@article {2470,
	title = {Data-driven characterization of knee structures using non-negative matrix factorization of 3D multi-echo UTE MRI},
	journal = {NMR in Biomedicine},
	year = {In Press},
	author = {C{\'e}line Smekens and Pieter Van Dyck and Patrick S Fuchs and Jan Sijbers and Thomas Janssens and Ben Jeurissen}
}
@mastersthesis {2465,
	title = {Accurate and precise parameter estimation for diffusion magnetic resonance imaging},
	year = {2026},
	month = {January, 2026},
	type = {PhD thesis},
	abstract = {			
Diffusion magnetic resonance imaging (dMRI) offers a unique way of imaging the human brain noninvasively. By carefully controlling various acquisition parameters, the dMRI signal can be used to probe the movement of water molecules inside biological tissues, revealing important information about tissue structure. However, this signal is subject to noise and other undesired electromagnetic effects that reduce the signal-to-noise ratio. Efficient acquisition schemes and robust estimators are required to obtain accurate and precise tissue maps of the human brain. In this thesis, we have aimed our efforts towards improving the accuracy, precision, generalizability and applicability of several dMRI analysis methods. Constrained spherical deconvolution (CSD) is a popular dMRI technique that can be used to infer the local tissue densities and their orientations in the human brain, which consists of cerebrospinal fluid, anisotropic white matter and isotropic gray matter. This is achieved by densely sampling q-space, the space of q-vectors representing the direction and strength of diffusion weighting. After sampling q-space in multiple shells, tissue densities and orientations can be estimated by deconvolving this multi-shell dMRI signal with the respective response functions for white matter, gray matter and cerebrospinal fluid. These response functions are typically represented using spherical harmonics (SH) basis functions. However, when sampling is nonspherical, either due to inhomogeneous gradients or by design such as Cartesian sampling, the estimated tissue maps suffer from biases. To counter this issue, we adopted a compact response function model that accounts for nonspherical sampling. On multi-shell data, our approach provides fiber orientation density functions and tissue densities indistinguishable from those estimated using SH. On Cartesian data, estimates are on par with those obtained from shell-wise data, significantly broadening the range of data sets analyzable using CSD. In addition, inhomogeneous gradients can be accounted for, resulting in more accurate apparent tissue densities and connectivity metrics. Q-space trajectory imaging is a dMRI technique that uses time-varying q-vectors to sensitize the dMRI signal to microscopic variations in heterogeneous tissues. By modelling the dMRI signal with a diffusion tensor distribution (DTD), this approach allows teasing apart variations in diffusivity from microscopic anisotropy, orientation dispersion, and mixtures of multiple isotropic diffusivities. To improve the estimation of the DTD parameters, we propose an efficient acquisition scheme optimized for the most used QTI-derived microstructural parameters. A constrained iteratively reweighted least squares estimator is used to further improve the bias and precision of the DTD parameters.},
	author = {Jan Morez}
}
@mastersthesis {2438,
	title = {Advancement of Defect Detection and Quantifying Measurement Uncertainty in X-Ray CT Inspection},
	volume = {PhD in Sciences: Physics},
	year = {2026},
	type = {PhD thesis},
	author = {Miroslav Yosifov}
}
@mastersthesis {2433,
	title = {Advances in Monte Carlo simulations for the design of X-ray phase contrast imaging systems},
	volume = {PhD in Science: Physics},
	year = {2026},
	type = {PhD thesis},
	author = {Jonathan Sanctorum}
}
@article {2472,
	title = {Anatomical exploration of Vipera ammodytes (family Viperidae) horn supports cephalic appendage sensory function in non-aquatic snakes},
	journal = {Journal of Anatomy},
	year = {2026},
	abstract = {Several snakes possess cephalic appendages over their eyes and snouts, but their exact function remains largely unknown. In one aquatic species, such structures have been associated with a mechanosensory role, and this suggests that other horned snakes may have evolved analogous appendages. To be considered sensory structures, cephalic appendages should be highly innervated by specific cranial nerves like the trigeminal, and perhaps carry specialised integumentary sense organs. The identification of trigeminal branches reaching the cephalic appendages would be consistent with sensory capability of the structures. Here we tested whether the rostral appendage of the nose-horned viper (Vipera ammodytes) could serve such a sensory function. We conducted an integrative anatomical exploration of the species{\textquoteright} head and appendage using a combination of diffusible iodine-based contrast-enhanced micro computed tomography, classical histology and scanning electron microscopy and found support for our initial hypothesis. The rostral appendage of V. ammodytes is highly innervated by the ophthalmic branch of the trigeminal nerve, and we also found dermal sensory organs over the snake{\textquoteright} snout and appendage scales. These findings suggest that a sensory function for cephalic appendages could also be possible in non-aquatic species, hinting that more analogous sensory structures may have evolved across horned snakes and other reptile groups.},
	keywords = {cranial nerves, diceCT, functional morphology, histology, mechanosensation, SEM, sense organs, sensory ecology, trigeminal},
	doi = {10.1111/joa.70132},
	url = {https://onlinelibrary.wiley.com/doi/10.1111/joa.70132},
	author = {Federico Banfi and Paul M. Gignac and Tom Hellebuyck and Joaquim G. Sanctorum and Barbara De Kegel and Dominique Adriaens and Raoul Van Damme}
}
@article {2436,
	title = {Combining magnetic resonance imaging and evoked potentials enhances machine learning prediction of multiple sclerosis disability worsening},
	journal = {Frontiers in Immunology},
	volume = {17},
	year = {2026},
	doi = {https://doi.org/10.3389/fimmu.2026.1625837},
	author = {Sofie Aerts and Lorin Werthen-Brabants and Hamza Khan and Diana Giraldo and Edward De Brouwer and Lotte Geys and Veronica Popescu and Jan Sijbers and Henry C. Woodruff and Tom Dhaene and Dirk Deschrijver and Bart Van Wijmeersch and Philippe Lambin and Liesbet M. Peeters}
}
@inproceedings {2442,
	title = {Cycloidal Acquisition for Inline Edge Illumination X-ray Phase Contrast Computed Tomography: A Simulation Study},
	booktitle = {15th Conference on Industrial Computed Tomography (iCT)},
	volume = {31},
	year = {2026},
	month = {February},
	address = {Linz, Austria},
	keywords = {computed tomography, edge illumination, inline acquisition, X-ray phase contrast imaging},
	doi = {10.58286/32604},
	url = {https://doi.org/10.58286/32604},
	author = {Verstraete, J. and Francken, N. and Jan Sijbers and Jan De Beenhouwer}
}
@mastersthesis {2437,
	title = {Edge illumination phase contrast imaging  with continuous motion},
	volume = {PhD in Sciences: Physics},
	year = {2026},
	month = {February, 2026},
	type = {PhD thesis},
	author = {Ben Huyge}
}
@article {2423,
	title = {Framework for optimal design of a multi-source rectangular X-ray cargo scanning system},
	journal = {Radiation Physics and Chemistry},
	volume = {239},
	year = {2026},
	pages = {113332},
	doi = {https://doi.org/10.1016/j.radphyschem.2025.113332 },
	author = {Caroline Bossuyt and Arnold Jan den Dekker and Domenico Iuso and Jan De Beenhouwer and Jan Sijbers}
}
@article {2428,
	title = {From Skin to Skeleton: A Statistical Shape Modelling Approach for Predicting Hand and Foot Bony Geometry},
	journal = {Annals of Biomedical Engineering},
	volume = {54},
	year = {2026},
	pages = {284-302},
	doi = {https://doi.org/10.1007/s10439-025-03882-0},
	author = {Kate Duquesne and Molnar Adris and Roel Huysentruyt and Aline Van Oevelen and Jing Li and Jan Sijbers and Wim Van Paepegem and Emmanuel Audenaert}
}
@inproceedings {2447,
	title = {GEO-ASTRA: a toolbox for non-conventional XCT scanning and reconstruction},
	booktitle = { 15th Conference on Industrial Computed Tomography (iCT), 10 - 13 February 2026, Linz, Austria (iCT 2026), e-Journal of Nondestructive Testing},
	volume = {31 (3)},
	year = {2026},
	doi = {https://doi.org/10.58286/32567},
	author = {Joaquim G. Sanctorum and Bjorn De Samber and Pavel Paramonov and Jan De Beenhouwer and Jan Sijbers}
}
@inproceedings {2443,
	title = {Inline Edge Illumination Computed Tomography: A Simulation Study},
	booktitle = {2026 IEEE 23rd Mediterranean Electrotechnical Conference (MELECON)},
	year = {2026},
	address = {Cairo, Egypt},
	keywords = {edge illumination, Industry 4.0, Inline inspection, Non-destructive testing, X-ray phase contrast computed tomography},
	doi = {https://doi.org/10.1109/MELECON64486.2026.11418832},
	author = {Verstraete, J. and Francken, N. and Jan Sijbers and Jan De Beenhouwer}
}
@article {2432,
	title = {Investigating the Influence of Individual Physical Effects on Dimensional Measurement Deviations Using XCT Simulations},
	journal = {Measurement,},
	volume = {263},
	year = {2026},
	doi = {https://doi.org/10.1016/j.measurement.2025.120168},
	author = {Miroslav Yosifov and Thiago Linhares Fernandes and Filippo Zanini and Simone Carmignato and Jan De Beenhouwer and Jan Sijbers and Johann Kastner and Christoph Heinzl}
}
@article {2453,
	title = {Longitudinal brain-age predictions comprising long-duration spaceflight missions},
	journal = {npj Microgravity},
	volume = {12},
	year = {2026},
	month = {Jan-12-2026},
	doi = {10.1038/s41526-026-00575-3},
	url = {https://www.nature.com/articles/s41526-026-00575-3https://www.nature.com/articles/s41526-026-00575-3.pdfhttps://www.nature.com/articles/s41526-026-00575-3https://www.nature.com/articles/s41526-026-00575-3.pdf},
	author = {Tang, Ge and Patil, Kaustubh R. and Hoffstaedter, Felix and More, Shammi and Eickhoff, Simon B. and Steven Jillings and Ben Jeurissen and Tomilovskaya, Elena and Gerlach, Darius and Nosikova, Inna and Riabova, Alexandra and Pechenkova, Ekaterina and Petrovichev, Viktor and Rukavishnikov, Ilya and Makovskaya, Lyudmila and Ombergen, Angelique Van and Wuyts, Floris L. and Eulenburg, Peter zu}
}
@article {2431,
	title = {Physiological cerebrospinal fluid interactions between brain and eye structures are altered after long-duration spaceflight},
	journal = {Experimental Physiology},
	year = {2026},
	doi = {http://doi.org/10.1113/EP093112},
	author = {Ge Tang and Steven Jillings and Ben Jeurissen and Elena Tomilovskaya and Inna Nosikova and Alexandra Riabova and Ekaterina V. Pechenkova and Petrovichev, Victor and Ilya Rukavishnikov and Stefan Sunaert and Paul M Parizel and Lyudmila Makovskaya and Valentin Sinitsyn and Jan Sijbers and Jitka Annen and Steven S L Laureys and Angelique Van Ombergen and Seyed-Ahmad Ahmadi and Floris L Wuyts and Peter zu Eulenburg}
}
@article {2439,
	title = {Spatio-temporal characterization of gastric distensibility in upper endoscopy identifies the presence of Helicobacter pylori},
	journal = {IEEE Transactions on Medical Imaging},
	year = {2026},
	pages = {1-1},
	abstract = {Assessment of gastric distensibility is crucial for understanding various gastrointestinal disorders, particularly those affecting gastric motility and reflux conditions. Gastric distensibility can be altered by pathological conditions such as linitis plastica or gastric mucosal inflammation, as seen in Helicobacter pylori infections. Although distensibility is evaluated during routine endoscopy, the assessment remains purely visual. Moreover, some specialized endoscopic tools assess only specific gastric regions and are often unavailable. Artificial intelligence tools may used to estimate the gastric three-dimensional surface from the endoscopy video and capture temporal distensibility changes of the gastric surface. This paper introduces a novel spatio-temporal characterization of the gastric distensibility during a routine endoscopy procedure. The method reconstructs the gastric surface for each frame of a short endoscopy video using a shape-from- shading network trained with a synthetic endoscopy collection. Local and regional changes in surface bending are then quantified, which herein depend on the distensibility. This spatio-temporal characterization was applied to a group of 160 patients, among them 60 diagnosed with Helicobacter pylori confirmed by pathology examination. The obtained characteristics in three different regions: body, antrum, and pylorus, demonstrated significant differences between patients with and without Helicobacter pylori under a t-test (p < 0.05). The proposed method effectively captures subtle gastric wall bending from routine endoscopy videos, providing a strategy for analyzing distensibility in pathological conditions across various gastric regions without requiring specialized tools.},
	keywords = {Bending, Cameras, Endoscopes, Endoscopy, Gastric distensibility, Gastroenterology, Helicobacter pylori, image reconstruction, Protocols, Spatio-temporal characterization, Stomach, Surface reconstruction, Three-dimensional displays, Three-dimensional reconstruction, Videos},
	doi = {10.1109/TMI.2026.3672198},
	author = {Ruano-Balseca, Josu{\'e} and Bravo, Diego and Diana Giraldo and G{\'o}mez, Mart{\'\i}n and Gonzalez, Fabio A. and Romero, Eduardo}
}
@inproceedings {2448,
	title = {Towards X-ray-based inspection of corrosion in maritime infrastructure},
	booktitle = {15th Conference on Industrial Computed Tomography (iCT), 10 - 13 February 2026, Linz, Austria (iCT 2026) Special Issue of e-Journal of Nondestructive Testing (eJNDT) ISSN 1435-4934 },
	volume = {31(3)},
	year = {2026},
	doi = {https://doi.org/10.58286/32583},
	author = {Joaquim G. Sanctorum and Nathan Bekaert and Wikke Witteveen and Geert Potters and Jan Sijbers and Jan De Beenhouwer}
}
@article {2455,
	title = {Considerations and recommendations from the ISMRM diffusion study group for preclinical diffusion MRI: Part 1: In vivo small-animal imaging},
	journal = {Magnetic Resonance in Medicine},
	volume = {93},
	year = {2025},
	month = {Jan-06-2025},
	pages = {2507 - 2534},
	issn = {0740-3194},
	doi = {10.1002/mrm.v93.610.1002/mrm.30429},
	url = {https://onlinelibrary.wiley.com/toc/15222594/93/6https://onlinelibrary.wiley.com/doi/10.1002/mrm.30429https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.30429},
	author = {Jelescu, Ileana O. and Grussu, Francesco and Ianus, Andrada and Hansen, Brian and Barrett, Rachel L. C. and Aggarwal, Manisha and Michielse, Stijn and Nasrallah, Fatima and Syeda, Warda and Wang, Nian and Veraart, Jelle and Roebroeck, Alard and Bagdasarian, Andrew F. and Eichner, Cornelius and Sepehrband, Farshid and Zimmermann, Jan and Soustelle, Lucas and Bowman, Christien and Tendler, Benjamin C. and Hertanu, Andreea and Ben Jeurissen and Verhoye, Marleen and Frydman, Lucio and van de Looij, Yohan and Hike, David and Dunn, Jeff F. and Miller, Karla and Landman, Bennett A. and Shemesh, Noam and Anderson, Adam and McKinnon, Emilie and Farquharson, Shawna and Dell{\textquoteright}Acqua, Flavio and Pierpaoli, Carlo and Drobnjak, Ivana and Leemans, Alexander and Harkins, Kevin D. and Descoteaux, Maxime and Xu, Duan and Huang, Hao and Santin, Mathieu D. and Grant, Samuel C. and Obenaus, Andre and Kim, Gene S. and Wu, Dan and Le Bihan, Denis and Blackband, Stephen J. and Ciobanu, Luisa and Els Fieremans and Bai, Ruiliang and Leergaard, Trygve B. and Zhang, Jiangyang and Dyrby, Tim B. and Johnson, G. Allan and Cohen-Adad, Julien and Budde, Matthew D. and Schilling, Kurt G.}
}
@article {2456,
	title = {Considerations and recommendations from the ISMRM diffusion study group for preclinical diffusion MRI: Part 2{\textemdash}Ex vivo imaging: Added value and acquisition},
	journal = {Magnetic Resonance in Medicine},
	volume = {93},
	year = {2025},
	month = {Jan-06-2025},
	pages = {2535 - 2560},
	issn = {0740-3194},
	doi = {10.1002/mrm.v93.610.1002/mrm.30435},
	url = {https://onlinelibrary.wiley.com/toc/15222594/93/6https://onlinelibrary.wiley.com/doi/10.1002/mrm.30435https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.30435},
	author = {Schilling, Kurt G. and Grussu, Francesco and Ianus, Andrada and Hansen, Brian and Howard, Amy F. D. and Barrett, Rachel L. C. and Aggarwal, Manisha and Michielse, Stijn and Nasrallah, Fatima and Syeda, Warda and Wang, Nian and Veraart, Jelle and Roebroeck, Alard and Bagdasarian, Andrew F. and Eichner, Cornelius and Sepehrband, Farshid and Zimmermann, Jan and Soustelle, Lucas and Bowman, Christien and Tendler, Benjamin C. and Hertanu, Andreea and Ben Jeurissen and Verhoye, Marleen and Frydman, Lucio and van de Looij, Yohan and Hike, David and Dunn, Jeff F. and Miller, Karla and Landman, Bennett A. and Shemesh, Noam and Anderson, Adam and McKinnon, Emilie and Farquharson, Shawna and Dell{\textquoteright}Acqua, Flavio and Pierpaoli, Carlo and Drobnjak, Ivana and Leemans, Alexander and Harkins, Kevin D. and Descoteaux, Maxime and Xu, Duan and Huang, Hao and Santin, Mathieu D. and Grant, Samuel C. and Obenaus, Andre and Kim, Gene S. and Wu, Dan and Le Bihan, Denis and Blackband, Stephen J. and Ciobanu, Luisa and Els Fieremans and Bai, Ruiliang and Leergaard, Trygve B. and Zhang, Jiangyang and Dyrby, Tim B. and Johnson, G. Allan and Cohen-Adad, Julien and Budde, Matthew D. and Jelescu, Ileana O.}
}
@article {2457,
	title = {Considerations and recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 3{\textemdash}Ex vivo imaging: Data processing, comparisons with microscopy, and tractography},
	journal = {Magnetic Resonance in Medicine},
	volume = {93},
	year = {2025},
	month = {Jan-06-2025},
	pages = {2561 - 2582},
	issn = {0740-3194},
	doi = {10.1002/mrm.v93.610.1002/mrm.30424},
	url = {https://onlinelibrary.wiley.com/toc/15222594/93/6https://onlinelibrary.wiley.com/doi/10.1002/mrm.30424https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.30424},
	author = {Schilling, Kurt G. and Howard, Amy F. D. and Grussu, Francesco and Ianus, Andrada and Hansen, Brian and Barrett, Rachel L. C. and Aggarwal, Manisha and Michielse, Stijn and Nasrallah, Fatima and Syeda, Warda and Wang, Nian and Veraart, Jelle and Roebroeck, Alard and Bagdasarian, Andrew F. and Eichner, Cornelius and Sepehrband, Farshid and Zimmermann, Jan and Soustelle, Lucas and Bowman, Christien and Tendler, Benjamin C. and Hertanu, Andreea and Ben Jeurissen and Verhoye, Marleen and Frydman, Lucio and van de Looij, Yohan and Hike, David and Dunn, Jeff F. and Miller, Karla and Landman, Bennett A. and Shemesh, Noam and Anderson, Adam and McKinnon, Emilie and Farquharson, Shawna and Dell{\textquoteright}Acqua, Flavio and Pierpaoli, Carlo and Drobnjak, Ivana and Leemans, Alexander and Harkins, Kevin D. and Descoteaux, Maxime and Xu, Duan and Huang, Hao and Santin, Mathieu D. and Grant, Samuel C. and Obenaus, Andre and Kim, Gene S. and Wu, Dan and Le Bihan, Denis and Blackband, Stephen J. and Ciobanu, Luisa and Els Fieremans and Bai, Ruiliang and Leergaard, Trygve B. and Zhang, Jiangyang and Dyrby, Tim B. and Johnson, G. Allan and Cohen-Adad, Julien and Budde, Matthew D. and Jelescu, Ileana O.}
}
@inproceedings {2401,
	title = {Correcting for focal spot drift in edge illumination X-ray phase contrast imaging},
	booktitle = {14th Conference on Industrial Computed Tomography (iCT), 4 - 7 February 2025, Antwerp, Belgium},
	year = {2025},
	doi = {doi.org/10.58286/30746},
	author = {Nicholas Francken and Jonathan Sanctorum and Ben Huyge and Jan Sijbers and Jan De Beenhouwer}
}
@article {2412,
	title = {DAMMER: Direct Adaptive Multi-resolution MEsh Reconstruction from X-ray measurements},
	journal = {IEEE Transactions on Computational Imaging },
	volume = {11},
	year = {2025},
	pages = {926 - 941},
	doi = {10.1109/TCI.2025.3587408},
	author = {Jannes Merckx and Arnold Jan den Dekker and Jan Sijbers and Jan De Beenhouwer}
}
@article {2351,
	title = {Deep Learning-Enhanced Accelerated 2D TSE and 3D Super-Resolution Dixon TSE for Rapid Comprehensive Knee Joint Assessment},
	journal = {Investigative Radiology},
	volume = {60},
	year = {2025},
	pages = {220-233},
	doi = {10.1097/RLI.0000000000001118},
	author = {C{\'e}line Smekens and Quinten Beirinckx and Frederik Bosmans and Floris Vanhevel and Annemie Snoeckx and Jan Sijbers and Ben Jeurissen and Thomas Janssens and Pieter Van Dyck}
}
@article {2383,
	title = {Degradation Detection in Rice Products via Shape Variations in XCT Simulation-Empowered AI},
	journal = {Journal of Nondestructive Evaluation},
	volume = {44},
	year = {2025},
	doi = {https://doi.org/10.1007/s10921-024-01147-9},
	author = {Miroslav Yosifov and Thomas Lang and Virginia Florian and Stefan Gerth and Jan De Beenhouwer and Jan Sijbers and Johann Kastner and Christoph Heinzl}
}
@inproceedings {2391,
	title = {Dual Domain Swin Transformer based Reconstruction method for Sparse-View Computed Tomography},
	booktitle = {iCT 2025 - 14th Conference on Industrial Computed Tomography (iCT), 4 - 7 February 2025, Antwerp, Belgium},
	year = {2025},
	doi = {doi.org/10.58286/30751},
	author = {Jonas Van der Rauwelaert and Caroline Bossuyt and Jan Sijbers}
}
@mastersthesis {2415,
	title = {Edge illumination X-ray phase contrast imaging: Advancements in simulations and inline acquisitions},
	volume = {PhD in Sciences: Physics},
	year = {2025},
	type = {PhD thesis},
	author = {Nicholas Francken}
}
@article {2417,
	title = {Edge illumination X-ray phase contrast imaging with continuous mask motion},
	journal = {Optics Express},
	volume = {33},
	year = {2025},
	pages = {41271-41286},
	doi = {https://doi.org/10.1364/OE.570476},
	author = {Ben Huyge and Nicholas Francken and Joaquim Sanctorum and Jonathan Sanctorum and Jan De Beenhouwer and Jan Sijbers}
}
@article {2385,
	title = {Efficient X-ray dark field contrast simulations using a condensed history approach},
	journal = {Optics Express},
	volume = {33},
	year = {2025},
	month = {01/2025},
	pages = {3562-3576},
	doi = {10.1364/OE.544864},
	url = {https://opg.optica.org/oe/fulltext.cfm?uri=oe-33-2-3562\&id=567367},
	author = {Nicholas Francken and Jonathan Sanctorum and Ben Huyge and Jan Sijbers and Jan De Beenhouwer}
}
@conference {2452,
	title = {Enhancing AdAPTS: Fast Field Evaporation Simulation with Parametric Local Electrodes for Atom Probe Tomography},
	year = {2025},
	abstract = {Atom probe tomography (APT) is a powerful technique for analyzing the composition of materials at the atomic level. However, no reconstruction method currently fully capitalizes on the spatial resolution potential of APT instrumentation. A model of field evaporation, efficiently and accurately describing ion trajectories, remains essential for reconstruction. To address this, we developed AdAPTS, a simulation tool that improves the efficiency of modeling individual evaporations compared to TAPSim. Both tools, however, lack a local electrode model, which influences ion trajectories. Directly incorporating a local electrode into the finite element mesh hinders simulation speed and memory efficiency. To overcome this, we introduce a local electrode feature in AdAPTS that leverages the superposition principle by projecting a solution of the field emitted by the local electrode onto a mesh of the experimental domain, resulting in constant computational overhead instead of recalculating the total electrostatic field in each iteration. The parametric definition of local electrodes allows for precise modeling of electrode geometries, providing a flexible framework for electrode design and potentially enabling more accurate 3D reconstruction. Future work will focus on integrating additional physical models in AdAPTS to expand its capabilities across diverse material systems.},
	url = {https://ge.iitm.ac.in/apt\&m-2025/schedule.html},
	author = {Julian L{\"u}ken and Claudia Fleischmann and Jan Sijbers and Jan De Beenhouwer}
}
@inproceedings {2450,
	title = {Exact reconstruction for helical cone-beam X-ray CT using the ASTRA toolbox},
	booktitle = {10th International Symposium on Digital Industrial Radiology and Computed Tomography, 1 {\textendash} 3 July 2025 in Paris, France (DIR 2025)},
	volume = {30(8)},
	year = {2025},
	url = {https://www.ndt.net/?id=31452},
	author = {Pavel Paramonov and Joaquim G. Sanctorum and Femke Danckaers and Jan De Beenhouwer and Jan Sijbers}
}
@article {2405,
	title = {Gratings for multiresolution edge illumination X-ray phase contrast imaging: concept and simulation},
	journal = {Optics Express},
	volume = {33},
	year = {2025},
	pages = {17886-17902},
	doi = {10.1364/OE.550252},
	author = {Pieter-Jan Vanthienen and Nicholas Francken and Jonathan Sanctorum and Jan Sijbers and Jan De Beenhouwer}
}
@book {2458,
	title = {Handbook of Diffusion MR Tractography: Multishell models},
	year = {2025},
	pages = {201 - 220},
	publisher = {Elsevier},
	organization = {Elsevier},
	isbn = {9780128188941},
	doi = {10.1016/B978-0-12-818894-1.00008-2},
	author = {Morez, Jan and De Luca, Alberto and Pietsch, Maximillian and Christiaens, Daan and Baete, Steven H. and Reisert, Marco and Ben Jeurissen}
}
@article {2406,
	title = {Harmonization of diffusion MRI on healthy subjects using NeuroCombat and LongCombat: a B-Q MINDED Brain Intra-and Inter-Scanner Study},
	journal = {Frontiers in Neuroscience},
	volume = {19},
	year = {2025},
	doi = {10.3389/fnins.2025.1591169},
	author = {Maira Siqueira Pinto and Vincenzo Anania and Roberto Paolella and C{\'e}line Smekens and Thibo Billiet and Thomas Janssens and Arnold Jan den Dekker and Jan Sijbers and Pieter-Jan Guns and Pieter Van Dyck}
}
@unpublished {2346,
	title = {ICT 2025 template},
	year = {2025}
}
@mastersthesis {2430,
	title = {Improved Diagnosis and Prognosis of Mild Traumatic Brain Injury via Harmonised Diffusion MRI},
	year = {2025},
	type = {PhD thesis},
	author = {Maira Siqueira Pinto}
}
@inproceedings {2400,
	title = {In-depth imaging of in-vitro tissue models using electrical impedance tomography},
	booktitle = {iCT 2025 - 14th Conference on Industrial Computed Tomography},
	volume = {4 - 7 February},
	year = {2025},
	doi = {doi.org/10.58286/30763},
	author = {Jian Deng and Babu Linkoon P Meenaketan and Saeedeh Ebrahimi Takalloo and Yoke Chin Chai and Tom Torfs and Dries Braeken and Jan De Beenhouwer and Jan Sijbers}
}
@article {2426,
	title = {Leveraging Hand-Crafted Radiomics on Multicenter FLAIR MRI for Predicting Disability Worsening in People with Multiple Sclerosis},
	journal = {Front. Neurosci.},
	volume = {19},
	year = {2025},
	doi = {https://doi.org/10.3389/fnins.2025.1610401},
	author = {Hamza Khan and Diana Giraldo and Henry C. Woodruff and Lorin Werthen-Brabants and Shruti Atul Mali and Sina Amirrajab and Edward De Brouwer and Veronica Popescu and Bart Van Wijmeersch and Oliver Gerlach and Jan Sijbers and Liesbet M. Peeters and Philippe Lambin}
}
@article {2399,
	title = {Loss of the Ubiquitin-Associated Domain of sqstm1/p62 in Zebrafish Causes a Phenotype Resembling Paget{\textquoteright}s Disease of Bone},
	journal = {Calcified Tissue International},
	volume = {116},
	year = {2025},
	pages = {1-15},
	author = {Yentl Huybrechts and Rapha{\"e}l De Ridder and Dylan Bergen and Bjorn De Samber and Eveline Boudin and Francesca Tonelli and Dries Knapen and Lucia Vergauwen and Dorien Schepers and Evelien Van Dijck and Qiao Tong and Anja Verhulst and Jan De Beenhouwer and Jan Sijbers and Chrissy Hammond and Antonella Forlino and Geert Mortier and Paul Coucke and P Eckhard Witten and Ronald Young Kwon and Andy Willaert and Gretl Hendrickx and Wim Van Hul}
}
@article {2434,
	title = {Moisture Content Estimation of Porous Building Stones using Hyperspectral Imaging},
	journal = {Scientific Data},
	volume = {13},
	year = {2025},
	month = {Dec},
	pages = {102},
	abstract = {Moisture poses a major threat to built heritage through processes such as frost damage, salt crystallization and biological growth, which accelerate stone deterioration. Stone moisture content can be determined non-destructively by spectral reflectance in the shortwave infrared (SWIR) wavelength range. To validate this, large cubic and small cylindrical samples from six stone types, Brick, Euville, Massangis, Neubrunn, Obernkirchen, and Savonni{\`e}res, with different controlled moisture levels were prepared, and a comprehensive SWIR hyperspectral image dataset was created. The acquired hyperspectral images were processed using the Normalized Relative Arc Lengths (NRAL) method to generate moisture maps that illustrate the spatial distribution of water within each sample. Because moisture distribution is influenced by pore characteristics and grain arrangement, the moisture maps were validated through petrographic examination. For quantitative validation, the mean moisture content of each sample was compared with the corresponding gravimetric moisture content. The results demonstrated strong agreement between the estimated and gravimetric moisture values, with root mean square errors ranging from 1 and 2 g/g ~{\texttimes} 100.},
	issn = {2052-4463},
	doi = {10.1038/s41597-025-06416-4},
	url = {https://doi.org/10.1038/s41597-025-06416-4},
	author = {Chaghdo, Danish Ali and Koirala, Bikram and Cristina, Laura and De Kock, Tim and Fontaine, Laurent and Hayen, Roald and Paul Scheunders}
}
@article {2454,
	title = {MReye-Seg: development and validation of an automated MRI pipeline for standardised ocular and orbital morphometrics},
	journal = {Eye},
	volume = {39},
	year = {2025},
	month = {Jan-12-2025},
	pages = {3294 - 3305},
	issn = {0950-222X},
	doi = {10.1038/s41433-025-04044-1},
	url = {https://www.nature.com/articles/s41433-025-04044-1https://www.nature.com/articles/s41433-025-04044-1.pdfhttps://www.nature.com/articles/s41433-025-04044-1.pdfhttps://www.nature.com/articles/s41433-025-04044-1},
	author = {Tang, Ge and Ahmadi, Seyed-Ahmad and Steven Jillings and Ben Jeurissen and Tomilovskaya, Elena and Nosikova, Inna and Ryabova, Alexandra and Pechenkova, Ekaterina and Petrovichev, Viktor and Rukavishnikov, Ilya and Velten, Theresa and Gripshi, Marina and Van Ombergen, Angelique and Wuyts, Floris L. and zu Eulenburg, Peter}
}
@article {2388,
	title = {Multi-contrast benchmarking of edge illumination Monte Carlo simulations using virtual gratings},
	journal = {Journal of Applied Physics},
	volume = {137},
	year = {2025},
	pages = {104904},
	doi = {10.1063/5.0244152},
	author = {Jonathan Sanctorum and Jan Sijbers and Jan De Beenhouwer}
}
@article {2404,
	title = {A novel Soft Tissue-Integrated Kinematic Solver for skeletal motion: Validation and applications},
	journal = {Computer Methods and Programs in Biomedicine},
	volume = {265},
	year = {2025},
	chapter = {108766},
	doi = {https://doi.org/10.1016/j.cmpb.2025.108766},
	author = {Kate Duquesne and Aline Van Oevelen and Jan Sijbers and Wim Van Paepegem and Emmanuel Audenaert}
}
@article {2407,
	title = {A PHASE-CONTRAST X-RAY IMAGING SYSTEM FOR OBTAINING A DARK-FIELD IMAGE AND A METHOD THEREFOR},
	number = {EP 4 351 425 B1},
	year = {2025},
	author = {Jan De Beenhouwer and Jan Sijbers}
}
@inproceedings {2392,
	title = {Proximal Neural Networks based reconstruction for few-view CT applications},
	booktitle = {14th Conference on Industrial Computed Tomography (iCT), 4 - 7 February 2025, Antwerp, Belgium},
	year = {2025},
	author = {H. Le and Caroline Bossuyt and Julie Escoda and Marius Costin and Jan De Beenhouwer and Jan Sijbers}
}
@article {2384,
	title = {Proximity Adjusted Centroid Mapping for Accurate Detection of Nuclei in Dense 3D Cell Systems},
	journal = {Computers in Biology and Medicine},
	volume = {185},
	year = {2025},
	pages = {109561},
	doi = {https://doi.org/10.1016/j.compbiomed.2024.109561},
	author = {Tim Van De Looverbosch and Sarah De Beuckeleer and Frederik De Smet and Jan Sijbers and Winnok De Vos}
}
@mastersthesis {2416,
	title = {Shaping the future: 3D knee morphology analysis and predictive models for data-driven patient selection},
	volume = {PhD in Medicine and Health Sciences},
	year = {2025},
	type = {PhD thesis},
	author = {Jonas Grammens}
}
@inproceedings {2393,
	title = {Single X-ray Projection Material Decomposition using a Mesh Projector},
	booktitle = {14th Conference on Industrial Computed Tomography (iCT), 4 - 7 February 2025, Antwerp, Belgium (iCT 2025)},
	year = {2025},
	author = {Fleur Linsen and Domenico Iuso and Jan Sijbers}
}
@article {2422,
	title = {Soil Moisture Content Estimation From Hyperspectral Remote Sensing Data},
	journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
	volume = {18},
	year = {2025},
	pages = {22231-22240},
	abstract = {Because of its significant absorption power, especially in the shortwave infrared optical region, water dominates the optical reflectance properties of water-bearing materials. This allows us to study a material{\textquoteright}s water-related features, such as its moisture content, from optical reflectance. In this study, we proposed a framework to estimate soil moisture content from PRISMA hyperspectral remote sensing data. The proposed framework requires a dry endmember spectrum and an endmember spectrum of high soil moisture along with ground truth moisture content, obtained from ground measurements. The method takes into account the complex interaction of light with soils, the large variation of environmental conditions, leading to spectral variability and the soil-specific behavior of water. The framework is extensively validated using ground-measured soil moisture data from the International Soil Moisture Network database. A total of 1418 PRISMA images corresponding to 151 ground stations were analyzed. From 518 retained images, a total root-mean-squared error of 8.682 \% and R2 of 0.385 was obtained.},
	keywords = {Absorption, Hyperspectral, Hyperspectral imaging, Land surface, Machine learning regression, Optical imaging, Optical reflection, Optical sensors, PRISMA, Reflectivity, remote sensing, Soil measurements, Soil moisture, soil moisture content (SMC), vegetation mapping},
	doi = {10.1109/JSTARS.2025.3603841},
	author = {Jambhali, Ketaki Vinay and Koirala, Bikram and Bnoulkacem, Zakaria and Paul Scheunders}
}
@article {2421,
	title = {A Spectral-Spatial Attention Network for Hyperspectral Unmixing},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {63},
	year = {2025},
	pages = {1-15},
	abstract = {Hyperspectral unmixing, an essential and fundamental task in remote sensing, focuses on estimating endmembers (spectrally pure components) and their fractional abundances within each mixed pixel of a hyperspectral image. With the advent of deep learning (DL), the field of hyperspectral unmixing has made significant progress. Among DL approaches, autoencoder-based models have shown promising results. However, most unmixing methods estimate the endmembers by the weights of the linear layers in the decoder of their networks, making their performance highly dependent on weight initialization. Moreover, noise is not explicitly accounted for in most recent methods that use spectral angle distance (SAD) loss. To avoid the initialization problems, we developed an innovative inversion strategy to directly estimate the endmembers. Moreover, to optimally account for noise, an end-to-end network is proposed, which integrates both denoising and unmixing. Finally, for an improved feature extraction, a novel spectral-spatial attention module (SSAM) is integrated into the network. Extensive experiments on synthetic and three real datasets show that the proposed method significantly and consistently outperforms the compared state-of-the-art methods. The full code is available at https://github.com/xuanwentao for public evaluation.},
	keywords = {Attention module, autoencoder, Autoencoders, autonomous unmixing module (AUM), Convolutional neural networks, Data mining, Decoding, deep denoising module (DDM), Feature extraction, Hyperspectral imaging, Hyperspectral unmixing, image reconstruction, Loss measurement, Neural networks, Noise reduction},
	doi = {10.1109/TGRS.2025.3576479},
	author = {Tao, Xuanwen and Koirala, Bikram and Rasti, Behnood and Plaza, Antonio and Paul Scheunders}
}
@article {2425,
	title = {Superimposing Synthetic Defects into Real XCT Data and Segmentation-Based Comparison for Advanced Probability of Detection Evaluation},
	journal = { Journal of Nondestructive Evaluation },
	volume = {44},
	year = {2025},
	doi = {https://doi.org/10.1007/s10921-025-01262-1},
	author = {Miroslav Yosifov and Bernhard Fr{\"o}hler and Jan Sijbers and Jan De Beenhouwer and Johann Kastner and Christoph Heinzl}
}
@article {2411,
	title = {SwinIR-based Dual-Domain Reconstruction for Sparse-View Computed Tomography},
	journal = {Journal of Nondestructive Evaluation},
	volume = {44},
	year = {2025},
	doi = {https://doi.org/10.1007/s10921-025-01244-3},
	author = {Jonas Van der Rauwelaert and Caroline Bossuyt and Stijn Verleden and Jan Sijbers}
}
@mastersthesis {2414,
	title = {Towards adaptive multi-resolution edge illumination X-ray phase contrast imaging},
	volume = {PhD in Sciences: Physics},
	year = {2025},
	type = {PhD thesis},
	author = {Pieter-Jan Vanthienen}
}
@inproceedings {2418,
	title = {About rapid phase-wrapping in terahertz phase contrast imaging},
	booktitle = {SPIE},
	volume = {13141},
	year = {2024},
	pages = {69-73},
	abstract = {Terahertz (THz) imaging has emerged as a powerful technique in diverse fields, ranging from medical diagnostics to material characterization. Due to its lower frequency, compared to x-ray-based techniques, THz imaging provides an alternative view into the internal structures of samples composed of low-density materials. Notably, phase contrast imaging is advantageous as it generally exhibits reduced distortion from refraction and reflection losses compared to attenuation contrast. However, in the course of THz phase measurements, a fresh issue arises. Due to the relatively wide beam shape of THz imaging combined with rapid phase wrapping, a new type of artifact emerges, posing a significant hindrance to the accuracy and reliability of the imaging process. This paper explores the intricate interplay between beam shape dynamics and phase-wrapping mechanisms in THz imaging systems, unraveling the source of the artifacts that arise from this phenomenon.},
	doi = {https://doi.org/10.1117/12.3027665},
	url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/13141/131410F/About-rapid-phase-wrapping-in-terahertz-phase-contrast-imaging/10.1117/12.3027665.short},
	author = {Lars-Paul Lumbeeck and Pavel Paramonov and Jan Sijbers and Jan De Beenhouwer}
}
@conference {2379,
	title = {AdAPTS: An Adaptive Atom Probe Tomography Simulation Library},
	volume = {30},
	year = {2024},
	doi = {https://doi.org/10.1093/mam/ozae044.017},
	author = {Julian L{\"u}ken and Claudia Fleischmann and Jan Sijbers and Jan De Beenhouwer}
}
@conference {2451,
	title = {AdAPTS: An Adaptive Atom Probe Tomography Simulation Library},
	year = {2024},
	abstract = {In atom probe tomography (APT), spatial reconstruction enables volumetric insight into a specimen{\textquoteright}s nanostructure. To this day, a fast reconstruction method which utilizes the true potential of APT in terms of resolution does not exist. A model of its effective inverse, the field evaporation, which provides a physically accurate description of the ion trajectories, is a crucial component in reconstruction. The simulation of each individual evaporation, such as in TAPSim, while being able to model some cases accurately, has been time inefficient. Novel methods track surface meshes, achieving better time efficiency, although at the cost of spatial accuracy.

We introduce AdAPTS, an adaptive atom probe tomography simulation library. AdAPTS is a modular volumetric finite element simulation and specimen modeling library for APT written in C++. It is capable of generating accurate detector hit maps of various specimens, efficiently representing and simulating the experimental domain from specimen to detector. Using AdAPTS, we are able to accurately simulate the field evaporation of various specimens, revealing realistic poles and zone lines.

AdAPTS utilizes a unique high quality mesh generation strategy, in which each individual atom is represented by a vertex carrying material property information (e.g. atomic mass, relative permittivity). To constitute the surrounding vacuum chamber, additional vertices are generated using efficient variable density Poisson disk sampling. The software library TetGen is used to Delaunay-tetrahedralize all generated vertices. This yields a tetrahedral mesh in which each element inherits its material properties from the material property previously assigned to each vertex. The resulting vacuum vertices constitute less than 2\% of the total vertices, effectively allowing for a fast and full-scale simulation without employing the finite element method/boundary element method (FEM/BEM) coupling scheme proposed by Fletcher et al. Simultaneously, this method provides a rigid surface notion that preserves local protrusions due to lattice nanostructure similar to TAPSim by Oberdorfer et al., which in turn preserves zone lines and poles on the hit map.

The modeling of a specimen comprises two steps. In the first step, individual crystal lattices can be defined using Bravais lattice descriptors (e.g. bcc, fcc), the material lattice constant and the Miller indices of the crystallic plane facing the detector. In the second step, the shape of the specimen and dopants contained within are modeled using implicit surfaces and binary set operators (e.g. union, intersection). However, for amorphous materials, the individual atomic positions can also be specified individually or generated by any other means. The height, tip radius and shank angle of an implicit  surface (a truncated cone with a quasi-hemispherical tip) that represents the specimen shape can be specified.

The modular finite element methods library MFEM is utilized to determine the electric field by solving Poisson{\textquoteright}s equation given an electric potential Dirichlet boundary condition at the base of the specimen. Using an Euler-forward scheme with variable time steps, the trajectory starting at each evaporation site is calculated. The trajectory integration is terminated when the trajectory intersects with the detector plane. To conclude, AdAPTS provides an efficient and accurate framework which combines specimen modeling with realistic field evaporation due to voltage pulses using state-of-the-art mesh generation and numerical simulation tools. A thorough quantitative comparison in terms of simulation time of the different simulators will be provided by the time of the conference.},
	url = {https://plan.events.mpg.de/event/261/contributions/1337/},
	author = {Julian L{\"u}ken and Claudia Fleischmann and Jan Sijbers and Jan De Beenhouwer}
}
@article {2343,
	title = {Automated landmark annotation for morphometric analysis of distal femur and proximal tibia},
	journal = {Journal of Imaging},
	volume = {10},
	year = {2024},
	doi = {10.3390/jimaging10040090 },
	author = {Jonas Grammens and Annemieke Van Haver and Imelda Lumban-Gaol and Femke Danckaers and Peter Verdonk and Jan Sijbers}
}
@mastersthesis {2330,
	title = {Automating the planning of transcatheter Mitral Valve Interventions},
	volume = {Engineering Science},
	year = {2024},
	type = {PhD thesis},
	author = {Lopes, Patricia}
}
@article {2323,
	title = {CAD-ASTRA: A versatile and efficient mesh projector for X-ray tomography with the ASTRA-toolbox},
	journal = {Optics Express},
	volume = {32},
	year = {2024},
	pages = {3425-3439},
	doi = {https://doi.org/10.1364/OE.498194},
	author = {Pavel Paramonov and Nicholas Francken and Jens Renders and Domenico Iuso and Tim Elberfeld and Jan De Beenhouwer and Jan Sijbers}
}
@inproceedings {2419,
	title = {Coherent detection terahertz imaging with a continuous wave source},
	booktitle = {SPIE},
	volume = {13141},
	year = {2024},
	pages = {63-68},
	abstract = {The unique properties of terahertz (THz) radiation, coupled with its non-ionizing nature, enable high-contrast imaging of soft materials with less stringent safety precautions than those required for x-ray systems. In this study, we introduce novel methods for THz imaging using a system that employs two photoconductive antennas (PCAs): one as a Continuous-Wave (CW) source and the other as a coherent detector. To enable precise multiband imaging with high frequency resolution, a fiber stretcher for rapid phase modulation was included. This system allows for the registration of two types of imaging contrasts: light attenuation and phase shift. We outline methods for extracting these contrasts and evaluate the system{\textquoteright}s capabilities through experiments involving various materials and frequencies. Furthermore, its application in computed tomography is demonstrated. To the best of our knowledge, this is the first application of a CW setup based on PCAs for non-destructive inspection and imaging of concealed objects.},
	doi = {https://doi.org/10.1117/12.3027653},
	url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/13141/131410E/Coherent-detection-terahertz-imaging-with-a-continuous-wave-source/10.1117/12.3027653.short},
	author = {Lars-Paul Lumbeeck and Pavel Paramonov and Jan Sijbers and Jan De Beenhouwer}
}
@inproceedings {2374,
	title = {Deep image prior for sparse-view reconstruction in static, rectangular multi-source x-ray CT systems for cargo scanning},
	booktitle = {SPIE Developments in X-Ray Tomography XV},
	volume = {13152},
	year = {2024},
	doi = {https://doi.org/10.1117/12.3027668},
	author = {Caroline Bossuyt and Arnold Jan den Dekker and Domenico Iuso and T. Le Hoang and Julie Escoda and Marius Costin and Jan Sijbers and Jan De Beenhouwer}
}
@article {2370,
	title = {Determination of volumetric abundance of intimate mixture using Bayesian MCMC},
	journal = {IEEE Sensors Journal},
	year = {2024},
	pages = {1-1},
	keywords = {Grain size, Hyperspectral, intimate mixture, Iron, Minerals, Powders, radiative transfer, Reflectivity, Sensors, Uncertainty},
	doi = {10.1109/JSEN.2024.3463401},
	author = {Schmidt, Fr{\'e}d{\'e}ric and Koirala, Bikram and Andrieu, Fran{\c c}ois}
}
@mastersthesis {2386,
	title = {Development of spoiled gradient echo sequences for MRI relaxometry},
	volume = {PhD in Biomedical Sciences},
	year = {2024},
	type = {PhD thesis},
	author = {Marco Andrea Zampini}
}
@article {2459,
	title = {Diffusion MRI marks progressive alterations in fiber integrity in the zQ175DN mouse model of Huntington{\textquoteright}s disease},
	journal = {Neurobiology of Disease},
	volume = {193},
	year = {2024},
	month = {Jan-04-2024},
	pages = {106438},
	issn = {09699961},
	doi = {10.1016/j.nbd.2024.106438},
	author = {Vidas-Guscic, Nicholas and van Rijswijk, Jo{\"e}lle and Van Audekerke, Johan and Ben Jeurissen and Nnah, Israel and Tang, Haiying and Mu{\~n}oz-Sanjuan, Ignacio and Pustina, Dorian and Cachope, Roger and Van der Linden, Annemie and Bertoglio, Daniele and Verhoye, Marleen}
}
@article {2360,
	title = {Direct reconstruction of wet foam from sparse-view, dynamic X-ray CT scans},
	journal = {Advanced Engineering Materials},
	volume = {2400957},
	year = {2024},
	pages = {1-10},
	doi = {https://doi.org/10.1002/adem.202400957},
	author = {Jens Renders and Lucia Mancini and Jan De Beenhouwer and Jan Sijbers}
}
@inproceedings {2376,
	title = {Dynamic flat field correction in edge illumination imaging},
	booktitle = {SPIE Developments in X-Ray Tomography XV},
	volume = {13152},
	year = {2024},
	doi = {https://doi.org/10.1117/12.3028319},
	author = {Didem G{\"o}kbel Kekliko{\u g}lu and Nicholas Francken and Ben Huyge and Ziya Merdan and Jan De Beenhouwer and Jan Sijbers}
}
@article {2329,
	title = {Edge illumination x-ray phase contrast simulations using the CAD-ASTRA toolbox},
	journal = {Optics Express},
	volume = {32},
	year = {2024},
	pages = {10005-10021},
	doi = {10.1364/OE.516138},
	url = {https://opg.optica.org/oe/abstract.cfm?doi=10.1364/OE.516138},
	author = {Nicholas Francken and Jonathan Sanctorum and Pavel Paramonov and Jan Sijbers and Jan De Beenhouwer}
}
@article {2362,
	title = {An efficient method for water content estimation of building materials from spectral reflectance},
	journal = {NDT \& E International},
	volume = {147},
	year = {2024},
	pages = {103214},
	abstract = {We propose a nondestructive methodology to accurately estimate the water content of building materials from spectral reflectance in the shortwave infrared. The water content of a wet sample is estimated from the relative position of its reflectance spectrum on the curve between 2 reference spectra with known water content. By design, the approach is invariant to variations in illumination and acquisition conditions. Validation is done on datasets of clay powders and bricks. The experimental results provide confirmation of the effectiveness of the proposed method.},
	keywords = {Brick, Clay, Cultural heritage, Hyperspectral, remote sensing, Water content},
	issn = {0963-8695},
	doi = {https://doi.org/10.1016/j.ndteint.2024.103214},
	url = {https://www.sciencedirect.com/science/article/pii/S0963869524001798},
	author = {Bikram Koirala and Paul Scheunders}
}
@mastersthesis {2413,
	title = {Ensuring Flawlessness in Additive Manufacturing: Advances in X-ray Inspection Techniques for Efficient Defect Detection},
	volume = {PhD in Sciences},
	year = {2024},
	type = {PhD thesis},
	author = {Domenico Iuso}
}
@inproceedings {2440,
	title = {Estimating Polyp Size From a Single Colonoscopy Image Using a Shape-From-Shading Model},
	booktitle = {2024 IEEE International Symposium on Biomedical Imaging (ISBI)},
	year = {2024},
	pages = {1-5},
	keywords = {Accuracy, Colonoscopy, Depth estimation, image segmentation, polyp size estimation, Solid modeling, Surface reconstruction, Surveillance, Synthetic database, Three-dimensional displays},
	doi = {10.1109/ISBI56570.2024.10635358},
	author = {Ruano-Balseca, Josu{\'e} and Bravo, Diego and Diana Giraldo and G{\'o}mez, Mart{\'\i}n and Gonzalez, Fabio A. and Manzanera, Antoine and Romero, Eduardo}
}
@inproceedings {2398,
	title = {Exploring the Potential of Hyperspectral Imaging to Estimate the Moisture Content in Natural and Historical Stones},
	booktitle = {2024 14th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)},
	year = {2024},
	pages = {1-5},
	keywords = {Building Physics, Cultural differences, Damage Monitoring, Estimation, Hyperspectral imaging, Moisture, Moisture content, Monitoring, Protocols, Reflectivity, Reliability, Signal processing, Water conservation},
	doi = {10.1109/WHISPERS65427.2024.10876512},
	author = {Chaghdo, Danish Ali and Koirala, Bikram and Cristina, Laura and Hayen, Roald and Paul Scheunders}
}
@inproceedings {2375,
	title = {Fast and efficient tetrahedral volume mesh reconstruction with CAD-ASTRA},
	booktitle = {SPIE Developments in X-Ray Tomography XV},
	volume = {13152},
	year = {2024},
	address = {San Diego, USA},
	doi = {https://doi.org/10.1117/12.3027610},
	author = {Jannes Merckx and Arnold Jan den Dekker and Jan De Beenhouwer and Jan Sijbers}
}
@conference {2460,
	title = {Fixel-based analysis of multi-shell diffusion MRI reveals white matter changes after long-duration spaceflight},
	year = {2024},
	pages = {2057},
	author = {Ben Jeurissen and Steven Jillings and Giraldo, Diana L and Van Ombergen, Angelique and Tomilovskaya, Elena and Pechenkova, Ekaterina and Rukavishnikov, Ilya and Petrovichev, Victor and Sijbers, Jan and zu Eulenburg, Peter and Wuyts, Floris L}
}
@article {2359,
	title = {Foot shape assessment techniques for orthotic and footwear applications: a methodological literature review},
	journal = {Frontiers in Bioengineering and Biotechnology},
	volume = {12},
	year = {2024},
	month = {Sept / 2024},
	pages = {1-15},
	doi = {https://doi.org/10.3389/fbioe.2024.1416499},
	author = {Femke Danckaers and Kristina Stankovi{\'c} and Toon Huysmans and Brian G. Booth and Jan Sijbers}
}
@inproceedings {2350,
	title = {High-resolution tiled X-ray cone-beam CT using the ASTRA toolbox},
	booktitle = {13th Conference on Industrial Computed Tomography (iCT) 2024},
	year = {2024},
	month = {03/2024},
	address = {School of Engineering, Wels Campus, Austria},
	abstract = {Dealing with large and/or elongated objects presents a challenge in micro computed tomography ({\textmu}CT). In conventional CT, the necessity for the X-ray beam to fully cover the scanned object which has to remain well within the detector boundaries is essential to avoid region-of-interest artifacts in the reconstructed images. Consequently, the magnification of the object and thereby the spatial resolution are limited by the physical dimensions and degrees of freedom of the imaging system. A possible way to increase the magnification and thus the resolution of a {\textmu}CT image is to perform a tiled scan of the object. In this paper, we propose an iterative reconstruction approach that yields a high-resolution image from such a tiled CT scan. The approach utilizes the ASTRA toolbox, leveraging its key features: the vector-based definition of projection geometry and the linear tomography operators. Our simulations demonstrate that the proposed approach achieves higher resolution through increased magnification.},
	keywords = {cone beam, iterative reconstruction, micro computed tomography, X-ray tomography},
	isbn = {1435-4934},
	doi = {10.58286/29258 },
	author = {Pavel Paramonov and Joaquim G. Sanctorum and Domenico Iuso and Jan Sijbers and Jan De Beenhouwer}
}
@article {2347,
	title = {Imaging Algorithm and Optimal Measurement Protocol for 3D Impedance Tomography on 2D High Density Micro Electrode Array},
	journal = {IEEE Sensors},
	volume = {24},
	year = {2024},
	pages = {24452-24465},
	doi = {10.1109/JSEN.2024.3417289},
	author = {Jian Deng and Jens Renders and Babu Linkoon P Meenaketan and Saeedeh Ebrahimi Takalloo and Dries Braeken and Jan De Beenhouwer and Jan Sijbers}
}
@mastersthesis {2363,
	title = {Improved 4DCT reconstruction algorithms for the imaging of foam microstructure formation},
	volume = {PhD in Sciences},
	year = {2024},
	type = {PhD thesis},
	author = {Jens Renders}
}
@mastersthesis {2326,
	title = {Improved X-ray CT reconstruction techniques with non-linear imaging models},
	year = {2024},
	type = {PhD thesis},
	author = {Nathana{\"e}l Six}
}
@inproceedings {2397,
	title = {Improving Spectral Unmixing Performance by Frequency Component Reduction},
	booktitle = {2024 14th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)},
	year = {2024},
	pages = {1-5},
	keywords = {Fast Fourier Transform, Fast Fourier transforms, Genetic algorithms, Hyperspectral, Hyperspectral imaging, intimate mixtures, Noise, Noise level, Redundancy, Robustness, Signal processing algorithms, Stability analysis, Surface treatment, unmixing},
	doi = {10.1109/WHISPERS65427.2024.10876530},
	author = {Bnoulkacem, Zakaria and Koirala, Bikram and Paul Scheunders}
}
@inproceedings {2468,
	title = {Inline edge illumination through sample mask misalignment: a proof of concept},
	booktitle = {Proceedings of the 6th International Conference on X-ray and Neutron Phase Imaging with Gratings, Shenzhen, China},
	year = {2024},
	abstract = {Edge illumination (EI) is an X-ray phase contrast imaging setup that allows
measuring two additional complimentary contrasts. The setup consists of two absorbing
masks with slit shaped apertures. Typically, one of the masks is stepped during scans,
complicating the application of EI in inline settings. Current solutions exist, but require
either limiting the number of retrieved contrasts or fabricating specialized masks. In this
work, an EI scanning method with purposely misaligned conventional EI masks is
proposed to enable inline EI. Good agreement is found between conventional and inline EI
flatfield parameters, and first inline scan results are presented.},
	keywords = {X-ray phase contrast imaging},
	url = {https://files.sciconf.cn/upload/file/20240415/20240415160406_47435.pdf},
	author = {Nicholas Francken and Jan De Beenhouwer and Jan Sijbers}
}
@article {2348,
	title = {Inline edge illumination X-ray phase contrast imaging through mask misalignment},
	journal = {Optics Express},
	volume = {32},
	year = {2024},
	pages = {32048-32061},
	doi = {10.1364/OE.525730},
	author = {Nicholas Francken and Jonathan Sanctorum and Joaquim Sanctorum and Pieter-Jan Vanthienen and Jan Sijbers and Jan De Beenhouwer}
}
@article {2321,
	title = {Joint multi-contrast CT for edge illumination X-ray phase contrast imaging using split Barzilai-Borwein steps},
	journal = {Optics Express},
	volume = {32},
	year = {2024},
	pages = {1135-1150},
	doi = {https://doi.org/10.1364/OE.502542},
	author = {Nathana{\"e}l Six and Jens Renders and Jan De Beenhouwer and Jan Sijbers}
}
@article {2381,
	title = {Longitudinal Magnetic Resonance Imaging Method},
	number = {WO2024100240A1},
	year = {2024},
	chapter = {WO2024100240A1},
	author = {Jan Sijbers and Arnold Jan den Dekker}
}
@article {2467,
	title = {Mask assembly for edge illumination phase contrast radiation imaging},
	number = {EP2025062978W},
	year = {2024},
	month = {11/2025},
	edition = {G01N23/041 },
	chapter = {WO2025237942A1},
	abstract = {In general, the technology disclosed herein relates object inspection utilizing radiation imaging, more specifically, edge illumination phase contrast X-ray imaging. The apparatus and methods described in the present disclosure can be applied for inspection of various objects in different industrial settings, such as manufacturing, food, agriculture, aerospace, automotive, electronics, healthcare, transport, logistics, security, and others. An aspect of the present invention relates to a mask assembly for radiation phase contrast imaging apparatus, comprising a first mask comprising a plurality of X-ray absorbing element and apertures arranged in a manner that gradually diverges from one portion to an opposite portion of the body, resulting in a variable pitch perpendicular to the optical axis direction; and a mask alignment mechanism configured to adjust a position and/or orientation of the first mask to align the variable pitch of the first mask for phase contrast imaging.},
	author = {Jan De Beenhouwer and Jan Sijbers and Pieter-Jan Vanthienen}
}
@article {2380,
	title = {Methods and systems for determining an anatomical reconstruction of a broken bone},
	year = {2024},
	edition = {WO2024068984A1},
	chapter = {PCT/EP2023/077151},
	author = {Jan Sijbers and Femke Danckaers and Jana Osstyn}
}
@article {2325,
	title = {Model-based super-resolution reconstruction for pseudo-continuous Arterial Spin Labeling},
	journal = {NeuroImage},
	volume = {286},
	year = {2024},
	month = {01/2024},
	pages = {120506},
	abstract = {Arterial spin labeling (ASL) is a promising, non-invasive perfusion magnetic resonance imaging technique for quantifying cerebral blood flow (CBF). Unfortunately, ASL suffers from an inherently low signal-to-noise ratio (SNR) and spatial resolution, undermining its potential. Increasing spatial resolution without significantly sacrificing SNR or scan time represents a critical challenge towards routine clinical use. In this work, we propose a model-based super-resolution reconstruction (SRR) method with joint motion estimation that breaks the traditional SNR/resolution/scan-time trade-off. From a set of differently oriented 2D multi-slice pseudo-continuous ASL images with a low through-plane resolution, 3D-isotropic, high resolution, quantitative CBF maps are estimated using a Bayesian approach. Experiments on both synthetic whole brain phantom data, and on in vivo brain data, show that the proposed SRR Bayesian estimation framework outperforms state-of-the-art ASL quantification.},
	keywords = {Arterial spin labeling, CBF mapping, Model-based reconstruction, Perfusion, Quantitative MRI, super-resolution},
	issn = {1053-8119},
	doi = {10.1016/j.neuroimage.2024.120506},
	author = {Quinten Beirinckx and Piet Bladt and Merlijn C E van der Plas and M.J.P van Osch and Ben Jeurissen and Arnold Jan den Dekker and Jan Sijbers}
}
@inproceedings {2378,
	title = {Multi-resolution gratings for edge illumination x-ray phase contrast imaging: a simulation study},
	booktitle = {Developments in X-Ray Tomography XV},
	volume = {13152},
	year = {2024},
	pages = {1315208},
	publisher = {International Society for Optics and Photonics},
	organization = {International Society for Optics and Photonics},
	keywords = {cone-beam, edge illumination, grating design, magnification, mask, monte-carlo, multi-resolution, Phase contrast},
	doi = {10.1117/12.3025968},
	url = {https://doi.org/10.1117/12.3025968},
	author = {Pieter-Jan Vanthienen and Nicholas Francken and Jan Sijbers and Jan De Beenhouwer}
}
@article {2382,
	title = {A New Dual-Feature Fusion Network for Enhanced Hyperspectral Unmixing},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	year = {2024},
	pages = {1-1},
	keywords = {Accuracy, dual-feature fusion, Estimation, Feature extraction, Hyperspectral imaging, Hyperspectral unmixing, image reconstruction, Noise reduction, Reflectivity, Scattering, similarity, Transformers, Transforms, unmixing module},
	doi = {10.1109/TGRS.2024.3505292},
	author = {Tao, Xuanwen and Koirala, Bikram and Plaza, Antonio and Paul Scheunders}
}
@article {2361,
	title = {Nonlinear Spectral Unmixing Using B{\'e}zier Surfaces},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {62},
	year = {2024},
	pages = {1-16},
	keywords = {B{\'e}zier surface, Data models, Hyperspectral, Hyperspectral imaging, Manifolds, mineral powder mixtures, mixing models, nonlinearity, Reflectivity, Sensor phenomena and characterization, spectral variability, Surface treatment, Training},
	doi = {10.1109/TGRS.2024.3422495},
	author = {Koirala, Bikram and Rasti, Behnood and Bnoulkacem, Zakaria and Paul Scheunders}
}
@article {2358,
	title = {PACS: Projection-driven with Adaptive CADs X-ray Scatter compensation for additive manufacturing inspection},
	journal = {Precision Engineering},
	volume = {90},
	year = {2024},
	pages = {108-121},
	doi = {https://doi.org/10.1016/j.precisioneng.2024.08.006},
	author = {Domenico Iuso and Pavel Paramonov and Jan De Beenhouwer and Jan Sijbers}
}
@article {2364,
	title = {Perceptual super-resolution in multiple sclerosis MRI},
	journal = {Frontiers in Neuroscience},
	volume = {18},
	year = {2024},
	doi = {10.3389/fnins.2024.1473132},
	author = {Diana Giraldo and Hamza Khan and Gustavo Pineda and Liang, Zhihua and Alfonso Lozano and Bart Van Mijweersch and Henry C. Woodruff and Philippe Lambin and Romero, Eduardo and Liesbet M. Peeters and Jan Sijbers}
}
@article {2336,
	title = {Practical multi-mesh registration for few-view poly-chromatic X-ray inspection},
	journal = {Journal of Non-destructive Testing},
	volume = {43},
	year = {2024},
	chapter = {63},
	doi = {10.1007/s10921-024-01071-y},
	author = {Domenico Iuso and Pavel Paramonov and Jan De Beenhouwer and Jan Sijbers}
}
@article {2296,
	title = {A preparation pulse for fast steady state approach in Actual Flip angle Imaging},
	journal = {Medical Physics},
	volume = {51},
	year = {2024},
	pages = {306-318},
	doi = {https://doi.org/10.1002/mp.16624},
	author = {Marco Andrea Zampini and Jan Sijbers and Marleen Verhoye and Ruslan Garipov}
}
@conference {2337,
	title = {Reconstruction with rigid motion correction technique in CT imaging: A simulation and application study},
	year = {2024},
	address = {Vietnam Institute for Advanced Study in Mathematics (VIASM), Hanoi, Vietnam},
	author = {Anh-Tuan Nguyen and Jens Renders and Jan Sijbers and Jan De Beenhouwer}
}
@conference {2449,
	title = {Simulating Atom Probe Tomography Using MFEM},
	year = {2024},
	abstract = {In atom probe tomography (APT), spatial reconstruction enables volumetric insight into a specimen{\textquoteright}s nanostructure. To this day, a fast reconstruction method which utilizes the true potential of APT in terms of resolution does not exist. A model of its effective inverse, the field evaporation, which provides a physically accurate description of the ion trajectories, is a crucial component in reconstruction. The simulation of each individual evaporation while has been time inefficient. We introduce AdAPTS, an adaptive atom probe tomography simulation library based on MFEM. AdAPTS is capable of generating accurate detector hit maps of various specimens, efficiently representing and simulating the experimental domain from specimen to detector. Using AdAPTS, we are able to accurately simulate the field evaporation of various specimens, revealing realistic poles and zone lines. },
	url = {https://mfem.org/workshop24/$\#$agenda},
	author = {Julian L{\"u}ken and Claudia Fleischmann and Jan Sijbers and Jan De Beenhouwer}
}
@inproceedings {2466,
	title = {A simulation study of optimized-aperture gratings for edge illumination cone-beam phase contrast x-ray imaging},
	booktitle = {Proceedings of the 6th International Conference on X-ray and Neutron Phase Imaging with Gratings},
	year = {2024},
	month = {04/2024},
	address = {Shenzhen, China},
	abstract = {Edge illumination (EI) is an established x-ray phase-contrast imaging method that relies on gratings to obtain attenuation, phase and dark field contrast. Despite the successful transition from synchrotron to lab sources, the cone-beam geometry of lab systems limits the effectiveness of conventional gratings. The non-parallel incidence of X-rays on planar gratings with equally sized apertures introduces unwanted shadowing effects. In this paper, a grating design with optimized position-dependent apertures is introduced and compared to a conventional grating and a grating with fan-beam shaped gratings bars. Numerical simulations show that this optimized-aperture design must be considered when constructing an EI setup.},
	author = {Pieter-Jan Vanthienen and Jan Sijbers and Jan De Beenhouwer}
}
@inproceedings {2327,
	title = {Statistical shape model guided virtual reduction of displaced distal radius fractures},
	booktitle = {IEEE International Symposium on Biomedical Imaging},
	year = {2024},
	pages = {1-5},
	address = {Athens, Greece},
	doi = {10.1109/ISBI56570.2024.10635672},
	author = {Jana Osstyn and Femke Danckaers and A Verstreken and Annemieke Van Haver and Matthias Vanhees and Jan Sijbers}
}
@article {2344,
	title = {A Supervised Approach for Estimating Fractional Abundances of Binary Intimate Mixtures},
	journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
	year = {2024},
	pages = {1-11},
	keywords = {Earth, Estimation, Hyperspectral, Hyperspectral imaging, Mathematical models, mineral powder mixtures, mixing models, nonlinearity, Powders, Reflectivity, spectral variability, Vectors},
	doi = {10.1109/JSTARS.2024.3387750},
	author = {Koirala, Bikram and Rasti, Behnood and Paul Scheunders}
}
@inproceedings {2353,
	title = {Synthesizing fractional anisotropy maps from T1-weighted magnetic resonance images using a simplified generative adversarial network},
	booktitle = {Medical Imaging 2024: Clinical and Biomedical Imaging},
	volume = {12930},
	year = {2024},
	pages = {129302P},
	publisher = {International Society for Optics and Photonics},
	organization = {International Society for Optics and Photonics},
	keywords = {Diffusion weighted imaging, Fractional anisotropy, Generative adversarial networks, Image-to-image translation, MRI synthesis, Structural MRI},
	doi = {10.1117/12.3009706},
	url = {https://doi.org/10.1117/12.3009706},
	author = {Murcia-Tapias, Alyhuwert and Diana Giraldo and Romero, Eduardo}
}
@mastersthesis {2356,
	title = {Technological breakthroughs towards motion-robust super-resolution quantitative magnetic resonance imaging for improved detection of brain diseases},
	year = {2024},
	month = {07/2024},
	pages = {268},
	school = {University of Antwerp},
	type = {PhD Thesis},
	abstract = {Magnetic resonance imaging (MRI) is one of the most widely used neuroimaging techniques, thanks to its exceptional soft tissue contrast and intrinsic safety for patients. Unfortunately, signal intensities in conventional MRI images are expressed in relative units that depend on scanner hardware and software. While this poses no issue for visual inspection of anatomy, it complicates the quantitative comparison of signal intensities within a scan, between successive scans, and among different subjects. In contrast, quantitative MRI (qMRI) generates quantitative maps of absolute biophysical tissue parameters by voxel-wise fitting a biophysical signal model to a series of contrast-weighted scans. Increasing evidence suggests that qMRI can detect and quantify subtle microscopic tissue damage, aiding in the early detection and accurate diagnosis of various neurodegenerative diseases such as Alzheimer{\textquoteright}s or multiple sclerosis. The impact of these diseases is growing in our rapidly aging society. However, current qMRI techniques require long scan times due to the need for multiple contrast-weighted scans with high-resolution and high signal-to-noise ratio (SNR). Long scan times increase the risk of motion artifacts, reduce patient throughput, and decrease patient comfort, hindering the clinical adoption of qMRI. This work therefore investigates the use of model-based super-resolution reconstruction (SRR) for qMRI, aiming to optimize the trade-off between spatial resolution, SNR, and scan time. SRR enables the reconstruction of a high-resolution scan or parameter map from a series of low-resolution MRI scans, with typically shorter scan time per image. As an additional innovation in this thesis, patient movement is jointly estimated, which is crucial to avoid motion artifacts in the reconstruction. The new SRR framework is extensively tested and compared with existing SRR methods, both in computer simulations and using real brain MRI data. Furthermore, the new framework uses Bayesian statistics to incorporate prior knowledge about noise and brain tissue characteristics into the reconstruction process. This allows for the reconstruction of a 3D high-resolution quantitative tissue parameter map from motion-affected low-resolution MRI scans. Application to real brain MRI shows that the proposed framework significantly improves the accuracy of tissue parameter quantification compared to methods with a separate motion correction step. Finally, the SRR framework is extended to perfusion MRI for improved quantification of cerebral blood flow (CBF), in combination with Arterial Spin Labeling (ASL). As a result, a high-resolution quantitative CBF parameter map can be directly reconstructed from low-resolution ASL MRI scans, significantly improving SNR and CBF accuracy over traditional ASL methods.},
	doi = {10.63028/10067/2069070151162165141},
	url = {https://repository.uantwerpen.be/desktop/irua},
	author = {Quinten Beirinckx}
}
@article {2355,
	title = {Three-dimensional bone morphology is a risk factor for medial postmeniscectomy syndrome: A retrospective cohort study},
	journal = {Journal of Experimental Orthopaedics},
	volume = {11},
	year = {2024},
	pages = {e12090},
	doi = {https://doi.org/10.1002/jeo2.12090},
	author = {Jonas Grammens and Annemieke Van Haver and Femke Danckaers and Kristien Vuylsteke and Jan Sijbers and Lotem Mahluf and Peter Angele and Elizaveta Kon and Peter Verdonk}
}
@inproceedings {2441,
	title = {Towards dopaminergic SPECT estimation from structural MRI sequences to characterize Parkinson{\textquoteright}s disease},
	booktitle = {2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)},
	year = {2024},
	pages = {1-4},
	keywords = {Codes, Diseases, Engineering in medicine and biology, Estimation, Magnetic Resonance Imaging, Magnetic resonance imaging (MRI), Motors, Neurons, non-aligned projections, Parkinson{\textquoteright}s disease, Proposals, Resonance, self-supervised learning, Single photon emission computed tomography, Single-Photon Emission Computed Tomography (SPECT)},
	doi = {10.1109/EMBC53108.2024.10782073},
	author = {Sierra-Jerez, Franklin and Valencia, Yeferson and Diana Giraldo and Mart{\'\i}nez, Fabio}
}
@conference {2394,
	title = {Validation of abundance determination of granular mixture using radiative transfer and Bayesian MCMC},
	year = {2024},
	abstract = {We used the semi-analytical reflectance model from (Hapke, 2012), which is a good compromise between physical realism and efficient computation time. In Andrieu et al., 2022, we demonstrated that the usual gradient-descent method is not helpful because the non-linearities are so strong that the results mainly depend on the initialization. So the analysis is done by a Monte Carlo Bayesian approach to propagate the uncertainties from the measurement in reflectance to the parameters (Cruz Mermy et al., 2023). This operation is sometimes called inversion or assimilation.},
	url = {https://meetingorganizer.copernicus.org/EPSC2024/EPSC2024-902.html},
	author = {Fr{\'e}d{\'e}ric Schmidt and Bikram Koirala and Fran{\c c}ois Andrieu}
}
@article {2349,
	title = {Voxel-wise segmentation for porosity investigation of additive manufactured parts with 3D unsupervised and (deeply) supervised neural models},
	journal = {Applied Intelligence},
	volume = {54},
	year = {2024},
	pages = { 13160{\textendash}13177},
	doi = {https://doi.org/10.1007/s10489-024-05647-z},
	author = {Domenico Iuso and Soumick Chatterjee and Sven Cornelissen and Dries Verhees and Jan De Beenhouwer and Jan Sijbers}
}
@inproceedings {2373,
	title = {X-ray image reconstruction for continuous acquisition with accelerated rotation},
	booktitle = {SPIE Developments in X-Ray Tomography XV},
	volume = {13152},
	year = {2024},
	doi = {https://doi.org/10.1117/12.3027622},
	author = {Ben Huyge and Jens Renders and Jan De Beenhouwer and Jan Sijbers}
}
@article {2365,
	title = {X-ray image reconstruction for continuous acquisitions with a generalized motion model},
	journal = {Optics Express},
	volume = {32},
	year = {2024},
	pages = {39192-39207},
	doi = {https://doi.org/10.1364/OE.531999},
	author = {Ben Huyge and Jens Renders and Joaquim Sanctorum and Jan De Beenhouwer and Jan Sijbers}
}
@conference {2291,
	title = {Accelerated Dual-contrast Three-dimensional Knee Magnetic Resonance Imaging Using Super-resolution Reconstructed Deep Learning-enhanced Two-dimensional Dixon Turbo Spin-echo Imaging},
	volume = {27},
	year = {2023},
	month = {05/2023},
	pages = {1-24},
	publisher = {Thieme Medical Publishers, Inc.},
	abstract = {Recent work on three-dimensional (3D) super-resolution reconstruction (SRR) of conventional two-dimensional (2D) turbo spin-echo (TSE) knee magnetic resonance imaging (MRI) shows that high-resolution isotropic knee MRI is technically feasible. Yet the use of acceleration techniques and contrast optimization are needed for clinical validation of this 3D method. With the advent of deep learning (DL) image reconstruction techniques, high acceleration of SRR input data is now achievable through extended use of simultaneous multislice and parallel imaging methods. Moreover, the combination of accelerated TSE with the Dixon method allows us to acquire fat-suppressed and non-fat-suppressed data within a single acquisition that further accelerates the dual-contrast SRR protocol.

This study evaluated the technical feasibility of 3D SRR MRI based on DL-enhanced highly accelerated 2D Dixon TSE MRI. It also compared image quality and diagnostic confidence of this dual-contrast 3D technique to (accelerated) conventional 2D TSE knee MRI. We hypothesized that the SRR method provides the required contrasts needed for comprehensive knee joint evaluation in a competitive acquisition time.},
	doi = {10.1055/s-0043-1770025},
	url = {https://www.thieme-connect.com/products/ejournals/abstract/10.1055/s-0043-1770025},
	author = {C{\'e}line Smekens and Floris Vanhevel and Quinten Beirinckx and Thomas Janssens and Pieter Van Dyck}
}
@inproceedings {2333,
	title = {Acritical Comparison of Linear and Nonlinear Unmixing for Intimate Mixtures},
	booktitle = {2023 13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)},
	year = {2023},
	pages = {1-5},
	keywords = {Conferences, Estimation, hyperspectral image, Hyperspectral imaging, intimate mixtures, linear and nonlinear unmixing, Powders, Reflectivity, Signal processing, Spectroradiometers},
	doi = {10.1109/WHISPERS61460.2023.10431354},
	author = {Koirala, Bikram and Das, Samiran and Rasti, Behnood and Ghamisi, Pedram and Gloaguen, Richard and Paul Scheunders}
}
@inproceedings {2295,
	title = {Adapting an XCT-scanner to enable edge illumination X-ray phase contrast imaging},
	booktitle = {e-Journal of Nondestructive Testing},
	volume = {28},
	year = {2023},
	issn = {1435-4934},
	doi = {doi.org/10.58286/27755},
	author = {Ben Huyge and Pieter-Jan Vanthienen and Nathana{\"e}l Six and Jan Sijbers and Jan De Beenhouwer}
}
@article {2256,
	title = {ADEPT: Accurate Diffusion EPI with multi-contrast shoTs},
	journal = {Magnetic Resonance in Medicine},
	volume = {89},
	year = {2023},
	pages = {396-410},
	doi = {10.1002/mrm.29398},
	author = {Banafshe Shafieizargar and Ben Jeurissen and Dirk H J Poot and Stefan Klein and Johan Van Audekerke and Verhoye, Marleen and Arnold Jan den Dekker and Jan Sijbers}
}
@inproceedings {2282,
	title = {Automated virtual reduction of displaced distal radius fractures},
	booktitle = {IEEE International Symposium on Biomedical Imaging},
	year = {2023},
	pages = {1-4},
	address = {Cartagena, Colombia},
	doi = {10.1109/ISBI53787.2023.10230399},
	author = {Jana Osstyn and Femke Danckaers and Annemieke Van Haver and Jose Oramas and Matthias Vanhees and Jan Sijbers}
}
@inproceedings {2352,
	title = {Comparison of diffusion kurtosis tensor imaging and multi-tissue CSD for the investigation of group differences in Alzheimer{\textquoteright}s disease},
	booktitle = {International Society of Magnetic Resonance in Medicine (ISMRM)},
	year = {2023},
	address = {Toronto, ON, Canada},
	url = {https://submissions.mirasmart.com/ISMRM2023/Itinerary/PresentationDetail.aspx?evdid=6363},
	author = {Diana Giraldo and Robert E. Smith and Hanne Struyfs and Ellis Niemantsverdriet and Ellen De Roeck and Maria Bjerke and Sebastiaan Engelborghs and Eduardo Romero and Jan Sijbers and Ben Jeurissen}
}
@inproceedings {2287,
	title = {A condensed history approach to x-ray dark field effects in edge illumination phase contrast simulations},
	booktitle = {45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society},
	year = {2023},
	author = {Nicholas Francken and Jonathan Sanctorum and Jens Renders and Pavel Paramonov and Jan Sijbers and Jan De Beenhouwer}
}
@inproceedings {2335,
	title = {Corrosion detection using hyperspectral VNIR and SWIR imaging for drone-based inspection of electrical pylons},
	booktitle = {EUROCORR 2023, European Corrosion Congress, Proceedings},
	year = {2023},
	pages = {2},
	author = {Bnoulkacem, Zakaria and Vlaminck, Michiel and Luzardo Morocho, Gonzalo Raimundo and Zivariadab, Hamed and Zahiri, Zohreh and Koirala, Bikram and Platisa, Ljiljana and Luong, Hiep and Paul Scheunders and Mangialetto, Fr{\'e}d{\'e}ric and Bufi, Irid}
}
@inproceedings {2292,
	title = {The Deep Steerable Convolutional Framelet Network for Suppressing Directional Artifacts in X-ray Tomosynthesis},
	booktitle = {31st European Signal Processing Conference, EUSIPCO},
	year = {2023},
	author = {Luis Filipe Alves Pereira and Jan De Beenhouwer and Jan Sijbers}
}
@inproceedings {2293,
	title = {Defect detectability analysis via Probability of defect detection between traditional and deep learning methods in numerical simulations},
	booktitle = {e-Journal of Nondestructive Testing},
	volume = {28},
	year = {2023},
	issn = {1435-4934 },
	doi = {doi.org/10.58286/27716},
	author = {Miroslav Yosifov and Patrick Weinberger and Michael Reiter and Bernhard Fr{\"o}hler and Jan De Beenhouwer and Jan Sijbers and Johann Kastner and Christoph Heinzl}
}
@inproceedings {2283,
	title = {DELTA-MRI: Direct deformation Estimation from LongiTudinally Acquired k-space data},
	booktitle = {IEEE International Symposium on Biomedical Imaging},
	year = {2023},
	pages = {1-4},
	doi = {10.1109/ISBI53787.2023.10230697},
	author = {Jens Renders and Banafshe Shafieizargar and Marleen Verhoye and Jan De Beenhouwer and Arnold Jan den Dekker and Jan Sijbers}
}
@inproceedings {2334,
	title = {Drone-Based Corrosion Detection on High-Voltage Transmission Towers Using Hyperspectral Imaging},
	booktitle = {2023 13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)},
	year = {2023},
	pages = {1-5},
	keywords = {Corrosion, corrosion detection, Drones, High-voltage techniques, high-voltage tower, Hyperspectral imaging, Inspection, Pipelines, Poles and towers, UAV},
	doi = {10.1109/WHISPERS61460.2023.10431111},
	author = {Vlaminck, Michiel and Bnoulkacem, Zakaria and Luzardo, Gonzalo and Zivariadab, Hamed and Zahiri, Zohreh and Koirala, Bikram and Mangialetto, Fr{\'e}d{\'e}ric and Bufi, Irid and Platisa, Ljiljana and Jayapala, Murali and Paul Scheunders and Luong, Hiep}
}
@article {2297,
	title = {dtiRIM: A generalisable deep learning method for diffusion tensor imaging},
	journal = {Neuroimage},
	volume = {269},
	year = {2023},
	chapter = {119900},
	doi = {10.1016/j.neuroimage.2023.119900},
	author = {Emanoel Ribeiro Sabidussi and Stefan Klein and Ben Jeurissen and Dirk H J Poot}
}
@article {2305,
	title = {Efficient iterative reconstruction with beam shape compensation for THz computed tomography},
	journal = {Applied Optics},
	volume = {62},
	year = {2023},
	month = {April 14, 2023},
	pages = {F31-F40},
	chapter = {F31},
	abstract = {Terahertz (THz) computed tomography is an emerging nondestructive and non-ionizing imaging method. Most THz reconstruction methods rely on the Radon transform, originating from x-ray imaging, in which x rays propagate in straight lines. However, a THz beam has a finite width, and ignoring its shape results in blurred reconstructed images. Moreover, accounting for the THz beam model in a straightforward way in an iterative reconstruction method results in extreme demands in memory and in slow convergence. In this paper, we propose an efficient iterative reconstruction that incorporates the THz beam shape, while avoiding the above disadvantages. Both simulation and real experiments show that our approach results in improved resolution recovery in the reconstructed image. Furthermore, we propose a suitable preconditioner to improve the convergence speed of our reconstruction.},
	keywords = {Beam shaping, computed tomography, Image quality, image reconstruction, THz imaging},
	doi = {https://doi.org/10.1364/AO.482511},
	url = {https://opg.optica.org/viewmedia.cfm?r=1\&rwjcode=ao\&uri=ao-62-17-F31\&seq=0},
	author = {Lars-Paul Lumbeeck and Pavel Paramonov and Jan Sijbers and Jan De Beenhouwer}
}
@inproceedings {2299,
	title = {Enhancing industrial inspection with efficient edge illumination x-ray phase contrast simulations},
	booktitle = {IEEE EUROCON 2023 -20th International Conference on Smart Technologies, Torino, Italy},
	year = {2023},
	author = {Nicholas Francken and Pavel Paramonov and Jan Sijbers and Jan De Beenhouwer}
}
@inproceedings {2354,
	title = {Exploring anatomical brain heterogeneity in subjects with mild cognitive impairment},
	booktitle = {2023 19th International Symposium on Medical Information Processing and Analysis (SIPAIM)},
	year = {2023},
	pages = {1-5},
	doi = {10.1109/SIPAIM56729.2023.10373453},
	author = {Pabon Ochoa, German A. and Diana Giraldo and Romero, Eduardo}
}
@conference {2300,
	title = {Exploring the Correlation between Disability Status and Brain Volumetric Measurements Using Real-World Retrospective Magnetic Resonance Images in People with Multiple Sclerosis},
	number = {1637},
	year = {2023},
	address = {October 11-13, Milan, Italy},
	abstract = {This study investigated whether retrospective real-world MR image databases can be used to obtain MR-based biomarkers of multiple sclerosis (MS) disability by examining correlations between Expanded Disability Status Scale (EDSS) scores and volumetric measurements from reconstructed T2-weighted fluid-attenuated inversion recovery (FLAIR) in people with MS (PwMS).},
	author = {Hamza Khan and Diana Giraldo and Quinten Beirinckx and Jan Sijbers and Philippe Lambin and Henry C. Woodruff and Liesbet M. Peeters}
}
@inproceedings {2317,
	title = {An Extensive Multisensor Hyperspectral Benchmark Datasets of Intimate Mixtures of Mineral Powders},
	booktitle = { IEEE International Geoscience and Remote Sensing Symposium},
	year = {2023},
	month = {20 October 2023},
	pages = {5890-5893},
	abstract = {Since many materials behave as heterogeneous intimate mixtures with which each photon interacts differently, the relationship between spectral reflectance and material composition is very complex. Quantitative validation of spectral unmixing algorithms requires high-quality ground truth fractional abundance data, which are very difficult to obtain.In this work, we generated a comprehensive hyperspectral dataset of intimate mineral powder mixtures by homogeneously mixing five different clay powders (Kaolin, Roof clay, Red clay, mixed clay, and Calcium hydroxide). In total 325 samples were prepared. Among the 325 samples, 60 mixtures were binary, 150 were ternary, 100 were quaternary, and 15 were quinary. For each mixture (and pure clay powder), reflectance spectra are acquired by 13 different sensors, with a broad wavelength range between the visible and the long-wavelength infrared regions (i.e., between 350 nm and 15385 nm) and with a large variation in sensor types, platforms, and acquisition conditions. We will make this dataset public, to be used by the community for the validation of nonlinear unmixing methodologies (https://github.com/VisionlabUA/Multisensor_datasets)},
	doi = {10.1109/IGARSS52108.2023.10281467},
	author = {Bikram Koirala and Behnood Rasti and Zakaria Bnoulkacem and Andr{\'e}a De Lima Ribeiro and Yuleika Madriz and Erik Herrmann and Arthur Gestels and Thomas De Kerf and Koen Janssens and Gunther Steenackers and Richard Gloaguen and Paul Scheunders}
}
@article {2264,
	title = {Fast and accurate pose estimation of additive manufactured objects from few X-ray projections},
	journal = {Expert Systems With Applications},
	volume = {213},
	year = {2023},
	pages = {1-10},
	doi = {https://doi.org/10.1016/j.eswa.2022.118866},
	author = {Alice Presenti and Liang, Zhihua and Luis Filipe Alves Pereira and Jan Sijbers and Jan De Beenhouwer}
}
@article {2298,
	title = {Fiber Orientation Estimation from X-ray Dark Field Images of Fiber Reinforced Polymers using Constrained Spherical Deconvolution},
	journal = {Polymers},
	volume = {15},
	year = {2023},
	pages = {2887},
	doi = {https://doi.org/10.3390/polym15132887},
	author = {Ben Huyge and Jonathan Sanctorum and Ben Jeurissen and Jan De Beenhouwer and Jan Sijbers}
}
@inproceedings {2446,
	title = {Fitting a 2D mesh to X-ray measurements},
	booktitle = { 			 17th International Meeting on Fully 3D Image Reconstruction in Radiology and Nuclear Medicine},
	year = {2023},
	address = {Stony Brook},
	author = {Merckx, Jannes and Jan Sijbers and Jan De Beenhouwer}
}
@article {2301,
	title = {Grating designs for cone beam edge illumination X-ray phase contrast imaging: a simulation study},
	journal = {Optics Express},
	volume = {31},
	year = {2023},
	pages = {28051-28064},
	abstract = {Edge illumination is an emerging X-ray phase contrast imaging technique providing attenuation, phase and dark field contrast. Despite the successful transition from synchrotron to lab sources, the cone beam geometry of lab systems limits the effectiveness of using conventional planar gratings. The non-parallel incidence of X-rays introduces shadowing effects, worsening with increasing cone angle. To overcome this limitation, several alternative grating designs can be considered. In this paper, the effectiveness of three alternative designs is compared to conventional gratings using numerical simulations. Improvements in flux and contrast are discussed, taking into account practical considerations concerning the implementation of the designs.},
	doi = {10.1364/OE.495789},
	url = {https://opg.optica.org/oe/fulltext.cfm?uri=oe-31-17-28051\&id=536127},
	author = {Pieter-Jan Vanthienen and Jonathan Sanctorum and Ben Huyge and Nathana{\"e}l Six and Jan Sijbers and Jan De Beenhouwer}
}
@mastersthesis {2339,
	title = {Hyperspectral imaging for automated inspection of offshore wind infrastructure},
	year = {2023},
	type = {PhD thesis},
	author = {Thomas De Kerf}
}
@article {2304,
	title = {ImWIP: open-source image warping toolbox with adjoints and derivatives},
	journal = {SoftwareX},
	volume = {24},
	year = {2023},
	pages = {101524},
	doi = {https://doi.org/10.1016/j.softx.2023.101524},
	author = {Jens Renders and Ben Jeurissen and Anh-Tuan Nguyen and Jan De Beenhouwer and Jan Sijbers}
}
@conference {2461,
	title = {Iterative model-based Rician bias correction and its application to denoising in diffusion MRI},
	year = {2023},
	pages = {3795},
	author = {Tournier, Jacques-Donald and Ben Jeurissen and Christiaens, Daan}
}
@article {2324,
	title = {A Multisensor Hyperspectral Benchmark Dataset For Unmixing of Intimate Mixtures},
	journal = {IEEE Sensors Journal},
	year = {2023},
	month = {2023/12/28},
	abstract = {Optical hyperspectral cameras capture the spectral reflectance of materials. Since many materials behave as heterogeneous intimate mixtures with which each photon interacts differently, the relationship between spectral reflectance and material composition is very complex. Quantitative validation of spectral unmixing algorithms requires high-quality ground truth fractional abundance data, which are very difficult to obtain. 

In this work, we generated a comprehensive laboratory ground truth dataset of intimately mixed mineral powders. For this, five clay powders (Kaolin, Roof clay, Red clay, mixed clay, and Calcium hydroxide) were mixed homogeneously to prepare 325 samples of 60 binary, 150 ternary, 100 quaternary, and 15 quinary mixtures. Thirteen different hyperspectral sensors have been used to acquire the reflectance spectra of these mixtures in the visible, near, short, mid, and long-wavelength infrared regions (350-15385) nm. Overlaps in wavelength regions due to the operational ranges of each sensor and variations in acquisition conditions resulted in a large amount of spectral variability. Ground truth composition is given by construction, but to verify that the generated samples are sufficiently homogeneous, XRD and XRF elemental analysis is performed. We believe these data will be beneficial for validating advanced methods for nonlinear unmixing and material composition estimation, including studying spectral variability and training supervised unmixing approaches. The datasets can be downloaded from the following link: https://github.com/VisionlabHyperspectral/Multisensor_datasets.},
	author = {Bikram Koirala and Behnood Rasti and Zakaria Bnoulkacem and Andr{\'e}a De Lima Ribeiro and Yuleika Madriz and Erik Herrmann and Arthur Gestels and Thomas De Kerf and Sandra Lorenz and Margret Fuchs and Koen Janssens and Gunther Steenackers and Richard Gloaguen and Paul Scheunders}
}
@article {2274,
	title = {Optimal experimental design and estimation for q-space trajectory imaging},
	journal = {Human Brain Mapping},
	volume = {44},
	year = {2023},
	pages = {1793-1809},
	abstract = {Tensor-valued diffusion encoding facilitates data analysis by q-space trajectory imaging. By modeling the diffusion signal of heterogeneous tissues with a diffusion tensor distribution (DTD) and modulating the encoding tensor shape, this novel approach allows disentangling variations in diffusivity from microscopic anisotropy, orientation dispersion, and mixtures of multiple isotropic diffusivities. To facilitate the estimation of the DTD parameters, a parsimonious acquisition scheme coupled with an accurate and precise estimation of the DTD is needed. In this work, we create two precision-optimized acquisition schemes: one that maximizes the precision of the raw DTD parameters, and another that maximizes the precision of the scalar measures derived from the DTD. The improved precision of these schemes compared to a naive sampling scheme is demonstrated in both simulations and real data. Furthermore, we show that the weighted linear least squares (WLLS) estimator that uses the squared reciprocal of the noisy signal as weights can be biased, whereas the iteratively WLLS estimator with the squared reciprocal of the predicted signal as weights outperforms the conventional unweighted linear LS and nonlinear LS estimators in terms of accuracy and precision. Finally, we show that the use of appropriate constraints can considerably increase the precision of the estimator with only a limited decrease in accuracy.},
	doi = {10.1002/hbm.26175},
	author = {Jan Morez and Szczepankiewicz, Filip and Arnold Jan den Dekker and Floris Vanhevel and Jan Sijbers and Ben Jeurissen}
}
@mastersthesis {2290,
	title = {Parametric Fiber Analysis for Glass Fiber-reinforced Composite Tomographic Images},
	year = {2023},
	type = {PhD thesis},
	author = {Tim Elberfeld}
}
@article {2270,
	title = {Prolonged microgravity induces reversible and persistent changes on human cerebral connectivity},
	journal = {Communications Biology},
	volume = {6},
	year = {2023},
	doi = {https://doi.org/10.1038/s42003-022-04382-w},
	author = {Steven Jillings and Ekaterina V. Pechenkova and Elena Tomilovskaya and Ilya Rukavishnikov and Ben Jeurissen and Angelique Van Ombergen and Nosikova, Inna and Alena Rumshiskaya and Liudmila Litvinova and Annen, Jitka and Chloe De Laet and Catho Schoenmaekers and Jan Sijbers and Petrovichev, Victor and Stefan Sunaert and Paul M Parizel and Valentin Sinitsyn and zu Eulenburg, P and Steven S L Laureys and Athena Demertzi and Floris L Wuyts}
}
@article {2306,
	title = {A reconstruction method for atom probe tomography},
	number = {18017575},
	year = {2023},
	edition = {US20230307207A1},
	chapter = {US Patent App. 18/017,575, 2023},
	author = {Jan Sijbers and Jan De Beenhouwer and Yu-Ting Ling and Wilfried Vandervorst}
}
@inproceedings {2279,
	title = {Region-based motion-compensated iterative reconstruction technique for dynamic computed tomography},
	booktitle = {IEEE International Symposium on Biomedical Imaging (ISBI)},
	year = {2023},
	address = {Cartagena de Indias, Colombia},
	doi = {https://doi.org/10.1109/ISBI53787.2023.10230608},
	author = {Anh-Tuan Nguyen and Jens Renders and Jan Sijbers and Jan De Beenhouwer}
}
@article {2402,
	title = {Shadow-aware nonlinear spectral unmixing with spatial regularization},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {61},
	year = {2023},
	pages = {5517516},
	author = {Guichen Zhang and Paul Scheunders and Daniele Cerra}
}
@inproceedings {2331,
	title = {Soil Moisture Content Estimation from Hyperspectral Remote Sensing Data},
	booktitle = {2023 13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)},
	year = {2023},
	pages = {1-5},
	keywords = {Hyperion EO-1, Hyperspectral, Hyperspectral imaging, machine learning, Moisture, Optical reflection, Optical saturation, Optical sensors, Optical signal processing, remote sensing, Soil moisture, soil moisture content},
	doi = {10.1109/WHISPERS61460.2023.10431194},
	author = {Jambhali, Ketaki Vinay and Koirala, Bikram and Bnoulkacem, Zakaria and Paul Scheunders}
}
@inproceedings {2281,
	title = {Sparse-view Medical Tomosynthesis via Mixed Scale Dense Convolutional Framelet Networks},
	booktitle = {IEEE International Symposium on Biomedical Imaging (ISBI)},
	year = {2023},
	pages = {880-884},
	doi = { https://doi.org/10.23919/EUSIPCO58844.2023.10289781},
	author = {Luis Filipe Alves Pereira and Jan De Beenhouwer and Jan Sijbers}
}
@article {2318,
	title = {Study on the Potential of Oil Spill Monitoring in a Port Environment Using Optical Reflectance},
	journal = {Remote Sensing},
	year = {2023},
	month = {2023/10/13},
	abstract = {In this work, we studied the potential of the visible, near-infrared, and shortwave infrared wavelength regions for monitoring oil spill incidents using optical reflectance. First, a simple physical model was designed for accurate oil thickness and volume estimation using optical reflectance. The developed method was made invariant to changes in acquisition and illumination conditions. In the next step, an algorithm based on an artificial neural network was designed to detect spilled oil. The training samples that are required to optimize the parameters of the network were generated by utilizing the proposed physical model. To validate the method, experiments were conducted in laboratory and outdoor scenarios for detection and thickness/volume estimation on four different oil types. In particular, we developed hyperspectral datasets of oil samples with varying thickness between 500 {\textmu}m and 5000 {\textmu}m acquired using two different sensors, an Agrispec spectrometer and an Imec snapscan shortwave infrared hyperspectral camera, in strictly controlled experimental settings. To demonstrate the potential of the proposed method in outdoor environments using solely the visible wavelength region, we monitored the evolution of artificially spilled oil in an outdoor scene with an RGB camera mounted on a drone.},
	doi = {10.3390/rs15204950},
	author = {Bikram Koirala and Nicholus Mboga and Robrecht Moelans and Els Knaeps and Seppe Sels and Frederik Winters and Svetlana Samsonova and Steve Vanlanduit and Paul Scheunders}
}
@inproceedings {2288,
	title = {Super-resolution reconstruction of multi-slice T2-w FLAIR MRI improves Multiple Sclerosis lesion segmentation},
	booktitle = {45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society},
	year = {2023},
	address = {Sydney, Australia},
	abstract = {Due to acquisition time constraints, T2-w FLAIR MRI of Multiple Sclerosis (MS) patients is often acquired with multi-slice 2D protocols with a low through-plane resolution rather than with high-resolution 3D protocols. Automated lesion segmentation on such low-resolution (LR) images, however, performs poorly and leads to inaccurate lesion volume estimates. Super-resolution reconstruction (SRR) methods can then be used to obtain a high-resolution (HR) image from multiple LR images to serve as input for lesion segmentation. In this work, we evaluate the effect on MS lesion segmentation of three SRR approaches: one based on interpolation, a state-of-the-art self-supervised CNN-based strategy, and a recently proposed model-based SRR method. These SRR strategies were applied to LR acquisitions simulated from 3D T2-w FLAIR MRI of MS patients. Each SRR method was evaluated in terms of image reconstruction quality and posterior lesion segmentation performance. When compared to segmentation on LR images, the three considered SRR strategies demonstrate improved lesion segmentation. Furthermore, in some scenarios, SRR achieves a similar segmentation performance compared to segmentation of HR images.},
	author = {Diana Giraldo and Quinten Beirinckx and Arnold Jan den Dekker and Ben Jeurissen and Jan Sijbers}
}
@mastersthesis {2277,
	title = {Surface and image-based registration methods with statistical modeling for biomedical applications},
	volume = {PhD},
	year = {2023},
	type = {PhD thesis},
	author = {Jeroen Van Houtte}
}
@article {2289,
	title = {Systematic review of reconstruction techniques for accelerated quantitative MRI},
	journal = {Magnetic Resonance in Medicine},
	volume = {90},
	year = {2023},
	pages = {1172-1208},
	doi = {https://doi.org/10.1002/mrm.29721},
	author = {Banafshe Shafieizargar and Riwaj Byanju and Jan Sijbers and Stefan Klein and Arnold Jan den Dekker and Dirk H J Poot}
}
@article {2255,
	title = {Tabu-DART: A dynamic update strategy for efficient discrete algebraic reconstruction},
	journal = {The Visual Computer},
	volume = {39},
	year = {2023},
	pages = {4671{\textendash}4683},
	doi = {10.1007/s00371-022-02616-w},
	author = {Daniel Frenkel and Nathana{\"e}l Six and Jan De Beenhouwer and Jan Sijbers}
}
@inproceedings {2294,
	title = {Toward denoising of 3D CT scans with few data},
	booktitle = {e-Journal of Nondestructive Testing},
	volume = {28},
	year = {2023},
	issn = {1435-4934},
	doi = {doi.org/10.58286/27741},
	author = {Liang, Zhihua and Anneke Van Heteren and Jan Sijbers and Jan De Beenhouwer}
}
@article {2285,
	title = {Towards material and process agnostic features for the classification of pore types in metal additive manufacturing},
	journal = {Materials \& Design},
	volume = {227},
	year = {2023},
	pages = {111757},
	doi = {10.1016/j.matdes.2023.111757},
	author = {M. Vandecasteele and R. Heylen and Domenico Iuso and A. Thanki and W. Philips and A. Witvrouw and D. Verhees and B. G. Booth}
}
@inproceedings {2332,
	title = {Unmixing Intimate Mixtures Using B{\'e}zier Surfaces},
	booktitle = {2023 13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)},
	year = {2023},
	pages = {1-5},
	keywords = {hyperspectral image, Hyperspectral imaging, intimate mixtures, Lighting, nonlinear unmixing, Reflectivity, Signal processing, Surface reconstruction, Surface treatment, Training},
	doi = {10.1109/WHISPERS61460.2023.10431306},
	author = {Koirala, Bikram and Rasti, Behnood and Bnoulkacem, Zakaria and Paul Scheunders}
}
@mastersthesis {2338,
	title = {Unsupervised machine/deep learning mapping (clustering) for single/multi-source remote sensing data },
	year = {2023},
	type = {PhD thesis},
	author = {Kasra Rafiezadeh Shahi}
}
@article {2286,
	title = {Use of support vector machines approach via ComBat harmonized diffusion tensor imaging for the diagnosis and prognosis of mild traumatic brain injury: a CENTER-TBI study},
	journal = {Journal of Neurotrauma},
	volume = {40},
	year = {2023},
	pages = {1317-1338},
	doi = {https://doi.org/10.1089/neu.2022.0365},
	author = {Maira Siqueira Pinto and Stefan Winzeck and Evgenios N. Kornaropoulos and Sophie Richter and Roberto Paolella and Marta M. Correia and Ben Glocker and Guy Williams and Anne Vik and Jussi Posti and Asta Kristine H{\r a}berg and Jonas Stenberg and Pieter-Jan Guns and Arnold Jan den Dekker and David K. Menon and Jan Sijbers and Pieter Van Dyck and Virginia F. J. Newcombe}
}
@article {2219,
	title = {Using particle systems for mitral valve segmentation from 3D transoesophageal echocardiography (3D TOE) - a proof of concept},
	journal = {Computer Methods in Biomechanics and Biomedical Engineering: Imaging \& Visualization},
	volume = {11},
	year = {2023},
	pages = {112-120},
	doi = {10.1080/21681163.2022.2058416},
	url = {https://doi.org/10.1080/21681163.2022.2058416},
	author = {Patricia Lopes and Paul Van Herck and Eefje Verhoelst and Roel Wirix-Speetjens and Jan Sijbers and Johan Bosmans and Jos Vander Sloten}
}
@article {2204,
	title = {3D total variation denoising in X-CT imaging applied to pore extraction in additively manufactured parts},
	journal = {Measurement Science and Technology},
	volume = {33},
	year = {2022},
	pages = {1-12},
	doi = {10.1088/1361-6501/ac459a},
	author = {Rob Heylen and Aditi Thanki and Dries Verhees and Domenico Iuso and Jan De Beenhouwer and Jan Sijbers and Ann Witvrouw and Han Haitjema and Abdellatif Bey-Temsamani}
}
@mastersthesis {2280,
	title = {3D X-ray radiography-based inspection of manufactured objects},
	volume = {PhD},
	year = {2022},
	type = {PhD thesis},
	author = {Alice Presenti}
}
@inproceedings {2239,
	title = {An accelerated motion-compensated iterative reconstruction technique for dynamic computed tomography},
	booktitle = {Proc. SPIE 12242, Developments in X-Ray Tomography XIV, 122421F},
	year = {2022},
	address = {San Diego, CA, United States},
	doi = {https://doi.org/10.1117/12.2631570},
	author = {Anh-Tuan Nguyen and Jens Renders and Jeroen Soete and Martine Wevers and Jan Sijbers and Jan De Beenhouwer}
}
@conference {2268,
	title = {Adaptive triangular mesh for phase contrast imaging},
	year = {2022},
	url = {https://ictms2022.sciencesconf.org/385325/document},
	author = {Jannes Merckx and Bart van Lith and Jan Sijbers and Jan De Beenhouwer}
}
@mastersthesis {2278,
	title = {Advances in biplanar X-ray imaging: calibration and 2D/3D registration},
	volume = {PhD},
	year = {2022},
	type = {PhD thesis},
	author = {Van Nguyen}
}
@inproceedings {2262,
	title = {Alternative grating designs for cone-beam edge illumination X-ray phase contrast imaging},
	booktitle = { Proc. SPIE 12242, Developments in X-Ray Tomography XIV},
	volume = {12242},
	year = {2022},
	month = {10/2022},
	pages = {122420Z},
	publisher = {SPIE},
	organization = {SPIE},
	address = {San Diego, USA},
	doi = {10.1117/12.2632301},
	author = {Pieter-Jan Vanthienen and Jonathan Sanctorum and Ben Huyge and Nathana{\"e}l Six and Jan Sijbers and Jan De Beenhouwer}
}
@inproceedings {2227,
	title = {Analysis of Plant Stress Response Using Hyperspectral Imaging and Kernel Ridge Regression},
	booktitle = {11th International Conference on Robotics, Vision, Signal Processing and Power Applications},
	volume = {829},
	year = {2022},
	publisher = {Lecture Notes in Electrical Engineering, Springer},
	organization = {Lecture Notes in Electrical Engineering, Springer},
	address = {Singapore},
	doi = {10.1007/978-981-16-8129-5_66},
	author = {Mohd Shahrimie Mohd Asaari and Stien Mertens and Stijn Dhondt and Dirk Inze and Paul Scheunders}
}
@inproceedings {2192,
	title = {Analytic derivatives of scaling motion-compensated projection operators for dynamic computed tomography},
	booktitle = {e-Journal of Nondestructive Testing},
	volume = { 27 (3)},
	year = {2022},
	pages = {1-5},
	doi = {https://doi.org/10.58286/26588},
	url = {https://www.ndt.net/search/docs.php3?id=26588},
	author = {Anh-Tuan Nguyen and Jens Renders and Jeroen Soete and Martine Wevers and Jan Sijbers and Jan De Beenhouwer}
}
@inproceedings {2237,
	title = {Augmenting a conventional X-ray scanner with edge illumination based phase contrast imaging: how to design the gratings?},
	booktitle = {Proc. SPIE 12242, Developments in X-Ray Tomography XIV},
	year = {2022},
	month = {10/2022},
	pages = {1224218},
	publisher = {SPIE},
	organization = {SPIE},
	address = {San Diego, USA},
	doi = {10.1117/12.2633455},
	author = {Jonathan Sanctorum and Nathana{\"e}l Six and Jan Sijbers and Jan De Beenhouwer}
}
@article {2273,
	title = {Automated Mitral Valve Assessment for Transcatheter Mitral Valve Replacement (TMVR) Planning},
	journal = {Frontiers in Bioengineering and Biotechnology},
	volume = {16},
	year = {2022},
	pages = {1-13},
	doi = {https://doi.org/10.3389/fbioe.2022.1033713},
	author = {Lopes, Patricia and Paul Van Herck and J. Ooms and N. Van Mieghem and Wirix-Speetjens, Roel and Jan Sijbers and Vander Sloten, Jos and Bosmans, Johan}
}
@article {2236,
	title = {Automatic anomaly detection from X-ray images based on autoencoder},
	journal = {Nondestructive Testing and Evaluation},
	volume = {37},
	year = {2022},
	doi = {10.1080/10589759.2022.2074415},
	author = {Alice Presenti and Zhihua Liang and Luis Filipe Alves Pereira and Jan Sijbers and Jan De Beenhouwer}
}
@article {2257,
	title = {Automatic landmark detection and mapping for 2D/3D registration with BoneNet},
	journal = {Frontiers Veterinary Science},
	year = {2022},
	doi = {https://doi.org/10.3389/fvets.2022.923449},
	author = {Van Nguyen and Luis Filipe Alves Pereira and Liang, Zhihua and Falk Mielke and Jeroen Van Houtte and Jan Sijbers and Jan De Beenhouwer}
}
@inproceedings {2315,
	title = {Blind Nonlinear Unmixing For Intimate Mixtures Using Hapke Model And CNN},
	booktitle = {Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)},
	year = {2022},
	month = {22 November 2022},
	pages = {1-5},
	abstract = {We propose a blind nonlinear unmixing technique for intimate mixtures. We use the Hapke model and a fully convolutional neural networks (HapkeCNN). The proposed loss function contains 1) A quadratic term based on the Hapke model, 2) reconstruction error, and 3) a minimum volume term. The first term captures the nonlinearity, the second ensures the fidelity of the reconstructed reflectance, and the latter term exploits the geometrical information. The proposed method is evaluated using a simulated and a real datasets. We compare the results of endmember and abundance estimation with bilinear, multilinear, nonlinear, and projection-based linear unmixing techniques. The experimental results confirm that HapkeCNN considerably outperforms the state-of-the-art nonlinear approaches in terms of spectral angle distance and root mean square error. HapkeCNN was implemented in Python (3.9) using PyTorch as the platform for the deep network and is available online: https://github.com/BehnoodRasti/HapkeCNN.},
	doi = {10.1109/WHISPERS56178.2022.9955081},
	author = {Behnood Rasti and Bikram Koirala}
}
@article {2212,
	title = {A Bottom-Up Volume Reconstruction Method for Atom Probe Tomography},
	journal = {Microscopy and Microanalysis},
	volume = {28},
	year = {2022},
	pages = {1-14},
	doi = {10.1017/S1431927621012836},
	author = {Yu-Ting Ling and Siegfried Cools and Janusz Bogdanowicz and Claudia Fleischmann and Jan De Beenhouwer and Jan Sijbers and Wilfried Vandervorst}
}
@article {2216,
	title = {Brain Connectometry Changes in Space Travelers After Long-Duration Spaceflight},
	journal = {Front. Neural Circuits},
	volume = {16},
	year = {2022},
	doi = {https://doi.org/10.3389/fncir.2022.815838},
	author = {Andrei Doroshin and Steven Jillings and Ben Jeurissen and Elena Tomilovskaya and Ekaterina V. Pechenkova and Nosikova, Inna and Alena Rumshiskaya and Liudmila Litvinova and Ilya Rukavishnikov and Chloe De Laet and Catho Schoenmaekers and Jan Sijbers and Steven S L Laureys and Petrovichev, Victor and Angelique Van Ombergen and Jitka Annen and Stefan Sunaert and Paul M Parizel and Valentin Sinitsyn and zu Eulenburg, Peter and Karol Osipowicz and Floris L Wuyts}
}
@inproceedings {2194,
	title = {CNN-based pose estimation from a single X-ray projection for 3D inspection of manufactured objects},
	booktitle = {11th Conference on Industrial Computed Tomography},
	year = {2022},
	author = {Alice Presenti and Zhihua Liang and Luis Filipe Alves Pereira and Jan Sijbers and Jan De Beenhouwer}
}
@mastersthesis {2218,
	title = {Computational anatomy strategies for characterization of brain patterns associated with Alzheimer{\textquoteright}s disease},
	volume = {PhD in Science},
	year = {2022},
	type = {PhD thesis},
	author = {Diana Giraldo}
}
@article {2276,
	title = {cuPARE: Parametric Reconstruction of Curved Fibres from Glass fibre-reinforced Composites},
	journal = {Nondestructive Testing and Evaluation},
	year = {2022},
	doi = {10.1080/10589759.2022.2155647},
	author = {Tim Elberfeld and Bernhard Fr{\"o}hler and Christoph Heinzl and Jan Sijbers and Jan De Beenhouwer}
}
@conference {2462,
	title = {Data-driven characterization of knee structures using non-negative matrix factorization of quantitative UTE Spiral VIBE MRI},
	year = {2022},
	author = {Smekens, C{\'e}line and Van Dyck, Pieter and Janssens, Thomas and Sijbers, Jan and Ben Jeurissen}
}
@inproceedings {2311,
	title = {Deep Blind Unmixing using Minimum Simplex Convolutional Network},
	booktitle = {IEEE International Geoscience and Remote Sensing Symposium},
	year = {2022},
	month = {28/09/2022},
	pages = {28-31},
	abstract = {This paper proposes a deep blind hyperspectral unmixing network for datasets without pure pixels called minimum simplex convolutional network (MiSiCNet). MiSiCNet is the first deep learning-based blind unmixing method proposed in the literature which incorporates both spatial and geometrical information of the hyperspectral data, in addition to the spectral information. The proposed convolutional encoder-decoder architecture incorporates the spatial information using convolutional filters and implicitly applying a prior on the abundances. We added a minimum simplex volume penalty term to the loss function to exploit the geometrical information. We evaluate the performance of MiSiCNet on simulated and real datasets. The experimental results confirm the robustness of the proposed method to both noise and absence of pure pixels. Additionally, MiSiCNet considerably outperforms the state-of-the-art unmixing approaches. The results are given in terms of spectral angle distance in degree for the endmember estimation, and root mean square error in percentage for the abundance estimation. MiS-iCNet was implemented in Python (3.8) using PyTorch as the platform for the deep network and is available online: https://github.com/BehnoodRasti/MiSiCNet.},
	doi = {10.1109/IGARSS46834.2022.9883117},
	author = {Behnood Rasti and Bikram Koirala and Paul Scheunders and Jocelyn Chanussot}
}
@article {2213,
	title = {Deep learning-based 2D/3D registration of an atlas to biplanar X-ray images},
	journal = {International Journal of Computer Assisted Radiology and Surgery},
	volume = {309},
	year = {2022},
	pages = {1333{\textendash}1342},
	doi = {10.1007/s11548-022-02586-3},
	author = {Jeroen Van Houtte and Emmanuel Audenaert and Guoyan Zheng and Jan Sijbers}
}
@article {2193,
	title = {Discrete Terahertz tomography: a simulation study},
	journal = {e-Journal of Nondestructive Testing},
	volume = {27},
	year = {2022},
	issn = {1435-4934},
	doi = {doi.org/10.58286/26608},
	author = {Jana Christopher and Lars-Paul Lumbeeck and Pavel Paramonov and Jan De Beenhouwer and Jan Sijbers}
}
@conference {2463,
	title = {dtiRIM: A recurrent inference machine for diffusion tensor estimation},
	year = {2022},
	pages = {1981},
	author = {Sabidussi, E R and Klein, S and Ben Jeurissen and Poot, D H J}
}
@conference {2269,
	title = {Edge Illumination Phase Contrast Simulations Using the OptiX GPU Ray Tracing Engine},
	year = {2022},
	author = {Nicholas Francken and Pavel Paramonov and Jan Sijbers and Jan De Beenhouwer}
}
@article {2215,
	title = {The effect of prolonged Spaceflight on Cerebrospinal Fluid and Perivascular Spaces of Astronauts and Cosmonauts},
	journal = {PNAS},
	volume = {119},
	year = {2022},
	chapter = {e2120439119},
	doi = {https://doi.org/10.1073/pnas.2120439119},
	author = {Giuseppe Barisano and Farshid Sepehrband and Heather R. Collins and Steven Jillings and Ben Jeurissen and Andrew J. Taylor and Catho Schoenmaekers and Chloe De Laet and Ilya Rukavishnikov and Inna Nosikova and Alena Rumshiskaya and Jitka Annen and Jan Sijbers and Steven S L Laureys and Angelique Van Ombergen and Petrovichev, Victor and Valentin Sinitsyn and Ekaterina V. Pechenkova and Alexey Grishin and Peter zu Eulenburg and Meng Law and Stefan Sunaert and Paul M. Parizel and Elena Tomilovskaya and Donna Roberts and Floris L Wuyts}
}
@inproceedings {2240,
	title = {Efficient X-ray projection of triangular meshes based on ray tracing and rasterization},
	booktitle = {SPIE Optical Engineering: Developments in X-Ray Tomography XIV },
	volume = {12242},
	year = {2022},
	pages = {122420W },
	doi = {https://doi.org/10.1117/12.2633448},
	author = {Pavel Paramonov and Jens Renders and Tim Elberfeld and Jan De Beenhouwer and Jan Sijbers}
}
@inproceedings {2209,
	title = {Evaluation of a Morphable Anthropomorphic Articulated Total Body Model},
	booktitle = {Design Tools and Methods in Industrial Engineering II - Proceedings of the 2nd International Conference on Design Tools and Methods in Industrial Engineering, ADM 2021},
	year = {2022},
	pages = {761{\textendash}772},
	publisher = {Springer},
	organization = {Springer},
	abstract = {In this work a new approach for the creation of Articulated Total Body (ATB) models for person-specific multi-body simulations is presented, with the main aim of overcoming limitations related to classical multi-ellipsoids ATB models, based on regression equations having only the weight and the height of the subject as input. The new methodology is based on a Statistical Shape Model (SSM), morphable according to up to 24 input parameters: the SSM was obtained from Principal Component Analysis (PCA), applied on a wide database of 3D human scans (CAESAR). The so obtained geometry can be segmented automatically to generate body segments with the respective inertial properties (mass, principal moments of inertia, and centres of mass location). The routine has been tested on a random set of 20 male subjects and the classical multi-ellipsoids models were compared to these in terms of inertial properties and 3D external geometry: the highest differences were registered at the abdomen and the thighs for what concerns the mass (60\%), principal moments (75\%) and centres of mass (50 mm) properties; the trunk, the shoulder and the calves are the most critical areas for the external geometry (average distance between the anthropomorphic and ellipsoids models equal to 50 mm). A contribution has been made to build person-specific multibody models. This is a valuable method since approximations made by multi-ellipsoidal models have resulted to be relevant at specific body areas, and personalised models can be a support to design and to forensic analyses.},
	keywords = {3D parametric human model, Articulated total body, Forensic biomechanics, Multibody analysis, principal component analysis (PCA)},
	isbn = {978-3-030-91233-8},
	doi = {10.1007/978-3-030-91234-5_77},
	author = {Giulia Pascoletti and Toon Huysmans and Paolo Conti and Zanetti, Elisabetta M.},
	editor = {Caterina Rizzi and Francesca Campana and Michele Bici and Francesco Gherardini and Tommaso Ingrassia and Paolo Cicconi}
}
@inproceedings {2243,
	title = {Evaluation of deeply supervised neural networks for 3D pore segmentation in additive manufacturing},
	booktitle = {SPIE Optical Engineering: Developments in X-Ray Tomography XIV },
	volume = {12242},
	year = {2022},
	pages = {122421K},
	doi = {https://doi.org/10.1117/12.2633318},
	author = {Domenico Iuso and Soumick Chatterjee and Rob Heylen and Sven Cornelissen and Jan De Beenhouwer and Jan Sijbers}
}
@article {2210,
	title = {Feature preserving non-rigid iterative weighted closest point and semi-curvature registration},
	journal = {IEEE Transactions on Image Processing},
	year = {2022},
	abstract = {Preserving features of a surface as characteristic local shape properties captured e.g. by curvature, during non-rigid registration is always difficult where finding meaningful correspondences, assuring the robustness and the convergence of the algorithm while maintaining the quality of mesh are often challenges due to the high degrees of freedom and the sensitivity to features of the source surface. In this paper, we present a non-rigid registration method utilizing a newly defined semi-curvature, which is inspired by the definition of the Gaussian curvature. In the procedure of establishing the correspondences, for each point on the source surface, a corresponding point on the target surface is selected using a dynamic weighted criterion defined on the distance and the semi-curvature. We reformulate the cost function as a combination of the semi-curvature, the stiffness, and the distance terms, and ensure to penalize errors of both the distance and the semi-curvature terms in a guaranteed stable region. For a robust and efficient optimization process, we linearize the semi-curvature term, where the region of attraction is defined and the stability of the approach is proven. Experimental results show that features of the local areas on the original surface with higher curvature values are better preserved in comparison with the conventional methods. In comparison with the other methods, this leads to, on average, 75\%, 8\% and 82\% improvement in terms of quality of correspondences selection, quality of surface after registration, and time spent of the convergence process respectively, mainly due to that the semi-curvature term logically increases the constraints and dependency of each point on the neighboring vertices based on the point{\textquoteright}s degree of curvature.},
	keywords = {curvature, non-linearity, Non-rigid registration, region of attraction},
	issn = {1057-7149},
	doi = {10.1109/TIP.2022.3148822},
	author = {Farzam Tajdari and Toon Huysmans and Yusheng Yang and Yu Song}
}
@inproceedings {2272,
	title = {Fiber orientation estimation by constrained spherical deconvolution of the anisotropic edge illumination x-ray dark field signal},
	booktitle = {SPIE: Developments in X-Ray Tomography XIV},
	volume = {12242},
	year = {2022},
	pages = {122420V },
	doi = {ttps://doi.org/10.1117/12.2633482},
	author = {Ben Huyge and Ben Jeurissen and Jan De Beenhouwer and Jan Sijbers}
}
@article {2261,
	title = {HapkeCNN: Blind nonlinear unmixing for intimate mixtures using Hapke model and convolutional neural network},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	year = {2022},
	author = {Behnood Rasti and Bikram Koirala and Paul Scheunders}
}
@article {2410,
	title = {HapkeCNN: Blind Nonlinear Unmixing for Intimate Mixtures Using Hapke Model and Convolutional Neural Network},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {60},
	year = {2022},
	pages = {1-15},
	keywords = {Albedo, Blind unmixing, convolutional neural network, deep learning (DL), endmember estimation, Estimation, Fans, Hapke model, hyperspectral image, image reconstruction, Kernel, minimum simplex volume, nonlinear unmixing, Photonics, Reflectivity, Solid modeling},
	doi = {10.1109/TGRS.2022.3202490},
	author = {Rasti, Behnood and Koirala, Bikram and Paul Scheunders}
}
@inproceedings {2231,
	title = {Hyperspectral clustering using atrous spatial-spectral convolutional network},
	booktitle = {IGARSS 2022, International Geoscience and Remote Sensing Symposium},
	year = {2022},
	address = {Kuala Lumpur, Malaysia},
	author = {Kasra Rafiezadeh Sahi and Pedram Ghamisi and Behnood Rasti and Paul Scheunders and Richard Gloaguen}
}
@article {2313,
	title = {Hyperspectral Unmixing Using Transformer Network},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {60},
	year = {2022},
	month = { 03 August 2022},
	pages = {1-16},
	abstract = {Transformers have intrigued the vision research community with their state-of-the-art performance in natural language processing. With their superior performance, transformers have found their way into the field of hyperspectral image classification and achieved promising results. In this article, we harness the power of transformers to conquer the task of hyperspectral unmixing and propose a novel deep neural network-based unmixing model with transformers. A transformer network captures nonlocal feature dependencies by interactions between image patches, which are not employed in convolutional neural network (CNN) models, and hereby has the ability to enhance the quality of the endmember spectra and the abundance maps. The proposed model is a combination of a convolutional autoencoder and a transformer. The hyperspectral data is encoded by the convolutional encoder. The transformer captures long-range dependencies between the representations derived from the encoder. The data are reconstructed using a convolutional decoder. We applied the proposed unmixing model to three widely used unmixing datasets, that is, Samson, Apex, and Washington DC Mall, and compared it with the state-of-the-art in terms of root mean squared error and spectral angle distance. The source code for the proposed model will be made publicly available at https://github.com/preetam22n/DeepTrans-HSU .},
	doi = {10.1109/TGRS.2022.3196057},
	author = {Preetam Ghosh and Swalpa Kumar Roy and Bikram Koirala and Behnood Rasti and Paul Scheunders}
}
@article {2208,
	title = {Identification of corrosion minerals usning shortwave infrared hyperspectral imaging},
	journal = {Sensors},
	volume = {22},
	year = {2022},
	pages = {407},
	abstract = {In this study, we propose a new method to identify corrosion minerals in carbon steel using hyperspectral imaging (HSI) in the shortwave infrared range (900{\textendash}1700 nm). Seven samples were artificially corroded using a neutral salt spray test and examined using a hyperspectral camera. A normalized cross-correlation algorithm is used to identify four different corrosion minerals (goethite, magnetite, lepidocrocite and hematite), using reference spectra. A Fourier Transform Infrared spectrometer (FTIR) analysis of the scraped corrosion powders was used as a ground truth to validate the results obtained by the hyperspectral camera. This comparison shows that the HSI technique effectively detects the dominant mineral present in the samples. In addition, HSI can also accurately predict the changes in mineral composition that occur over time. },
	author = {Thomas De Kerf and G. Pipintakos and Zohreh Zahiri and Steve Vanlanduit and Paul Scheunders}
}
@article {2235,
	title = {Improved diffusion parameter estimation by incorporating T2 relaxation properties into the DKI-FWE model},
	journal = {NeuroImage},
	volume = {256},
	year = {2022},
	pages = {119219},
	doi = {https://doi.org/10.1016/j.neuroimage.2022.119219},
	author = {Vincenzo Anania and Quinten Collier and Jelle Veraart and Annemieke Eline Buikema and Floris Vanhevel and Thibo Billiet and Ben Jeurissen and Arnold Jan den Dekker and Jan Sijbers}
}
@article {2234,
	title = {Inline nondestructive internal disorder detection in pear fruit using explainable deep anomaly detection on X-ray images},
	journal = {Computers and Electronics in Agriculture},
	volume = {197},
	year = {2022},
	pages = {1-14},
	doi = {https://doi.org/10.1016/j.compag.2022.106962},
	author = {Tim Van De Looverbosch and Jiaqi, He and Astrid Tempelaere and Klaas Kelchtermans and Pieter Verboven and Tinne Tuytelaars and Jan Sijbers and Bart Nicolai}
}
@article {2265,
	title = {Investigating tissue-specific abnormalities in Alzheimer{\textquoteright}s disease with multi-shell diffusion MRI},
	journal = {Journal of Alzheimer{\textquoteright}s Disease},
	volume = {90},
	year = {2022},
	pages = {1771-1791},
	doi = {10.3233/JAD-220551},
	author = {Diana Giraldo and Robert Elton Smith and Hanna Struyfs and Ellis Niemantsverdriet and Ellen De Roeck and Maria Bjerke and Sebastiaan Engelborghs and Romero, Eduardo and Jan Sijbers and Ben Jeurissen}
}
@inproceedings {2242,
	title = {Joint reconstruction of attenuation, refraction and dark field X-ray phase contrasts using split Barzilai-Borwein steps},
	booktitle = {SPIE Optical Engineering: Developments in X-Ray Tomography XIV },
	volume = {12242},
	year = {2022},
	pages = {122420O},
	doi = {https://doi.org/10.1117/12.2633587},
	author = {Nathana{\"e}l Six and Jens Renders and Jan De Beenhouwer and Jan Sijbers}
}
@article {2220,
	title = {MiSiCNet: Minimum Simplex Convolutional Network for Deep Hyperspectral Unmixing},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {60},
	year = {2022},
	doi = {https://doi.org/10.1109/TGRS.2022.3146904},
	author = {Behnood Rasti and Bikram Koirala and Paul Scheunders and Jocelyn Chanussot}
}
@article {2408,
	title = {MiSiCNet: Minimum Simplex Convolutional Network for Deep Hyperspectral Unmixing},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {60},
	year = {2022},
	pages = {1-15},
	keywords = {Blind unmixing, convolutional neural network, Data mining, deep learning, deep prior, endmember extraction, Estimation, hyperspectral image, Hyperspectral imaging, Indexes, Libraries, Minimization, minimum simplex volume, Optimization, unmixing},
	doi = {10.1109/TGRS.2022.3146904},
	author = {Rasti, Behnood and Koirala, Bikram and Paul Scheunders and Chanussot, Jocelyn}
}
@article {2247,
	title = {Model-based super-resolution reconstruction with joint motion estimation for improved quantitative MRI parameter mapping},
	journal = {Computerized Medical Imaging and Graphics},
	volume = {100},
	year = {2022},
	month = {09/2022},
	pages = {1-16},
	chapter = {102071},
	abstract = {Quantitative Magnetic Resonance (MR) imaging provides reproducible measurements of biophysical parameters, and has become an essential tool in clinical MR studies. Unfortunately, 3D isotropic high resolution (HR) parameter mapping is hardly feasible in clinical practice due to prohibitively long acquisition times. Moreover, accurate and precise estimation of quantitative parameters is complicated by inevitable subject motion, the risk of which increases with scanning time. In this paper, we present a model-based super-resolution reconstruction (SRR) method that jointly estimates HR quantitative parameter maps and inter-image motion parameters from a set of 2D multi-slice contrast-weighted images with a low through-plane resolution. The method uses a Bayesian approach, which allows to optimally exploit prior knowledge of the tissue and noise statistics. To demonstrate its potential, the proposed SRR method is evaluated for a T1 and T2 quantitative mapping protocol. Furthermore, the method{\textquoteright}s performance in terms of precision, accuracy, and spatial resolution is evaluated using simulated as well as real brain imaging experiments. Results show that our proposed fully flexible, quantitative SRR framework with integrated motion estimation outperforms state-of-the-art SRR methods for quantitative MRI.},
	issn = {0895-6111},
	doi = {https://doi.org/10.1016/j.compmedimag.2022.102071},
	author = {Quinten Beirinckx and Ben Jeurissen and Michele Nicastro and Dirk H J Poot and Marleen Verhoye and Arnold Jan den Dekker and Jan Sijbers}
}
@article {2260,
	title = {MS2A-Net: multi-view spectral-spatial association network for hyperspectral image clustering},
	journal = {IEEE-JSTARS, Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
	volume = {15},
	year = {2022},
	pages = {6518-6530},
	author = {Kasra Rafiezadeh Sahi and Pedram Ghamisi and Behnood Rasti and Richard Gloaguen and Paul Scheunders}
}
@article {2221,
	title = {Non-Destructive Analysis of Plant Physiological Traits Using Hyperspectral Imaging: A Case Study on Drought Stress},
	journal = {Computers and Electronics in Agriculture},
	volume = {195},
	year = {2022},
	doi = {10.1016/j.compag.2022.106806},
	author = {Mohd Shahrimie Mohd Asaari and Stien Mertens and Lennart Verbraeken and Stijn Dhondt and Dirk Inze and Bikram Koirala and Paul Scheunders}
}
@article {2409,
	title = {Non-destructive analysis of plant physiological traits using hyperspectral imaging: A case study on drought stress},
	journal = {Computers and Electronics in Agriculture},
	volume = {195},
	year = {2022},
	pages = {106806},
	abstract = {Conventional methods to access plant physiological traits are based on destructive measurements by means of biochemical extraction or leaf clipping, thereby limiting the throughput capability. With advances in hyperspectral imaging sensor, fast, non-invasive and non-destructive measurements of a plant{\textquoteright}s physiological status became feasible. In this work, a non-destructive method for the characterization of a plant{\textquoteright}s status from hyperspectral images is presented. A supervised data-driven method based on Machine Learning Regression (MLR) algorithms was developed to generate prediction models of four targeted physiological traits: water potential, effective quantum yield of photosystem II, transpiration rate and stomatal conductance. Standard Normal Variate (SNV) transformed reflectance spectra were used as the input variables for building the regression model. Three MLR algorithms: Gaussian Process Regression (GPR), Kernel Ridge Regression (KRR), and Partial Least Squares Regression (PLSR) were explored as candidate methods for building the prediction model of the targeted physiological traits. Validation results show that the non-linear prediction models, developed based on the GPR algorithm produced the best estimation accuracy on all plant traits. The best prediction models were applied to a small-scale phenotyping experiment to study drought stress responses in maize plants. Results show that all estimated traits revealed a significant difference between plants under drought stress and normal growth dynamics as early as after 3 days of drought induction.},
	keywords = {Drought stress, Hyperspectral imaging, Machine learning regression, Non-destructive measurements, Physiological traits, Plant phenotyping},
	issn = {0168-1699},
	doi = {https://doi.org/10.1016/j.compag.2022.106806},
	url = {https://www.sciencedirect.com/science/article/pii/S0168169922001235},
	author = {Mohd Shahrimie Mohd Asaari and Stien Mertens and Lennart Verbraeken and Stijn Dhondt and Dirk Inze and Koirala Bikram and Paul Scheunders}
}
@conference {2233,
	title = {Optimal acquisition settings for simultaneous diffusion kurtosis, free water fraction and T2 estimation},
	year = {2022},
	author = {Vincenzo Anania and Ben Jeurissen and Jan Morez and Annemieke Eline Buikema and Thibo Billiet and Jan Sijbers and Arnold Jan den Dekker}
}
@inproceedings {2241,
	title = {Optimization of a multi-source rectangular X-ray CT geometry for inline inspection},
	booktitle = {SPIE Optical Engineering: Developments in X-Ray Tomography XIV },
	volume = {12242},
	year = {2022},
	pages = {1224219 },
	doi = {https://doi.org/10.1117/12.2633523},
	author = {Caroline Bossuyt and Jan De Beenhouwer and Jan Sijbers}
}
@article {2246,
	title = {Probability of Detection applied to X-ray inspection using numerical simulations},
	journal = {Nondestructive Testing and Evaluation},
	volume = {37},
	year = {2022},
	pages = {536-551},
	doi = {10.1080/10589759.2022.2071892},
	author = {Miroslav Yosifov and M. Reiter and S. Heupl and C. Gusenbauer and Bernhard Fr{\"o}hler and R. Fernandez- Gutierrez and Jan De Beenhouwer and Jan Sijbers and Johann Kastner and Christoph Heinzl}
}
@mastersthesis {2271,
	title = {Quantitative assessment of 3D foot shape using statistical shape analysis},
	volume = {PhD in Sciences},
	year = {2022},
	month = {10/2022},
	type = {PhD thesis},
	author = {Kristina Stankovi{\'c}}
}
@article {2403,
	title = {Quantitative detection of corrosion minerals in carbon steel using shortwave infrared hyperspectral imaging},
	journal = {RSC Advances},
	volume = {12},
	year = {2022},
	pages = {32775-32783},
	author = {Thomas De Kerf and Arthur Gestels and Janssens, Koen and Gunther Steenackers and Paul Scheunders and Steve Vanlanduit}
}
@article {2316,
	title = {A Robust Supervised Method for Estimating Soil Moisture Content From Spectral Reflectance},
	journal = {IEEE Transactions on Geoscience and Remote Sensing },
	volume = {60},
	year = {2022},
	month = {05 October 2022},
	pages = {1-13},
	abstract = {Due to the complex interaction of light with moist soils, the soil moisture content (SMC) is hard to estimate from the soil spectral reflectance. Spectral variability, caused by variations in viewing and illumination angle and between-sensor variability, further complicates the estimation. In this work, we developed a supervised methodology to accurately estimate SMC from spectral reflectance. The method determines a proxy for the SMC of moist soil, making use of the reflectance spectra of an air-dried and saturated soil sample. The proxy is made invariant to illumination and viewing angle, and sensor type. In the next step, the proxy is normalized with respect to the ground-truth SMC of the saturated soil to make the technique less dependent on the soil type. The normalized proxy can be directly used as an estimate of SMC. Alternatively, the nonlinear relationship between the normalized proxy and the actual SMC can be learned by supervised regression. Experiments are conducted on real moist soil data. In particular, we developed datasets of moist minerals, acquired by two different sensors, an Agrispec spectrometer and an Imec snapscan shortwave infrared (SWIR) hyperspectral camera, under strictly controlled experimental settings. The proposed methodology is also validated on the available real moist soil data from the literature. Compared to state-of-the-art methods, the proposed method accurately estimates the SMC.},
	doi = {10.1109/TGRS.2022.3212600},
	author = {Bikram Koirala and Zohreh Zahiri and Paul Scheunders}
}
@inproceedings {2312,
	title = {A Robust Supervised Method to Estimate Chlorophyll Ab Content from Spectral Reflectance},
	booktitle = {IEEE International Geoscience and Remote Sensing Symposium},
	year = {2022},
	month = {28/09/2022},
	pages = {326-329},
	abstract = {Leaf chlorophyll ab content is an important indicator of vegetation physiological status and is generally obtained from spectral reflectance. For non-destructive estimation of chlorophyll ab content, physical leaf reflectance models, such as the PROSPECT model and supervised methods have been applied. While the former generally does not perform optimal, the latter only performs well when trained on similar data. In this work, we developed a robust supervised method that overcomes this problem. The method derives a proxy for chlorophyll ab content as the relative position of a leaf reflectance spectrum on the arc spanned by the two extremes, containing high and low chlorophyll ab content. This proxy is found to be unaffected by spectral variability, caused by environmental and acquisition conditions. The relation between this proxy and the actual chlorophyll ab content is obtained by a supervised regression model, that is trained on a single leaf reflectance dataset, and that is transferable to other datasets. The proposed method is validated on seven real hyperspectral datasets.},
	doi = {10.1109/IGARSS46834.2022.9883839},
	author = {Bikram Koirala and Paul Scheunders}
}
@article {2248,
	title = {Self-calibration of C-arm imaging system using interventional instruments during an intracranial biplane angiography.},
	journal = {Int J Comput Assist Radiol Surg},
	year = {2022},
	month = {2022 Mar 12},
	abstract = {To create an accurate 3D reconstruction of the vascular trees, it is necessary to know the exact geometrical parameters of the angiographic imaging system. Many previous studies used vascular structures to estimate the system{\textquoteright}s exact geometry. However, utilizing interventional devices and their relative features may be less challenging, as they are unique in different views. We present a semi-automatic self-calibration approach considering the markers attached to the interventional instruments to estimate the accurate geometry of a biplane X-ray angiography system for neuroradiologic use.</p><p><b>METHODS: </b>A novel approach is proposed to detect and segment the markers using machine learning classification, a combination of support vector machine and boosted tree. Then, these markers are considered as reference points to optimize the acquisition geometry iteratively.</p><p><b>RESULTS: </b>The method is evaluated on four clinical datasets and three pairs of phantom angiograms. The mean and standard deviation of backprojection error for the catheter or guidewire before and after self-calibration are [Formula: see text]~mm and [Formula: see text]~mm, respectively. The mean and standard deviation of the 3D root-mean-square error (RMSE) for some markers in the phantom reduced from [Formula: see text] to [Formula: see text]~mm.</p><p><b>CONCLUSION: </b>A semi-automatic approach to estimate the accurate geometry of the C-arm system was presented. Results show the reduction in the 2D backprojection error as well as the 3D RMSE after using our proposed self-calibration technique. This approach is essential for 3D reconstruction of the vascular trees or post-processing techniques of angiography systems that rely on accurate geometry parameters.},
	issn = {1861-6429},
	doi = {10.1007/s11548-022-02580-9},
	author = {Chabi, Negar and Domenico Iuso and Beuing, Oliver and Preim, Bernhard and Saalfeld, Sylvia}
}
@article {2258,
	title = {Shadow-aware nonlinear spectral unmixing for hyperspectral imagery},
	journal = {IEEE-JSTARS, Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
	volume = {15},
	year = {2022},
	pages = {5514-5533},
	author = {Guichen Zhang and Paul Scheunders and Daniele Cerra and Rupert Muller}
}
@inproceedings {2320,
	title = {Sparse Unmixing using Deep Convolutional Networks},
	booktitle = {IEEE International Geoscience and Remote Sensing Symposium},
	year = {2022},
	month = {28/09/2022},
	pages = {24-27},
	abstract = {This paper proposes a sparse unmixing technique using a convolutional neural network (SUnCNN). We reformulate the sparse unmixing problem into an optimization over the parameters of a convolutional network. Relying on a spectral library, the deep network learns in an unsuper-vised manner a mapping from a fixed input to the sparse abundances. Moreover, SUnCNN fulfills the sum-to-one constraint using a softmax activation layer. We compare SUnCNN with the state-of-the-art using a simulated and a real dataset. The experimental results show that the proposed deep learning-based unmixing method outperforms the oth-ers in terms of signal to reconstruction error. Additionally, SUnCNN is visually superior to the competing techniques. SUnCNN was implemented in Python (3.8) using PyTorch as the platform for the deep network and is available online: https://github.com/BehnoodRasti/SUnCNN.},
	doi = {10.1109/IGARSS46834.2022.9884790},
	author = {Behnood Rasti and Bikram Koirala and Paul Scheunders}
}
@conference {2217,
	title = {Sparse view rectangular X-ray CT for cargo inspection using the ASTRA toolbox},
	year = {2022},
	author = {Caroline Bossuyt and Jan De Beenhouwer and Jan Sijbers}
}
@article {2249,
	title = {The sqstm1tmΔUBA zebrafish model, a proof-of-concept in vivo model for Paget{\textquoteright}s disease of bone?},
	journal = {Bone Reports},
	volume = {16},
	year = {2022},
	pages = {75-76},
	doi = {10.1016/j.bonr.2022.101483},
	author = {Yentl Huybrechts and Rapha{\"e}l De Ridder and Bjorn De Samber and Eveline Boudin and Francesca Tonelli and Dries Knapen and Dorien Schepers and Jan De Beenhouwer and Jan Sijbers and Antonella Forlino and Paul Coucke and P. Eckhard Witten and Ronald Kwon and Andy Willaert and Gretl Hendrickx and Wim Van Hul}
}
@article {2275,
	title = {To shift or to rotate? Comparison of acquisition strategies for multi-slice super-resolution magnetic resonance imaging},
	journal = {Frontiers in Neuroscience},
	year = {2022},
	pages = {1-18},
	doi = {https://doi.org/10.3389/fnins.2022.1044510},
	author = {Michele Nicastro and Ben Jeurissen and Quinten Beirinckx and C{\'e}line Smekens and Dirk H J Poot and Jan Sijbers and Arnold Jan den Dekker}
}
@article {2207,
	title = {Unsupervised Data Fusion with Deeper Perspective: A Novel Multi-Sensor Deep Clustering Algorithm},
	journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing },
	volume = {15},
	year = {2022},
	month = {1/2022},
	pages = {284-296},
	abstract = {The ever-growing developments in technology to capture different types of image data (e.g., hyperspectral imaging and Light Detection and Ranging (LiDAR)-derived digital surface model (DSM)), along with new processing techniques, have led to a rising interest in imaging applications for Earth observation. However, analyzing such datasets in parallel, remains a challenging task. In this paper, we propose a multi-sensor deep clustering (MDC) algorithm for the joint processing of multi-source imaging data. The architecture of MDC is inspired by autoencoder (AE)-based networks. The MDC paradigm includes three parallel networks, a spectral network using an autoencoder structure, a spatial network using a convolutional autoencoder structure, and lastly, a fusion network that reconstructs the concatenated image information from the concatenated latent features from the spatial and spectral network. The proposed algorithm combines the reconstruction losses obtained by the aforementioned networks to optimize the parameters (i.e., weights and bias) of all three networks simultaneously. To validate the performance of the proposed algorithm, we use two multi-sensor datasets from different applications (i.e., geological and rural sites) as benchmarks. The experimental results confirm the superiority of our proposed deep clustering algorithm compared to a number of state-of-the-art clustering algorithms. The code will be available at: https://github.com/Kasra2020/MDC.},
	doi = {10.1109/JSTARS.2021.3132856},
	author = {Kasra Rafiezadeh Sahi and Pedram Ghamisi and Behnood Rasti and Paul Scheunders and Richard Gloaguen}
}
@article {2263,
	title = {Virtual grating approach for Monte Carlo simulations of edge illumination-based x-ray phase contrast imaging},
	journal = {Optics Express},
	volume = {31},
	year = {2022},
	pages = {38695-38708},
	doi = {https://doi.org/10.1364/OE.472145},
	author = {Jonathan Sanctorum and Jan Sijbers and Jan De Beenhouwer}
}
@inproceedings {2176,
	title = {2D/3D registration with a statistical deformation model prior using deep learning},
	booktitle = {the IEEE International Conference on Biomedical and Health Informatics (BHI{\textquoteright}21) },
	year = {2021},
	pages = {1-4},
	doi = {10.1109/BHI50953.2021.9508540},
	author = {Jeroen Van Houtte and Xiaoru Gao and Jan Sijbers and Guoyan Zheng}
}
@conference {2170,
	title = {3D atomic resolution tomography from iDPC-STEM images using multiple atom model prior},
	year = {2021},
	author = {Shiva Hosseinnejad and E. G. T. Bosch and H. Kohr and I. Lazi{\'c} and V. Zharinov and E. Franken and Jan Sijbers and Jan De Beenhouwer}
}
@inproceedings {2186,
	title = {3D THz Tomography Incorporating the Beam Shape},
	booktitle = {2021 OSA Imaging and Applied Optics Congress},
	year = {2021},
	doi = {10.1364/AIS.2021.JTu5A.36},
	author = {Lars-Paul Lumbeeck and Pavel Paramonov and Jan Sijbers and Jan De Beenhouwer}
}
@conference {2184,
	title = {Accelerated multi-shot diffusion weighted imaging with joint estimation of diffusion and phase parameters},
	volume = {34},
	year = {2021},
	pages = {S57-S58},
	doi = {10.1007/s10334-021-00947-8},
	author = {Banafshe Shafieizargar and Ben Jeurissen and Dirk H J Poot and Johan Van Audekerke and Marleen Verhoye and Arnold Jan den Dekker and Jan Sijbers}
}
@article {2079,
	title = {Accelerating in vivo fast spin echo high angular resolution diffusion imaging with an isotropic resolution in mice through compressed sensing},
	journal = {Magnetic Resonance in Medicine},
	volume = {85},
	year = {2021},
	pages = {1397-1413},
	doi = {https://doi.org/10.1002/mrm.28520},
	author = {Maarten Naeyaert and Jan Aelterman and Vladimir Golkov and Daniel Cremers and Aleksandra Pizurica and Jan Sijbers and Marleen Verhoye}
}
@article {2144,
	title = {Adjoint image warping using multivariate splines with application to 4D-CT},
	journal = {Medical Physics},
	volume = {48},
	year = {2021},
	pages = {6362-6374},
	doi = {10.1002/mp.14765},
	author = {Jens Renders and Jan Sijbers and Jan De Beenhouwer}
}
@inproceedings {2098,
	title = {Analysis of flat fields in edge illumination phase contrast imaging},
	booktitle = {2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)},
	year = {2021},
	pages = {1310-1313},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Nice, France},
	doi = {10.1109/ISBI48211.2021.9433849},
	url = {https://ieeexplore.ieee.org/document/9433849},
	author = {Ben Huyge and Jonathan Sanctorum and Nathana{\"e}l Six and Jan De Beenhouwer and Jan Sijbers}
}
@article {2143,
	title = {Associations between different white matter properties and reward-based performance modulation},
	journal = {Brain Structure and Function},
	year = {2021},
	month = {Apr-02-2021},
	issn = {1863-2653},
	doi = {10.1007/s00429-021-02222-x},
	url = {https://link.springer.com/content/pdf/10.1007/s00429-021-02222-x.pdf},
	author = {Park, Haeme R. P. and Verhelst, Helena and Quak, Michel and Ben Jeurissen and Krebs, Ruth M.}
}
@inproceedings {2307,
	title = {BOOSTING HYPERSPECTRAL IMAGE UNMIXING USING DENOISING: FOUR SCENARIOS},
	booktitle = {IEEE International Geoscience and Remote Sensing Symposium IGARSS},
	year = {2021},
	month = {12 October 2021},
	abstract = {We present an analysis of the influence of noise on the unmixing of hyperspectral data. We propose four scenarios to 1) investigate the effect of noise reduction as a preprocessing step on the performance of hyperspectral unmixing and 2) study the relation between noise and different endmembers selection strategies. Experiments are conducted on a simu-1ated and a real datasets with a wide range of signal to noise ratios (from 10 to 50 dB).},
	doi = {10.1109/IGARSS47720.2021.9553942},
	author = {Behnood Rasti and Bikram Koirala and Paul Scheunders and Pedram Ghamisi and Richard Gloaguen}
}
@inproceedings {2172,
	title = {CAD-based scatter compensation for polychromatic reconstruction of additive manufactured parts},
	booktitle = {IEEE ICIP},
	year = {2021},
	pages = {2948-2952},
	doi = {10.1109/ICIP42928.2021.9506536},
	author = {Domenico Iuso and Ehsan Nazemi and Nathana{\"e}l Six and Bjorn De Samber and Jan De Beenhouwer and Jan Sijbers}
}
@inbook {2154,
	title = {Chapter Eight - General conclusions and future perspectives},
	booktitle = {Advances in Imaging and Electron Physics},
	volume = {217},
	year = {2021},
	publisher = {Science Direct Elsevier},
	organization = {Science Direct Elsevier},
	chapter = {Chapter Eight - General conclusions and future perspectives},
	doi = {10.1016/bs.aiep.2021.01.008},
	author = {Annick De Backer and Jarmo Fatermans and Arnold Jan den Dekker and Sandra Van Aert}
}
@inbook {2152,
	title = {Chapter Five - Optimal experiment design for nanoparticle atom counting from ADF STEM images},
	booktitle = {Advances in Imaging and Electron Physics},
	volume = {217},
	year = {2021},
	publisher = {Science Direct Elsevier},
	organization = {Science Direct Elsevier},
	chapter = {Chapter Five - Optimal experiment design for nanoparticle atom counting from ADF STEM images},
	doi = {10.1016/bs.aiep.2021.01.005},
	author = {Annick De Backer and Jarmo Fatermans and Arnold Jan den Dekker and Sandra Van Aert}
}
@inbook {2150,
	title = {Chapter Four - Atom counting},
	booktitle = {Advances in Imaging and Electron Physics,},
	volume = {217},
	year = {2021},
	publisher = {Science Direct Elsevier},
	organization = {Science Direct Elsevier},
	chapter = {Chapter Four - Atom counting},
	doi = {10.1016/bs.aiep.2021.01.004},
	author = {Annick De Backer and Jarmo Fatermans and Arnold Jan den Dekker and Sandra Van Aert}
}
@inbook {2149,
	title = {Chapter One - Introduction},
	booktitle = {Advances in Imaging and Electron Physics},
	volume = {217},
	year = {2021},
	publisher = {Science Direct Elsevier},
	organization = {Science Direct Elsevier},
	chapter = {One-introduction},
	doi = {10.1016/bs.aiep.2021.01.001},
	author = {Annick De Backer and Jarmo Fatermans and Arnold Jan den Dekker and Sandra Van Aert}
}
@inbook {2153,
	title = {Chapter Seven - Image-quality evaluation and model selection with maximum a posteriori probability},
	booktitle = {Advances in Imaging and Electron Physics},
	volume = {217},
	year = {2021},
	publisher = {Science Direct Elsevier},
	organization = {Science Direct Elsevier},
	chapter = {Chapter Seven - Image-quality evaluation and model selection with maximum a posteriori probability},
	doi = {10.1016/bs.aiep.2021.01.007},
	author = {Jarmo Fatermans and Annick De Backer and Arnold Jan den Dekker and Sandra Van Aert}
}
@inbook {2156,
	title = {Chapter Six - Atom column detection},
	booktitle = {Advances in Imaging and Electron Physics},
	volume = {217},
	year = {2021},
	publisher = {Science Direct Elsevier},
	organization = {Science Direct Elsevier},
	chapter = {Chapter Six - Atom column detection},
	doi = {10.1016/bs.aiep.2021.01.006},
	author = {Jarmo Fatermans and Annick De Backer and Arnold Jan den Dekker and Sandra Van Aert}
}
@inbook {2155,
	title = {Chapter Three - Efficient fitting algorithm},
	booktitle = {Advances in Imaging and Electron Physics},
	volume = {217},
	year = {2021},
	publisher = {Science Direct Elsevier},
	organization = {Science Direct Elsevier},
	chapter = {Chapter Three - Efficient fitting algorithm},
	doi = {10.1016/bs.aiep.2021.01.003},
	author = {Annick De Backer and Jarmo Fatermans and Arnold Jan den Dekker and Sandra Van Aert}
}
@inbook {2151,
	title = {Chapter Two - Statistical parameter estimation theory: principles and simulation studies},
	booktitle = {Advances in Imaging and Electron Physics},
	volume = {217},
	year = {2021},
	publisher = {Science Direct Elsevier},
	organization = {Science Direct Elsevier},
	chapter = {Chapter Two - Statistical parameter estimation theory: principles and simulation studies},
	doi = {10.1016/bs.aiep.2021.01.002},
	author = {Annick De Backer and Jarmo Fatermans and Arnold Jan den Dekker and Sandra Van Aert}
}
@inproceedings {2201,
	title = {CNN-based Pose Estimation of Manufactured Objects During Inline X-ray Inspection},
	booktitle = {2021 IEEE 6th International Forum on Research and Technology for Society and Industry (RTSI)},
	year = {2021},
	abstract = {X-ray Computed Tomography (CT) is a nondestructive technique widely used for inspection of manufactured objects generated from a reference computer-aided-design (CAD) representation. In a conventional CT inspection framework, a volumetric reconstruction is computed from a large number of X-ray projections of the object. Afterwards, a surface is extracted, aligned and compared to the CAD model. For an accurate comparison, a high-resolution reconstruction is needed, requiring hundreds of projections, making this procedure not suitable for real-time inspection. In contrast to CT-based inspection, radiograph-based inspection only requires a few radiographs that then can be compared with simulated projections from the reference CAD model. For an effective comparison, however, an accurate 3D pose estimation of the object and consequent alignment between the measured object and the reference model are crucial. In this paper, we present an inline projection-based 3D pose estimation framework using convolutional neural networks (CNNs). Through realistic simulation experiments, we show that, with only two projections, estimation of the pose of the object is possible at the resolution of the acquisition system.},
	author = {Alice Presenti and Zhihua Liang and Luis Filipe Alves Pereira and Jan Sijbers and Jan De Beenhouwer}
}
@article {2135,
	title = {Combination of X-ray tube and GMDH neural network as a nondestructive and potential technique for measuring characteristics of gas-oil{\textendash}water three phase flows},
	journal = {Measurement},
	volume = {168},
	year = {2021},
	chapter = {108427},
	abstract = {In this investigation, a fan-beam photon attenuation based system, including one X-ray tube and two sodium iodide crystal detectors, combined with group method of data handling (GMDH) neural network is proposed to recognize type of flow regime and predict gas-oil{\textendash}water volume fractions of a three phase flow. One GMDH neural network was considered for recognizing flow patterns and two GMDH networks were implemented to predict the volume fractions. The recorded photon energy spectra from the two sodium iodide detectors were defined as the inputs of the three GMDH neural networks. The type of flow pattern and volume fractions were the output obtained from the first and the other two GMDH neural networks, respectively. Through the application of the proposed methodology, all of the flow patterns were recognized correctly except one single case. The volume fraction was also predicted with RMS error of less than 3.1.},
	doi = {10.1016/j.measurement.2020.108427},
	author = {Mohammadmehdi Roshani and Giang Phan and Gholam Hossein Roshani and Robert Hanus and Enrico Corniani and Ehsan Nazemi}
}
@inproceedings {2159,
	title = {Comparison of MR acquisition strategies for super-resolution reconstruction using the Bayesian Mean Squared Error},
	booktitle = { International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine},
	year = {2021},
	abstract = {In multi-slice super-resolution reconstruction (MS-SRR), a high resolution image, referred to as SRR image, is estimated from a series of multi-slice images with a low through-plane resolution. This work proposes a framework based on the Bayesian mean squared error of the Maximum A Posteriori estimator of a SRR image to compare the accuracy and precision of two commonly adopted MR acquisition strategies in MS-SRR. The first strategy consists in acquiring a set of multi-slice
images, where each image is shifted in the through-plane direction by a different, sub-pixel distance. The latter consists in acquiring a set of multi-slice images, where each image is rotated around the frequency or phase-encoding axis by a different rotation angle. Results show that MS-SRR based on rotated multi-slice images outperforms MS-SRR based on shifted multi-slice images in terms of accuracy, precision and mean squared error of the reconstructed image.},
	author = {Michele Nicastro and Ben Jeurissen and Quinten Beirinckx and C{\'e}line Smekens and Dirk H J Poot and Jan Sijbers and Arnold Jan den Dekker}
}
@inproceedings {2099,
	title = {Dark field sensitivity in single mask edge illumination lung imaging},
	booktitle = {2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)},
	year = {2021},
	pages = {775-778},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Nice, France},
	doi = {10.1109/ISBI48211.2021.9434024},
	url = {https://ieeexplore.ieee.org/document/9434024},
	author = {Jonathan Sanctorum and Jan Sijbers and Jan De Beenhouwer}
}
@conference {2180,
	title = {Decoding Multiple Sclerosis EDSS disability scores from MRI using Deep Learning},
	volume = {34},
	year = {2021},
	pages = {S57-S58},
	doi = {10.1007/s10334-021-00947-8},
	author = {Roberto Paolella and Ezequiel de la Rosa and Diana M. Sima and Dominique Dive and Francoise Durand-Dubief and Dominique Sappey-Marinier and Ben Jeurissen and Jan Sijbers and Thibo Billiet}
}
@inproceedings {2158,
	title = {A Deep Convolutional Framelet Network based on Tight Steerable Wavelet: application to sparse-view medical tomosynthesis},
	booktitle = { International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine},
	year = {2021},
	author = {Luis Filipe Alves Pereira and Vincent Van Nieuwenhove and Jan De Beenhouwer and Jan Sijbers}
}
@inproceedings {2223,
	title = {Destriping Hyperspectral Imagery By Adaptive Anisotropic Total Variation And Truncated Nuclear Norm},
	booktitle = {Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)},
	year = {2021},
	doi = {10.1109/WHISPERS52202.2021.9484033},
	author = {Ting Hu and N. Liu and W. Li and R. Tao and F. Zhang and Paul Scheunders}
}
@article {2049,
	title = {Diffusion Tensor Imaging of the Anterior Cruciate Ligament Following Primary Repair with Internal Bracing: a Longitudinal Study},
	journal = {Journal of Orthopaedic Research },
	volume = {39},
	year = {2021},
	pages = {1318{\textendash}1330},
	doi = {10.1002/jor.24684},
	author = {Pieter Van Dyck and Martijn Froeling and Christiaan H.W. Heusdens and Jan Sijbers and Annemie Ribbens and Thibo Billiet}
}
@article {2171,
	title = {Dynamic few-view X-ray imaging for inspection of CAD-based objects},
	journal = {Expert Systems with Applications},
	volume = {180},
	year = {2021},
	pages = {115012},
	keywords = {CAD, Dynamic acquisition, Few-view inspection, Pose estimation, Visibility angles},
	issn = {0957-4174},
	doi = {https://doi.org/10.1016/j.eswa.2021.115012},
	url = {https://www.sciencedirect.com/science/article/pii/S095741742100453X},
	author = {Alice Presenti and Jan Sijbers and Jan De Beenhouwer}
}
@conference {2183,
	title = {EPIFANI for ultrafast B1-corrected T1 and PD mapping},
	volume = {34},
	year = {2021},
	pages = {S59-S60},
	doi = {10.1007/s10334-021-00947-8},
	author = {Marco Andrea Zampini and Jan Sijbers and Marleen Verhoye and Ruslan Garipov}
}
@article {2146,
	title = {EquiSim: An open-source articulatable statistical model of the equine distal limb},
	journal = {Frontiers in Veterinary Science },
	volume = {8},
	year = {2021},
	doi = {10.3389/fvets.2021.623318},
	author = {Jeroen Van Houtte and Filip Vandenberghe and Guoyan Zheng and Toon Huysmans and Jan Sijbers}
}
@article {2137,
	title = {Evaluation of flow pattern recognition and void fraction measurement in two phase flow independent of oil pipeline{\textquoteright}s scale layer thickness},
	journal = {Alexandria Engineering Journal},
	volume = {60},
	year = {2021},
	abstract = {The main objective of the present research is to combine the effect of scale thickness on the flow pattern and characteristics of two-phase flow that is used in oil industry. In this regard, an intelligent nondestructive technique based on combination of gamma radiation attenuation and artificial intelligence is proposed to determine the type of flow pattern and gas volume percentage in two phase flow independent of petroleum pipeline{\textquoteright}s scale layer thickness. The proposed system includes a dual energy gamma source, composed of Barium-133 and Cesium-137 radioisotopes, and two sodium iodide detectors for recording the transmitted and scattered photons. Support Vector Machine was implemented for regime identification and Multi-Layer Perceptron with Levenberg Marquardt algorithm was utilized for void fraction prediction. Total count in the scattering detector and counts under photo peaks of Barium-133 and Cesium-137 were assigned as the inputs of networks. The results show the ability of presented system to identify the annular regime and measure the void fraction independent of petroleum pipeline{\textquoteright}s scale layer thickness.},
	author = {Mohammadmehdi Roshani and Giang T.T. Phan and Gholam Hossein Roshani and Robert Hanus and Trung Duong and Enrico Corniani and Ehsan Nazemi and ElMostafa Kalmouni}
}
@article {2188,
	title = {Extended imaging volume in cone-beam x-ray tomography using the weighted simultaneous iterative reconstruction technique},
	journal = {Physics in Medicine and Biology},
	volume = {66},
	year = {2021},
	chapter = {165008},
	doi = {10.1088/1361-6560/ac16bc},
	author = {Joaquim Sanctorum and Sam Van Wassenbergh and Van Nguyen and Jan De Beenhouwer and Jan Sijbers and Joris J. J. Dirckx}
}
@conference {2464,
	title = {On false positive control in Fixel-Based Analysis},
	volume = {29},
	year = {2021},
	author = {Smith, R E and Christiaens, D and Ben Jeurissen and Pietsch, M and Vaughan, D N and Jackson, G and Tournier, J D}
}
@conference {2147,
	title = {Faster and better HARDI using FSE and holistic reconstruction},
	year = {2021},
	author = {Maarten Naeyaert and Vladimir Golkov and Daniel Cremers and Jan Sijbers and Marleen Verhoye}
}
@article {2092,
	title = {FleXCT: a Flexible X-ray CT scanner with 10 degrees of freedom},
	journal = {Optics Express},
	volume = {29},
	year = {2021},
	pages = {3438-3457},
	doi = {https://doi.org/10.1364/OE.409982},
	author = {Bjorn De Samber and Jens Renders and Tim Elberfeld and Yves Maris and Jonathan Sanctorum and Nathana{\"e}l Six and Liang, Zhihua and Jan De Beenhouwer and Jan Sijbers}
}
@article {2142,
	title = {Fracture patterns in midshaft clavicle fractures},
	journal = {Acta Orthop Belg },
	volume = {87},
	year = {2021},
	pages = {501-507},
	doi = {https://doi.org/10.52628/87.3.16},
	author = {Van Tongel, Alexander and Lieven De Wilde and Yasunori Shimamura and Jan Sijbers and Toon Huysmans}
}
@article {2203,
	title = {Gauss-Newton-Krylov for Reconstruction of Polychromatic X-ray CT Images},
	journal = {IEEE Transactions on Computational Imaging},
	volume = {7},
	year = {2021},
	pages = {1304-1313},
	doi = {10.1109/TCI.2021.3133226},
	author = {Nathana{\"e}l Six and Jens Renders and Jan Sijbers and Jan De Beenhouwer}
}
@article {2179,
	title = {On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: chronicles of the MEMENTO challenge},
	journal = {NeuroImage},
	volume = {240},
	year = {2021},
	doi = {https://doi.org/10.1016/j.neuroimage.2021.118367},
	author = {Alberto De Luca and Andrada Ianus and Alexander Leemans and Marco Palombo and Noam Shemesh and Hui Zhang and Daniel C Alexander and Markus Nilsson and Martijn Froeling and Geert-Jan Biessels and Mauro Zucchelli and Matteo Frigo and Enes Albay and Sara Sedlar and Abib Alimi and Samuel Deslauriers-Gauthier and Rachid Deriche and Rutger Fick and Maryam Afzali and Tomasz Pieciak and Fabian Bogusz and Santiago Aja-Fernandez and Evren Ozarslan and Derek K. Jones and Haoze Chen and Mingwu Jin and Zhijie Zhang and Fenxiang Wang and Vishwesh Nath and Prasanna Parvathaneni and Jan Morez and Jan Sijbers and Ben Jeurissen and Shreyas and Fadnavis and Stefan Endres and Ariel Rokem and Eleftherios Garyfallidis and Irina Sanchez and Vesna Prchkovska and Paulo Rodrigues and Bennet A. Landman and Kurt G Schilling}
}
@article {2148,
	title = {Geometry Calibration of a Modular Stereo Cone-Beam X-ray CT System},
	journal = {Journal of Imaging},
	volume = {7},
	year = {2021},
	pages = {1-12},
	doi = {https://doi.org/10.3390/jimaging7030054},
	author = {Van Nguyen and Joaquim Sanctorum and Sam Van Wassenbergh and Joris J. J. Dirckx and Jan Sijbers and Jan De Beenhouwer}
}
@inproceedings {2200,
	title = {Graphical User Interface for Joint Space Width Assessment by Optical Marker Tracking},
	booktitle = {4th International Conference on Bio-engineering for Smart Technologies},
	year = {2021},
	author = {Jeroen Van Houtte and Jan Sijbers and Guoyan Zheng}
}
@conference {2205,
	title = {High-resolution T2* mapping of the knee based on UTE Spiral VIBE MRI},
	volume = {34},
	year = {2021},
	pages = {S53-S54},
	author = {C{\'e}line Smekens and Quinten Beirinckx and Floris Vanhevel and Pieter Van Dyck and Arnold Jan den Dekker and Jan Sijbers and Thomas Janssens and Ben Jeurissen}
}
@article {2206,
	title = {Hyperspectral and multispectral image fusion using coupled non-negative tucker tensor decomposition},
	journal = {Remote Sensing},
	volume = {13},
	year = {2021},
	abstract = {Fusing a low spatial resolution hyperspectral image (HSI) with a high spatial resolution multispectral image (MSI), aiming to produce a super-resolution hyperspectral image, has recently attracted increasing research interest. In this paper, a novel approach based on coupled non-negative tensor decomposition is proposed. The proposed method performs a tucker tensor factorization of a low resolution hyperspectral image and a high resolution multispectral image under the constraint of non-negative tensor decomposition (NTD). The conventional matrix factorization methods essentially lose spatio-spectral structure information when stacking the 3D data structure of a hyperspectral image into a matrix form. Moreover, the spectral, spatial, or their joint structural features have to be imposed from the outside as a constraint to well pose the matrix factorization problem. The proposed method has the advantage of preserving the spatio-spectral structure of hyperspectral images. In this paper, the NTD is directly imposed on the coupled tensors of the HSI and MSI. Hence, the intrinsic spatio-spectral structure of the HSI is represented without loss, and spatial and spectral information can be interdependently exploited. Furthermore, multilinear interactions of different modes of the HSIs can be exactly modeled with the core tensor of the Tucker tensor decomposition. The proposed method is straightforward and easy to implement. Unlike other state-of-the-art approaches, the complexity of the proposed approach is linear with the size of the HSI cube. Experiments on two well-known datasets give promising results when compared with some recent methods from the literature.},
	doi = {https://doi.org/10.3390/rs13152930},
	author = {M. Zare and M. Sadegh Helfroush and K. Kazemi and Paul Scheunders}
}
@article {2066,
	title = {Hyperspectral Image Restoration Using Adaptive Anisotropy Total Variation and Nuclear Norms},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {59},
	year = {2021},
	pages = {1516-1533},
	author = {T. Hu and W. Li and N. Liu and R. Tao and F. Zhang and Paul Scheunders}
}
@mastersthesis {2140,
	title = {The impact of long-duration spaceflight on brain structure and function},
	volume = {Doctor of Science},
	year = {2021},
	school = {University of Antwerp},
	type = {PhD thesis},
	abstract = {In over half a century of crewed missions to space, many different effects of spaceflight on the human body have been uncovered so far. However, little focus has been directed to investigating how space stressors affect the human brain. The largest body of work in this dissertation describes pioneering findings on brain structural and functional changes after spaceflight in Roscosmos cosmonauts by means of multi-modal magnetic resonance imaging (MRI) in a longitudinal and prospective design. Structural MRI modalities, such as T1-weighted and diffusion MRI, were used to unravel macroscopic volume and microstructural brain tissue composition changes. We found a widespread redistribution of the cerebrospinal fluid (CSF) with secondary mechanistic effects on the grey matter (GM) tissue. We also revealed increased neural tissue volume in motor regions of the brain that suggest evidence for structural brain adaptations, also known as neuroplasticity, associated with altered motor strategies in space. Most CSF changes after spaceflight were still detectable more than half a year after return to Earth, while the GM changes after spaceflight partially reversed in the long term. In addition, functional MRI data was acquired in these cosmonauts to study functional reorganisation of the brain after spaceflight, showing numerous functional connectivity (FC) alterations after spaceflight. Some of these changes persisted in the longer-term, whereas other changes returned back to pre-flight levels. Furthermore, this work also describes the experimental work and preliminary analyses of several Earth-based models. One is a longitudinal MRI pilot study in hindlimb-unloaded (HLU) mice, inducing fluid shifts to the head region, in order to better understand the consequence of these fluid shifts on the brain. A second study was performed in fighter pilots as a model for exposure to high g-levels and sensory conflicts, in which FC was compared to that in a control group. This work rendered a vast increase in available information on structural and functional brain changes after spaceflight compared to several years ago. In the future, the underlying mechanisms of the observed findings need to be understood in more detail. Ultimately, we aim to characterise the effects space stressors have on the brain, to then attempt to mitigate these changes through countermeasures and characterise beneficial coping mechanisms that we can enhance, in order to be fully prepared for future exploration missions into deep space.},
	author = {Steven Jillings}
}
@article {2096,
	title = {Joint Deblurring and Denoising of THz Time-Domain Images},
	journal = {IEEE Access},
	volume = {9},
	year = {2021},
	pages = {162-176},
	doi = {10.1109/ACCESS.2020.3045605},
	author = {Marina Ljubenovi{\'c} and Lina Zhuang and Jan De Beenhouwer and Jan Sijbers}
}
@inbook {2197,
	title = {Just enough physics},
	booktitle = {Computed Tomography: Algorithms, Insight, and Just Enough Theory },
	volume = {4},
	year = {2021},
	publisher = {SIAM},
	organization = {SIAM},
	isbn = {978-1-611976-66-3},
	issn = {978-1-611976-66-3},
	doi = {https://doi.org/10.1137/1.9781611976670},
	author = {J S Jorgensen and Jan Sijbers}
}
@inproceedings {2198,
	title = {Mesh-based reconstruction of dynamic foam images using X-ray CT},
	booktitle = {International Conference on 3D Vision (3DV2021)},
	year = {2021},
	pages = {1312-1320},
	doi = {10.1109/3DV53792.2021.00138},
	author = {Jens Renders and Jan De Beenhouwer and Jan Sijbers}
}
@article {2163,
	title = {Monte-Carlo-Based Estimation of the X-ray Energy Spectrum for CT Artifact Reduction},
	journal = {Applied Sciences},
	volume = {11},
	year = {2021},
	chapter = {3145},
	doi = {https://doi.org/10.3390/app11073145},
	author = {Ehsan Nazemi and Nathana{\"e}l Six and Domenico Iuso and Bjorn De Samber and Jan Sijbers and Jan De Beenhouwer}
}
@conference {2178,
	title = {Motion compensating X-ray micro-CT of diamonds in a processing stage},
	year = {2021},
	month = {September},
	author = {Jens Renders and Anh-Tuan Nguyen and Jan De Beenhouwer and Jan Sijbers}
}
@inproceedings {2189,
	title = {Multi-contrast multi-shot EPI for accelerated diffusion MRI},
	booktitle = {43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society},
	year = {2021},
	pages = {3869-3872},
	doi = {10.1109/EMBC46164.2021.9630069},
	author = {Banafshe Shafieizargar and Ben Jeurissen and Dirk H J Poot and Arnold Jan den Dekker and Jan Sijbers}
}
@inproceedings {2222,
	title = {A Multi-Sensor Subspace-Based Clustering Algorithm Using RGB and Hyperspectral Data},
	booktitle = {Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)},
	year = {2021},
	doi = {10.1109/WHISPERS52202.2021.9483953},
	author = {Kasra Rafiezadeh Sahi and Pedram Ghamisi and R. Jackisch and Behnood Rasti and Paul Scheunders and Richard Gloaguen}
}
@article {2232,
	title = {Multi-tissue spherical deconvolution of tensor-valued diffusion MRI.},
	journal = {Neuroimage},
	volume = {245},
	year = {2021},
	month = {2021 12 15},
	pages = {118717},
	abstract = {Multi-tissue constrained spherical deconvolution (MT-CSD) leverages the characteristic b-value dependency of each tissue type to estimate both the apparent tissue densities and the white matter fiber orientation distribution function from diffusion MRI data. In this work, we generalize MT-CSD to tensor-valued diffusion encoding with arbitrary b-tensor shapes. This enables the use of data encoded with mixed b-tensors, rather than being limited to the subset of linear (conventional) b-tensors. Using the complete set of data, including all b-tensor shapes, provides a categorical improvement in the estimation of apparent tissue densities, fiber ODF, and resulting tractography. Furthermore, we demonstrate that including multiple b-tensor shapes in the analysis provides improved contrast between tissue types, in particular between gray matter and white matter. We also show that our approach provides high-quality apparent tissue density maps and high-quality fiber tracking from data, even with sparse sampling across b-tensors that yield whole-brain coverage at 2~mm isotropic resolution in approximately 5:15~min.},
	keywords = {Brain Mapping, diffusion tensor imaging, Healthy Volunteers, Humans, Image Processing, Computer-Assisted, white matter},
	issn = {1095-9572},
	doi = {10.1016/j.neuroimage.2021.118717},
	author = {Ben Jeurissen and Szczepankiewicz, Filip}
}
@article {2164,
	title = {Non-destructive internal disorder detection of Conference pears by semantic segmentation of X-ray CT scans using deep learning},
	journal = {Expert Systems with Applications},
	volume = {176},
	year = {2021},
	pages = {1-12},
	doi = {https://doi.org/10.1016/j.eswa.2021.114925},
	author = {Tim Van De Looverbosch and Ellen Raeymaekers and Pieter Verboven and Jan Sijbers and Bart Nicolai}
}
@conference {2182,
	title = {Optimal experimental design for the T2-weighted diffusion kurtosis imaging free water elimination model},
	volume = {34},
	year = {2021},
	pages = {S54-S55},
	doi = {https://doi.org/10.1007/s10334-021-00947-8},
	author = {Vincenzo Anania and Ben Jeurissen and Jan Morez and Annemieke Eline Buikema and Thibo Billiet and Jan Sijbers and Arnold Jan den Dekker}
}
@conference {2174,
	title = {Outcome prediction in Mild Traumatic Brain Injury patients using conventional and diffusion MRI via Support Vector Machine: A CENTER-TBI study},
	year = {2021},
	author = {Maira Siqueira Pinto and Stefan Winzeck and Marta M. Correia and Evgenios N. Kornaropoulos and David K. Menon and Ben Glocker and Arnold Jan den Dekker and Jan Sijbers and Pieter-Jan Guns and Pieter Van Dyck and Virginia F. J. Newcombe}
}
@conference {2185,
	title = {Outcome prediction of mild traumatic brain injury using support vector machine based on longitudinal MRdiffusion imaging from CENTER-TBI},
	volume = {34},
	year = {2021},
	pages = {S54},
	doi = {10.1007/s10334-021-00947-8},
	author = {Maira Siqueira Pinto and Stefan Winzeck and S. Richter and Marta M. Correia and Evgenios N. Kornaropoulos and David K. Menon and Ben Glocker and Pieter-Jan Guns and Arnold Jan den Dekker and Jan Sijbers and Virginia F. J. Newcombe and Pieter Van Dyck}
}
@article {2095,
	title = {Outlier detection for foot complaint diagnosis: modeling confounding factors using metric learning},
	journal = {IEEE Intelligent Systems},
	volume = {36},
	year = {2021},
	pages = {41-49},
	doi = {10.1109/MIS.2020.3046431},
	author = {Brian G. Booth and No{\"e}l L.W. Keijsers and Jan Sijbers}
}
@article {2071,
	title = {Projection-angle-dependent distortion correction in high-speed image-intensifier-based x-ray computed tomography},
	journal = {Measurement Science and Technology},
	volume = {32},
	year = {2021},
	pages = {1-11},
	doi = {10.1088/1361-6501/abb33e},
	author = {Joaquim Sanctorum and Sam Van Wassenbergh and Van Nguyen and Jan De Beenhouwer and Jan Sijbers and Joris J. J. Dirckx}
}
@article {2196,
	title = {Quantification of cognitive impairment to characterize heterogeneity of patients at risk of developing Alzheimer{\textquoteright}s disease dementia},
	journal = {Alzheimer{\textquoteright}s \& Dementia: Diagnosis, Assessment \& Disease Monitoring},
	volume = {13},
	year = {2021},
	pages = {e12237},
	doi = {https://doi.org/10.1002/dad2.12237},
	author = {Diana Giraldo and Jan Sijbers and Romero, Eduardo}
}
@conference {2173,
	title = {RAMSES: Relaxation Alternate Mapping of Spoiled Echo Signals sequence for simultaneous accurate T1 and T2* mapping},
	year = {2021},
	month = {May 2021},
	author = {Marco Andrea Zampini and Jan Sijbers and Marleen Verhoye and Ruslan Garipov}
}
@article {2190,
	title = {Recurrent Inference Machines as inverse problem solvers for MR relaxometry},
	journal = {Medical Image Analysis},
	volume = {74},
	year = {2021},
	pages = {1-11},
	doi = {https://doi.org/10.1016/j.media.2021.102220},
	author = {Emanoel Ribeiro Sabidussi and Stefan Klein and Matthan Caan and Shabab Bazrafkan and Arnold Jan den Dekker and Jan Sijbers and Wiro J Niessen and Dirk H J Poot}
}
@inproceedings {2169,
	title = {Recurrent Inference Machines as Inverse Problem Solvers for MR Relaxometry},
	booktitle = { MIDL 2021 - Medical Imaging with Deep Learning},
	year = {2021},
	author = {Emanoel Ribeiro Sabidussi and Matthan Caan and Shabab Bazrafkan and Arnold Jan den Dekker and Jan Sijbers and Wiro J Niessen and Dirk H J Poot}
}
@article {2086,
	title = {Robust supervised method for nonlinear spectral unmixing accounting for endmember variability},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {59},
	year = {2021},
	pages = {7434-7448},
	abstract = {Due to the complex interaction of light with mixed materials, reflectance spectra are highly nonlinearly related to the pure material endmember spectra, making it hard to estimate the fractional abundances of the materials. Changing illumination conditions and cross-sensor situations cause spectral variability, further complicating the unmixing procedure. In this work, we propose a supervised approach to unmix mineral powder mixtures, containing endmember variability.

First, the abundances are estimated by calculating the geodesic distances between the mixtures and the endmembers. It is argued and experimentally validated that the estimated geodesic abundances, although not correct, are invariant to external spectral variability.  Then, a supervised approach is applied to learn a mapping from the obtained geodesic abundances to spectra that follow a linear model. To learn this mapping, ground truth fractional abundances of a number of training samples are required. 
Although any nonlinear regression method can be used to learn the mapping, Gaussian process is found to be suitable when a limited number of training samples are available. The trained model is applicable to all manifolds that contain a similar nonlinear behavior as the trained manifold, e.g. when the same mixtures are measured by another sensor. Using the output spectra, a simple inversion of the linear model reveals the true abundances.

Experiments are conducted on simulated and real mineral mixtures. In particular, we developed data sets of homogeneously mixed mineral powder mixtures, acquired by 2 different sensors, an Agrispec spectrometer and a snapscan shortwave infrared hyperspectral camera, under strictly controlled experimental settings. The proposed approach is compared to other supervised approaches and nonlinear mixture models.  },
	doi = {https://doi.org/10.1109/TGRS.2020.3031012},
	author = {Bikram Koirala and Zohreh Zahiri and Alfredo Lamberti and Paul Scheunders}
}
@conference {2181,
	title = {Rotated or shifted sets of multi-slice MR images for super-resolution reconstruction? A Bayesian answer},
	volume = {34},
	year = {2021},
	pages = {S56-S57},
	doi = {10.1007/s10334-021-00947-8},
	author = {Michele Nicastro and Ben Jeurissen and Quinten Beirinckx and C{\'e}line Smekens and Dirk H J Poot and Jan Sijbers and Arnold Jan den Dekker}
}
@inproceedings {2254,
	title = {Spectral Unmixing Using Deep Convolutional Encoder-Decoder},
	booktitle = {2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS},
	year = {2021},
	month = {12 October 2021},
	pages = {3829-3832},
	abstract = {In this paper, we introduce {\textquoteleft}Unmixing Deep Image Prior{\textquoteright} (UnDIP), a deep learning-based technique for the linear hyperspectral unmixing problem. The proposed method contains two steps. First, the endmembers are extracted using a geometric endmember extraction method, i.e. a simplex volume maximization in a subspace of the dataset. Then, the abundances are estimated using a deep image prior. The proposed deep image prior uses a convolutional neural network to estimate the fractional abundances, relying on the extracted endmembers and the observed hyperspectral dataset. The results show considerable improvements compared to state-of-the-art methods.},
	doi = {10.1109/IGARSS47720.2021.9553425},
	author = {Behnood Rasti and Bikram Koirala and Paul Scheunders and Pedram Ghamisi}
}
@inproceedings {2100,
	title = {Statistical shape and pose model of the forearm for custom splint design},
	booktitle = {IEEE International Symposium on Biomedical Imaging (ISBI)},
	year = {2021},
	pages = {1669-1672},
	doi = {10.1109/ISBI48211.2021.9434067},
	author = {Femke Danckaers and Jeroen Van Houtte and Brian G Booth and Frederik Verstreken and Jan Sijbers}
}
@mastersthesis {2145,
	title = {Strategies for efficient acquisition and reconstruction of structural and quantitative MRI},
	volume = {PhD in Biomedical Sciences},
	year = {2021},
	type = {PhD thesis},
	author = {Maarten Naeyaert}
}
@inproceedings {2187,
	title = {A study of terahertz beam simulation with ray tracing for computed tomography},
	booktitle = {2021 OSA Imaging and Applied Optics Congress},
	year = {2021},
	doi = {10.1364/AIS.2021.JTu5A.37},
	author = {Pavel Paramonov and Lars-Paul Lumbeeck and Jan Sijbers and Jan De Beenhouwer}
}
@article {2308,
	title = {SUnCNN: Sparse Unmixing Using Unsupervised Convolutional Neural Network},
	journal = {IEEE Geoscience and Remote Sensing Letters},
	volume = {19},
	year = {2021},
	month = {06 August 2021},
	pages = {1-5},
	abstract = {In this letter, we propose a sparse unmixing technique using a convolutional neural network (SUnCNN) for hyperspectral images. SUnCNN is the first deep learning-based technique proposed for sparse unmixing. It uses a deep convolutional encoder{\textendash}decoder to generate the abundances relying on a spectral library. We reformulate the sparse unmixing into an optimization over the deep network{\textquoteright}s parameters. Therefore, the deep network learns in an unsupervised manner to map a fixed input into the sparse optimum abundances. Additionally, SUnCNN holds the sum-to-one constraint using a softmax activation layer. The proposed method is compared with the state-of-the-art using two synthetic datasets and one real hyperspectral dataset. The overall results confirm that the proposed method outperforms the other ones in terms of signal to reconstruction error (SRE). Additionally, SUnCNN shows visual superiority for both real and synthetic datasets compared with the competing techniques. The proposed method was implemented in Python (3.8) using PyTorch as the platform for the deep network and is available online: https://github.com/BehnoodRasti/SUnCNN .},
	doi = {10.1109/LGRS.2021.3100992},
	author = {Behnood Rasti and Bikram Koirala}
}
@conference {2160,
	title = {Super-resolution T2* mapping of the knee using UTE Spiral VIBE MRI},
	year = {2021},
	pages = {3920},
	abstract = {T2* mapping using ultrashort echo time (UTE) MRI allows for quantitative evaluation of collagen-rich knee structures with short mean transverse relaxation times. However, acquisitions with low through-plane resolution are commonly used to obtain T2* maps within reasonable scan times, affecting the accuracy of the estimations because of partial volume effects. In this work, model-based super-resolution reconstructions based on UTE Spiral VIBE MRI were performed to obtain high-resolution T2* maps of knee structures within a reasonable scan time. The obtained T2* maps are comparable to maps generated with direct 3D UTE Spiral VIBE acquisitions while requiring approximately 25\% less scan time.},
	author = {C{\'e}line Smekens and Quinten Beirinckx and Floris Vanhevel and Pieter Van Dyck and Arnold Jan den Dekker and Jan Sijbers and Thomas Janssens and Ben Jeurissen}
}
@inproceedings {2157,
	title = {Tabu-DART: a dynamic update strategy for the Discrete Algebraic Reconstruction Technique based on Tabu-search},
	booktitle = { International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine},
	year = {2021},
	author = {Daniel Frenkel and Jan De Beenhouwer and Jan Sijbers}
}
@article {2087,
	title = {To Recurse or not to Recurse A Low Dose CT Study},
	journal = {Progress in Artificial Intelligence},
	volume = {10},
	year = {2021},
	pages = {65{\textendash}81},
	doi = {https://doi.org/10.1007/s13748-020-00224-0},
	author = {Shabab Bazrafkan and Vincent Van Nieuwenhove and Joris Soons and Jan De Beenhouwer and Jan Sijbers}
}
@article {2167,
	title = {UnDIP: hyperspectral unmixing using deep image prior},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {60},
	year = {2021},
	month = {31 March 2021},
	pages = {1-15},
	abstract = {In this article, we introduce a deep learning-based technique for the linear hyperspectral unmixing problem. The proposed method contains two main steps. First, the endmembers are extracted using a geometric endmember extraction method, i.e., a simplex volume maximization in the subspace of the data set. Then, the abundances are estimated using a deep image prior. The main motivation of this work is to boost the abundance estimation and make the unmixing problem robust to noise. The proposed deep image prior uses a convolutional neural network to estimate the fractional abundances, relying on the extracted endmembers and the observed hyperspectral data set. The proposed method is evaluated on simulated and three real remote sensing data for a range of SNR values (i.e., from 20 to 50 dB). The results show considerable improvements compared to state-of-the-art methods. The proposed method was implemented in Python (3.8) using PyTorch as the platform for the deep network and is available online: https://github.com/BehnoodRasti/UnDIP.},
	doi = {10.1109/TGRS.2021.3067802},
	author = {Behnood Rasti and Bikram Koirala and Paul Scheunders and Pedram Ghamisi}
}
@inproceedings {2224,
	title = {When is the right time to apply denoising?},
	booktitle = {IGARSS 2021, International Geoscience and Remote Sensing Symposium},
	year = {2021},
	address = {Brussels, Belgium},
	doi = {10.1109/IGARSS47720.2021.9553263},
	author = {Kasra Rafiezadeh Sahi and Behnood Rasti and Pedram Ghamisi and Paul Scheunders and Richard Gloaguen}
}
@conference {2081,
	title = {3D Atomic Scale Quantification of Nanostructures and their Dynamics Using Model-based STEM},
	year = {2020},
	author = {Sandra Van Aert and Annick De Backer and De wael, A and Jarmo Fatermans and Friedrich, T and Ivan Lobato and O{\textquoteright}Leary, C M. and Varambhia, A and Thomas Altantzis and Jones, L and Arnold Jan den Dekker and Peter D Nellist and Sara Bals}
}
@mastersthesis {2069,
	title = {Accurate and precise perfusion parameter estimation in pseudo-continuous arterial spin labeling MRI},
	volume = {PhD in Sciences/Physics},
	year = {2020},
	type = {PhD thesis},
	author = {Piet Bladt}
}
@inproceedings {2053,
	title = {Accurate terahertz simulation with ray tracing incorporating beam shape and refraction},
	booktitle = {IEEE ICIP },
	year = {2020},
	pages = {3035-3039},
	doi = {10.1109/ICIP40778.2020.9190937},
	author = {Pavel Paramonov and Lars-Paul Lumbeeck and Jan De Beenhouwer and Jan Sijbers}
}
@inproceedings {1979,
	title = {An adaptive probability map for the Discrete Algebraic Reconstruction Technique},
	booktitle = {10th Conference on Industrial Computed Tomography (ICT 2020)},
	year = {2020},
	pages = {1-6},
	author = {Daniel Frenkel and Jan De Beenhouwer and Jan Sijbers}
}
@inproceedings {2028,
	title = {Adjoint pairs of image warping operators for motion modeling in 4D-CT},
	booktitle = {6th International Conference on Image Formation in X-Ray Computed Tomography},
	year = {2020},
	author = {Jens Renders and Jan Sijbers and Jan De Beenhouwer}
}
@article {2057,
	title = {Analysis and Comparison of Algorithms for the Tomographic Reconstruction of Curved Fibres},
	journal = {Nondestructive Testing and Evaluation},
	volume = {35},
	year = {2020},
	pages = {328-341},
	doi = {https://doi.org/10.1080/10589759.2020.1774583},
	author = {Bernhard Fr{\"o}hler and Tim Elberfeld and Torsten M{\"o}ller and Hans-Christian Hege and Jan De Beenhouwer and Jan Sijbers and Johann Kastner and Christoph Heinzl}
}
@inproceedings {2084,
	title = {Aperture size selection for improved brain tumor detection and quantification in multi-pinhole 123I-CLINDE SPECT imaging},
	booktitle = {IEEE Nuclear Science Symposium and Medical Imaging Conference, Boston, USA (2020)},
	year = {2020},
	author = {Benjamin Auer and Kesava Kalluri and Aly H. Abayazeed and Jan De Beenhouwer and Navid Zeraatkar and Clifford Lindsay and Neil Momsen and R. Garrett Richards and Micaehla May and Matthew A. Kupinski and Philip H. Kuo and Lars R. Furenlid and Michael A. King}
}
@article {2136,
	title = {Application of GMDH neural network technique to improve measuring precision of a simplified photon attenuation based two-phase flowmeter},
	journal = {Flow Measurement and Instrumentation},
	volume = {75},
	year = {2020},
	abstract = {Multiphase flowmeters have an important role to play in the industry and any attempts that lead to improvements in this field are of great interest. In the current study, group method of data handling (GMDH) technique was applied in order to increase measuring precision of a simple photon attenuation based two-phase flowmeter that has the ability to estimate the gas volumetric percentage in a two-phase flow without any dependency to flow regime pattern. The simple photon attenuation based system is comprised of a cobalt-60 radioisotope and only one 25.4 mm {\texttimes} 25.4 mm sodium iodide crystal detector. Four extracted features from recorded photon spectrum in sodium iodide crystal detector were used as the inputs of GMDH neural network. Equations related to the combination of the features and the error rate of each approximation is also reported in this paper. Applying the mentioned technique, the gas volumetric percentage in an oil-gas two phase flow was determined with the root mean square error of less than 2.71 without any dependency to the flow pattern. The obtained measuring precision in this study is at least 2.1 times better than reported in previous studies.},
	doi = {10.1016/j.flowmeasinst.2020.101804},
	author = {Mohammadmehdi Roshani and Mohammad Amir Sattari and Peshawa Jammal Muhammad Ali and Gholam Hossein Roshani and Enrico Corniani and Ehsan Nazemi}
}
@article {2063,
	title = {Atom column detection from simultaneously acquired ABF and ADF STEM images},
	journal = {Ultramicroscopy},
	volume = {219},
	year = {2020},
	pages = {113046},
	doi = {https://doi.org/10.1016/j.ultramic.2020.113046},
	author = {Fatermans, J. and Arnold Jan den Dekker and M{\"u}ller-Caspary, K. and N Gauquelin and Jo Verbeeck and Sandra Van Aert}
}
@conference {2091,
	title = {Bayesian model selection for atom column detection from ABF-ADF STEM images},
	year = {2020},
	author = {Fatermans, J. and Arnold Jan den Dekker and N Gauquelin and Jo Verbeeck and Sandra Van Aert}
}
@inproceedings {2027,
	title = {BeadNet: a network for automated spherical marker detection in radiographs for geometry calibration},
	booktitle = {6th International Conference on Image Formation in X-Ray Computed Tomography},
	year = {2020},
	pages = {518-521},
	author = {Van Nguyen and Jan De Beenhouwer and Shabab Bazrafkan and A-T. Hoang and Sam Van Wassenbergh and Jan Sijbers}
}
@article {2211,
	title = {Brain Computer Interface},
	year = {2020},
	month = {01/2020},
	publisher = {Universiteit Antwerpen},
	chapter = {EP 3 597 102 A1; A61B 5/04 (2006.01),A61B 5/0476 (2006.01),A61B 5/00 (2006.01)"},
	address = {Belgium},
	abstract = {The present invention relates to an interfacingsystem for interfacing a plurality of elements to a surfaceof interest. The elements thereby are suitable for locallysensing a signal of the surface of interest or for locallyactuating the surface of interest. The interfacing systemcomprises a plurality of element holders, each elementholder being configured for holding an element for interfacingwith the surface of interest and each element holderbeing configured for providing a pressure exerted onthe element for interfacing the element with the surfaceof interest, the pressure being based on a fluidic actionin the element holder. The interfacing system also comprisesan outer shell interconnecting the plurality of elementholders. At least two of the plurality of element holdersfurther are fluidically interconnected so as to link thefluidic action in the at least two element holders therebylinking the pressure exerted by the element holders onthe elements.},
	author = {Stijn Verwulgen and Daniel Lacko and Jochen Vleugels and Toon Huysmans and Steven Truijen}
}
@inproceedings {2034,
	title = {CNN-based Deblurring of Terahertz Images},
	booktitle = {Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP)},
	volume = {4},
	year = {2020},
	pages = {323-330},
	doi = {10.5220/0008973103230330},
	author = {Marina Ljubenovi{\'c} and Shabab Bazrafkan and Jan De Beenhouwer and Jan Sijbers}
}
@article {2032,
	title = {Co valence transformation in isopolar LaCoO3/LaTiO3 perovskite heterostructures via interfacial engineering},
	journal = {Phys. Rev. Materials},
	volume = {4},
	year = {2020},
	chapter = {026001},
	doi = {https://doi.org/10.1103/PhysRevMaterials.4.026001},
	author = {G Araizi-Kanoutas and J Geessinck and N Gauquelin and S Smit and X H Verbeeck and S K Mishra and P Bencok and C Schlueter and T-L Lee and D Krishnan and J Fatermans and Jo Verbeeck and G Rijnders and G Koster and M S Golden}
}
@inproceedings {1951,
	title = {A Comparative Study Between Three Measurement Methods to Predict 3D Body Dimensions Using Shape Modelling},
	booktitle = {Advances in Additive Manufacturing, Modeling Systems and 3D Prototyping},
	volume = {975},
	year = {2020},
	pages = {464{\textendash}470},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {In times of online shopping, it is a challenge to select the right size of the desired clothing without fitting it before ordering. Therefore, this study describes three techniques to predict 3D upper body dimensions. The first method used basic personal parameters (gender, age, weight and length), the second technique used also the shoulder width and the last method used a 3D Styku scan to add extra input parameters. The accuracy of the three prediction methods was compared against hand measurements for 17 upper body dimensions of 37 subjects. The Intraclass Correlation Coefficient increases with 11.2\% for the Styku method compared to the other methods. For chest, hip and waist measurements, the basic method is reliable to predict 3D body dimensions and indicate the right size from an existing collection. For more accurate upper body dimensions as needed for producing custom made clothing, a 3D Styku scan can supply the desired input.},
	isbn = {978-3-030-20216-3},
	doi = {10.1007/978-3-030-20216-3_43},
	author = {Peeters, Thomas and Vleugels, Jochen and Stijn Verwulgen and Femke Danckaers and Toon Huysmans and Jan Sijbers and De Bruyne, Guido},
	editor = {Di Nicolantonio, Massimo and Rossi, Emilio and Alexander, Thomas}
}
@article {2080,
	title = {Constrained spherical deconvolution of non-spherically sampled diffusion MRI data},
	journal = {Human Brain Mapping},
	volume = {42},
	year = {2020},
	pages = {521{\textendash}538},
	doi = {10.1002/hbm.25241},
	author = {Jan Morez and Jan Sijbers and Floris Vanhevel and Ben Jeurissen}
}
@article {1956,
	title = {The costs and benefits of estimating T1 of tissue alongside cerebral blood flow and arterial transit time in pseudo-continuous arterial spin labeling},
	journal = {NMR in Biomedicine},
	volume = {33},
	year = {2020},
	pages = {1-17},
	doi = {https://doi.org/10.1002/nbm.4182},
	author = {Piet Bladt and Arnold Jan den Dekker and Clement, Patricia and Eric Achten and Jan Sijbers}
}
@article {2093,
	title = {Data Fusion Using a Multi-Sensor Sparse-Based Clustering Algorithm},
	journal = {Remote Sensing},
	volume = {12 (23)},
	year = {2020},
	author = {Kasra Rafiezadeh Sahi and Pedram Ghamisi and Behnood Rasti and R. Jackisch and Paul Scheunders and Richard Gloaguen}
}
@inproceedings {1977,
	title = {Deep learning-based 2D-3D sample pose estimation for X-ray 3DCT},
	booktitle = {10th Conference on Industrial Computed Tomography (ICT 2020)},
	year = {2020},
	author = {Alice Presenti and Shabab Bazrafkan and Jan Sijbers and Jan De Beenhouwer}
}
@article {2058,
	title = {Deep neural network and data augmentation methodology for off-axis iris segmentation in wearable headsets},
	journal = {Science Direct Elsevier Neural Networks},
	volume = {121},
	year = {2020},
	month = {01/2020},
	pages = {101-121},
	chapter = {101-121},
	doi = {https://doi.org/10.1016/j.neunet.2019.07.020},
	url = {https://www.sciencedirect.com/science/article/pii/S0893608019302163?via\%3Dihub$\#$!},
	author = {Viktor Varkarakis and Shabab Bazrafkan and Peter Corcoran}
}
@mastersthesis {2341,
	title = { Development of advanced hyperspectral unmixing methods },
	year = {2020},
	type = {PhD thesis},
	author = {Bikram Koirala}
}
@conference {Jillings2020-nb,
	title = {Diffusion MRI reveals macro- and microstructural changes in cosmonauts{\textquoteright} brains after long-duration spaceflight},
	year = {2020},
	pages = {4531},
	author = {Steven Jillings and Angelique Van Ombergen and Tomilovskaya, E and Rumshiskaya, A and Litvinova, L and Nosikova, I and Pechenkova, E and Rukavishnikov, I and Kozlovskaya, I and Stefan Sunaert and Paul M Parizel and Valentin Sinitsyn and Petrovichev, V and Laureys, S and zu Eulenburg, P and Jan Sijbers and Floris L Wuyts and Ben Jeurissen}
}
@article {2064,
	title = {Diffusion tensor imaging of the anterior cruciate ligament graft following reconstruction: a longitudinal study},
	journal = {European Radiology},
	volume = {34},
	year = {2020},
	pages = {6673{\textendash}6684},
	doi = {10.1007/s00330-020-07051-w},
	author = {Pieter Van Dyck and Thibo Billiet and Damien Desbuquoit and Peter Verdonk and Christiaan H. Heusdens and Ella Roelant and Jan Sijbers and Martijn Froeling}
}
@article {2011,
	title = {The effect of nasal shape on the thermal conditioning of inhaled air: Using clinical tomographic data to build a large-scale statistical shape model},
	journal = {Computers in Biology and Medicine},
	volume = {117},
	year = {2020},
	pages = {1-13},
	doi = {https://doi.org/10.1016/j.compbiomed.2020.103600},
	author = {Keustermans, William and Toon Huysmans and Bert Schmelzer and Jan Sijbers and Joris J. J. Dirckx}
}
@article {2016,
	title = {Effect of sample preparation techniques upon single cell chemical imaging: A practical comparison between synchrotron radiation based X-ray fluorescence (SR-XRF) and Nanoscopic Secondary Ion Mass Spectrometry (nano-SIMS)},
	journal = {Analytica Chimica Acta},
	volume = {1106},
	year = {2020},
	pages = {22-32},
	abstract = {Analytical capabilities of Nanoscopic Secondary Ion Mass Spectrometry (nano-SIMS) and Synchrotron Radiation based X-ray Fluorescence (SR nano-XRF) techniques were compared for nanochemical imaging of polymorphonuclear human neutrophils (PMNs). PMNs were high pressure frozen (HPF), cryosubstituted, embedded in Spurr{\textquoteright}s resin and cut in thin sections (500 nm and 2 mm for both techniques resp.) Nano-SIMS enabled nanoscale mapping of isotopes of C, N, O, P and S, while SR based nano-XRF enabled trace level imaging of metals like Ca, Mn, Fe, Ni, Cu and Zn at a resolution of approx. 50 nm.

The obtained elemental distributions were compared with those of whole, cryofrozen PMNs measured at the newly developed ID16A nano-imaging beamline at the European Synchrotron Radiation Facility (ESRF) in Grenoble, France. Similarities were observed for elements more tightly bound to the cell structure such as phosphorus and sulphur, while differences for mobile ions such as chlorine and potassium were more pronounced. Due to the observed elemental redistribution of mobile ions such as potassium and chlorine, elemental analysis of high pressure frozen (HPF), cryo-substituted and imbedded cells should be interpreted critically. Although decreasing analytical sensitivity occurs due to the presence of ice, analysis of cryofrozen cells - close to their native state - remains the golden standard. In general, we found nanoscale secondary ion mass spectrometry (nano-SIMS) and synchrotron radiation based nanoscopic X-ray fluorescence (SR nano-XRF) to be two supplementary alternatives for nanochemical imaging of single cells at the nanoscale.},
	doi = {10.1016/j.aca.2020.01.054},
	author = {Bjorn De Samber and Riet De Rycke and Michiel De Bruyne and Michiel Kienhuis and Linda Sandblad and Sylvain Bohic and Peter Cloetens and Constantin Urban and Lubos Polerecky and Laszlo Vincze}
}
@inproceedings {2072,
	title = {Extreme Sparse X-ray Computed Laminography Via Convolutional Neural Networks},
	booktitle = {ICTAI 2020},
	year = {2020},
	author = {Luis Filipe Alves Pereira and Jan De Beenhouwer and Johann Kastner and Jan Sijbers}
}
@article {2037,
	title = {Harmonisation of Brain Diffusion MRI: Concepts and Methods},
	journal = {Frontiers in Neuroscience },
	volume = {14},
	year = {2020},
	month = {03/2020},
	pages = {1-17},
	doi = {https://doi.org/10.3389/fnins.2020.00396},
	author = {Maira Siqueira Pinto and Roberto Paolella and Thibo Billiet and Pieter Van Dyck and Pieter-Jan Guns and Ben Jeurissen and Annemie Ribbens and Arnold Jan den Dekker and Jan Sijbers}
}
@article {2059,
	title = {How Hyperspectral Image Unmixing and Denoising Can Boost Each Other},
	journal = {Remote Sensing},
	volume = {12},
	year = {2020},
	month = {28 May 2020},
	abstract = {Hyperspectral linear unmixing and denoising are highly related hyperspectral image (HSI) analysis tasks. In particular, with the assumption of Gaussian noise, the linear model assumed for the HSI in the case of low-rank denoising is often the same as the one used in HSI unmixing. However, the optimization criterion and the assumptions on the constraints are different. Additionally, noise reduction as a preprocessing step in hyperspectral data analysis is often ignored. The main goal of this paper is to study experimentally the influence of noise on the process of hyperspectral unmixing by: (1) investigating the effect of noise reduction as a preprocessing step on the performance of hyperspectral unmixing; (2) studying the relation between noise and different endmember selection strategies; (3) investigating the performance of HSI unmixing as an HSI denoiser; (4) comparing the denoising performance of spectral unmixing, state-of-the-art HSI denoising techniques, and the combination of both. All experiments are performed on simulated and real datasets.},
	keywords = {hyperspectral image; unmixing; denoising; linear mixing model; low-rank model; noise reduction; abundance estimation},
	doi = {https://doi.org/10.3390/rs12111728},
	url = {https://www.mdpi.com/2072-4292/12/11/1728$\#$cite},
	author = {Behnood Rasti and Bikram Koirala and Paul Scheunders and Pedram Ghamisi}
}
@mastersthesis {2340,
	title = {Hyperspectral image mixture analysis using notions of sparsity, nonlinearity and decision fusion },
	year = {2020},
	type = {PhD thesis},
	author = {Vera Andrejchenko}
}
@conference {2021,
	title = {Improved voxel-wise quantification of diffusion and kurtosis metrics in the presence of noise and intensity outliers},
	year = {2020},
	url = {https://www.ismrm-benelux.org/wp-content/uploads/2020/01/Proceedings2020.pdf},
	author = {Vincenzo Anania and Thibo Billiet and Ben Jeurissen and Annemie Ribbens and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {2024,
	title = {Joint estimation of phase and diffusion tensor parameters from multi-shot k-q-space data: a proof of concept},
	volume = {28},
	year = {2020},
	abstract = {To address the issue of phase induced artifacts in multi-shot diffusion weighted imaging, we propose a model-based framework which enables the joint estimation of diffusion and phase parameters
directly from the multi-shot k-q-space. In a simulation study, we show that using this framework, diffusion parameters can be estimated more accurately and precisely than with the conventional
method (image reconstruction followed by voxel-wise model fitting) that ignores phase differences.},
	keywords = {Diffusion MRI, MRI, Phase estimation},
	author = {Banafshe Shafieizargar and Ben Jeurissen and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {2025,
	title = {Joint estimation of phase and diffusion tensor parameters from multi-shot k-q-space data: a proof of concept},
	volume = {12},
	year = {2020},
	abstract = {To address the issue of phase induced artifacts in multi-shot diffusion weighted imaging, we propose a model-based framework which enables the joint estimation of diffusion and phase parameters
directly from the multi-shot k-q-space. In a simulation study, we show that using this framework, diffusion parameters can be estimated more accurately and precisely than with the conventional
method (image reconstruction followed by voxel-wise model fitting) that ignores phase differences.},
	keywords = {Diffusion MRI, MRI, Phase estimation},
	author = {Banafshe Shafieizargar and Ben Jeurissen and Arnold Jan den Dekker and Jan Sijbers}
}
@article {1971,
	title = {Joint Maximum Likelihood estimation of motion and T1 parameters from magnetic resonance images in a super-resolution framework: a simulation study},
	journal = {Fundamenta Informaticae},
	volume = {172},
	year = {2020},
	pages = {105{\textendash}128},
	abstract = {Magnetic resonance imaging (MRI) based T1 mapping allows spatially resolved quantification of the tissue-dependent spin-lattice relaxation time constant T1, which is a potential biomarker of various neurodegenerative diseases, including Multiple Sclerosis, Alzheimer disease, and Parkinson{\textquoteright}s disease. In conventional T1 MR relaxometry, a quantitative T1 map is obtained from a series of T1-weighted MR images. Acquiring such a series, however, is time consuming. This has sparked the development of more efficient T1 mapping methods, one of which is a super-resolution reconstruction (SRR) framework in which a set of low resolution (LR) T1-weighted images is acquired and from which a high resolution (HR) T1 map is directly estimated. In this paper, the SRR T1 mapping framework is augmented with motion estimation. That is, motion between the acquisition of the LR T1-weighted images is modeled and the motion parameters are estimated simultaneously with the T1 parameters. Based on Monte Carlo simulation experiments, we show that such an integrated motion/relaxometry estimation approach yields more accurate T1 maps compared to a previously reported SRR based T1 mapping approach.},
	doi = {10.3233/FI-2020-1896},
	author = {Quinten Beirinckx and Gabriel Ramos-Llord{\'e}n and Ben Jeurissen and Dirk H J Poot and Paul M Parizel and Marleen Verhoye and Jan Sijbers and Arnold Jan den Dekker}
}
@article {2083,
	title = {Keel-Edge Height Selection for Improved Multi-Pinhole 123I Brain SPECT Imaging},
	journal = {Journal of Nuclear Medicine},
	volume = {61},
	year = {2020},
	pages = {573},
	abstract = {573Objectives: Given its excellent resolution versus sensitivity trade-off, multi-pinhole SPECT has become a powerful tool for clinical imaging of small human structures such as the brain [1]. Our research team is designing and constructing a next-generation multi-pinhole system, AdaptiSPECT-C, for quantitative brain imaging. In this context, keel-edge pinhole has proven to increase significantly attenuation of gamma rays through the edges of the pinhole aperture compared to the most commonly clinically used knife-edge profile [2-4]. In this work, we investigate the potential improvement in imaging performance of multiple keel-edge pinhole profiles as a function of keel height for AdaptiSPECT-C compared to a knife-edge collimation for 123I-IMP brain perfusion. Methods: The prototype AdaptiSPECT-C system used herein is composed of 23 hexagonal detector modules hemi-spherically arranged along 3 rings. For modeling in GATE simulation (GS) [5], each of these modules is composed of 1.5 mm radius pinhole and a 1 cm thick NaI(Tl) crystal with a 5 cm thick back-scattering compartment, which was considered to simulate 123I down-scatter interactions. Multiple keel-edge heights, corresponding to 0.0 (knife edge), 0.375, 0.75, 1.0, 1.125, 1.5, 1.875, and 2.25 mm were studied. We evaluated the volumetric sensitivity and relative amount of collimator penetration for a 15\% energy window centered at 159 keV in simulated projections of a 21 cm diameter spherical source (e.g. corresponding to the system{\textquoteright}s volume of interest) centered at the focal point of the pinholes. For reconstruction, an approach developed in our group was employed for modeling using GS the system response and especially collimator penetration into the system matrix [6,7] for the knife and the keel-edge designs. An XCAT [8] brain phantom with source distribution for the perfusion imaging agent 123I-IMP was simulated using the pinhole designs. Projection were acquired considering two scenarios: noise free (S1), and equal imaging time comparison for a realistic clinical scan time (e.g. 30 min [9,10]) (S2). Reconstructions were performed with a customized 3D-MLEM software into images of 1203 voxels of (2 mm)3. The reconstructed images were then compared to the ground truth image in terms of the normalized root mean squared error (NRMSE) and activity recovery (\%AR) for selected three-dimensional brain regions. Results: A keel-edge height of 0.375-0.75 mm represents the best choice leading to a significant reduction of the amount of penetration (up to  50\%) at the expense of sensitivity (-20\%) compared to a knife-edge profile. Visually, for all scenarios, the use of such a keel-edge profile leads to better separation of the brain structures, especially the caudate and the putamen. When sensitivity is not taken into account (e.g. noise free scenario), increasing the keel height improves NRMSE results. For an equal imaging time comparison, lowest NRMSE values are achieved for a 0.375-0.75 mm keel-height. A further keel-height increase degrades the NRMSE results due to significant loss of counts compared to knife-edge design. A 0.75 mm keel height leads on average to the best \%ARs (e.g. closest value to 100\%), especially for the striatum and putamen. For cortex and cerebellum regions, \%ARs are comparable with those obtained for a knife-edge design. Conclusion: In this work, we demonstrated that the use of a  0.75 mm height keel-edge profile for AdaptiSPECT-C incorporating 1.5 mm radius pinholes leads to superior imaging performance compared to knife-edge collimation for clinical 123I brain perfusion imaging. A range of aperture radii from 0.5 to 3.5 mm for each design have been investigated and will be shown at the time of the conference. We are currently working on performing a numerical-observer task-performance study of defect-detection in perfusion. Research Support: National Institute of Biomedical Imaging and Bioengineering (NIBIB), National Institutes of Health, Grant No R01 EB022521. Volumetric Sensitivities, Amount of Penetration, and Lowest NRMSE for the designs investigated},
	url = {http://jnm.snmjournals.org/content/61/supplement_1/573.abstract},
	author = {Benjamin Auer and Kesava Kalluri and Jan De Beenhouwer and Navid Zeraatkar and Neil Momsen and Philip H. Kuo and Lars R. Furenlid and Michael A. King}
}
@article {2055,
	title = {A low-cost geometry calibration procedure for a modular cone-beam X-ray CT system},
	journal = {Nondestructive Testing and Evaluation },
	volume = {35},
	year = {2020},
	pages = { 252-265},
	doi = {https://doi.org/10.1080/10589759.2020.1774580},
	author = {Van Nguyen and Jan De Beenhouwer and Joaquim Sanctorum and Sam Van Wassenbergh and Shabab Bazrafkan and Joris J. J. Dirckx and Jan Sijbers}
}
@article {2010,
	title = {A Machine Learning Approach to Growth Direction Finding for Automated Planting of Bulbous Plants},
	journal = {Scientific Reports},
	volume = {10},
	year = {2020},
	doi = {https://doi.org/10.1038/s41598-019-57405-8},
	author = {Brian G. Booth and Jan Sijbers and Jan De Beenhouwer}
}
@article {2048,
	title = {A Machine Learning Framework for Estimating Leaf Biochemical Parameters From Its Spectral Reflectance and Transmission Measurements},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {58},
	year = {2020},
	month = {04/2020},
	pages = {7393-7405},
	abstract = {Spectral measurements are commonly applied for the nondestructive estimation of leaf parameters, such as the concentrations of chlorophyll a and b, carotenoid, anthocyanin, brown pigment, leaf water content, and leaf mass per area for the quantification of vegetation physiology. The most popular way to estimate these parameters is by using spectral vegetation indices. The use of biochemical models allows us to use the full wavelength range (400-2500 nm) and to physically interpret the result. However, their performance is usually lower than that of supervised machine learning regression techniques. Machine learning regression techniques, on the other hand, have the disadvantage that the relationship between estimated parameters and the reflectance/transmission spectra is unclear. In this article, a hybrid between a supervised learning method and physical modeling for the estimation of leaf parameters is proposed. In this method, a machine learning regression technique is applied to learn a mapping from the true hyperspectral data set to a data set that follows the PROSPECT model. The PROSPECT model then reveals the actual leaf parameters. Two mapping methods, based on Gaussian processes (GPs) and kernel ridge regression (KRR) are proposed. As an alternative, mapping onto the leaf absorption spectra is proposed as well. The proposed methodology not only estimates the leaf parameters with a lower error but also solves the interpretation problem of the parameters estimated by the advanced machine learning regression techniques. This method is validated on the ANGERS and LOPEX data set.},
	doi = {10.1109/TGRS.2020.2982263},
	url = {https://www.researchgate.net/publication/340391024_A_Machine_Learning_Framework_for_Estimating_Leaf_Biochemical_Parameters_From_Its_Spectral_Reflectance_and_Transmission_Measurements},
	author = {Bikram Koirala and Zohreh Zahiri and Paul Scheunders}
}
@article {2074,
	title = {Macro- and microstructural changes in cosmonauts{\textquoteright} brains after long-duration spaceflight},
	journal = {Science Advances},
	volume = {6},
	year = {2020},
	month = {Apr-09-2020},
	pages = {eaaz9488},
	abstract = {Long-duration spaceflight causes widespread physiological changes, although its effect on brain structure remains poorly understood. In this work, we acquired diffusion magnetic resonance imaging to investigate alterations of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) compositions in each voxel, before, shortly after, and 7 months after long-duration spaceflight. We found increased WM in the cerebellum after spaceflight, providing the first clear evidence of sensorimotor neuroplasticity. At the region of interest level, this increase persisted 7 months after return to Earth. We also observe a widespread redistribution of CSF, with concomitant changes in the voxel fractions of adjacent GM. We show that these GM changes are the result of morphological changes rather than net tissue loss, which remained unclear from previous studies. Our study provides evidence of spaceflight-induced neuroplasticity to adapt motor strategies in space and evidence of fluid shift{\textendash}induced mechanical changes in the brain.},
	doi = {10.1126/sciadv.aaz9488},
	url = {https://advances.sciencemag.org/content/6/36/eaaz9488},
	author = {Steven Jillings and Angelique Van Ombergen and Tomilovskaya, Elena and Rumshiskaya, Alena and Litvinova, Liudmila and Nosikova, Inna and Pechenkova, Ekaterina and Rukavishnikov, Ilya and Kozlovskaya, Inessa B. and Manko, Olga and Danilichev, Sergey and Stefan Sunaert and Paul M Parizel and Valentin Sinitsyn and Petrovichev, Victor and Laureys, Steven and zu Eulenburg, Peter and Jan Sijbers and Floris L Wuyts and Ben Jeurissen}
}
@inproceedings {2133,
	title = {Newton-Krylov Methods For Polychromatic X-Ray CT},
	booktitle = {2020 IEEE International Conference on Image Processing (ICIP)},
	year = {2020},
	pages = {3045-3049},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Abu Dhabi},
	keywords = {Attenuation, computed tomography, image reconstruction, Jacobian matrices, Linear programming, Mathematical model, Structural beams},
	isbn = {978-1-7281-6396-3},
	doi = {10.1109/ICIP40778.2020.9190717},
	url = {https://ieeexplore.ieee.org/abstract/document/9190717},
	author = {Nathana{\"e}l Six and Jens Renders and Jan Sijbers and Jan De Beenhouwer}
}
@article {2018,
	title = {Nondestructive internal quality inspection of pear fruit by X-ray CT using machine learning},
	journal = {Food Control},
	volume = {113},
	year = {2020},
	pages = {1-13},
	doi = {https://doi.org/10.1016/j.foodcont.2020.107170},
	author = {Tim Van De Looverbosch and Hafizur Rahman Bhuiyan and Pieter Verboven and Manuel Dierick and Van Loo, D. and Jan De Beenhouwer and Jan Sijbers and Bart Nicolai}
}
@conference {2019,
	title = {Optimal design of a T1 super-resolution reconstruction experiment: a simulation study},
	year = {2020},
	author = {Michele Nicastro and Quinten Beirinckx and Piet Bladt and Ben Jeurissen and Stefan Klein and Jan Sijbers and Dirk H J Poot and Arnold Jan den Dekker}
}
@conference {Morez2020-al,
	title = {Optimal experimental design for multi-tissue spherical deconvolution of diffusion MRI},
	year = {2020},
	pages = {4321},
	author = {Jan Morez and Jan Sijbers and Ben Jeurissen}
}
@article {2017,
	title = {PAPPI: Personalized analysis of plantar pressure images using statistical modelling and parametric mapping},
	journal = {PlosOne},
	volume = {15},
	year = {2020},
	pages = {e0229685},
	doi = {https://doi.org/10.1371/journal.pone.0229685},
	author = {Brian G Booth and Eva Hoefnagels and Toon Huysmans and Jan Sijbers and No{\"e}l L.W. Keijsers}
}
@inproceedings {2052,
	title = {Projection angle adapted distortion correction in high-speed image-intensifier based tomography},
	booktitle = {6th International Conference on Image Formation in X-Ray Computed Tomography},
	year = {2020},
	author = {Joaquim Sanctorum and Van Nguyen and Jan Sijbers and Sam Van Wassenbergh and Joris J. J. Dirckx}
}
@article {2134,
	title = {Proposing a gamma radiation based intelligent system for simultaneous analyzing and detecting type and amount of petroleum by-products},
	journal = {Nuclear Engineering and Technology},
	year = {2020},
	type = {Research regular paper},
	abstract = {It is important for operators of poly-pipelines in petroleum industry to continuously monitor characteristics of transferred fluid such as its type and amount. To achieve this aim, in this study a dual energy gamma attenuation technique in combination with artificial neural network (ANN) is proposed to simultaneously determine type and amount of four different petroleum by-products. The detection system is composed of a dual energy gamma source, including americium-241 and barium-133 radioisotopes, and one 2.54 cm {\texttimes} 2.54 cm sodium iodide detector for recording the transmitted photons. Two signals recorded in transmission detector, namely the counts under photo peak of Americium-241 with energy of 59.5 keV and the counts under photo peak of Barium-133 with energy of 356 keV, were applied to the ANN as the two inputs and volume percentages of petroleum by-products were assigned as the outputs.},
	author = {Mohammadmehdi Roshani and Giang Phan and Rezhna Hassan Faraj and Nhut-Huan Phan and Gholam Hossein Roshani and Enrico Corniani and Ehsan Nazemi}
}
@inproceedings {2054,
	title = {The Radon Transform for Terahertz Computed Tomography Incorporating the Beam Shape},
	booktitle = {IEEE ICIP},
	year = {2020},
	pages = {3040-3044},
	doi = {10.1109/ICIP40778.2020.9191175},
	author = {Lars-Paul Lumbeeck and Pavel Paramonov and Jan Sijbers and Jan De Beenhouwer}
}
@article {2082,
	title = {Reconstruction using Depth of Interaction Information of Curved and Flat Detector Designs for Quantitative Multi-Pinhole Brain SPECT},
	journal = {Journal of Nuclear Medicine},
	volume = {61},
	year = {2020},
	pages = {103},
	abstract = {103Objectives: Brain SPECT has many clinical applications, especially for cerebral blood flow and dopamine transporter imaging [1,2]. In this context, a dedicated brain imaging, multi-pinhole system, AdaptiSPECT-C, is being developed by our group. Recent studies in cardiac and small animal imaging have demonstrated that the use of curved detector could improve image quality, by reducing parallax errors due to the depth of interaction (DOI) effect [3,4]. In this simulation study, we proposed to investigate using reconstruction with DOI modeling the potential advantage in imaging performance of curved over flat detectors for 123I-IMP perfusion imaging using the AdaptiSPECT-C system. Methods: The AdaptiSPECT-C design used in this work consists of 26 detector modules, 158 by 158 mm2 in size, arranged around the patient{\textquoteright}s head in three rings. The simulated detector modules were composed of a 8 mm thick NaI(Tl) crystal coupled to a 5 cm thick back-scattering compartment, representing components behind the crystal, to model 123I down-scatter interactions. Each detector module is associated with a 1.36 mm radius direct knife-edge pinhole aperture collimator. Two different system designs were considered, one based on curved detectors, and the other on flat detectors. The curved detectors were designed so that the radius of curvature corresponds to the detector to system center distance (e.g. 30.5 cm). This distance was the same for the flat detectors. GATE simulation [5] was employed to compute the system matrix [6,7] for both detector designs by forming the system response for the activity within each three-dimensional image-voxel of a 24 cm diameter sphere, thus including DOI variations and corrections in reconstruction [6,7]. An XCAT brain phantom [8] emulating 123I-IMP perfusion source distribution was simulated using the two designs. Data were acquired following three scenarios: noise free case (S1), equal number of counts comparison (S2) (e.g. 5.5M detected counts [9]), and equal imaging time comparison for the typical scan time (e.g. 30 min [10]) (S3). For S3, the total number of counts for the curved and flat detector designs, were respectively 9.24M and 9.17M. Projections were reconstructed with 3D-MLEM into images of 1203 voxels of (2 mm)3 and reconstruction compared to the ground truth image. The normalized root mean squared error (NRMSE) as well as percentage of activity recovery (\%AR) for several brain regions were used to evaluate the image quality. Results: Only a small gain in volumetric sensitivity ( 0.8\%) was obtained with the curved detector design. Qualitatively, the reconstructions for the curved and flat detector designs appear similar for all the 3 noise scenarios. Differences are mostly for the peripheral regions of the head where the differences in the obliquity of the gamma-rays passing through the apertures would be the greatest. Due to lower activity, those regions are also more impacted by noise. Quantitatively, a slight NRMSE improvement using curved detectors was seen. The curved detector design leads on average to the best ARs, especially for the striatum and putamen. Regions at the edges of the brain (e.g. cortex and cerebellum), more impacted by DOI effect, are similarly recovered by the two designs. Conclusion: We demonstrated that using curved instead of flat detector for AdaptiSPECT-C with solely centered pinholes leads to small improvement in sensitivity and image quality based on visual inspection, NRMSE, and activity recovery analysis. Flat detector associated with a sophisticated DOI correction was found to lead to similar results than those obtained with the curved detector. Further investigation will be performed using additional pinholes irradiating the 4 quadrants of the detectors which will increase the obliquity of the rays striking the detectors and may thus result in larger difference. Research Support: Grant No R01 EB022521 (NIBIB).},
	url = {http://jnm.snmjournals.org/content/61/supplement_1/103.abstract},
	author = {Benjamin Auer and Kesava Kalluri and Jan De Beenhouwer and Kimberly Doty and Navid Zeraatkar and Philip H. Kuo and Lars R. Furenlid and Michael A. King}
}
@inproceedings {2029,
	title = {Ring Artifact Reduction in Sinogram Space Using Deep Learning},
	booktitle = {6th International Conference on Image Formation in X-Ray Computed Tomography},
	year = {2020},
	author = {Maxime Nauwynck and Shabab Bazrafkan and Anneke Van Heteren and Jan De Beenhouwer and Jan Sijbers}
}
@inproceedings {2068,
	title = {Ringing Artefact Removal From Sparse View Tomosynthesis using Deep Neural Networks},
	booktitle = {The 6th International Conference on Image Formation in X-Ray Computed Tomography},
	year = {2020},
	pages = {380-383},
	author = {Shabab Bazrafkan and Vincent Van Nieuwenhove and Joris Soons and Jan De Beenhouwer and Jan Sijbers}
}
@conference {Smekens2020-mo,
	title = {Short T2* quantification of knee structures based on accelerated UTE Spiral VIBE MRI with SPIRiT reconstruction},
	year = {2020},
	pages = {2687},
	author = {C{\'e}line Smekens and Floris Vanhevel and Ben Jeurissen and Pieter Van Dyck and Jan Sijbers and Janssens, T}
}
@conference {2026,
	title = {Short T2* quantification of knee structures based on accelerated UTE Spiral VIBE MRI with SPIRiT reconstruction},
	year = {2020},
	author = {C{\'e}line Smekens and Floris Vanhevel and Ben Jeurissen and Pieter Van Dyck and Jan Sijbers and Thomas Janssens}
}
@article {2070,
	title = {Small medial femoral condyle morphotype is associated with medial compartment degeneration and distinct morphological characteristics: a comparative pilot study},
	journal = {Knee Surgery, Sports Traumatology, Arthroscopy},
	year = {2020},
	doi = {https://doi.org/10.1007/s00167-020-06218-8},
	author = {Jonas Grammens and Annemieke Van Haver and Femke Danckaers and Brian G Booth and Jan Sijbers and Peter Verdonk}
}
@conference {2094,
	title = {Strategies for quantifying the 3D atomic structure and the dynamics of nanomaterials using model-based STEM},
	year = {2020},
	author = {Sandra Van Aert and Annick De Backer and De wael, A and Jarmo Fatermans and Arslan Irmak, E and Friedrich, T and Ivan Lobato and Jones, L and Arnold Jan den Dekker and Peter D Nellist and Sara Bals}
}
@article {2030,
	title = {Subject-specific identification of three dimensional foot shape deviations using statistical shape analysis},
	journal = {Expert Systems With Applications},
	volume = {151},
	year = {2020},
	pages = {1-11},
	doi = {https://doi.org/10.1016/j.eswa.2020.113372},
	author = {Kristina Stankovi{\'c} and Toon Huysmans and Femke Danckaers and Jan Sijbers and Brian G Booth}
}
@article {2031,
	title = {Super-Resolution Magnetic Resonance Imaging of the Knee Using 2-Dimensional Turbo Spin Echo Imaging},
	journal = {Investigative Radiology},
	volume = {55},
	year = {2020},
	pages = {481-493},
	doi = {10.1097/RLI.0000000000000676},
	author = {Pieter Van Dyck and C{\'e}line Smekens and Floris Vanhevel and Eline De Smet and Ella Roelant and Jan Sijbers and Ben Jeurissen}
}
@conference {Bladt2020-mp,
	title = {Super-resolution reconstruction of single-PLD pseudo-continuous ASL images},
	year = {2020},
	pages = {3293},
	author = {Bladt, P and Beirinckx, Q and Van der Plas, M and Schmid, S and Teeuwisse, W and Ben Jeurissen and Arnold Jan den Dekker and Jan Sijbers and van Osch, M}
}
@conference {2139,
	title = {Super-resolution reconstruction of single-PLD pseudo-continuous ASL images},
	year = {2020},
	pages = {3293},
	abstract = {Super-resolution reconstruction (SRR) allows for 3D high-resolution image reconstruction from a set of low-resolution multi-slice images with different orientations. Arterial spin labeling (ASL) is an interesting albeit complicated candidate for SRR, as it relies on subtraction. SRR-ASL can be performed on low-SNR subtracted or on low-contrast unsubtracted ASL data. Different ASL-SRR implementations were applied to single-PLD PCASL data and validated against traditional ASL-scans. Combining motion correction, super-resolution post-processing and pairwise subtraction of label-control pairs in a single framework yielded comparable CBF maps as with traditional HR-ASL. Furthermore, in certain slices, SRR-ASL appears to reconstruct the anatomical structure with higher fidelity.},
	author = {Piet Bladt and Quinten Beirinckx and Merlijn C E van der Plas and Sophie Schmid and Wouter M Teeuwisse and Ben Jeurissen and Arnold Jan den Dekker and Jan Sijbers and M.J.P van Osch}
}
@conference {2015,
	title = {Super-resolution strategies for single-PLD pseudo-continuous ASL},
	year = {2020},
	address = {Arnhem, The Netherlands},
	abstract = {Super-resolution reconstruction (SRR) allows for 3D high-resolution image reconstruction from a set of low-resolution multi-slice images with different orientations. Arterial spin labeling (ASL) is an interesting albeit complicated candidate for SRR, as it relies on subtraction. SRR-ASL can be performed on low-SNR subtracted or on low-contrast unsubtracted ASL data. Different ASL-SRR implementations were applied to single-PLD PCASL data and validated against traditional ASL-scans. Combining motion correction, super-resolution post-processing and pairwise subtraction of label-control pairs in a single framework yielded comparable CBF maps as with traditional HR-ASL. Furthermore, in certain slices, SRR-ASL appears to reconstruct the anatomical structure with higher fidelity.},
	author = {Quinten Beirinckx and Piet Bladt and Merlijn C E van der Plas and Sophie Schmid and Wouter M Teeuwisse and Ben Jeurissen and Arnold Jan den Dekker and Jan Sijbers and M.J.P van Osch}
}
@article {2051,
	title = {Supporting measurements or more averages? How to quantify cerebral blood flow most reliably in 5 minutes by arterial spin labeling},
	journal = {Magnetic Resonance in Medicine},
	volume = {84},
	year = {2020},
	pages = {2523-2536},
	doi = {10.1002/mrm.28314},
	author = {Piet Bladt and M.J.P van Osch and Clement, Patricia and Eric Achten and Jan Sijbers and Arnold Jan den Dekker}
}
@conference {2101,
	title = {Tracking Off the Beaten Track},
	year = {2020},
	pages = {e975},
	address = {Sydney, Australia},
	author = {Ben Jeurissen}
}
@article {2076,
	title = {Unveiling Water Dynamics in Fuel Cells from Time-Resolved Tomographic Microscopy Data},
	journal = {Scientific Reports},
	volume = {10},
	year = {2020},
	doi = {https://doi.org/10.1038/s41598-020-73036-w},
	author = {Minna B{\"u}hrer and Hong Xu and Jens Eller and Jan Sijbers and Marco Stampanoni and Federica Marone}
}
@article {2077,
	title = {X-ray phase contrast simulation for grating-based interferometry using GATE},
	journal = {Optics Express},
	volume = {28},
	year = {2020},
	pages = {33390-33412},
	doi = {https://doi.org/10.1364/OE.392337},
	author = {Jonathan Sanctorum and Jan De Beenhouwer and Jan Sijbers}
}
@inproceedings {1931,
	title = {A 3D Printed Thermal Manikin Head for Evaluating Helmets for Convective and Radiative Heat Loss},
	booktitle = {Proceedings of the 20th Congress of the International Ergonomics Association (IEA 2018) Volume VII},
	volume = {VII},
	year = {2019},
	pages = {592{\textendash}602},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {Thermal performance of three bicycle helmets for radiative and convective heat loss was evaluated through heat loss experiments in a wind tunnel. A 3D printed thermal manikin head of a 50th percentile western male population was developed. Thermal performance of a helmet was quantified by comparing the manikin head heat losses with and without helmet. Experiments were performed for two air velocities: 1.6 m/s and 6 m/s. An infrared heat lamp positioned above the manikin simulated the effect of solar load. The results from the experiments showed a convective cooling efficiency between 89{\%} and 96{\%} for open helmets and between 78{\%} and 83{\%} for closed helmets. The radiative heat gain ranged from 3.5 W to 4.5 W for open helmets and 5 W to 8 W for closed helmets.},
	keywords = {Convective heat loss, Heat transfer, Helmet thermal performance, Radiative heat gain, Thermal manikin head, Wind tunnel},
	isbn = {978-3-319-96070-8},
	doi = {10.1007/978-3-319-96071-5_63},
	author = {Shriram Mukunthan and Jochen Vleugels and Toon Huysmans and Mayor, Tiago Sotto and De Bruyne, Guido}
}
@inbook {1960,
	title = {Adaptable digital human models from 3D body scans},
	booktitle = {DHM and Posturography},
	volume = {VI},
	year = {2019},
	pages = {459-470},
	publisher = {Academic Press},
	organization = {Academic Press},
	chapter = {33},
	doi = {https://doi.org/10.1016/B978-0-12-816713-7.00033-7},
	author = {Femke Danckaers and Toon Huysmans and Jan Sijbers}
}
@conference {1924,
	title = {Advancing Analysis Techniques for Plantar Pressure Videos via Open-Access Contributions},
	year = {2019},
	author = {Brian G Booth and No{\"e}l L.W. Keijsers and Toon Huysmans and Jan Sijbers}
}
@article {1964,
	title = {Alterations of Functional Brain Connectivity After Long-Duration Spaceflight as Revealed by fMRI.},
	journal = {Front Physiol},
	volume = {10},
	year = {2019},
	pages = {1-23},
	abstract = {<p>The present study reports alterations of task-based functional brain connectivity in a group of 11 cosmonauts after a long-duration spaceflight, compared to a healthy control group not involved in the space program. To elicit the postural and locomotor sensorimotor mechanisms that are usually most significantly impaired when space travelers return to Earth, a plantar stimulation paradigm was used in a block design fMRI study. The motor control system activated by the plantar stimulation involved the pre-central and post-central gyri, SMA, SII/operculum, and, to a lesser degree, the insular cortex and cerebellum. While no post-flight alterations were observed in terms of activation, the network-based statistics approach revealed task-specific functional connectivity modifications within a broader set of regions involving the activation sites along with other parts of the sensorimotor neural network and the visual, proprioceptive, and vestibular systems. The most notable findings included a post-flight increase in the stimulation-specific connectivity of the right posterior supramarginal gyrus with the rest of the brain; a strengthening of connections between the left and right insulae; decreased connectivity of the vestibular nuclei, right inferior parietal cortex (BA40) and cerebellum with areas associated with motor, visual, vestibular, and proprioception functions; and decreased coupling of the cerebellum with the visual cortex and the right inferior parietal cortex. The severity of space motion sickness symptoms was found to correlate with a post- to pre-flight difference in connectivity between the right supramarginal gyrus and the left anterior insula. Due to the complex nature and rapid dynamics of adaptation to gravity alterations, the post-flight findings might be attributed to both the long-term microgravity exposure and to the readaptation to Earth{\textquoteright}s gravity that took place between the landing and post-flight MRI session. Nevertheless, the results have implications for the multisensory reweighting and gravitational motor system theories, generating hypotheses to be tested in future research.</p>},
	issn = {1664-042X},
	doi = {10.3389/fphys.2019.00761},
	author = {Pechenkova, Ekaterina and Nosikova, Inna and Rumshiskaya, Alena and Litvinova, Liudmila and Rukavishnikov, Ilya and Mershina, Elena and Valentin Sinitsyn and Angelique Van Ombergen and Ben Jeurissen and Steven Jillings and Laureys, Steven and Jan Sijbers and Grishin, Alexey and Chernikova, Ludmila and Naumov, Ivan and Kornilova, Ludmila and Floris L Wuyts and Tomilovskaya, Elena and Kozlovskaya, Inessa}
}
@article {2007,
	title = {Analysis of hyperspectral images for detection of drought stress and recovery in maize plants in a high-throughput phenotyping platform},
	journal = {Computers and Electronics in Agriculture},
	volume = {162},
	year = {2019},
	pages = {749-758},
	doi = {https://doi.org/10.1016/j.compag.2019.05.018},
	author = {Mohd Shahrimie Mohd Asaari and Stien Mertens and Stijn Dhondt and Dirk Inze and Nathalie Wuyts and Paul Scheunders}
}
@mastersthesis {2342,
	title = {Analysis of hyperspectral images for high-throughput plant phenotyping },
	year = {2019},
	type = {PhD thesis},
	author = {Shahrimie Mohd. Asaari}
}
@article {1955,
	title = {Aortic root sizing for transcatheter aortic valve implantation using a shape model parameterisation},
	journal = {Medical \& Biological Engineering \& Computing},
	volume = {57},
	year = {2019},
	month = {Jul},
	pages = {2081{\textendash}2092},
	abstract = {During a transcatheter aortic valve implantation, an axisymmetric implant is placed in an irregularly shaped aortic root. Implanting an incorrect size can cause complications such as leakage of blood alongside or through the implant. The aim of this study was to construct a method that determines the optimal size of the implant based on the three-dimensional shape of the aortic root. Based on the pre-interventional computed tomography scan of 89 patients, a statistical shape model of their aortic root was constructed. The weights associated with the principal components and the volume of calcification in the aortic valve were used as parameters in a classification algorithm. The classification algorithm was trained using the patients with no or mild leakage after their intervention. Subsequently, the algorithms were applied to the patients with moderate to severe leakage. Cross validation showed that a random forest classifier assigned the same size in 65 {\textpm} 7\% of the training cases, while 57 {\textpm} 8\% of the patients with moderate to severe leakage were assigned a different size. This initial study showed that this semi-automatic method has the potential to correctly assign an implant size. Further research is required to assess whether the different size implants would improve the outcome of those patients.},
	issn = {1741-0444},
	doi = {10.1007/s11517-019-01996-x},
	author = {Bosmans, Bart and Toon Huysmans and Lopes, Patricia and Verhoelst, Eva and Dezutter, Tim and de Jaegere, Peter and Jan Sijbers and Vander Sloten, Jos and Bosmans, Johan}
}
@inproceedings {1854,
	title = {An Articulating Statistical Shape Model of the Human Hand},
	booktitle = {Advances in Human Factors in Simulation and Modeling (AHFE 2018)},
	volume = {780},
	year = {2019},
	pages = {433{\textendash}445},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {This paper presents a registration framework for the construction of a statistical shape model of the human hand in a standard pose. It brings a skeletonized reference model of an individual human hand into correspondence with optical 3D surface scans of hands by sequentially applying articulation-based registration and elastic surface registration. Registered surfaces are then fed into a statistical shape modelling algorithm based on principal component analysis. The model-building technique has been evaluated on a dataset of optical scans from 100 healthy individuals, acquired with a 3dMD scanning system. It is shown that our registration framework provides accurate geometric and anatomical alignment, and that the shape basis of the resulting statistical model provides a compact representation of the specific population. The model also provides insight into the anatomical variation of the lower arm and hand, which is useful information for the design of well-fitting products.},
	isbn = {978-3-319-94223-0},
	doi = {10.1007/978-3-319-94223-0_41},
	author = {Jeroen Van Houtte and Kristina Stankovi{\'c} and Brian G Booth and Femke Danckaers and Bertrand, V{\'e}ronique and Frederik Verstreken and Jan Sijbers and Toon Huysmans},
	editor = {Cassenti, Daniel N.}
}
@article {1947,
	title = {Assessment of Anterior Cruciate Ligament Graft Maturity With Conventional Magnetic Resonance Imaging},
	journal = {The Orthopaedic Journal of Sports Medicine},
	volume = {7},
	year = {2019},
	pages = {1-9},
	doi = {https://doi.org/10.1177/2325967119849012},
	author = {Pieter Van Dyck and Katja Zazulia and C{\'e}line Smekens and Christiaan H.W. Heusdens and Thomas Janssens and Jan Sijbers}
}
@article {1927,
	title = {An assessment of the information lost when applying data reduction techniques to dynamic plantar pressure measurements},
	journal = {Journal of Biomechanics },
	volume = {87},
	year = {2019},
	pages = {161-166},
	doi = {https://doi.org/10.1016/j.jbiomech.2019.02.008},
	author = {Brian G Booth and No{\"e}l L.W. Keijsers and Jan Sijbers and Toon Huysmans}
}
@conference {1974,
	title = {Atom column detection from STEM images using the maximum a posteriori probability rule},
	year = {2019},
	author = {J Fatermans and Arnold Jan den Dekker and O{\textquoteright}Leary, C M. and Peter D Nellist and Sandra Van Aert}
}
@conference {1975,
	title = {Atom detection from electron microscopy images},
	year = {2019},
	pages = {15},
	author = {J Fatermans and Arnold Jan den Dekker and Sandra Van Aert}
}
@inproceedings {1852,
	title = {Automatic Generation of Statistical Shape Models in Motion},
	booktitle = {Advances in Human Factors in Simulation and Modeling (AHFE 2018)},
	volume = {780},
	year = {2019},
	pages = {170{\textendash}178},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {Statistical body shape modeling (SBSM) is a well-known technique to map out the variability of body shapes and is commonly used in 3D anthropometric analyses. In this paper, a new approach to integrate movement acquired by a motion capture system with a body shape is proposed. This was done by selecting landmarks on a body shape model, and predicting a body shape based on features. Then, a virtual skeleton was generated relative to those landmarks. This skeleton was parented to a body shape, allowing to modify its pose and to add pre-recorded motion to different body shapes in a realistic way.},
	isbn = {978-3-319-94223-0},
	doi = {10.1007/978-3-319-94223-0_16},
	author = {Femke Danckaers and Scataglini, Sofia and Haelterman, Robby and Van Tiggelen, Damien and Toon Huysmans and Jan Sijbers},
	editor = {Cassenti, Daniel N.}
}
@conference {1993,
	title = {Beyond the consensus: is sacrificing part of the PCASL scan time for measurement of labeling efficiency and T1 of blood beneficial?},
	year = {2019},
	author = {Piet Bladt and M.J.P van Osch and Eric Achten and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {1994,
	title = {Beyond the consensus: should measurement of T1 of blood and labeling efficiency be included and should a single- or multi-PLD protocol be used in a five-minute protocol for PCASL?},
	year = {2019},
	author = {Piet Bladt and M.J.P van Osch and Eric Achten and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {1992,
	title = {Beyond the consensus: what to include when 5 minutes are available for perfusion imaging by PCASL?},
	year = {2019},
	author = {Piet Bladt and M.J.P van Osch and Eric Achten and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {2038,
	title = {BQ-MINDED: Introducing Quantitative MRI in routine clinical practice},
	year = {2019},
	author = {Michele Nicastro and Maira Siqueira Pinto}
}
@article {1941,
	title = {Brain ventricular volume changes induced by long-duration spaceflight},
	journal = {Proceedings of the National Academy of Sciences},
	volume = {116},
	year = {2019},
	pages = {10531-10536},
	abstract = {Long-duration spaceflight induces detrimental changes in human physiology due to microgravity. One example is a cephalic fluid shift. Here, we prospectively investigated the quantitative changes in cerebrospinal fluid (CSF) volume of the brain ventricular regions in space crew by means of a region of interest, observer-independent analysis on structural brain MRI scans. MRI scans were collected before the mission, shortly after and 7 mo after return to Earth. We found a significant increase in lateral and third ventricles at postflight and a trend to normalization at follow-up, but still significantly increased ventricular volumes. The observed spatiotemporal pattern of CSF compartment enlargement and recovery points to a reduced CSF resorption in microgravity as the underlying cause.Long-duration spaceflight induces detrimental changes in human physiology. Its residual effects and mechanisms remain unclear. We prospectively investigated the changes in cerebrospinal fluid (CSF) volume of the brain ventricular regions in space crew by means of a region of interest analysis on structural brain scans. Cosmonaut MRI data were investigated preflight (n = 11), postflight (n = 11), and at long-term follow-up 7 mo after landing (n = 7). Post hoc analyses revealed a significant difference between preflight and postflight values for all supratentorial ventricular structures, i.e., lateral ventricle (mean \% change {\textpm} SE = 13.3 {\textpm} 1.9), third ventricle (mean \% change {\textpm} SE = 10.4 {\textpm} 1.1), and the total ventricular volume (mean \% change {\textpm} SE = 11.6 {\textpm} 1.5) (all P \&lt; 0.0001), with higher volumes at postflight. At follow-up, these structures did not quite reach baseline levels, with still residual increases in volume for the lateral ventricle (mean \% change {\textpm} SE = 7.7 {\textpm} 1.6; P = 0.0009), the third ventricle (mean \% change {\textpm} SE = 4.7 {\textpm} 1.3; P = 0.0063), and the total ventricular volume (mean \% change {\textpm} SE = 6.4 {\textpm} 1.3; P = 0.0008). This spatiotemporal pattern of CSF compartment enlargement and recovery points to a reduced CSF resorption in microgravity as the underlying cause. Our results warrant more detailed and longer longitudinal follow-up. The clinical impact of our findings on the long-term cosmonauts{\textquoteright} health and their relation to ocular changes reported in space travelers requires further prospective studies.},
	issn = {0027-8424},
	doi = {10.1073/pnas.1820354116},
	url = {https://www.pnas.org/content/early/2019/05/01/1820354116},
	author = {Angelique Van Ombergen and Steven Jillings and Ben Jeurissen and Tomilovskaya, Elena and Alena Rumshiskaya and Liudmila Litvinova and Nosikova, Inna and Ekaterina V. Pechenkova and Ilya Rukavishnikov and Manko, Olga and Danylichev, Sergey and R{\"u}hl, R. Maxine and Inessa B. Kozlovskaya and Stefan Sunaert and Paul M Parizel and Valentin Sinitsyn and Steven S L Laureys and Jan Sijbers and zu Eulenburg, Peter and Floris L Wuyts}
}
@inproceedings {2090,
	title = {CAD-based defect inspection with optimal view angle selection based on polychromatic X-ray projection images},
	booktitle = {9th Conference on Industrial Computed Tomography},
	year = {2019},
	pages = {1-5},
	address = {Padova, Italy},
	author = {Alice Presenti and Jan Sijbers and Arnold Jan den Dekker and Jan De Beenhouwer}
}
@article {1962,
	title = {Cognitive Training in Young Patients With Traumatic Brain Injury: A Fixel-Based Analysis.},
	journal = {Neurorehabil Neural Repair},
	year = {2019},
	month = {2019 Aug 16},
	abstract = {Traumatic brain injury (TBI) is associated with altered white matter organization and impaired cognitive functioning.
We aimed to investigate changes in white matter and cognitive functioning following computerized cognitive training.
Sixteen adolescents with moderate-to-severe TBI (age 15.6 {\textpm} 1.8 years, 1.2-4.6 years postinjury) completed the 8-week BrainGames program and diffusion weighted imaging (DWI) and cognitive assessment at time point 1 (before training) and time point 2 (after training). Sixteen healthy controls (HC) (age 15.6 {\textpm} 1.8 years) completed DWI assessment at time point 1 and cognitive assessment at time point 1 and 2. Fixel-based analyses were used to examine fractional anisotropy (FA), mean diffusivity (MD), and fiber cross-section (FC) on a whole brain level and in tracts of interest. 
Patients with TBI showed cognitive impairments and extensive areas with decreased FA and increased MD together with an increase in FC in the body of the corpus callosum and left superior longitudinal fasciculus (SLF) at time point 1. Patients improved significantly on the inhibition measure at time point 2, whereas the HC group remained unchanged. No training-induced changes were observed on the group level in diffusion metrics. Exploratory correlations were found between improvements on verbal working memory and reduced MD of the left SLF and between increased performance on an information processing speed task and increased FA of the right precentral gyrus.

Results are indicative of positive effects of BrainGames on cognitive functioning and provide preliminary evidence for neuroplasticity associated with cognitive improvements following cognitive intervention in TBI.},
	issn = {1552-6844},
	doi = {10.1177/1545968319868720},
	author = {Verhelst, Helena and Diana Giraldo and Vander Linden, Catharine and Vingerhoets, Guy and Ben Jeurissen and Caeyenberghs, Karen}
}
@article {1848,
	title = {Combination of shape and X-ray inspection for apple internal quality control: in silico analysis of the methodology based on X-ray computed tomography},
	journal = {Postharvest Biology and Technology},
	volume = {148},
	year = {2019},
	pages = {218-227},
	doi = {https://doi.org/10.1016/j.postharvbio.2018.05.020},
	author = {Mattias Van Dael and Pieter Verboven and Angelo Zanella and Jan Sijbers and Bart Nicolai}
}
@inproceedings {1932,
	title = {A Comparison Between Representative 3D Faces Based on Bi- and Multi-variate and Shape Based Analysis},
	booktitle = {Proceedings of the 20th Congress of the International Ergonomics Association (IEA 2018)- Volume VII},
	volume = {VII},
	year = {2019},
	pages = {1355{\textendash}1364},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {In Ergonomic product design, designers need to translate anthropometric data of the target population into product dimensions or sizing systems. Currently, sizing systems are often based on traditional anthropometric data and generally use the variation of one or two key body dimensions directly related to the product. For products that need to closely fit a certain part of the body it is relevant to incorporate multiple key dimensions. This can be realized by a multivariate approach such as a Principal Component Analysis. Over the past decades, there has been an increase in incorporating 3D imaging in anthropometric surveys. In order to integrate the use of 3D anthropometry in product sizing, representative models are used to visualize the variability of the target population. For the development of a ventilation mask for children, this study compares representative models of 3D faces based on a bivariate, multivariate and shape based analysis of 303 children{\textquoteright}s faces.},
	keywords = {3D anthropometry, Children, Design, Product sizing, Ventilation mask},
	isbn = {978-3-319-96070-8},
	doi = {10.1007/978-3-319-96071-5_137},
	author = {Ly{\`e} Goto and Toon Huysmans and Wonsup Lee and Molenbroek, Johan F. M. and Richard Goossens}
}
@conference {Morez2019-uz,
	title = {A comparison of response function tensor models for multi-tissue spherical deconvolution},
	volume = {32 (Suppl. 1)},
	year = {2019},
	pages = {S138{\textendash}S139},
	author = {Jan Morez and Jan Sijbers and Ben Jeurissen}
}
@article {1934,
	title = {Decision Fusion Framework for Hyperspectral Image Classification Based on Markov and Conditional Random Fields},
	journal = {Remote Sensing},
	volume = {11},
	year = {2019},
	chapter = {624},
	abstract = {Classification of hyperspectral images is a challenging task owing to the high dimensionality of the data, limited ground truth data, collinearity of the spectra and the presence of mixed pixels. Conventional classification techniques do not cope well with these problems. Thus, in addition to the spectral information, features were developed for a more complete description of the pixels, e.g., containing contextual information at the superpixel level or mixed pixel information at the subpixel level. This has encouraged an evolution of fusion techniques which use these myriad of multiple feature sets and decisions from individual classifiers to be employed in a joint manner. In this work, we present a flexible decision fusion framework addressing these issues. In a first step, we propose to use sparse fractional abundances as decision source, complementary to class probabilities obtained from a supervised classifier. This specific selection of complementary decision sources enables the description of a pixel in a more complete way, and is expected to mitigate the effects of small training samples sizes. Secondly, we propose to apply a fusion scheme, based on the probabilistic graphical Markov Random Field (MRF) and Conditional Random Field (CRF) models, which inherently employ spatial information into the fusion process. To strengthen the decision fusion process, consistency links across the different decision sources are incorporated to encourage agreement between their decisions. The proposed framework offers flexibility such that it can be extended with additional decision sources in a straightforward way. Experimental results conducted on two real hyperspectral images show superiority over several other approaches in terms of classification performance when very limited training data is available},
	keywords = {Classification, decision fusion, Hyperspectral},
	doi = {https://doi.org/10.3390/rs11060624},
	url = {https://www.mdpi.com/2072-4292/11/6/624},
	author = {Vera Andrejchenko and Wenzhi Liao and Wilfried Philips and Paul Scheunders}
}
@inproceedings {1966,
	title = {A Deep Learning Approach to Horse Bone Segmentation from Digitally Reconstructed Radiographs},
	booktitle = {International Conference on Image Processing Theory, Tools, and Applications},
	year = {2019},
	doi = {10.1109/IPTA.2019.8936082},
	author = {Jeroen Van Houtte and Shabab Bazrafkan and Filip Vandenberghe and Guoyan Zheng and Jan Sijbers}
}
@conference {1984,
	title = {A deep learning approach to T1 mapping in quantitative MRI},
	volume = {32 (Suppl. 1)},
	number = {S09.05},
	year = {2019},
	publisher = {Magn Reson Mater Phy},
	abstract = {Quantitative MRI aims to measure biophysical tissue parameters through the analysis of the MR signal. Conventional parameter estimation methods, which often rely on a voxel-wise mapping, ignores spatial redundancies. In this work, a deep learning method for T1 mapping is proposed to overcome this limitation.},
	doi = {10.1007/s10334-019-00754-2},
	author = {Ribeiro Sabidussi, Emanoel and Michele Nicastro and Shabab Bazrafkan and Quinten Beirinckx and Ben Jeurissen and Jan Sijbers and Arnold Jan den Dekker and Stefan Klein and Dirk H J Poot}
}
@conference {1965,
	title = {Deep learning based missing wedge artefact removal for electron tomography},
	year = {2019},
	pages = {660-661},
	author = {Juho Rimpelainen and Shabab Bazrafkan and Jan Sijbers and Jan De Beenhouwer}
}
@inbook {1961,
	title = {Design smart clothing using digital human models},
	booktitle = {DHM and Posturography},
	year = {2019},
	pages = {683-698},
	publisher = {Academic Press},
	organization = {Academic Press},
	doi = {https://doi.org/10.1016/B978-0-12-816713-7.00053-2},
	author = {Scataglini, Sofia and Femke Danckaers and Toon Huysmans and Jan Sijbers and Giuseppe Andreoni}
}
@inproceedings {1850,
	title = {Determining Comfortable Pressure Ranges for Wearable EEG Headsets},
	booktitle = {Advances in Human Factors in Wearable Technologies and Game Design},
	year = {2019},
	pages = {11{\textendash}19},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {Measuring and interpretation of brain wave signals through electroencephalography (EEG) is an emerging technology. The technique is traditionally applied in a clinical setting with EEG caps and conductive gels to ensure proper contact through a subject{\textquoteright}s hair, and anticipate inter-subject anthropometric variations. Development of dry electrodes offers the potential to develop wearable EEG headsets. Such devices could induce medical and commercial applications. In this paper, we evaluate a prototype EEG headset that actively places electrodes at standardized positions on the subject{\textquoteright}s head, where each electrode is applied with equal pressure. The system is designed for use with dry electrodes. Our research delivers a better understanding on the link between general level of comfort and possible useful clear data signals, that can be used in brain computer interfaces (BCI). The present study is confined to the impact of adjustable electrodes pressure on level of user comfort only. Levels of discomfort are assessed in twelve participants, wearing an EEG headset with controllable electrode pressure exerted at 14 locations. Of-the-shelf dry electrodes are used. In a first session, evenly distributed pressure is increased and afterwards decreased in fixed time intervals, going from 10~kPa to 30~kPa and vice versa with steps of 2~kPa. In a second session, a subject specific acceptable pressure level is retrieved from the data of the first session and constantly applied for 30~min. During this intervention, level of discomfort is assessed in a VAS-scale. Additional observation and surveys yields insights on user experience in wearing a pressure exerting EEG headset.},
	isbn = {978-3-319-94619-1},
	author = {Stijn Verwulgen and Daniel Lacko and Justine, Hoppenbrouwers and Kustermans, Siemon and Moons, Stine and Thys, Falk and Zelck, Sander and Vaes, Kristof and Toon Huysmans and Jochen Vleugels and Steven Truijen},
	editor = {Ahram, Tareq Z.}
}
@mastersthesis {1930,
	title = {The Development of 3D Statistical Shape Models for Diverse Applications},
	volume = {PhD in Sciences},
	year = {2019},
	type = {PhD thesis},
	author = {Femke Danckaers}
}
@article {1776,
	title = {Diffusion MRI fiber tractography of the brain},
	journal = {NMR in Biomedicine},
	year = {2019},
	doi = {10.1002/nbm.3785},
	author = {Ben Jeurissen and Maxime Descoteaux and Susumu Mori and Alexander Leemans}
}
@conference {1981,
	title = {Diffusion time dependence in the mid-time regime: a simulation study using PGSE.},
	number = {3572},
	year = {2019},
	address = {Montreal, Canada},
	abstract = {The purpose of this work is to study the effect of varying the diffusion time on the estimation of the parameters of the two-compartment diffusion tensor model in the mid-time regime. Simulation results show that the precision of the diffusion time-dependent compartmental parameter estimates increases when a variable echo time acquisition scheme is used. At low SNR, however, including diffusion time-dependence may lead to a high bias and variance compared to the more conventional non diffusion time-dependent model.},
	author = {Annelinde E. Buikema and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {Jillings2019-al,
	title = {Diffusion-weighted imaging reveals structural brain changes in cosmonauts after long-duration spaceflight},
	volume = {32 (Suppl. 1)},
	year = {2019},
	pages = {Magn Reson Mater Phy. 2019; 32(Suppl. 1):S101},
	author = {Steven Jillings and Angelique Van Ombergen and Tomilovskaya, E and Laureys, S and zu Eulenburg, P and Stefan Sunaert and Jan Sijbers and Floris L Wuyts and Ben Jeurissen}
}
@inproceedings {1945,
	title = {Dynamic angle selection for few-view X-ray inspection of CAD based objects},
	booktitle = {Proc. SPIE, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully3D)},
	volume = {11072},
	year = {2019},
	doi = {https://doi.org/10.1117/12.2534894},
	author = {Alice Presenti and Jan Sijbers and Jan De Beenhouwer}
}
@conference {1921,
	title = {Effect of diffusion time dependence on parameter estimation in the clinical time frame: a simulation study using PGSE},
	year = {2019},
	address = {Leiden, the Netherlands},
	abstract = {Fieremans et al. modelled the diffusion time dependence of diffusion longitudinal and transverse to white matter tracts, which can be interpreted as an effect of structural disorder at the mesoscopic scale. To our knowledge, this diffusion time-dependent (DT-dependent) model has not yet been studied in the mid-time regime (Δ=20-180ms) with varying echo times, which would be valuable for clinical practice. In this simulation study, we use the pulsed-gradient spin-echo (PGSE) sequence5 and study the effects of varying the diffusion time on the parameter estimation in a mid-time regime.},
	author = {Annelinde E. Buikema and Arnold Jan den Dekker and Jan Sijbers}
}
@inproceedings {1883,
	title = {Fast detection of cracks in ultrasonically welded parts by inline X-ray inspection},
	booktitle = {9th Conference on Industrial Computed Tomography},
	year = {2019},
	address = {Padova, Italy},
	url = {https://www.ndt.net/article/ctc2019/papers/iCT2019_Full_paper_48.pdf},
	author = {Eline Janssens and Jan Sijbers and Manuel Dierick and Jan De Beenhouwer}
}
@inproceedings {1946,
	title = {Fiber assignment by continuous tracking for parametric fiber reinforced polymer reconstruction},
	booktitle = {15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully3D)},
	volume = {11072},
	year = {2019},
	doi = {http://dx.doi.org/10.1117/12.2534836},
	author = {Tim Elberfeld and Jan De Beenhouwer and Jan Sijbers}
}
@inproceedings {1986,
	title = {Fractional abundance estimation of mixed and compound materials by hyperspectral imaging.},
	booktitle = {10th Workshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (WHISPERS), Sept 2019},
	year = {2019},
	pages = {pp. 1-5},
	address = {Amsterdam, Netherlands},
	abstract = {The mechanical and chemical properties of a compound material are determined by the fractional abundances of its components. In this work, we present a spectral unmixing technique to estimate the fractional abundances of the components of mixed and compound materials from hyperspectral images. The estimation of fractional abundances in mixed materials faces the main challenge of intimate mixing. In compound materials, the mixing with water causes changes in chemical properties resulting in spectral variability and non-linearity. To address these challenges, a supervised method is proposed that learns a mapping from the hyperspectral data to spectra that follow the linear mixing model. Then, a linear unmixing technique is applied on the mapped spectra to estimate the fractional abundances. To demonstrate the potential of the proposed method, experiments are conducted on hyperspectral images from mixtures of red and yellow clay powders and hardened mortar samples with varying water to cement ratios.},
	doi = {doi: 10.1109/WHISPERS.2019.8920893},
	url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=8920893\&isnumber=8920817},
	author = {Bikram Koirala and Zohreh Zahiri and Mahdi Khodadadzadeh and Paul Scheunders}
}
@article {1991,
	title = {Importance of pressure plasticity during compression of probiotic tablet formulations},
	journal = {European Journal of Pharmaceutics and Biopharmaceutics},
	volume = {145},
	year = {2019},
	pages = {7-11},
	doi = {https://doi.org/10.1016/j.ejpb.2019.10.001},
	author = {E Byl and Piet Bladt and S Lebeer and Filip Kiekens}
}
@conference {Jeurissen2019-sb,
	title = {Improved precision and accuracy in q-space trajectory imaging by model-based super-resolution reconstruction},
	year = {2019},
	pages = {556},
	author = {Ben Jeurissen and Westin, Carl-Fredrik and Jan Sijbers and Szczepankiewicz, Filip}
}
@inproceedings {1918,
	title = {An Interactive Visual Comparison Tool for 3D Volume Datasets represented by Nonlinearly Scaled 1D Line Plots through Space-filling Curves},
	booktitle = {9th Conference on Industrial Computed Tomography},
	year = {2019},
	address = {Padova, Italy},
	author = {Johannes Weissenb{\"o}ck and Bernhard Fr{\"o}hler and Eduard Gr{\"o}ller and Jonathan Sanctorum and Jan De Beenhouwer and Jan Sijbers and Santhosh Ayalur Karunakaran and Helmuth Hoeller and Johann Kastner and Christoph Heinzl}
}
@inproceedings {1989,
	title = {Investigation of a Monte Carlo simulation and an analytic-based approach for modeling the system response for clinical I-123 brain SPECT imaging},
	booktitle = {15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine},
	volume = {11072},
	year = {2019},
	pages = {187 {\textendash} 190},
	publisher = {International Society for Optics and Photonics},
	organization = {International Society for Optics and Photonics},
	keywords = {image reconstruction, Monte-Carlo simulation, SPECT I-123 brain imaging, System response modeling, Variance reduction technique (forced detection)},
	doi = {10.1117/12.2534881},
	url = {https://doi.org/10.1117/12.2534881},
	author = {Benjamin Auer and Navid Zeraatkar and Jan De Beenhouwer and Kesava Kalluri and Philip H. Kuo and Lars R. Furenlid and Michael A. King}
}
@conference {1990,
	title = {Investigation of keel versus knife edge pinhole profiles for a next-generation SPECT system dedicated to clinical brain imaging},
	year = {2019},
	month = {2019},
	author = {Benjamin Auer and Kesava Kalluri and Jan De Beenhouwer and Navid Zeraatkar and Arda K{\"o}nik and Philip H. Kuo and Lars R. Furenlid and Michael A. King}
}
@inproceedings {1851,
	title = {Latent Heat Loss of a Virtual Thermal Manikin for Evaluating the Thermal Performance of Bicycle Helmets},
	booktitle = {Advances in Human Factors in Simulation and Modeling},
	year = {2019},
	pages = {66{\textendash}78},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {Thermal performance of three bicycle helmets for latent heat loss was evaluated through a virtual testing methodology using Computational fluid dynamics (CFD) simulations. The virtual thermal manikin was prescribed with a constant sweat rate of 2~g/h and a constant sweat film thickness of 0.3~mm. The simulations were carried out at 6~m/s until convergence was achieved. The results from steady state simulations show heat loss of 158~W from manikin without helmet and approximately 135~W with helmets. However, the thermal performance of helmets with a sweating manikin has been reduced from 89{\textendash}93{\%} to 84{\textendash}87{\%}. These results imply that evaporative/latent heat loss plays a significant role in thermal performance of helmets. Therefore, thermal performance tests for helmets should also include testing of helmets for evaporative heat loss.},
	isbn = {978-3-319-94223-0},
	author = {Mukunthan, Shriram and Jochen Vleugels and Toon Huysmans and Guido De Bruyne},
	editor = {Cassenti, Daniel N.}
}
@inproceedings {1885,
	title = {A low-cost and easy-to-use phantom for cone-beam geometry calibration of a tomographic X-ray system},
	booktitle = {9th Conference on Industrial Computed Tomography},
	year = {2019},
	address = {Padova, Italy},
	url = {https://www.ndt.net/article/ctc2019/papers/iCT2019_Full_paper_54.pdf},
	author = {Van Nguyen and Jan De Beenhouwer and Joaquim Sanctorum and Sam Van Wassenbergh and Peter Aerts and Joris J. J. Dirckx and Jan Sijbers}
}
@article {1914,
	title = {Matlab{\textregistered} toolbox for semi-automatic segmentation of the human nasal cavity based on active shape modeling},
	journal = {Computers in Biology and Medicine},
	volume = {105},
	year = {2019},
	pages = {27-38},
	doi = {10.1016/j.compbiomed.2018.12.008},
	author = {Keustermans, William and Toon Huysmans and Bert Schmelzer and Jan Sijbers and Joris J. J. Dirckx}
}
@article {1925,
	title = {The maximum a posteriori probability rule for atom column detection from HAADF STEM images},
	journal = {Ultramicroscopy},
	volume = {201},
	year = {2019},
	pages = {81-91},
	abstract = {Recently, the maximum a posteriori (MAP) probability rule has been proposed as an objective and quantitative method to detect atom columns and even single atoms from high-resolution high-angle annular dark-field (HAADF) scanning transmission electron microscopy (STEM) images. The method combines statistical parameter estimation and model-order selection using a Bayesian framework and has been shown to be especially useful for the analysis of the structure of beam-sensitive nanomaterials. In order to avoid beam damage, images of such materials are usually acquired using a limited incoming electron dose resulting in a low contrast-to-noise ratio (CNR) which makes visual inspection unreliable. This creates a need for an objective and quantitative approach. The present paper describes the methodology of the MAP probability rule, gives its step-by-step derivation and discusses its algorithmic implementation for atom column detection. In addition, simulation results are presented showing that the performance of the MAP probability rule to detect the correct number of atomic columns from HAADF STEM images is superior to that of other model-order selection criteria, including the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). Moreover, the MAP probability rule is used as a tool to evaluate the relation between STEM image quality measures and atom detectability resulting in the introduction of the so-called integrated CNR (ICNR) as a new image quality measure that better correlates with atom detectability than conventional measures such as signal-to-noise ratio (SNR) and CNR.},
	keywords = {Atom detectability, Atom detection, Model selection, Scanning transmission electron microscopy (STEM)},
	issn = {0304-3991},
	doi = {https://doi.org/10.1016/j.ultramic.2019.02.003},
	url = {http://www.sciencedirect.com/science/article/pii/S0304399118304236},
	author = {J Fatermans and Sandra Van Aert and Arnold Jan den Dekker}
}
@article {1944,
	title = {Methods for characterization and optimisation of measuring performance of stereoscopic x-ray systems with image intensifiers},
	journal = {Measurement Science and Technology},
	volume = {30},
	year = {2019},
	doi = {https://doi.org/10.1088/1361-6501/ab23e7},
	author = {Joaquim Sanctorum and Dominique Adriaens and Joris J. J. Dirckx and Jan Sijbers and Chris Van Ginneken and Peter Aerts and Sam Van Wassenbergh}
}
@conference {1952,
	title = {Mixed-Scale Dense Convolutional Neural Network based Improvement of Glass Fiber-reinforced Composite CT Images},
	year = {2019},
	month = {07/2019},
	abstract = {For the study of glass fiber-reinforced polymers (GFRP), {\textmu}CT is the method of choice. Obtaining
GFRP parameters from a {\textmu}CT scan is difficult, due to the presence of noise and artifacts. We propose a method
to improve GFRP image quality using a recently introduced deep neural network. We describe the network{\textquoteright}s
setup and the data generation and show how the trained network improves the reconstruction.},
	author = {Tim Elberfeld and Shabab Bazrafkan and Jan De Beenhouwer and Jan Sijbers}
}
@inproceedings {1873,
	title = {Moving Statistical Body Shape Models Using Blender},
	booktitle = {Proceedings of the 20th Congress of the International Ergonomics Association (IEA 2018)},
	year = {2019},
	pages = {28{\textendash}38},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {In this paper, we present a new framework to integrate movement acquired by a motion capture system to a statistical body shape model using Blender. This provides a visualization of a digital human model based upon anthropometry and biomechanics of the subject. A moving statistical body shape model helps to visualize physical tasks with inter-individual variability in body shapes as well as anthropometric dimensions. This parametric modeling approach is useful for reliable prediction and simulation of the body shape movement of a specific population with a few given predictors such as stature, body mass index and age.},
	isbn = {978-3-319-96077-7},
	doi = {10.1007/978-3-319-96077-7_4},
	author = {Scataglini, Sofia and Femke Danckaers and Haelterman, Robby and Toon Huysmans and Jan Sijbers},
	editor = {Bagnara, Sebastiano and Tartaglia, Riccardo and Albolino, Sara and Alexander, Thomas and Fujita, Yushi}
}
@article {1963,
	title = {MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation.},
	journal = {Neuroimage},
	year = {2019},
	month = {2019 Aug 29},
	pages = {116137},
	abstract = {<p>MRtrix3 is an open-source, cross-platform software package for medical image processing, analysis and visualization, with a particular emphasis on the investigation of the brain using diffusion MRI. It is implemented using a fast, modular and flexible general-purpose code framework for image data access and manipulation, enabling efficient development of new applications, whilst retaining high computational performance and a consistent command-line interface between applications. In this article, we provide a high-level overview of the features of the MRtrix3 framework and general-purpose image processing applications provided with the software.</p>},
	issn = {1095-9572},
	doi = {10.1016/j.neuroimage.2019.116137},
	author = {Tournier, J-Donald and Smith, Robert and Raffelt, David and Tabbara, Rami and Dhollander, Thijs and Pietsch, Maximilian and Christiaens, Daan and Ben Jeurissen and Yeh, Chun-Hung and Connelly, Alan}
}
@inproceedings {1853,
	title = {Multi-patch B-Spline Statistical Shape Models for CAD-Compatible Digital Human Modeling},
	booktitle = {Advances in Human Factors in Simulation and Modeling},
	year = {2019},
	pages = {179{\textendash}189},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {Parametric 3D human body models are valuable tools for ergonomic product design and statistical shape modelling (SSM) is a powerful technique to build realistic body models from a database of 3D scans. Like the underlying 3D scans, body models built from SSMs are typically represented with triangle meshes. Unfortunately, triangle meshes are not well supported by CAD software where spline geometry dominates. Therefore, we propose a methodology to convert databases of pre-corresponded triangle meshes into multi-patch B-spline SSMs. An evaluation on four 3D scan databases shows that our method is able to generate accurate and water-tight models while preserving inter-subject correspondences by construction. In addition, we demonstrate that such SSMs can be used to generate design manikins which can be readily used in SolidWorks for designing well conforming product parts.},
	isbn = {978-3-319-94223-0},
	doi = {10.1007/978-3-319-94223-0_17},
	author = {Toon Huysmans and Femke Danckaers and Jochen Vleugels and Daniel Lacko and Guido De Bruyne and Stijn Verwulgen and Jan Sijbers}
}
@article {1913,
	title = {Nonlinear hyperspectral unmixing with graphical models},
	journal = {IEEE Transaction on Geoscience and Remote Sensing},
	volume = {57},
	year = {2019},
	pages = {4844-4856},
	author = {Rob Heylen and Vera Andrejchenko and Zohreh Zahiri and Mario Parente and Paul Scheunders}
}
@article {1916,
	title = {Normalized Averaged Range (nAR), a Robust Quantification Method for MPIO-content},
	journal = {Journal of Magnetic Resonance},
	volume = {300},
	year = {2019},
	pages = {18-27},
	doi = {10.1016/j.jmr.2018.12.019},
	author = {Maarten Naeyaert and Dimitri Roose and Zhenhua Mai and Aneta J Keliris and Jan Sijbers and Annemie Van Der Linden and Marleen Verhoye}
}
@conference {Wuyts2019-xr,
	title = {Novel insight on effect and recovery of long-duration spaceflight on the ventricles of the space traveller{\textquoteright}s brain},
	volume = {2019},
	year = {2019},
	pages = {IAC{\textendash}19_A1_2_4_x51230},
	publisher = {International Astronautical Federation},
	author = {Floris L Wuyts and Steven Jillings and Angelique Van Ombergen and Ben Jeurissen and Tomilovskaya, Elena and Rumshiskaya, Alena and Litvinova, Liudmila and Nosikova, Inna and Pechenkova, Ekaterina and Rukavishnikov, Ilya and others}
}
@conference {1982,
	title = {Optimal design of a blended diffusion/relaxometry experiment},
	year = {2019},
	address = {Rotterdam, the Netherlands},
	author = {Annelinde E. Buikema and Arnold Jan den Dekker and Jan Sijbers}
}
@article {1973,
	title = {poly-DART: A discrete algebraic reconstruction technique for polychromatic X-ray CT},
	journal = {Optics Express},
	volume = {27},
	year = {2019},
	pages = {33427-33435},
	doi = {https://doi.org/10.1364/OE.27.033670},
	author = {Nathana{\"e}l Six and Jan De Beenhouwer and Jan Sijbers}
}
@article {1893,
	title = {Posture normalization of 3D body scans},
	journal = {Ergonomics},
	volume = {62},
	year = {2019},
	pages = {834-848},
	doi = {10.1080/00140139.2019.1581262},
	author = {Femke Danckaers and Toon Huysmans and Ann Hallemans and Guido De Bruyne and Steven Truijen and Jan Sijbers}
}
@inproceedings {1988,
	title = {Preliminary investigation of attenuation and scatter correction strategies for a next-generation SPECT system dedicated to quantitative clinical brain imaging},
	booktitle = {IEEE Nuclear Science Symposium and Medical Imaging Conference},
	year = {2019},
	month = {11/2019},
	address = {Manchester, UK},
	author = {Benjamin Auer and Jan De Beenhouwer and Navid Zeraatkar and Philip H. Kuo and Lars R. Furenlid and Michael A. King}
}
@article {2085,
	title = {Primary, scatter, and penetration characterizations of parallel-hole and pinhole collimators for I-123 SPECT},
	journal = {Physics in Medicine \& Biology},
	volume = {64},
	year = {2019},
	pages = {245001},
	abstract = {Multi-pinhole (MPH) collimators are known to provide better trade-off between sensitivity and resolution for preclinical, as well as for smaller regions in clinical SPECT imaging compared to conventional collimators. In addition to this geometric advantage, MPH plates typically offer better stopping power for penetration than the conventional collimators, which is especially relevant for I-123 imaging. The I-123 emits a series of high-energy (>300 keV,  2.5\% abundance) gamma photons in addition to the primary emission (159 keV, 83\% abundance). Despite their low abundance, high-energy photons penetrate through a low-energy parallel-hole (LEHR) collimator much more readily than the 159 keV photons, resulting in large downscatter in the photopeak window. In this work, we investigate the primary, scatter, and penetration characteristics of a single pinhole collimator that is commonly used for I-123 thyroid imaging and our two MPH collimators designed for I-123 DaTscan imaging for Parkinson{\textquoteright}s Disease, in comparison to three different parallel-hole collimators through a series of experiments and Monte Carlo simulations. The simulations of a point source and a digital human phantom with DaTscan activity distribution showed that our MPH collimators provide superior count performance in terms of high primary counts, low penetration, and low scatter counts compared to the parallel-hole and single pinhole collimators. For example, total scatter, multiple scatter, and collimator penetration events for the LEHR were 2.5, 7.6 and 14 times more than that of MPH within the 15\% photopeak window. The total scatter fraction for LEHR was 56\% where the largest contribution came from the high-energy scatter from the back compartments (31\%). For the same energy window, the total scatter for MPH was 21\% with only 1\% scatter from the back compartments. We therefore anticipate that using MPH collimators, higher quality reconstructions can be obtained in a substantially shorter acquisition time for I-123 DaTscan and thyroid imaging.},
	doi = {10.1088/1361-6560/ab58fe},
	url = {https://doi.org/10.1088\%2F1361-6560\%2Fab58fe},
	author = {Arda K{\"o}nik and Benjamin Auer and Jan De Beenhouwer and Kesava Kalluri and Navid Zeraatkar and Lars R. Furenlid and Michael A. King}
}
@conference {1959,
	title = {Quantifying 3D atomic structures of nanomaterials and their dynamics using model-based scanning transmission electron microscopy},
	year = {2019},
	author = {Sandra Van Aert and De wael, A and J Fatermans and Ivan Lobato and Annick De Backer and Jones, L and Arnold Jan den Dekker and Peter D Nellist}
}
@mastersthesis {1976,
	title = {Quantitative atom detection from atomic-resolution transmission electron microscopy images},
	volume = {Doctor of Science/Physics},
	year = {2019},
	type = {PhD thesis},
	author = {J Fatermans}
}
@article {1909,
	title = {Reproducibility and intercorrelation of graph theoretical measures in structural brain connectivity networks},
	journal = {Medical Image Analysis},
	volume = {52},
	year = {2019},
	pages = {56-67},
	doi = {https://doi.org/10.1016/j.media.2018.10.009},
	author = {Timo Roine and Ben Jeurissen and Daniele Perrone and Jan Aelterman and Wilfried Philips and Jan Sijbers and Alexander Leemans}
}
@conference {2020,
	title = {Robust outlier detection for diffusion kurtosis MRI based on IRLLS},
	volume = {32},
	number = {1},
	year = {2019},
	url = {https://link.springer.com/article/10.1007/s10334-019-00756-0},
	author = {Vincenzo Anania and Thibo Billiet and Ben Jeurissen and Jan Sijbers and Arnold Jan den Dekker}
}
@inproceedings {1985,
	title = {A semi-supervised method for Nonlinear Hyperspectral Unmixing},
	booktitle = {IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Yokohama, Japan, 2019},
	year = {2019},
	pages = {pp. 361-364},
	abstract = {As the interaction of light with the Earth surface is very complex, spectral reflectances are composed of nonlinear mixtures of the observed materials. Nonlinear mixing models have the disadvantage that not all spectra of a hyperspectral dataset necessarily follow the same particular mixing model. Moreover, most models lack a proper interpretation of the estimated parameters in terms of fractional abundances.  
In this paper, we present a semi-supervised nonlinear unmixing technique that overcomes these problems. In a first step, we apply a kernelized simplex volume maximization to select an overcomplete set of endmembers that precisely describe the hyperspectral data manifold. 
In a second step, this set is used as ground truth data in a supervised learning approach to generate fractional abundance maps from the entire dataset. For this, three methods are presented, based on kernelized sparse unmixing, feedforward neural networks, and gaussian processes.
The proposed method is validated on simulated data, a dataset obtained by ray tracing, and a real hyperspectral image.},
	doi = {doi: 10.1109/IGARSS.2019.8898846},
	author = {Bikram Koirala and Paul Scheunders}
}
@inproceedings {1884,
	title = {Simulated grating-based x-ray phase contrast images of CFRP-like objects},
	booktitle = {9th Conference on Industrial Computed Tomography},
	year = {2019},
	pages = {1-8},
	address = {Padova, Italy},
	url = {https://www.ndt.net/article/ctc2019/papers/iCT2019_Full_paper_45.pdf},
	author = {Jonathan Sanctorum and Jan De Beenhouwer and Johannes Weissenb{\"o}ck and Christoph Heinzl and Johann Kastner and Jan Sijbers}
}
@inproceedings {2008,
	title = {A spectral mixing model accounting for multiple reflections and shadow},
	booktitle = {IGARSS 2019, International Geoscience and Remote Sensing Symposium},
	year = {2019},
	pages = {286-289},
	address = {Yokohama, Japan},
	doi = {10.1109/IGARSS.2019.8897856},
	author = {Vera Andrejchenko and Zohreh Zahiri and Rob Heylen and Paul Scheunders}
}
@conference {1958,
	title = {Strategies for quantifying 3D atomic structures of nanomaterials and their dynamics using dose-efficient ADF STEM},
	year = {2019},
	author = {Sandra Van Aert and De wael, A and J Fatermans and Ivan Lobato and Annick De Backer and Jones, L and Arnold Jan den Dekker and Peter D Nellist}
}
@conference {Dhondt2019-jv,
	title = {Structural adaptations of cognitive emotional brain regions are linked to endogenous pain modulation: a psychophysical and brain imaging study in healthy people and in low back pain},
	year = {2019},
	pages = {399},
	author = {Dhondt, E and Ben Jeurissen and Danneels, L and Van Oosterwijck, J}
}
@conference {1983,
	title = {Super-resolution T1 mapping with integrated motion compensation in a joint maximum likelihood framework},
	volume = {32 (Suppl. 1)},
	number = {S14.05},
	year = {2019},
	publisher = {Magn Reson Mater Phy},
	abstract = {To date, 3D high resolution (HR) quantitative T1 mapping is not feasible in clinical practice due to prohibitively long acquisition times. Recent work has shown that super-resolution reconstruction (SRR), in which a 3D HR T1 map is directly estimated from a set of low through-plane resolution (LR) multi-slice (ms) T1-weighted (T1w) images with different slice orientations, can improve the trade-off between SNR, spatial resolution, and acquisition time. In that work, however, inter-image motion compensation for SRR is performed in a preprocessing step in which the transformation parameters of each LR image are updated after image registration. As a result, potential registration errors might propagate in the T1 estimation as no feedback mechanism is in place. Moreover, due to missing subvoxel accuracy no HR information is readily available during preprocessing. In the current work, we explore the potential of an improved SRR T1 mapping method that aims at more accurate T1 maps by combining T1 and motion estimation in a joint Maximum Likelihood estimation (jMLE) framework.
},
	doi = {10.1007/s10334-019-00754-2},
	author = {Quinten Beirinckx and Ben Jeurissen and Marleen Verhoye and Arnold Jan den Dekker and Jan Sijbers}
}
@article {1987,
	title = {A supervised method for nonlinear hyperspectral unmixing},
	journal = {Remote Sensing},
	volume = {11},
	year = {2019},
	month = {10/2019},
	abstract = {Due to the complex interaction of light with the Earth surface, reflectance spectra can be described as highly nonlinear mixtures of the reflectances of the material constituents occuring in a given resolution cell of hyperspectral data. Our aim is to estimate the fractional abundance maps of the materials from the nonlinear hyperspectral data. The main disadvantage of using nonlinear mixing models is that the model parameters are not properly interpretable in terms of fractional abundances. Moreover, not all spectra of a hyperspectral dataset necessarily follow the same particular mixing model. In this work, we present a supervised method for nonlinear spectral unmixing. The method learns a mapping from a true hyperspectral dataset to corresponding linear spectra, composed of the same fractional abundances. A simple linear unmixing then reveals the fractional abundances. To learn this mapping, ground truth information is required, in the form of actual spectra and corresponding fractional abundances, along with spectra of the pure materials, obtained from a spectral library or available in the dataset. Three methods are presented for learning the nonlinear mapping, based on gaussian processes, kernel ridge regression, and feedforward neural networks. Experimental results conducted on an artificial dataset, a data set obtained by ray tracing, and a drill core hyperspectral dataset show that this novel methodology is very promising.},
	doi = {https://doi.org/10.3390/rs11202458},
	url = {https://www.mdpi.com/2072-4292/11/20/2458},
	author = {Bikram Koirala and Mahdi Khodadadzadeh and Cecilia Contreras and Zohreh Zahiri and Richard Gloaguen and Paul Scheunders}
}
@inproceedings {1917,
	title = {Tools for the Analysis of Datasets from X-Ray Computed Tomography based on Talbot-Lau Grating Interferometry},
	booktitle = {9th Conference on Industrial Computed Tomography},
	year = {2019},
	address = {Padova, Italy},
	url = {https://www.ndt.net/article/ctc2019/papers/iCT2019_Full_paper_52.pdf},
	author = {Bernhard Fr{\"o}hler and Lucas da Cunha Melo and Johannes Weissenb{\"o}ck and Johann Kastner and Torsten M{\"o}ller and Hans-Christian Hege and Eduard Gr{\"o}ller and Jonathan Sanctorum and Jan De Beenhouwer and Jan Sijbers and Christoph Heinzl}
}
@conference {1980,
	title = {A Trajectory Based Bottom-Up Volume Reconstruction Method for Atom Probe Tomography},
	year = {2019},
	author = {Yu-Ting Ling and Siegfried Cools and Jan De Beenhouwer and Jan Sijbers and Wilfried Vandervorst}
}
@inproceedings {1874,
	title = {Using 3D Statistical Shape Models for Designing Smart Clothing},
	booktitle = {Proceedings of the 20th Congress of the International Ergonomics Association (IEA 2018)},
	year = {2019},
	pages = {18{\textendash}27},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {In this paper we present an innovative approach to design smart clothing using statistical body shape modeling (SBSM) from the CAESAR{\texttrademark} dataset. A combination of different digital technologies and applications are used to create a common co-design workflow for garment design. User and apparel product design and developers can get personalized prediction of cloth sizing, fitting and aesthetics.},
	isbn = {978-3-319-96077-7},
	doi = {10.1007/978-3-319-96077-7_3},
	author = {Scataglini, Sofia and Femke Danckaers and Haelterman, Robby and Toon Huysmans and Jan Sijbers and Andreoni, Giuseppe},
	editor = {Bagnara, Sebastiano and Tartaglia, Riccardo and Albolino, Sara and Alexander, Thomas and Fujita, Yushi}
}
@article {1923,
	title = {A Visual Tool for the Analysis of Algorithms for Tomographic Fiber Reconstruction in Materials Science},
	journal = {Computer Graphics Forum},
	volume = {38},
	year = {2019},
	pages = {273-283},
	doi = {10.1111/cgf.13688},
	author = {Bernhard Fr{\"o}hler and Tim Elberfeld and Torsten M{\"o}ller and Johannes Weissenb{\"o}ck and Jan De Beenhouwer and Jan Sijbers and Hans-Christian Hege and Johann Kastner and Christoph Heinzl}
}
@conference {2022,
	title = {Voxelwise harmonisation of FA on a cohort of 605 healthy subjects using ComBat: an exploratory study},
	year = {2019},
	author = {Maira Siqueira Pinto and Roberto Paolella and Thibo Billiet and Pieter Van Dyck and Pieter-Jan Guns and Ben Jeurissen and Annemie Ribbens and Arnold Jan den Dekker and Jan Sijbers}
}
@article {2000,
	title = {Women{\textquoteright}s preferences for male facial masculinity are not condition-dependent in a large online study},
	journal = {The All Results Journals: Biol},
	volume = {10},
	year = {2019},
	type = {				 A3 Journal article },
	abstract = {While several studies have found evidence for conditional-dependent effect on women{\textquoteright}s preferences for male masculinity, others have questioned the relative importance of these effects. In this study, we evaluated variation in women{\textquoteright}s preference for male facial masculinity in a forced-choice experiment. Nearly 1200 participants scored preference for manipulated photos and surface scans. Between-individual variation in preferences were relatively small, especially for the evaluation of the surface scans. Nevertheless, preferences from the evaluations of photos and scans correlated positively, indicating that both stimuli provide similar biological information. Only few condition-dependent variables correlated significantly with preference for masculinity, and not all in predicted directions. Stronger preference for masculine male faces {\textendash} albeit only significant for the photos {\textendash} with higher own women attractiveness was observed as expected. Yet, for perceived infectability, consistently across the photos and scans, a negative association with preference for masculine faces was observed, which is opposite to theoretical predictions. In addition, no effects of pathogen exposure, germ aversion (a correlate of disgust), relational status, preference for short term relationships and sociosexuality were detected. Thus, overall, our study is in line with recent large studies that also find only very weak conditiondependent effects, if any.},
	issn = {21724784},
	author = {Van Dongen, Stefan and Femke Danckaers and Beerten, Jessie and Toon Huysmans}
}
@conference {1995,
	title = {Absolute CBF quantification in multi-time point ASL: the T1 issue},
	year = {2018},
	author = {Piet Bladt and Arnold Jan den Dekker and Clement, Patricia and Eric Achten and Jan Sijbers}
}
@conference {1827,
	title = {Accurate and precise MRI relaxometry: the often disregarded but critical role of statistical parameter estimation},
	year = {2018},
	pages = {5664},
	address = {Paris, France},
	author = {Gabriel Ramos-Llord{\'e}n and Quinten Beirinckx and Arnold Jan den Dekker and Jan Sijbers}
}
@proceedings {1899,
	title = {ACIVS 2018, Advanced Concepts for Intelligent Vision Systems },
	volume = {11182},
	year = {2018},
	author = {J Blanc-Talon and D Helbert and Wilfried Philips and D Popescu and Paul Scheunders}
}
@article {1857,
	title = {Advanced X-ray tomography: experiment, modeling, and algorithms},
	journal = {Measurement Science and Technology},
	volume = {29},
	year = {2018},
	pages = {080101},
	doi = {https://doi.org/10.1088/1361-6501/aacd25},
	author = {Kees Joost Batenburg and Francesco De Carlo and Lucia Mancini and Jan Sijbers}
}
@mastersthesis {1867,
	title = {Advances in X-ray reconstruction algorithms for limited data problems in conventional and non-conventional projection geometries},
	volume = {PhD in Sciences / Physics},
	year = {2018},
	type = {PhD thesis},
	author = {Eline Janssens}
}
@conference {2006,
	title = {Automatic Detection of Surface Damages on Steel Structures using Near Infrared Hyperspectral Imaging},
	year = {2018},
	abstract = {Surface damages such as corrosion and coating delamination are very common on steel structures and, if not prevented, can accelerate degradation and eventually lead to failure of the whole structure. Hence, field monitoring is of utmost importance to guarantee the structural health and reduce the maintenance cost. Visual inspection is very perilous (especially for offshore structures), time-consuming, and largely depends on the experience of the inspector (Price \& Figueira 2017). Current non-destructive testing (NDT) techniques (e.g. based on ultrasonic or eddy current technology, acoustic emission ([Calabrese et al 2016; Wu et al 2016] or Electrical Resistance [ Li et al 2007; Mathiesen et al 2016]), require a direct contact with the surface, which causes difficulties when the structure or parts of it are not accessible.
Image processing techniques have been considered as a fast and easier alternative to contact-based techniques. These techniques are usually based on measuring the textural and morphological changes in digital images from the structures (Feliciano et al 2015; Li \& Cheng 2016). However, in reality other materials on steel structures such as dirt, paint, bird droppings, oil leakage, and biofouling can significantly complicate the inspection. Moreover, some effects of corrosion not necessarily reveal themselves in the visible wavelength region. Hyperspectral imaging outside the visible range is an advanced imaging technique that can reveal chemical-mineralogical surface changes that are not observable by the naked eye.
This paper studied the feasibility of detecting coating damages on steel components using hyperspectral imaging. Two different samples of industrial components, provided by ENGIE Laborelec, were scanned with a hyperspectral camera: a part of a flame tube of a heavy-duty gas turbine as well as an artificially corroded sample of coated steel. The images were collected with a snapshot hyperspectral camera in the visible and Near Infrared range (manufactured by IMEC) and analyzed using a k-means clustering method. The outputs showed that despite the visual similarities between coating layers, corroded, and base material, hyperspectral imaging was capable to distinguish between them. Spectral plots showed that differences in spectral behavior of corroded and coated materials were more apparent when the wavelength shifts from the visible towards the near-infrared range. While further investigation is needed, the current results demonstrated that hyperspectral image analysis is a promising technology to complement or even replace digital image analysis within the visible range for characterizing steel surfaces in situ.},
	author = {Zohreh Zahiri and Bart Ribbens and Steve Vanlanduit and Paul Scheunders}
}
@conference {1890,
	title = {Bayesian analysis of noisy scanning transmission electron microscopy images for single atom detection},
	year = {2018},
	pages = {95},
	author = {J Fatermans and Arnold Jan den Dekker and M{\"u}ller-Caspary, K and Ivan Lobato and Sandra Van Aert}
}
@article {1908,
	title = {Brain Tissue{\textendash}Volume Changes in Cosmonauts},
	journal = {New England Journal of Medicine},
	volume = {379},
	year = {2018},
	pages = {1678 - 1680},
	issn = {0028-4793},
	doi = {10.1056/NEJMc1809011},
	url = {http://www.nejm.org/doi/10.1056/NEJMc1809011},
	author = {Angelique Van Ombergen and Steven Jillings and Ben Jeurissen and Tomilovskaya, Elena and R{\"u}hl, Maxine and Alena Rumshiskaya and Nosikova, Inna and Liudmila Litvinova and Annen, Jitka and Ekaterina V. Pechenkova and Inessa B. Kozlovskaya and Stefan Sunaert and Paul M Parizel and Valentin Sinitsyn and Steven S L Laureys and Jan Sijbers and zu Eulenburg, Peter and Floris L Wuyts}
}
@inproceedings {1933,
	title = {Capturing Joint Angles of the Off-Site Human Body},
	booktitle = {IEEE Sensors 2018},
	year = {2018},
	pages = {1{\textendash}4},
	publisher = {IEEE},
	organization = {IEEE},
	address = {United States},
	abstract = {Motion capture (mocap) is traditionally conducted by optical systems. These are expensive and usually limited to controlled environments. We investigated the accuracy of portable and inexpensive mocap sensor systems compared to benchmark optical systems with respect to tracking joint angles. This review summarizes the findings of 21 studies. In these studies, 228 subjects were employed, and 16 joints were tracked, spanning a range of activities. We did not find a system that is consistent and equally accurate across all joint angles for all activities (root mean square error up to 12.1 degrees). However, under some ideal conditions, the results are on par with optical mocap. Our recommendations for future research and development are to focus on tracking faster activities, activities in off-site conditions, and following standardized biomechanical models of joint angles.},
	keywords = {IMU, joint kinematics, motion capture, off-site, wearable},
	isbn = {978-1-5386-4708-0},
	doi = {10.1109/ICSENS.2018.8589711},
	author = {Raman Garimella and Thomas Peeters and Koen Beyers and Steven Truijen and Toon Huysmans and Stijn Verwulgen}
}
@conference {1949,
	title = {Changes in intrinsic functional brain connectivity after first-time exposure to parabolic flight},
	year = {2018},
	doi = {10.3389/conf.fphys.2018.26.00017},
	author = {Angelique Van Ombergen and Floris L Wuyts and Ben Jeurissen and Jan Sijbers and Floris Vanhevel and Steven Jillings and Paul M Parizel and Stefan Sunaert and Paul H Van de Heyning and Vincent Dousset and Steven S L Laureys and Athena Demertzi}
}
@inproceedings {2005,
	title = {Classification of hardened cement and lime mortar using short-wave infrared spectrometry data},
	booktitle = {11th international conference on Structural Analysis of Historical Constructions},
	year = {2018},
	abstract = {This paper evaluated the feasibility of using spectrometry data in the short-wave infrared range (1,300-2,200 nm) two distinguish lime mortar and Type S cement mortar. 42 samples of 40x40x40mm were created in the lab (21 lime-based, 21 cement-based). A Partial Least Square Discriminant Analysis model was developed using the mean spectra of 28 specimens as the calibration set. The results were tested on the mean spectra of the remaining 14 specimens as a validation set. The results showed that spectrometry data were able to fully distinguish modern mortars (made with cement) from historic lime mortars with a 100\% classification accuracy, which can be very useful in archaeological and architectural conservation applications. Specifically, being able to distinguish mortar composition in situ can provide critical information about the construction history of a structure as well as to inform an appropriate intervention scheme when historic material needs to be repaired or replaced.},
	author = {Zohreh Zahiri and Debra Laefer and Aoife Gowen}
}
@article {1901,
	title = {Close-range hyperspectral image analysis for the early detection of plant stress responses in individual plants in a high-throughput phenotyping platform},
	journal = {ISPRS Journal of Photogrammetry and Remote Sensing },
	volume = {138},
	year = {2018},
	pages = {121-138},
	author = {Mohd Shahrimie Mohd Asaari and Puneet Mishra and Stien Mertens and Stijn Dhondt and Dirk Inze and Nathalie Wuyts and Paul Scheunders}
}
@inproceedings {1770,
	title = {A Combined Statistical Shape Model of the Scalp and Skull of the Human Head},
	booktitle = { 8th International Conference on Applied Human Factors and Ergonomics (AHFE 2017)},
	volume = {591},
	year = {2018},
	pages = {538-548},
	publisher = {Springer},
	organization = {Springer},
	address = {Los Angeles, California, USA},
	doi = {10.1007/978-3-319-60591-3_49},
	author = {Femke Danckaers and Daniel Lacko and Stijn Verwulgen and Guido De Bruyne and Toon Huysmans and Jan Sijbers}
}
@inproceedings {1831,
	title = {Comparative Visualization of Orientation Tensors in Fiber-Reinforced Polymers},
	booktitle = {8th Conference on Industrial Computed Tomography, Wels, Austria },
	year = {2018},
	author = {Johannes Weissenb{\"o}ck and M Arikan and D Salaberger and Johann Kastner and Jan De Beenhouwer and Jan Sijbers and S Rauchenzauner and T Raab-Wernig and E Gr{\"o}ller and Christoph Heinzl}
}
@inbook {1900,
	title = {Contributions of Machine Learning to Remote Sensing Data Analysis},
	booktitle = {Comprehensive Remote Sensing},
	volume = {2},
	year = {2018},
	pages = {199-243},
	author = {Paul Scheunders and Devis Tuia and G Moser}
}
@inproceedings {1898,
	title = {Detection of plant responses to drought using close-range hyperspectral imaging in a high-throughput phenotyping platform},
	booktitle = {IEEE Whispers 2018, Workshop on Hyperspectral Image and Signal Processing, Amsterdam, 23-26 September  },
	year = {2018},
	author = {Mohd Shahrimie Mohd Asaari and Stien Mertens and S Dhondt and Nathalie Wuyts and Paul Scheunders}
}
@article {1817,
	title = {Diffusion kurtosis imaging allows the early detection and longitudinal follow-up of amyloid β-induced pathology.},
	journal = {Alzheimer{\textquoteright}s Research \& Therapy },
	volume = {10},
	year = {2018},
	pages = {1-16},
	doi = {10.1186/s13195-017-0329-8},
	author = {Jelle Praet and Nikolay Manyakov and Leacky Muchene and Zhenhua Mai and Vasilis Terzopoulos and Steve De Backer and An Torremans and Pieter-Jan Guns and Tom Van De Casteele and Astrid Bottelbergs and Bianca Van Broeck and Jan Sijbers and Dirk Smeets and Ziv Shkedy and Luc Bijnens and Darrel Pemberton and Mark Schmidt and Annemie Van Der Linden and Marleen Verhoye}
}
@article {1820,
	title = {Diffusion kurtosis imaging with free water elimination: a Bayesian estimation approach},
	journal = {Magnetic Resonance in Medicine},
	volume = {80},
	year = {2018},
	pages = {802-813},
	doi = {10.1002/mrm.27075},
	author = {Quinten Collier and Jelle Veraart and Ben Jeurissen and Floris Vanhevel and Pim Pullens and Paul M Parizel and Arnold Jan den Dekker and Jan Sijbers}
}
@article {1731,
	title = {Discrete tomography in an in vivo small animal bone study},
	journal = {Journal of Bone and Mineral Metabolism},
	volume = {36},
	year = {2018},
	pages = {40{\textendash}53},
	doi = {10.1007/s00774-017-0815-x},
	author = {Elke Van de Casteele and Egon Perilli and Wim Van Aarle and Karen Reynolds and Jan Sijbers}
}
@conference {1828,
	title = {An educational presentation on accurate and precise MRI relaxometry: the often disregarded but critical role of statistical parameter estimation},
	year = {2018},
	address = {Antwerp, Belgium},
	abstract = {MRI relaxometry holds the promise of providing biomarkers for monitoring, staging and follow up of diseases. Imperative to meet minimum standards for objective, reproducible, and reliable biomarkers is the need for accurate, precise, quantitative parameters maps, such as T1~or T2. While unrealistic physical modelling is often argued as the main cause of lack of accuracy, little effort has been made on discussing the impact that inadequate parameter estimation methods have on the accuracy and precision of MRI relaxometry techniques. This educational poster attempts to introduce young MR students/researchers into the basics of modern statistical parameter estimation theory, and its application for accurate and precise relaxometry. 
},
	author = {Gabriel Ramos-Llord{\'e}n and Quinten Beirinckx and Arnold Jan den Dekker and Jan Sijbers}
}
@inproceedings {1830,
	title = {An efficient CAD projector for X-ray projection based 3D inspection with the ASTRA Toolbox},
	booktitle = {8th Conference on Industrial Computed Tomography, Wels, Austria},
	year = {2018},
	author = {{\'A}rp{\'a}d Marinovszki and Jan De Beenhouwer and Jan Sijbers}
}
@article {1872,
	title = {Enhanced contrast in X-ray microtomographic images of the membranous labyrinth using different X-ray sources and scanning modes},
	journal = {Journal of Anatomy},
	volume = {233},
	year = {2018},
	pages = {770--782},
	doi = {doi: 10.1111/joa.12885},
	author = {Jana Goyens and Menelia Vasilopoulou-Kampitsi and Raf Claes and Jan Sijbers and Lucia Mancini}
}
@article {2004,
	title = {The feasibility of short-wave infrared spectrometry in assessing water-to-cement ratio and density of hardened concrete},
	journal = {Construction and Building Materials},
	year = {2018},
	abstract = {This paper describes the feasibility of using short-wave infrared (SWIR) spectrometry to classify concretes by their water-to-cement (w/c) ratios and predict their density. Concrete spectra of three w/c ratios (50\%, 65\%, 80\%) were studied in the 1300{\textendash}2200 nm range. A Partial Least Square Discriminant Analysis model was developed from the spectra of 36 samples, resulting in an 89\% correct classification for the 18 validation samples, thereby demonstrating that SWIR spectrometry can detect differences in initial w/c ratios for hardened concretes. Additionally, differences in density and compressive strength as a function of the w/c ratio could be indirectly estimated through SWIR spectrometry.},
	url = {https://www.sciencedirect.com/science/article/pii/S0950061818317598},
	author = {Zohreh Zahiri and Debra Laefer and Aoife Gowen}
}
@inproceedings {1769,
	title = {Full Body Statistical Shape Modeling with Posture Normalization},
	booktitle = {The 8th International Conference on Applied Human Factors and Ergonomics (AHFE 2017)},
	volume = {591},
	year = {2018},
	pages = {437-448},
	publisher = {Springer},
	organization = {Springer},
	address = {Los Angeles, California, USA},
	doi = {10.1007/978-3-319-60591-3_39},
	author = {Femke Danckaers and Toon Huysmans and Ann Hallemans and Guido De Bruyne and Steven Truijen and Jan Sijbers}
}
@inproceedings {1895,
	title = {Fusion of hyperspectral and Lidar images using non-subsampled shearlet transform},
	booktitle = {IEEE IGARSS 2018, International Geoscience and Remote Sensing Symposium, Valencia, Spain, July 23-27},
	year = {2018},
	author = {M. Reza Soleimanzadeh and Azam Karami and Paul Scheunders}
}
@article {1912,
	title = {High quality statistical shape modelling of the human nasal cavity and applications},
	journal = {Royal Society Open Science},
	volume = {5},
	year = {2018},
	doi = {https://doi.org/10.1098/rsos.181558},
	author = {Keustermans, William and Toon Huysmans and Femke Danckaers and Andrzej Zarowski and Bert Schmelzer and Jan Sijbers and Joris J. J. Dirckx}
}
@inproceedings {1896,
	title = {Hyperspectral and multispectral image fusion based on spectral matching in the shearlet domain},
	booktitle = {IEEE IGARSS 2018, International Geoscience and Remote Sensing Symposium, Valencia, Spain, July 23-27},
	year = {2018},
	author = {H. Rezaei and Azam Karami and Paul Scheunders}
}
@mastersthesis {1832,
	title = {Improved MRI Relaxometry through Statistical Signal Processing},
	volume = { Doctor of Science},
	year = {2018},
	month = {02/2018},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	abstract = {Magnetic Resonance Imaging (MRI) relaxometry is a quantitative MRI modality that deals with the estimation of the spin-lattice, T1, and the spin-spin, T2, relaxation times. Both relaxation times are fundamental parameters that describe the spin dynamics within a tissue during the relaxation process of the Nuclear Magnetic Resonance (NMR) phenomenon. During the last decades, spatial T1 and T2 maps have been analyzed to study and monitor the states of a multitude of human diseases. Those studies have shown that MRI relaxometry holds the promise of generating robust, objective image-based biomarkers for central nervous system pathologies, cardiovascular diseases and beyond. Unfortunately, quantitative biomarkers derived from MRI relaxometry are not yet sufficiently specific, sensitive, and robust to be routinely used in clinical practice. On top of that, high-resolution relaxation maps demand a clinically unfeasible long scanning time.

This PhD thesis tries to reduce the obstacles that preclude MRI relaxometry from being a fast, accurate, and precise quantitative MRI modality for clinical use by improving the way MR relaxometry data are acquired, processed and analyzed. In particular, by adopting a statistical signal processing approach, three contributions that address common problems of the field are given. Firstly, we present a unified relaxometry-based processing framework where the T1 map estimation and motion correction are accounted for in a synergistic manner, being both the T1 map and the motion parameters simultaneously estimated with a Maximum-Likelihood estimator. It is demonstrated that substantially more accurate T1 maps are obtained with our proposed integrated approach in comparison to the yet typical but suboptimal two-step approach: T1 model fitting after image registration.  Secondly, we developed a fast, robust, T1 estimator for Variable Flip Angle (VFA) T1 mapping that can provide statistically optimal T1 estimates with unprecedentedly short computation time, thereby enabling optimal, real-time, VFA T1 mapping.  Finally, we were able to reduce the long overall scanning time of MRI relaxometry studies using our novel k-space reconstruction technique that permits the reconstruction of individual MR images with less number of samples than it is commonly required.},
	author = {Gabriel Ramos-Llord{\'e}n}
}
@article {1791,
	title = {IntensityPatches and RegionPatches for Image Recognition},
	journal = {Applied Soft Computing},
	volume = {62},
	year = {2018},
	pages = {176-186},
	doi = {https://doi.org/10.1016/j.asoc.2017.09.046},
	url = {http://www.sciencedirect.com/science/article/pii/S1568494617305859},
	author = {Tiago B. A. de Carvalho and Maria A. Sibaldo and Ing Jyh Tsang and George D C Cavalcanti and Jan Sijbers and Ing Ren Tsang}
}
@conference {1860,
	title = {Investigating the relationship between shape and flow in the human nose using a statistical shape model},
	year = {2018},
	url = {http://www.eccm-ecfd2018.org/admin/files/fileabstract/a781.pdf},
	author = {Keustermans, William and Toon Huysmans and Femke Danckaers and Andrzej Zarowski and Bert Schmelzer and Jan Sijbers and Joris J. J. Dirckx}
}
@inbook {1835,
	title = {Iterative reconstruction methods in X-ray CT},
	booktitle = {Handbook of X-ray Imaging: Physics and Technology},
	year = {2018},
	pages = {693-712},
	publisher = {CRC Press},
	organization = {CRC Press},
	chapter = {34},
	author = {Van Eyndhoven, Geert and Jan Sijbers}
}
@inproceedings {1869,
	title = {Joint reconstruction and flat-field estimation using support estimation},
	booktitle = {IEEE Nuclear Science Symposium and Medical Imaging Conference},
	year = {2018},
	address = {Sydney, Australia},
	doi = {10.1109/NSSMIC.2018.8824406},
	author = {Nathana{\"e}l Six and Jan De Beenhouwer and Vincent Van Nieuwenhove and Wim Vanroose and Jan Sijbers}
}
@conference {1957,
	title = {Maximising dose efficiency in quantitative STEM to reveal the 3D atomic structure of nanomaterials},
	year = {2018},
	author = {Sandra Van Aert and J Fatermans and Annick De Backer and van den Bos, K. H. W. and O{\textquoteright}Leary, C M. and M{\"u}ller-Caspary, K and Jones, L and Ivan Lobato and B{\'e}ch{\'e}, A and Arnold Jan den Dekker and Sara Bals and Peter D Nellist}
}
@conference {1891,
	title = {The maximum a posteriori probability rule to detect single atoms from low signal-to-noise ratio scanning transmission electron microscopy images},
	year = {2018},
	author = {J Fatermans and Arnold Jan den Dekker and M{\"u}ller-Caspary, K and Ivan Lobato and Sandra Van Aert}
}
@article {1843,
	title = {Modeling brain dynamics in brain tumor patients using The Virtual Brain},
	journal = {eNeuro},
	year = {2018},
	abstract = {Presurgical planning for brain tumor resection aims at delineating eloquent tissue in the vicinity of the lesion to spare during surgery. To this end, non-invasive neuroimaging techniques such as functional MRI and diffusion weighted imaging fiber tracking are currently employed. However, taking into account this information is often still insufficient, as the complex non-linear dynamics of the brain impede straightforward prediction of functional outcome after surgical intervention. Large-scale brain network modeling carries the potential to bridge this gap by integrating neuroimaging data with biophysically based models to predict collective brain dynamics. As a first step in this direction, an appropriate computational model has to be selected, after which suitable model parameter values have to be determined. To this end, we simulated large-scale brain dynamics in 25 brain tumor patients and 11 control subjects using The Virtual Brain, an open-source neuroinformatics platform. Local and global model parameters of the Reduced Wong-Wang model were individually optimized and compared between brain tumor patients and control subjects. In addition, the relationship between model parameters and structural network topology and cognitive performance was assessed. Results showed (1) significantly improved prediction accuracy of individual functional connectivity when using individually optimized model parameters; (2) local model parameters can differentiate between regions directly affected by a tumor, regions distant from a tumor, and regions in a healthy brain; and (3) interesting associations between individually optimized model parameters and structural network topology and cognitive performance.},
	doi = {10.1101/265637},
	url = {https://www.biorxiv.org/content/early/2018/05/07/265637},
	author = {Aerts, Hannelore and Schirner, Michael and Ben Jeurissen and Van Roost, Dirk and Eric Achten and Ritter, Petra and Marinazzo, Daniele}
}
@inproceedings {1870,
	title = {MRF-based decision fusion for hyperspectral image classification},
	booktitle = {IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
	year = {2018},
	address = {Valencia, Spain},
	abstract = {The high dimensionality of hyperspectral images, the limited availability of ground-truth data as well as the low spatial resolution (causing pixels to contain mixtures of materials) hinder hyperspectral image classification. In this work we propose a novel hyperspectral classification method where we combine the outcome of spectral unmixing with the outcome
of a supervised classifier. In particular, we consider fractional abundances obtained from a Sparse Unmixing method along
with posterior probabilities acquired from a Multinomial Logistic Regression classifier. Both sources of information are
fused using a Markov Random Field framework. We conducted experiments on publicly available real hyperspectral
images: Indian Pines and University of Pavia using a very limited number of training samples. Our results indicate that
the proposed decision fusion approach significantly improves the classification result over using the individual sources and outperforms the state of the art methods.},
	url = {https://www.igarss2018.org/Papers/viewpapers.asp?papernum=2751},
	author = {Vera Andrejchenko and Rob Heylen and Wenzhi Liao and Wilfried Philips and Paul Scheunders}
}
@article {1816,
	title = {Neural network Hilbert transform based filtered backprojection for fast inline X-ray inspection},
	journal = {Measurement Science and Technology},
	volume = {29},
	year = {2018},
	doi = {10.1088/1361-6501/aa9de3},
	author = {Eline Janssens and Jan De Beenhouwer and Mattias Van Dael and Thomas De Schryver and Luc Van Hoorebeke and Pieter Verboven and Bart Nicolai and Jan Sijbers}
}
@inproceedings {1875,
	title = {A NEURAL NETWORK METHOD FOR NONLINEAR HYPERSPECTRAL UNMIXING},
	booktitle = {IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
	year = {2018},
	pages = {pp. 4233-4236},
	address = {Valencia, Spain},
	abstract = {Because of the complex interaction of light with the Earth surface, a hyperspectral pixel can be composed of a highly nonlinear mixture of the reflectances of the materials on the ground. When nonlinear mixing models are applied, the estimated model parameters are usually hard to interpret and to link to the actual fractional abundances. Moreover, not all spectral reflectances in a real scene follow the same particular mixing model. In this paper, we present a supervised learning method for nonlinear spectral unmixing. In this method, a neural network is applied to learn mappings of the true spectral reflectances to the reflectances that would be obtained if the mixture was linear. A simple linear unmixing then reveals the actual abundance fractions. This technique is model-independent and allows for an easy interpretation of the obtained abundance fractions. We validate this method on several artificial datasets, a data set obtained by ray tracing, and a real dataset.},
	doi = {doi: 10.1109/IGARSS.2018.8518995},
	url = {https://ieeexplore.ieee.org/document/8518995},
	author = {Bikram Koirala and Rob Heylen and Paul Scheunders}
}
@article {1866,
	title = {A new data structure and workflow for using 3D anthropometry in the design of wearable products},
	journal = {International Journal of Industrial Ergonomics},
	volume = {64},
	year = {2018},
	pages = {108 - 117},
	keywords = {3D anthropometry, CAD, EEG headsets, Mass-customization, Parameterized design, Statistical shape models},
	issn = {0169-8141},
	doi = {https://doi.org/10.1016/j.ergon.2018.01.002},
	url = {http://www.sciencedirect.com/science/article/pii/S0169814117302342},
	author = {Stijn Verwulgen and Daniel Lacko and Jochen Vleugels and Kristof Vaes and Femke Danckaers and Guido De Bruyne and Toon Huysmans}
}
@article {1902,
	title = {Noise reduction in hyperspectral imagery: overview and application},
	journal = {Remote Sensing },
	volume = {10},
	year = {2018},
	pages = {482},
	author = {B Rasti and Paul Scheunders and P Ghesami and G Licciardi and Jocelyn Chanussot}
}
@article {1842,
	title = {NOVIFAST: A fast algorithm for accurate and precise VFA MRI T1 mapping},
	journal = {IEEE Transactions on Medical Imaging},
	volume = {37},
	year = {2018},
	pages = {2414 - 2427},
	doi = {10.1109/TMI.2018.2833288},
	author = {Gabriel Ramos-Llord{\'e}n and Gonzalo Vegas-S{\'a}nchez-Ferrero and Marcus Bj{\"o}rk and Floris Vanhevel and Paul M Parizel and Raul San Jos{\'e} Est{\'e}par and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {1847,
	title = {Parametric Reconstruction of Advanced Glass Fiber-reinforced Polymer Composites from X-ray Images},
	year = {2018},
	address = {Wels, Austria},
	abstract = {A novel approach to the reconstruction of glass fiber-reinforced polymers (GFRP) from X-ray micro-computed tomography
(μCT) data is presented. The traditional fiber analysis workflow requires complete sample reconstruction, pre-processing and
segmentation, followed by the analysis of fiber distribution, orientation, and other features of interest. Each step in the chain
introduces errors that propagate through the pipeline and impair the accuracy of the estimation of those features. In the
approach presented in this paper, we combine iterative reconstruction techniques and a priori knowledge about the sample, to
reconstruct the volume and estimate the orientation of the fibers simultaneously. Fibers are modeled using rigid cylinders in
space whose orientation and position is then iteratively refined. The output of the algorithm is a non voxel-based dataset of the
fibers{\textquoteright} parametric representation, allowing to directly assess fiber features and distribution characteristics and to simulate the resulting material properties.},
	keywords = {GFRP, Materials Science, Modeling of Microstructures, Parametric Reconstruction, Tomography, {\textmu}CT},
	author = {Tim Elberfeld and Jan De Beenhouwer and Arnold Jan den Dekker and Christoph Heinzl and Jan Sijbers}
}
@article {1859,
	title = {Parametric Reconstruction of Glass Fiber-reinforced Polymer Composites from X-ray Projection Data - A Simulation Study},
	journal = {Journal of Nondestructive Evaluation},
	volume = {37},
	year = {2018},
	month = {Jul},
	pages = {1573-4862},
	abstract = {We present a new approach to estimate geometry parameters of glass fibers in glass fiber-reinforced polymers from simulated X-ray micro-computed tomography scans. Traditionally, these parameters are estimated using a multi-step procedure including image reconstruction, pre-processing, segmentation and analysis of features of interest. Each step in this chain introduces errors that propagate through the pipeline and impair the accuracy of the estimated parameters. In the approach presented in this paper, we reconstruct volumes from a low number of projection angles using an iterative reconstruction technique and then estimate position, direction and length of the contained fibers incorporating a priori knowledge about their shape, modeled as a geometric representation, which is then optimized. Using simulation experiments, we show that our method can estimate those representations even in presence of noisy data and only very few projection angles available.},
	keywords = {GFRP, Glass fiber reinforced polymer, Materials Science, Modeling of micro-structures, Parametric model, Tomography, {\textmu}CT},
	doi = {10.1007/s10921-018-0514-0},
	url = {https://doi.org/10.1007/s10921-018-0514-0},
	author = {Tim Elberfeld and Jan De Beenhouwer and Arnold Jan den Dekker and Christoph Heinzl and Jan Sijbers}
}
@inproceedings {1833,
	title = {pDART: Discrete algebraic reconstruction using a polychromatic forward model},
	booktitle = {The Fifth International Conference on Image Formation  in X-Ray Computed Tomography},
	year = {2018},
	address = {Salt Lake City, Utah, USA},
	author = {Nathana{\"e}l Six and Jan De Beenhouwer and Jan Sijbers}
}
@inproceedings {1882,
	title = {Performance and memory use trade-off in CPU and GPU implementations of a deformation operator for 4D-CT},
	booktitle = {8th Conference on Industrial Computed Tomography, Wels, Austria},
	year = {2018},
	author = {Stijn Manhaeve and Vincent Van Nieuwenhove and Jan Sijbers}
}
@inproceedings {1880,
	title = {Preliminary evaluation of surface mesh modeling of system geometry, anatomy phantom, and source activity for GATE simulations},
	booktitle = {IEEE Nuclear Science Symposium and Medical Imaging Conference},
	year = {2018},
	month = {11/2018},
	address = {Sydney, Australia},
	author = {Benjamin Auer and Arda K{\"o}nik and Timothy J. Fromme and Kesava Kalluri and Jan De Beenhouwer and George I. Zubal and Lars R. Furenlid and Michael A. King}
}
@inproceedings {1879,
	title = {Preliminary investigation of design parameters of an innovative multi- pinhole system dedicated to brain SPECT imaging},
	booktitle = {IEEE Nuclear Science Symposium and Medical Imaging Conference},
	year = {2018},
	month = {11/2018},
	address = {Sydney, Australia},
	author = {Benjamin Auer and Jan De Beenhouwer and Timothy J. Fromme and Kesava Kalluri and Justin C. Goding and George I. Zubal and Lars R. Furenlid and Michael A. King}
}
@article {1935,
	title = {Principal component analysis as a tool for determining optimal tibial baseplate geometry in modern TKA design},
	journal = {Acta Orthop Belg},
	volume = {84},
	year = {2018},
	pages = {452-460},
	author = {Sare Huijs and Toon Huysmans and A De Jong and Nele Arnout and Jan Sijbers and Johan Bellemans}
}
@mastersthesis {1911,
	title = {Robust estimation of diffusion tensor and diffusion kurtosis imaging parameters},
	volume = {PhD in Sciences/Physics},
	year = {2018},
	month = {Oct/2018},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	author = {Quinten Collier}
}
@article {1786,
	title = {The role of whole-brain diffusion MRI as a tool for studying human in vivo cortical segregation based on a measure of neurite density},
	journal = {Magnetic Resonance in Medicine},
	volume = {79},
	year = {2018},
	pages = {2738{\textendash}2744},
	keywords = {Brain, cortex, Diffusion MRI, fiber orientation distribution, myeloarchitecture, parcellation},
	issn = {1522-2594},
	doi = {10.1002/mrm.26917},
	url = {http://dx.doi.org/10.1002/mrm.26917},
	author = {Fernando Calamante and Ben Jeurissen and Robert Elton Smith and Tournier, Jacques-Donald and Connelly, Alan}
}
@article {1903,
	title = {A simple computer vision pipeline reveals the effects of isolation on social interaction dynamics in Drosophila},
	journal = {Plos Computational Biology},
	volume = {14},
	year = {2018},
	pages = {e1006410},
	author = {G Liu and Tanmay Nath and Z Guo and G Linneweber and A Claeys and J Li and M Bengochea and Steve De Backer and Barbara Weyn and M Sneyders and H Nicasy and P Yu and Paul Scheunders and Bassem Hassan}
}
@article {1877,
	title = {Simulations of a Multipinhole SPECT Collimator for Clinical Dopamine Transporter (DAT) Imaging},
	journal = {IEEE Transactions on Radiation and Plasma Medical Sciences},
	volume = {2},
	year = {2018},
	month = {Sept},
	pages = {444-451},
	keywords = {Analytical models, Apertures, Brain, Collimators, Logic gates, multipinhole (MPH), Sensitivity, simulation, Single photon emission computed tomography, SPECT},
	issn = {2469-7311},
	doi = {10.1109/TRPMS.2018.2831208},
	author = {Arda K{\"o}nik and Jan De Beenhouwer and J. M. Mukherjee and Kesava Kalluri and S Banerjee and N Zeraatkar and Timothy J. Fromme and Michael A. King}
}
@conference {1920,
	title = {Simultaneous T2/diffusion estimation: do we need a diffusion time dependent diffusion model?},
	year = {2018},
	address = {Antwerp, Belgium},
	abstract = {Joint estimation of the transverse relaxation (T2) and the diffusion tensor allows for probing white matter integrity in a more efficient way compared to estimating these parameters from sequential relaxometry and diffusion imaging protocols. For simultaneous estimation of diffusion and relaxation parameters, short echo times are needed and, as a consequence, short diffusion times. Therefore we need a flexible diffusion model that accounts for diffusion time dependence. Currently used diffusion models do not account for this dependence. An acknowledged model describing the properties of diffusion in coherently oriented fiber populations is the diffusion tensor. Alternative models are proposed, which allow to resolve multiple fiber directions, such as multiple tensor fitting and estimation of the fiber orientation distribution function using high angular resolution data acquisition. When the diffusion time is kept short enough, the diffusion behavior reflects that of free diffusion, meaning that the diffusion tensor is isotropic. With increasing diffusion time, diffusion is hindered by extracellular and intracellular components and the diffusion tensor evolves towards an ellipsoid. To the authors{\textquoteright} knowledge there is no model available to correct for diffusion time dependence in clinically short range diffusion times, allowing joint estimation of relaxation and diffusion properties. This work provides an overview of the state-of-the-art and challenges of diffusion time dependent diffusion modeling.},
	author = {Annelinde E. Buikema and Arnold Jan den Dekker and Jan Sijbers}
}
@article {1878,
	title = {Single Atom Detection from Low Contrast-to-Noise Ratio Electron Microscopy Images},
	journal = {Phys. Rev. Lett.},
	volume = {121},
	year = {2018},
	month = {Jul},
	pages = {056101},
	abstract = {Single atom detection is of key importance to solving a wide range of scientific and technological problems. The strong interaction of electrons with matter makes transmission electron microscopy one of the most promising techniques. In particular, aberration correction using scanning transmission electron microscopy has made a significant step forward toward detecting single atoms. However, to overcome radiation damage, related to the use of high-energy electrons, the incoming electron dose should be kept low enough. This results in images exhibiting a low signal-to-noise ratio and extremely weak contrast, especially for light-element nanomaterials. To overcome this problem, a combination of physics-based model fitting and the use of a model-order selection method is proposed, enabling one to detect single atoms with high reliability.},
	doi = {10.1103/PhysRevLett.121.056101},
	url = {https://link.aps.org/doi/10.1103/PhysRevLett.121.056101},
	author = {J Fatermans and Arnold Jan den Dekker and M{\"u}ller-Caspary, K. and Ivan Lobato and O{\textquoteright}Leary, C. M. and Peter D Nellist and Sandra Van Aert}
}
@inproceedings {1897,
	title = {Spectral variability in a multilinear mixing model},
	booktitle = {IEEE IGARSS 2018, International Geoscience and Remote Sensing Symposium, Valencia, Spain, July 23-27},
	year = {2018},
	author = {T Dox and Rob Heylen and Paul Scheunders}
}
@conference {Jeurissen2018-af,
	title = {Spherical deconvolution of diffusion MRI data with tensor-valued encodings},
	year = {2018},
	pages = {1559},
	author = {Ben Jeurissen and Szczepankiewicz, Filip}
}
@article {1837,
	title = {STAPP: SpatioTemporal Analysis of Plantar Pressure Measurements using Statistical Parametric Mapping},
	journal = {Gait and Posture},
	volume = {3},
	year = {2018},
	pages = {268-275},
	abstract = {Background: Pedobarography produces large sets of plantar pressure samples that are routinely subsampled (e.g. using regions of interest) or aggregated (e.g. center of pressure trajectories, peak pressure images) in order to simplify statistical analysis and provide intuitive clinical measures.

Research Question: We hypothesize that these data reductions discard gait information that can be used to differentiate between groups or conditions.

Methods: To test the hypothesis of null information loss, we created an implementation of statistical parametric mapping (SPM) for dynamic plantar pressure datasets (i.e. plantar pressure videos). Our SPM software framework brings all plantar pressure videos into anatomical and temporal correspondence, then performs statistical tests at each sampling location in space and time. Novelly, we introduce non-linear temporal registration into the framework in order to normalize for timing differences within the stance phase. We refer to our software framework as STAPP: spatiotemporal analysis of plantar pressure measurements. Using STAPP, we tested our hypothesis on plantar pressure videos from 33 healthy subjects walking at different speeds.

Results: As walking speed increased, STAPP was able to identify significant decreases in plantar pressure at mid-stance from the heel through the lateral forefoot. The extent of these plantar pressure decreases have not previously been observed using existing plantar pressure analysis techniques.

Significance: We therefore conclude that the subsampling of plantar pressure videos - a task which led to the discarding of gait information in our study - can be avoided using STAPP.},
	doi = {10.1016/j.gaitpost.2018.04.029},
	author = {Brian G Booth and No{\"e}l L.W. Keijsers and Jan Sijbers and Toon Huysmans}
}
@conference {Dhondt2018-hg,
	title = {Structural alterations of cognitive emotional brain regions are linked to the presence of spinal sensitization in low back pain},
	year = {2018},
	author = {Dhondt, Evy and Ben Jeurissen and Danneels, Lieven and Van Oosterwijck, Jessica}
}
@conference {Jeurissen2018-zz,
	title = {Super-resolution for spherical deconvolution of multi-shell diffusion MRI data},
	year = {2018},
	pages = {36},
	author = {Ben Jeurissen and Ramos-Llord{\'e}n, Gabriel and Floris Vanhevel and Paul M Parizel and Jan Sijbers}
}
@conference {Van_Dyck2018-st,
	title = {Super-resolution Reconstruction of Knee MRI},
	year = {2018},
	pages = {5184},
	author = {Pieter Van Dyck and Floris Vanhevel and De Smet, E and Paul M Parizel and Jan Sijbers and Ben Jeurissen}
}
@article {1865,
	title = {A three-dimensional digital neurological atlas of the mustached bat (Pteronotus parnellii)},
	journal = {NeuroImage},
	volume = {183},
	year = {2018},
	pages = {300-313},
	issn = {10538119},
	doi = {10.1016/j.neuroimage.2018.08.013},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1053811918307110https://api.elsevier.com/content/article/PII:S1053811918307110?httpAccept=text/xmlhttps://api.elsevier.com/content/article/PII:S1053811918307110?httpAccept=text/plain},
	author = {Washington, Stuart D. and Hamaide, Julie and Ben Jeurissen and Gwendolyn Van Steenkiste and Toon Huysmans and Jan Sijbers and Deleye, Steven and Kanwal, Jagmeet S. and De Groof, Geert and Liang, Sayuan and Johan Van Audekerke and Wenstrup, Jeffrey J. and Annemie Van Der Linden and Radtke-Schuller, Susanne and Marleen Verhoye}
}
@article {1838,
	title = {Three-dimensional quantitative analysis of healthy foot shape: a proof of concept study},
	journal = {Journal of Foot and Ankle Research},
	volume = {11},
	year = {2018},
	month = {Mar},
	pages = {1-13},
	abstract = {Background
Foot morphology has received increasing attention from both biomechanics researches and footwear manufacturers. Usually, the morphology of the foot is quantified by 2D footprints. However, footprint quantification ignores the foot{\textquoteright}s vertical dimension and hence, does not allow accurate quantification of complex 3D foot shape.

Methods
The shape variation of healthy 3D feet in a population of 31 adult women and 31 adult men who live in Belgium was studied using geometric morphometric methods. The effect of different factors such as sex, age, shoe size, frequency of sport activity, Body Mass Index (BMI), foot asymmetry, and foot loading on foot shape was investigated. Correlation between these factors and foot shape was examined using multivariate linear regression.

Results
The complex nature of a foot{\textquoteright}s 3D shape leads to high variability in healthy populations. After normalizing for scale, the major axes of variation in foot morphology are (in order of decreasing variance): arch height, combined ball width and inter-toe distance, global foot width, hallux bone orientation (valgus-varus), foot type (e.g. Egyptian, Greek), and midfoot width. These first six modes of variation capture 92.59\% of the total shape variation. Higher BMI results in increased ankle width, Achilles tendon width, heel width and a thicker forefoot along the dorsoplantar axis. Age was found to be associated with heel width, Achilles tendon width, toe height and hallux orientation. A bigger shoe size was found to be associated with a narrow Achilles tendon, a hallux varus, a narrow heel, heel expansion along the posterior direction, and a lower arch compared to smaller shoe size. Sex was found to be associated with differences in ankle width, Achilles tendon width, and heel width. Frequency of sport activity was associated with Achilles tendon width and toe height.

Conclusion
A detailed analysis of the 3D foot shape, allowed by geometric morphometrics, provides insights in foot variations in three dimensions that can not be obtained from 2D footprints. These insights could be applied in various scientific disciplines, including orthotics and shoe design.},
	issn = {1757-1146},
	doi = {10.1186/s13047-018-0251-8},
	url = {https://doi.org/10.1186/s13047-018-0251-8},
	author = {Kristina Stankovi{\'c} and Brian G Booth and Femke Danckaers and Fien Burg and Vermaelen, Philippe and Saartje Duerinck and Jan Sijbers and Toon Huysmans}
}
@article {1792,
	title = {TomoBank: A Tomographic Data Repository for Computational X-ray Science},
	journal = {Measurement Science and Technology},
	volume = {29},
	year = {2018},
	pages = {1-10},
	doi = {https://doi.org/10.1088/1361-6501/aa9c19},
	author = {Francesco De Carlo and Doga Gursoy and Daniel Ching and Kees Joost Batenburg and Ludwig, Wolfgang and Lucia Mancini and Federica Marone Welford and Rajmund Mokso and Daan Pelt and Jan Sijbers and Mark Rivers}
}
@article {2003,
	title = {Using Short-wave Infrared Range Spectrometry Data to Determine Brick Characteristics},
	journal = {INTERNATIONAL JOURNAL OF ARCHITECTURAL HERITAGE},
	year = {2018},
	abstract = {Characterizing material strength in-situ for existing structures poses a major problem for a range of civil engineering applications including structural modelling for tunnelling-vulnerability assessment and pre-earthquake resiliency evaluation, especially for unreinforced masonry buildings. Present methods require expensive testing equipment often requiring access to the structure and possible destruction of historic material. This article introduces spectrometry as a non-destructive means for identifying different brick clays and their firing levels, both of which influence the masonry{\textquoteright}s mechanical behavior. The experiments herein considered bricks of 2 clay groups (red and yellow) fired at 3 kiln temperatures (700{\textordmasculine}C, 950{\textordmasculine}C, 1,060{\textordmasculine}C). Samples were examined via spectrometry within the short-wave infrared range (1,300{\textendash}2,200 nm). A Partial Least Square Discriminant Analysis (PLS-DA) model was calibrated using 96 samples and tested on a set of 48 samples, resulting in a 98\% success rate in the classification of the two clay types and a 100\% success rate for classification among the 3 firing levels. The ability of the PLS-DA model to reliably distinguish well-fired bricks from other samples, irrespective of raw material configuration, shows the potential to use this approach as a new, non-destructive means for in-situ assessment of brick for architectural conservation, as well as for safety and serviceability assessments.},
	doi = {10.1080/15583058.2018.1503362},
	url = {https://doi.org/10.1080/15583058.2018.1503362},
	author = {Debra Laefer and Zohreh Zahiri and Aoife Gowen}
}
@article {1824,
	title = {White matter microstructural organization of interhemispheric pathways predicts different stages of bimanual coordination learning in young and older adults},
	journal = {European Journal of Neuroscience },
	volume = {47},
	year = {2018},
	pages = {446{\textendash}459},
	doi = {10.1111/ejn.13841},
	author = {Hamed Zivari Adab and Sima Chalavi and Iseult Beets and Jolien Gooijers and Inge Leunissen and Boris Cheval and Quinten Collier and Jan Sijbers and Ben Jeurissen and S. P. Swinnen and Matthieu Boisgontier}
}
@inproceedings {1858,
	title = {X-ray Phase-contrast Simulations of Fibrous Phantoms using GATE},
	booktitle = {2018 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)},
	year = {2018},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Sydney, Australia},
	doi = {10.1109/NSSMIC.2018.8824641},
	author = {Jonathan Sanctorum and Jan De Beenhouwer and Jan Sijbers}
}
@proceedings {1904,
	title = {ACIVS 2017, Advanced Concepts for Intelligent Vision Systems},
	volume = {10617},
	year = {2017},
	author = {J Blanc-Talon and R Penne and Wilfried Philips and D Popescu and Paul Scheunders}
}
@article {1752,
	title = {Altered functional brain connectivity in patients with visually induced dizziness},
	journal = {NeuroImage: Clinical},
	volume = {14},
	year = {2017},
	pages = {538{\textendash}545},
	doi = {http://dx.doi.org/10.1016/j.nicl.2017.02.020},
	author = {Angelique Van Ombergen and Lizette Heine and Steven Jillings and Edward Roberts and Ben Jeurissen and V. Van Rompaey and Viviana Mucci and Stefanie Vanhecke and Jan Sijbers and Floris Vanhevel and Stefan Sunaert and Mohamed Ali Bahri and Paul M Parizel and Paul H Van de Heyning and Steven S L Laureys and Floris L Wuyts}
}
@mastersthesis {1758,
	title = {The application of 3D anthropometry for the development of headgear: a case study on the design of ergonometric brain-computer interfaces},
	year = {2017},
	type = {PhD thesis},
	author = {Daniel Lacko}
}
@article {1915,
	title = {The arcuate fasciculus network and verbal deficits in psychosis},
	journal = {Translational Neuroscience},
	volume = {8},
	year = {2017},
	month = {Feb-11-2017},
	pages = {117-126},
	doi = {10.1515/tnsci-2017-0018},
	author = {Kenney, Joanne P.M. and McPhilemy, Genevieve and Scanlon, Cathy and Najt, Pablo and McInerney, Shane and Arndt, Sophia and Scherz, Elisabeth and Byrne, Fintan and Alexander Leemans and Ben Jeurissen and Hallahan, Brian and McDonald, Colm and Cannon, Dara M.}
}
@article {1712,
	title = {Atom-counting in High Resolution Electron Microscopy: TEM or STEM - that{\textquoteright}s the question},
	journal = {Ultramicroscopy},
	volume = {147},
	year = {2017},
	pages = {112{\textendash}120},
	doi = {http://dx.doi.org/10.1016/j.ultramic.2016.10.011},
	author = {Julie Gonnissen and Annick De Backer and Arnold Jan den Dekker and Jan Sijbers and Sandra Van Aert}
}
@article {1779,
	title = {Building 3D Statistical Shape Models of Horticultural Products},
	journal = {Food and Bioprocess Technology},
	volume = {10},
	year = {2017},
	pages = {2100-2112},
	doi = {10.1007/s11947-017-1979-z },
	author = {Femke Danckaers and Toon Huysmans and Mattias Van Dael and Pieter Verboven and Bart Nicolai and Jan Sijbers}
}
@article {1789,
	title = {Can portable tomosynthesis improve the diagnostic value of bedside chest X-ray in the intensive care unit? A proof of concept study},
	journal = {European Radiology Experimental},
	volume = {1},
	year = {2017},
	pages = {1-7},
	doi = {10.1186/s41747-017-0021-6},
	author = {Jeroen Cant and Annemie Snoeckx and Gert Behiels and Paul M Parizel and Jan Sijbers}
}
@conference {Adnan2017-uz,
	title = {The chronification of pain: are peripheral muscle dysfunction linked to central alteration in the brain},
	year = {2017},
	author = {Adnan, Rahmat and Dhondt, Evy and Danneels, Lieven and Hodges, Paul and Ben Jeurissen and Van Oosterwijck, Jessica}
}
@conference {Adnan2017-sq,
	title = {The chronification of pain : are peripheral muscle dysfunctions linked to central alteration in the brain?},
	year = {2017},
	author = {Adnan, Rahmat and Dhondt, Evy and Danneels, Lieven and Hodges, Paul and Ben Jeurissen and Van Oosterwijck, Jessica}
}
@inproceedings {1771,
	title = {A Comparison Between Physical and Virtual Experiments of Convective Heat Transfer Between Head and Bicycle Helmet},
	booktitle = {8th International Conference on Applied Human Factors and Ergonomics (AHFE 2017)},
	year = {2017},
	publisher = {Springer},
	organization = {Springer},
	address = {Los Angeles, California, USA},
	author = {Shriram Mukunthan and Kalev Kuklane and Toon Huysmans and Guido De Bruyne}
}
@inproceedings {1754,
	title = {Comparison of methods for online inspection of apple internal quality},
	booktitle = {7th Conference on Industrial Computed Tomography},
	year = {2017},
	address = {Leuven, Belgium},
	url = {http://www.ndt.net/events/iCT2017/app/content/Extended_Abstract/35_VanDael.pdf},
	author = {Mattias Van Dael and Pieter Verboven and Luc Van Hoorebeke and Jan Sijbers and Bart Nicolai}
}
@article {1727,
	title = {Data-Driven Affine Deformation Estimation and Correction in Cone Beam Computed Tomography},
	journal = {IEEE Transactions on Image Processing},
	volume = {26},
	year = {2017},
	pages = {1441-1451},
	doi = {10.1109/TIP.2017.2651370},
	author = {Vincent Van Nieuwenhove and Jan De Beenhouwer and Thomas De Schryver and Luc Van Hoorebeke and Jan Sijbers}
}
@conference {1889,
	title = {Detection of atomic columns from noisy STEM images},
	year = {2017},
	pages = {445-446},
	author = {J Fatermans and M{\"u}ller-Caspary, K and Arnold Jan den Dekker and Sandra Van Aert}
}
@conference {Van_Dyck2017-jo,
	title = {Diffusion Tensor Imaging of the Anterior Cruciate Ligament Graft},
	year = {2017},
	pages = {377},
	author = {Pieter Van Dyck and De Smet, Eline and Froeling, Martijn and Peter Verdonk and Torfs, Micha{\"e}l and Pim Pullens and Jan Sijbers and Paul M Parizel and Ben Jeurissen}
}
@article {1744,
	title = {Diffusion Tensor Imaging of the Anterior Cruciate Ligament Graft},
	journal = {Journal of Magnetic Resonance Imaging},
	volume = {46},
	year = {2017},
	pages = {1423{\textendash}1432},
	doi = {10.1002/jmri.25666},
	author = {Pieter Van Dyck and Martijn Froeling and Eline De Smet and Pim Pullens and Micha{\"e}l Torfs and Peter Verdonk and Jan Sijbers and Paul M Parizel and Ben Jeurissen}
}
@conference {2002,
	title = {Distinguishing Fa{\c c}ade Material Change using Hyperspectral Imaging},
	year = {2017},
	abstract = {To conduct city-scale computational modelling for infrastructure planning, micro-climate analysis, and disaster
mitigation, not only must the geometry of the built environment be detectable automatically but the component
materials must be as well. While extensive work has been undertaken for geometric recognition and feature
detection on buildings (e.g. VO et al., 2015), relatively little has been done for material identification (ZHU \&
WOODCOCK, 2014). Furthermore, most of that work has been for the classification of urban land cover, with
extremely limited analysis applied to building material identification.
Today, remote sensing data in the form of hyperspectral imagery are widely used for identification of materials
in agriculture, environment, geology, astronomy, and more. Despite the widespread application of hyperspectral
imaging in many areas, this method has seldom been used in building material detection. As aerial hyperspectral
imagers do not get adequate information from building fa{\c c}ades, close-range hyperspectral imaging, which is
a newer technique applied from the ground and recently used to study geological outcrops (KURZ et al.,
2013), can be applied to evaluate building fa{\c c}ade material. This paper will investigate how different building materials can be differentiated using close-range remote sensing technology in the form of hyperspectral (near-infrared) data. For this purpose, the fa{\c c}ade of a building containing multiple materials in Bergen, Norway, was scanned by a close-range, hyperspectral instrument. After masking non-building material, several pre-processing techniques were applied on the hyperspectral images including atmospheric and brightness correction, morphology effect removal, and bad pixel correction. Different materials on the building were then classified using supervised classification techniques (Linear Spectral Unmixing and Spectral Angle Mapper). The results showed the ability of hyperspectral data in the range of near-infrared to differentiate distinctive building materials.},
	url = {https://www.vrvis.at/publications}
}
@conference {1785,
	title = {Dual axis Dark Field Contrast Tomography for visualisation of scattering directions in a CFRP sample},
	year = {2017},
	pages = {79-80},
	address = {Z{\"u}rich, Switzerland},
	abstract = {In Carbon Fiber Reinforced Polymer (CFRP), the carbon fibers embedded into the polymer matrix cause scattering when inspected with X-rays. This scattering is captured within the dark field image when the sample is scanned with a grating based interferometer. The main disadvantage is, however, that only directional scattering information can be achieved due to the orientation of the gratings. By scanning the sample twice, with a 90 degrees rotation of the sample in between, information from two different scattering directions can be combined into one 3D reconstruction volume. In this abstract, such an approach is shown to improve the representation of scattering inside the sample.},
	keywords = {Dark field tomography, Dual axis, Phase contrast, Tomography, X-rays},
	url = {https://indico.psi.ch/getFile.py/access?resId=1\&materialId=2\&confId=5055},
	author = {Eline Janssens and Jan De Beenhouwer and Jonathan Sanctorum and Sascha Senck and Christoph Heinzl and Jan Sijbers}
}
@article {1756,
	title = {The effect of spaceflight and microgravity on the human brain},
	journal = {Journal of Neurology},
	volume = {246},
	year = {2017},
	pages = {18-22},
	doi = {10.1007/s00415-017-8427-x},
	author = {Angelique Van Ombergen and Athena Demertzi and Elena Tomilovskaya and Ben Jeurissen and Jan Sijbers and Inessa B. Kozlovskaya and Paul M Parizel and Paul H Van de Heyning and Stefan Sunaert and Steven S L Laureys and Floris L Wuyts}
}
@article {1676,
	title = {Ergonomic design of an EEG headset using 3D anthropometry},
	journal = {Applied Ergonomics},
	volume = {58},
	year = {2017},
	month = {2017},
	pages = {128{\textendash}136},
	doi = {doi:10.1016/j.apergo.2016.06.002},
	author = {Daniel Lacko and Jochen Vleugels and Erik Fransen and Toon Huysmans and Guido De Bruyne and Marc M. Van Hulle and Jan Sijbers and Stijn Verwulgen}
}
@inproceedings {1807,
	title = {Estimation of the intrinsic dimensionality in hyperspectral imagery via the hubness phenomenon},
	booktitle = {LVA ICA 2017, International conference on latent variable analysis and signal separation, Grenoble, France, February 21-23, Lecture Notes in Computer Science},
	volume = {10169},
	year = {2017},
	author = {Rob Heylen and Mario Parente and Paul Scheunders}
}
@article {1800,
	title = {Estimation of the number of endmembers in a hyperspectral image via the hubness phenomenon},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {55},
	year = {2017},
	pages = {2191-2200},
	author = {Rob Heylen and Mario Parente and Paul Scheunders}
}
@article {1806,
	title = {Evaluating palmiet wetland decline: a comparison of three methods},
	journal = {Remote Sensing Applications: Society and Environment},
	volume = {8},
	year = {2017},
	pages = {212-223},
	author = {A J Rebelo and Paul Scheunders and K J Esler and P Meire}
}
@article {1766,
	title = { Exploring sex differences in the adult zebra finch brain: In vivo diffusion tensor imaging and ex vivo super-resolution track density imaging},
	journal = {NeuroImage},
	volume = {146},
	year = {2017},
	pages = {789-803},
	doi = {10.1016/j.neuroimage.2016.09.067},
	author = {Hamaide, Julie and G. De Groof and Gwendolyn Van Steenkiste and Ben Jeurissen and Johan Van Audekerke and Maarten Naeyaert and Van Ruijssevelt, Lisbeth and Cornil, Charlotte and Jan Sijbers and Marleen Verhoye and Annemie Van Der Linden}
}
@inproceedings {1753,
	title = {Fast Reconstruction of CFRP X-ray Images based on a Neural Network Filtered Backprojection Approach},
	booktitle = {7th Conference on Industrial Computed Tomography},
	year = {2017},
	address = {Leuven, Belgium},
	url = {http://www.ndt.net/events/iCT2017/app/content/Extended_Abstract/63_Janssens_Rev4.pdf},
	author = {Eline Janssens and Sascha Senck and Christoph Heinzl and Johann Kastner and Jan De Beenhouwer and Jan Sijbers}
}
@conference {Giraldo2017-nq,
	title = {Fixel-Based Analysis of Alzheimer{\textquoteright}s Disease Using Multi-Tissue Constrained Spherical Deconvolution of Multi-Shell Diffusion MRI},
	year = {2017},
	pages = {400},
	author = {Diana Giraldo and Struyfs, Hanne and Raffelt, David and Paul M Parizel and Sebastiaan Engelborghs and Romero, Eduardo and Jan Sijbers and Ben Jeurissen}
}
@conference {1768,
	title = {Fracture Patterns In Midshaft Clavicle Fracture},
	year = {2017},
	author = {Van Tongel, Alexander and Lieven De Wilde and Ken De Smet and Thomas Decock and Edward Van Herzele and Robin Van Den Broecke and Yasunori Shimamura and Jan Sijbers and Toon Huysmans}
}
@article {1803,
	title = {Fusion of Hyperspectral and LiDAR Data for Classification of Cloud-Shadow Mixed Remote Sensing Scene},
	journal = {IEEE-JSTARS, Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
	volume = {10},
	year = {2017},
	pages = {3768-3781},
	author = {Renbo Luo and Wenzhi Liao and H Zhang and L Zhang and Y Pi and Paul Scheunders and Wilfried Philips}
}
@article {1802,
	title = {Habitat mapping and quality assessment of NATURA 2000 Heatland using airborne imaging spectroscopy},
	journal = {Remote Sensing},
	volume = {9},
	year = {2017},
	chapter = {266},
	author = {Birgen Haest and Jeroen Vanden Borre and Toon Spanhove and Guy Thoonen and Stephanie Delalieux and Kooistra, L. and C A M{\"u}cher and D Paelinckx and Paul Scheunders and Pieter Kempeneers}
}
@article {1718,
	title = {High angular resolution diffusion-weighted imaging in mild traumatic brain injury},
	journal = {NeuroImage: Clinical},
	volume = {13},
	year = {2017},
	month = {11/2016},
	pages = {174 - 180},
	abstract = {We sought to investigate white matter abnormalities in mild traumatic brain injury (mTBI) using diffusion-weighted magnetic resonance imaging (DW-MRI). We applied a global approach based on tract-based spatial statistics skeleton as well as constrained spherical deconvolution tractography. DW-MRI was performed on 102 patients with mTBI within two months post-injury and 30 control subjects. A robust global approach considering only the voxels with a single-fiber configuration was used in addition to global analysis of the tract skeleton and probabilistic whole-brain tractography. In addition, we assessed whether the microstructural parameters correlated with age, time from injury, patient{\textquoteright}s outcome and white matter \{MRI\} hyperintensities. We found that whole-brain global approach restricted to single-fiber voxels showed significantly decreased fractional anisotropy (FA) (p = 0.002) and increased radial diffusivity (p = 0.011) in patients with mTBI compared with controls. The results restricted to single-fiber voxels were more significant and reproducible than those with the complete tract skeleton or the whole-brain tractography. \{FA\} correlated with patient outcomes, white matter hyperintensities and age. No correlation was observed between \{FA\} and time of scan post-injury. In conclusion, the global approach could be a promising imaging biomarker to detect white matter abnormalities following traumatic brain injury.},
	keywords = {Diffusion-weighted magnetic resonance imaging, Global approach, Magnetic Resonance Imaging, probabilistic tractography, Traumatic brain injury},
	issn = {2213-1582},
	doi = {http://dx.doi.org/10.1016/j.nicl.2016.11.016},
	url = {http://www.sciencedirect.com/science/article/pii/S2213158216302248},
	author = {Mohammadian, Mehrbod and Timo Roine and Jussi Hirvonen and Timo Kurki and Henna Ala-Sepp{\"a}l{\"a} and Janek Frantz{\'e}n and Ari Katila and Anna Kyll{\"o}nen and Henna-Riikka Maanp{\"a}{\"a} and Jussi Posti and Riikka Takala and Jussi Tallus and Olli Tenovuo}
}
@article {1801,
	title = {Hyperspectral leaf reflectance of Carpines betulus L. saplings for urban air quality estimation},
	journal = {Environmental Pollution},
	volume = {220},
	year = {2017},
	pages = {159-167},
	author = {Melanka Brackx and Shari Van Wittenberghe and Jolien Verhelst and Paul Scheunders and Roeland Samson}
}
@mastersthesis {1777,
	title = {Improved reliability of fiber orientation estimation and graph theoretical analysis of structural brain networks with diffusion MRI},
	volume = {Doctor of Science},
	year = {2017},
	school = {University of Antwerp},
	type = {PhD thesis},
	author = {Timo Roine}
}
@article {1761,
	title = {Inline Discrete Tomography system: application to agricultural product inspection},
	journal = {Computers and Electronics in Agriculture},
	volume = {138},
	year = {2017},
	pages = {117{\textendash}126},
	doi = {https://doi.org/10.1016/j.compag.2017.04.010},
	author = {Luis Filipe Alves Pereira and Eline Janssens and George D C Cavalcanti and Ing Ren Tsang and Mattias Van Dael and Pieter Verboven and Bart Nicolai and Jan Sijbers}
}
@article {1762,
	title = {Intrinsic connectivity reduces in vestibular-related regions after first-time exposure to short-term gravitational alterations},
	journal = {Scientific Reports},
	volume = {7},
	year = {2017},
	doi = {10.1038/s41598-017-03170-5},
	author = {Angelique Van Ombergen and Floris L Wuyts and Ben Jeurissen and Jan Sijbers and Floris Vanhevel and Steven Jillings and Paul M Parizel and Stefan Sunaert and Paul H Van de Heyning and Vincent Dousset and Steven S L Laureys and Athena Demertzi}
}
@mastersthesis {1738,
	title = {Iterative reconstruction for mobile chest tomosynthesis},
	volume = {PhD in Sciences/Physics},
	year = {2017},
	type = {PhD thesis},
	author = {Jeroen Cant}
}
@conference {1996,
	title = {Maximizing precision in PCASL MRI using an optimized sampling strategy},
	year = {2017},
	author = {Piet Bladt and Arnold Jan den Dekker and Clement, Patricia and Eric Achten and Jan Sijbers}
}
@article {2047,
	title = {Mobile imaging of an object using penetrating radiation},
	number = {WO/2017/178334},
	year = {2017},
	chapter = {PCT/EP2017/058270},
	author = {Jan Sijbers and Jan De Beenhouwer}
}
@mastersthesis {1821,
	title = {Model-based reconstruction algorithms for dynamic X-ray CT},
	volume = {PhD in Sciences/Physics},
	year = {2017},
	type = {PhD thesis},
	author = {Vincent Van Nieuwenhove}
}
@article {1773,
	title = {MoVIT: A tomographic reconstruction framework for 4D-CT},
	journal = {Optics Express},
	volume = {25},
	year = {2017},
	pages = {19236-19250},
	abstract = {4D computed tomography (4D-CT) aims to visualise the temporal dynamics of a 3D sample with a sufficiently high temporal and spatial resolution.  Successive time frames are typically obtained by sequential scanning, followed by independent reconstruction of each 3D dataset. Such an approach requires a large number of projections for each scan to obtain images with sufficient quality (in terms of artefacts and SNR). Hence, there is a clear trade-off between the rotation speed of the gantry (i.e. time resolution) and the quality of the reconstructed images.
In this paper, the MotionVector-based Iterative Technique (MoVIT) is introduced which reconstructs a particular time frame by including the projections of neighbouring time frames as well. It is shown that such a  strategy improves the trade-off between the rotation speed and the SNR.
The framework is tested on both numerical simulations and on 4D X-ray CT datasets of polyurethane foam under compression.
Results show that reconstructions obtained with MoVIT have a significantly higher SNR compared to the SNR of conventional 4D reconstructions.},
	doi = {https://doi.org/10.1364/OE.25.019236},
	author = {Vincent Van Nieuwenhove and Jan De Beenhouwer and Jelle Vlassenbroeck and Mark Brennan and Jan Sijbers}
}
@article {1759,
	title = {Multisensor X-ray inspection of internal defects in horticultural products},
	journal = {Postharvest Biology and Technology},
	volume = {128},
	year = {2017},
	month = {June},
	pages = {33{\textendash}43},
	doi = {https://doi.org/10.1016/j.postharvbio.2017.02.002},
	author = {Mattias Van Dael and Pieter Verboven and Jelle Dhaene and Luc Van Hoorebeke and Jan Sijbers and Bart Nicolai}
}
@article {1775,
	title = {A nonlocal maximum likelihood estimation method for enhancing magnetic resonance phase maps},
	journal = {Signal Image and Video Processing},
	volume = {11},
	year = {2017},
	pages = { 913-920},
	author = {P V Sudeep and Palanisamy, P and Chandrasekharan Kesavadas and Jan Sijbers and Arnold Jan den Dekker and Jeny Rajan}
}
@inproceedings {1755,
	title = {A Novel adaptive PCA Based Denoising Technique for Ultra-High-Rate Computed Tomography},
	booktitle = {7th Conference on Industrial Computed Tomography (iCT 2017)},
	year = {2017},
	month = {02/2017},
	publisher = {ndt.net},
	organization = {ndt.net},
	address = {Leuven (Belgium)},
	abstract = {Increasing the X-ray tomography acquisition rate is of high importance especially in medical applications as well as dynamic processes investigation. Thanks to the high brilliance of synchrotron radiation, it is possible to reduce the exposure time and do a tomographic scan with a sub-second temporal resolution which allows following dynamic processes in 4D (3D space + time).
On the other hand, increasing the acquisition rate leads to more background noise which strictly limits the advantages of high rate scan. We apply a new fast denoising technique using universal properties of eigen-spectrum of random covariance matrices. Our proposed technique is established based on the principal component analysis (PCA) of redundant data which shows that most of the signal-related variance is contained in a few components, whereas the noise is spread over all components. Extensive numerical evaluations of the proposed technique on a real dataset were acquired at the TOMCAT beamline with its ultra-fast endstation, show significant improvement in the quality of reconstructed images and elimination of noise.},
	keywords = {denoising, eigenvalue, principal component analysis (PCA), ultra-fast scan},
	author = {Karim Zarei Zefreh and Federica Marone Welford and Jan Sijbers},
	editor = {Mehrdad Taki}
}
@mastersthesis {1783,
	title = {Online quality control of fruit and vegetables using X-ray imaging},
	volume = {Doctor of Bioscience Engineering},
	year = {2017},
	type = {PhD thesis},
	author = {Mattias Van Dael}
}
@conference {1997,
	title = {Optimal sampling strategy for pseudo-continuous arterial spin labeling MRI},
	year = {2017},
	author = {Piet Bladt and Arnold Jan den Dekker and Clement, Patricia and Eric Achten and Jan Sijbers}
}
@mastersthesis {1926,
	title = {Optimal statistical experiment design for detecting and locating light atoms using quantitative high resolution (scanning) transmission electron microscopy},
	volume = {PhD in Sciences/Physics},
	year = {2017},
	type = {PhD thesis},
	author = {Julie Gonnissen}
}
@article {1722,
	title = {Partial Discreteness: a Novel Prior for Magnetic Resonance Image Reconstruction},
	journal = {IEEE Transactions on Medical Imaging},
	volume = {36},
	year = {2017},
	pages = {1041 - 1053},
	doi = {10.1109/TMI.2016.2645122},
	author = {Gabriel Ramos-Llord{\'e}n and Arnold Jan den Dekker and Jan Sijbers}
}
@inproceedings {1808,
	title = {Pixel purity vertex component analysis},
	booktitle = {IEEE IGARSS 2017, International Geoscience and Remote Sensing Symposium, Fort Worth, USA, July 23-28},
	year = {2017},
	author = {Rob Heylen and Mario Parente and Paul Scheunders}
}
@article {1774,
	title = {Product sizing with 3D anthropometry and k-medoids clustering},
	journal = {Computer-Aided Design},
	volume = {91},
	year = {2017},
	pages = {60-74},
	doi = {https://doi.org/10.1016/j.cad.2017.06.004},
	author = {Daniel Lacko and Toon Huysmans and Jochen Vleugels and Guido De Bruyne and Marc M. Van Hulle and Jan Sijbers and Stijn Verwulgen}
}
@inproceedings {1743,
	title = {Registration Based SIRT: A reconstruction algorithm for 4D CT},
	booktitle = {7th Conference on Industrial Computed Tomography},
	year = {2017},
	address = {Leuven, Belgium},
	abstract = {The goal of 4D computed tomography (4D CT) is to study the temporal deformation of a 3D sample with a sufficiently high temporal and spatial resolution. Conventionally, the sample is sequentially scanned, resulting in datasets of successive time frames. Each of these datasets is then independently reconstructed. This framework results in a trade-off between the temporal resolution and the signal-to-noise ratio (SNR) of the reconstructed images. The proposed registration based simultaneous iterative reconstruction technique (RBSIRT) allows shortening the acquisition time per time frame, leading to improved temporal resolution at comparable SNR. To this end, the algorithm estimates the deformation field between different time frames, which allows incorporating projections of other time frames into the reconstruction of a particular time frame. The technique was validated on numeric simulations and on a real dynamic experiment of a polyurethane foam sample. The reconstructions obtained with RBSIRT have a significantly higher SNR compared to the SNR of conventional 4D reconstructions.},
	url = {http://www.ndt.net/events/iCT2017/app/content/Paper/42_VanNieuwenhove.pdf},
	author = {Vincent Van Nieuwenhove and Jan De Beenhouwer and Jelle Vlassenbroeck and Maarten Moesen and Mark Brennan and Jan Sijbers}
}
@article {1723,
	title = {A safe, cheap and easy-to-use isotropic diffusion phantom for clinical and multicenter studies},
	journal = {Medical Physics},
	volume = {44},
	year = {2017},
	pages = {1063{\textendash}1070},
	doi = {10.1002/mp.12101},
	author = {Pim Pullens and Piet Bladt and Jan Sijbers and Andrew I.R. Maas and Paul M Parizel}
}
@conference {1760,
	title = {Screw fixation of simulated scaphoid waist fractures: a biomechanical comparison of two screw lengths.},
	year = {2017},
	abstract = {Background: 
Sixty percent of carpal bone fractures affect the scaphoid. These fractures typically occur in a young and male population, with a mean age of 25 years. Eighty percent of scaphoid fractures are located at the waist and the majority of these fractures is nondisplaced and stable. The poor healing capacity of scaphoid fractures, frequently causes delayed- or nonunion, leading to impaired function, early degenerative changes and chronic wrist pain.. Adequate treatment is therefore essential.
Scaphoid fractures can be managed with a cast or with screw fixation.  The latter offers a shorter time of immobilisation with a faster return to function, whilst having the same long-term outcome. Ideal screw length is an area of discussion. Theoretically, longer screws provide more stability, but there is a higher risk of protrusion of the screw in the cartilage resulting in more adverse effects. 

Objectives: The goal of this study was to compare the fixation strength for long and short screws in order to evaluate if the choice for the longer screw is worth the increased risk of protrusion.

Study design \& methods: Thirteen pairs of fresh frozen cadaveric scaphoids were randomized to have one side fixed with a long and one with a shorter screw (longest screw possible and longest length minus 4 mm). Under fluoroscopic control a central guidewire was inserted and a cannulated screw placed. A wedge osteotomy was made to simulate a horizontal oblique fracture plane. The proximal pole of the scaphoid was placed into a fixture. Load was applied by using a load-controlled test protocol in a hydraulic testing machine and displacement was measured.

Results: There was no significant difference between the load at 1 and 2mm displacement for long and short screws. The load at 1 and 2 mm displacement was 40.9 N (SEM 7.0 N; 90\% CI 28.4-53.5 N) and 80.9 N (SEM 11.0 N; 90\% CI 61.1-100.7 N) for the short screws compared with 50.4 N (SEM 10.2 N; 32.0-68.8 N) and 92.6 N (SEM 15.6 N; 64.5-120.7 N) for the long screws. An equivalence test based on the mean and 90\% CI showed that these values are equivalent.

Conclusions: These data suggest that in oblique waist fracture, fixation with a long or a short screw are equivalent in strength. Given the higher risk of complications with longer screws, a shorter screw is advisable and has no negative impact on the stability of the construct.
},
	author = {Maarten Ouwendijk and Niek Slingerland and Job van Nistelrooij and Toon Huysmans and Elke Van de Casteele and Francis Van Glabbeek and Geert Meermans and Frederik Verstreken}
}
@conference {1764,
	title = {Solving the Free Water Elimination Estimation Problem by Incorporating T2 Relaxation Properties},
	year = {2017},
	author = {Quinten Collier and Jelle Veraart and Arnold Jan den Dekker and Floris Vanhevel and Paul M Parizel and Jan Sijbers}
}
@article {1804,
	title = {Spatial Resolution Enhancement of Hyperspectral Images Using Spectral Unmixing and Bayesian Sparse Representation},
	journal = {Remote Sensing},
	volume = {9},
	year = {2017},
	chapter = {541},
	author = {E.K. Ghasrodashti and Azam Karami and Rob Heylen and Paul Scheunders}
}
@conference {Morez2017-he,
	title = {Spherical Deconvolution of Non-Spherically Sampled Diffusion MRI Data},
	year = {2017},
	pages = {66},
	author = {Jan Morez and Jan Sijbers and Ben Jeurissen}
}
@conference {1794,
	title = {Spherical Deconvolution of Non-Spherically Sampled Diffusion MRI Data},
	year = {2017},
	author = {Jan Morez and Jan Sijbers and Ben Jeurissen}
}
@conference {1818,
	title = {Statistically optimal separation of multi-component MR signals with a Majorize-Minimize approach: application to MWF estimation},
	year = {2017},
	author = {Gabriel Ramos-Llord{\'e}n and Arnold Jan den Dekker and Piet Bladt and A. Cuyt and Jan Sijbers}
}
@conference {1826,
	title = {Super-resolution multi-PLD PCASL: a simulation study},
	volume = {30 (Suppl. 1)},
	number = {S396},
	year = {2017},
	publisher = {Magn Reson Mater Phy},
	abstract = {Cerebral blood flow (CBF) can be estimated non-invasively with arterial spin labeling (ASL). Multi-post-labeling-delay (PLD) pseudo-continuous ASL (PCASL) allows for accurate CBF estimation by sampling the dynamic perfusion signal at different PLDs and fitting a model to the perfusion data. Unfortunately, ASL difference images have a low SNR. Therefore, CBF estimation in multi-PLD PCASL is imprecise, unless a large number of images is acquired (long scan time) or spatial resolution is lowered significantly. It has been shown that model-based super-resolution reconstruction (SRR) techniques can improve the trade-off between SNR, spatial resolution and acquisition time. The results presented in this work show the promising potential of SRR ASL to outperform conventional ASL readout schemes in terms of achievable precision of HR perfusion measurements in a given acquisition time.},
	doi = {10.1007/s10334-017-0634-z},
	author = {Piet Bladt and Quinten Beirinckx and Gwendolyn Van Steenkiste and Ben Jeurissen and Eric Achten and Arnold Jan den Dekker and Jan Sijbers}
}
@article {1668,
	title = {Super-resolution T1 estimation: quantitative high resolution T1 mapping from a set of low resolution T1 weighted images with different slice orientations},
	journal = {Magnetic Resonance in Medicine},
	volume = {77},
	year = {2017},
	pages = {1818{\textendash}1830},
	doi = {10.1002/mrm.26262},
	author = {Gwendolyn Van Steenkiste and Dirk H J Poot and Ben Jeurissen and Arnold Jan den Dekker and Floris Vanhevel and Paul M Parizel and Jan Sijbers}
}
@article {1706,
	title = {A unified Maximum Likelihood framework for simultaneous motion and T1 estimation in quantitative MR T1 mapping},
	journal = {IEEE Transactions on Medical Imaging},
	volume = {36},
	year = {2017},
	pages = {433 - 446},
	doi = {10.1109/TMI.2016.2611653},
	author = {Gabriel Ramos-Llord{\'e}n and Arnold Jan den Dekker and Gwendolyn Van Steenkiste and Ben Jeurissen and Floris Vanhevel and Johan Van Audekerke and Marleen Verhoye and Jan Sijbers}
}
@article {1805,
	title = {On the use of dorsiventral reflectance asymmetry of hornbeam (Carpinus betulus L.) leaves  in air pollution estimation},
	journal = {Environmental Monitoring and Assessment},
	volume = {189},
	year = {2017},
	chapter = {472},
	author = {Melanka Brackx and Jolien Verhelst and Paul Scheunders and Roeland Samson}
}
@conference {1784,
	title = {A workflow to reconstruct grating-based X-ray phase contrast CT images: application to CFRP samples},
	year = {2017},
	pages = {139-140},
	address = {Z{\"u}rich, Switzerland},
	abstract = {Carbon fiber reinforced polymer (CFRP) is an extremely strong and lightweight plastic of which the strength depends on the distribution of its fibers. Fiber bundles can be visualized by means of phase contrast X-ray computed tomography (PCCT) based on grating-based interferometry (GBI). However, many steps are involved in the reconstruction of GBI-PCCT images. In this abstract, a workflow for the reconstruction of 3D CFRP phase contrast images based on GBI projection data is presented.},
	keywords = {Carbon fiber reinforced polymer, Image processing, Phase contrast, Tomography, X-rays},
	url = {https://indico.psi.ch/getFile.py/access?resId=1\&materialId=2\&confId=5055},
	author = {Jonathan Sanctorum and Eline Janssens and Arnold Jan den Dekker and Sascha Senck and Christoph Heinzl and Jan De Beenhouwer and Jan Sijbers}
}
@article {1644,
	title = {3D morphometric analysis of the human incudomallear complex using clinical cone-beam CT},
	journal = {Hearing research},
	volume = {340},
	year = {2016},
	pages = {79-88},
	abstract = {Human middle ears show large morphological variations. This could affect our perception of hearing and explain large variation in experimentally obtained transfer functions. Most morphological studies focus on capturing variation by using landmarks on cadaveric temporal bones. We present statistical shape analysis based on clinical cone beam CT (CBCT) scans of 100 patients. This allowed us to include surface information on the incudomallear (IM) complex (joint, ligaments and tendon not included) of 123 healthy ears with a scanning resolution of 150~μm and without a priori assumptions. Statistical shape modeling yields an average geometry for the IM complex and the variations present in the population with a high precision. Mean values, variation and correlations among anatomical features (length of manubrium, combined length of malleus head and neck, lengths of incus long and short process, enclosing angles, ossicular lever ratio, incudomallear angle, and principal moments of inertia) are reported and compared to results from the literature. Most variation is found in overall size and the angle between incus and malleus. The compact representation provided by statistical shape modeling is demonstrated and its benefits for surface modeling are discussed.},
	issn = {1878-5891},
	doi = {10.1016/j.heares.2016.01.014},
	author = {Joris Soons and Femke Danckaers and Keustermans, William and Toon Huysmans and Jan Sijbers and Casselman, Jan W and Joris J. J. Dirckx}
}
@proceedings {1905,
	title = {ACIVS 2016, Advanced Concepts for Intelligent Vision Systems},
	volume = {10016},
	year = {2016},
	author = {C Distante and J Blanc-Talon and Wilfried Philips and D Popescu and Paul Scheunders}
}
@article {VanAert:gq5005,
	title = {Advanced electron crystallography through model-based imaging},
	journal = {IUCrJ},
	volume = {3},
	number = {1},
	year = {2016},
	month = {Jan},
	abstract = {The increasing need for precise determination of the atomic arrangement of non-periodic structures in materials design and the control of nanostructures explains the growing interest in quantitative transmission electron microscopy. The aim is to extract precise and accurate numbers for unknown structure parameters including atomic positions, chemical concentrations and atomic numbers. For this purpose, statistical parameter estimation theory has been shown to provide reliable results. In this theory, observations are considered purely as data planes, from which structure parameters have to be determined using a parametric model describing the images. As such, the positions of atom columns can be measured with a precision of the order of a few picometres, even though the resolution of the electron microscope is still one or two orders of magnitude larger. Moreover, small differences in average atomic number, which cannot be distinguished visually, can be quantified using high-angle annular dark-field scanning transmission electron microscopy images. In addition, this theory allows one to measure compositional changes at interfaces, to count atoms with single-atom sensitivity, and to reconstruct atomic structures in three dimensions. This feature article brings the reader up to date, summarizing the underlying theory and highlighting some of the recent applications of quantitative model-based transmisson electron microscopy.},
	keywords = {experimental design, quantitative analysis, statistical parameter estimation, structure refinement, transmission electron microscopy},
	doi = {10.1107/S2052252515019727},
	url = {http://dx.doi.org/10.1107/S2052252515019727},
	author = {Sandra Van Aert and Annick De Backer and Martinez, Gerardo T. and Arnold Jan den Dekker and Dirk Van Dyck and Sara Bals and Van Tendeloo, Gustaaf}
}
@article {1686,
	title = {Advanced techniques for computational and information sciences},
	journal = {Mathematical Problems in Engineering},
	volume = {2016},
	year = {2016},
	edition = {Article ID 5623913},
	author = {W. Guo and C.-C. Hung and Paul Scheunders and B.-C. Kuo and Y. Lan and Z. Ma}
}
@inproceedings {1809,
	title = {Alternating angle minimization based unmixing with endmember variability},
	booktitle = {IEEE IGARSS 2016, International Geoscience and Remote Sensing Symposium, pp. 6974-6977, Beijing, July 10-15 },
	year = {2016},
	doi = {10.1109/IGARSS.2016.7730819},
	author = {Rob Heylen and Paul Scheunders and Alina Zare and Paul Gader}
}
@article {1793,
	title = {An alternative approach for ζ-factor measurement using pure element nanoparticles},
	journal = {Ultramicroscopy},
	volume = {164},
	year = {2016},
	pages = {11{\textendash}16},
	abstract = {It is very challenging to measure the chemical composition of hetero nanostructures in a reliable and quantitative manner. Here, we propose a novel and straightforward approach that can be used to quantify energy dispersive X-ray spectra acquired in a transmission electron microscope. Our method is based on a combination of electron tomography and the so-called ζ-factor technique. We will demonstrate the reliability of our approach as well as its applicability by investigating Au-Ag and Au-Pt hetero nanostructures. Given its simplicity, we expect that the method could become a new standard in the field of chemical characterization using electron microscopy.},
	keywords = {Bimetallic nanoparticles, EDXS quantification, Electron tomography},
	issn = {03043991},
	doi = {10.1016/j.ultramic.2016.03.002},
	url = {http://linkinghub.elsevier.com/retrieve/pii/S0304399116300134},
	author = {Daniele Zanaga and Thomas Altantzis and Jonathan Sanctorum and Bert Freitag and Sara Bals}
}
@article {1778,
	title = {Automated quality control and selection },
	number = {PCT/EP2016/055718},
	year = {2016},
	chapter = {WO2016146703 A1},
	author = {Mattias Van Dael and Pieter Verboven and Bart Nicolai and Jelle Dhaene and Luc Van Hoorebeke and Jan Sijbers}
}
@article {1641,
	title = {Automatic forensic analysis of automotive paints using optical microscopy},
	journal = {Forensic Science International},
	volume = {259},
	year = {2016},
	pages = {210-220},
	author = {Guy Thoonen and B. Nys and Y. Vander Haegen and G. De Roy and Paul Scheunders}
}
@inproceedings {1691,
	title = {Automatic geometric calibration of chest tomosynthesis using data consistency conditions},
	booktitle = {The 4th International Conference on Image Formation in X-Ray Computed Tomography},
	year = {2016},
	pages = {161-164},
	address = {Salt Lake City, Utah, USA},
	author = {Jeroen Cant and Gert Behiels and Jan Sijbers}
}
@conference {1888,
	title = {Bayesian model-order selection in electron microscopy to detect atomic columns in noisy images},
	year = {2016},
	pages = {53},
	author = {J Fatermans and Sandra Van Aert and Arnold Jan den Dekker}
}
@conference {1643,
	title = {Building 3D Statistical Shape Models of Vegetables and Fruit},
	year = {2016},
	abstract = {Introduction
Statistical shape modeling is a promising approach to map out the variability of a population. By adapting the parameters of the shape model, a new, realistic surface can be obtained. In this work, the framework for surface registration and building a statistical shape model of fruit is described.

Method
The framework consists of two parts. First, a reference surface is registered to each fruit. Based on the correspondences that resulted from this surface registration, a statistical shape model is built. In the surface registration part, a reference surface is registered to a target surface, such that the geometric distance between those surfaces becomes minimal while maintaining correspondences. The second part of our framework consists of building a statistical shape model based on the correspondences that resulted from the surface registration (Fig. 1). The model is built by performing principal
components analysis on the corresponding points matrix of the population. In this model, the mean surface and the main shape modes are captured.
Parametrization of a surface is the task of defining a map between the surface and a simple parameter domain, such as a cylinder or sphere, so each fruit in the model can be described using basis functions, such as B-splines. This is a very compact representation and is useful in CAD and finite-element environments, so the models can be used for simulations.

Results
Experiments resulted in better correspondences than the current state-of-the-art. This means that our shape model is a good representation of the population and adapting the shape model parameters will lead to a realistic surface.

Applications
Possible applications of a statistical shape model of fruit are predicting the final size of the fruit, search for correlations between stages of growth, estimate the volume from a single view and evaluation of the effect of the fruit shape on airflow characteristics to obtain cooling uniformity.},
	author = {Femke Danckaers and Toon Huysmans and Jan Sijbers}
}
@conference {1694,
	title = {Building a statistical shape model of the interior and exterior of the bell pepper},
	volume = {28-30 November},
	year = {2016},
	address = {Vienna, Austria},
	author = {Femke Danckaers and Toon Huysmans and Seppe Rogge and Mattias Van Dael and Pieter Verboven and Bart Nicolai and Jan Sijbers}
}
@article {1683,
	title = {Chronic exposure to haloperidol and olanzapine leads to common and divergent shape changes in the rat hippocampus in the absence of grey-matter volume loss},
	journal = {Psychological Medicine},
	volume = {46},
	year = {2016},
	pages = {3081-3093},
	doi = {10.1017/S0033291716001768},
	author = {William R Crum and Femke Danckaers and Toon Huysmans and Marie-Caroline Cotel and Sridhar Natesan and Michel M. Modo and Jan Sijbers and Steven C.R. Williams and Shitij Kapur and Anthony C. Vernon}
}
@inproceedings {1717,
	title = {Classification of hyperspectral images with very small training size using sparse unmixing},
	booktitle = {IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
	year = {2016},
	address = {Beijing, China},
	author = {Vera Andrejchenko and Rob Heylen and Paul Scheunders and Wilfried Philips and Wenzhi Liao}
}
@inproceedings {1737,
	title = {Close range hyperspectral imaging for plant phenotyping},
	booktitle = {Hyperspectral Imaging and Applications Conference},
	year = {2016},
	month = {October/2016},
	address = {Coventry, UK},
	author = {Puneet Mishra and Mohd Shahrimie Mohd Asaari and Stien Mertens and Nathalie Wuyts and Stijn Dhondt and Paul Scheunders}
}
@conference {1728,
	title = {Combining 3D vision and X-ray radiography for internal quality inspection of foods},
	year = {2016},
	month = {28-30 November},
	address = {Austrian Economic Chamber, Vienna, Austria},
	author = {Mattias Van Dael and Femke Danckaers and Thomas De Schryver and Toon Huysmans and Pieter Verboven and Luc Van Hoorebeke and Jan Sijbers and Bart Nicolai}
}
@inproceedings {1700,
	title = {Compression Of Hyperspectral Images Using Block Coordinate Descent Search And Compressed Sensing},
	booktitle = {8th workshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing},
	year = {2016},
	month = {08/2016},
	address = {Los Angeles, USA},
	author = {Shirin Hassanzadeh and Azam Karami and Rob Heylen and Paul Scheunders}
}
@article {1716,
	title = {Computerized Tomographic Image Exposure and Reconstruction Method},
	number = {PCT/EP2015/059956},
	year = {2016},
	abstract = {A method to improve reconstruction quality for continuous projections. The use of continuous projection
might be advantageous to use cheaper panels and simpler setups for tomosynthesis and CT reconstruction.},
	author = {Jeroen Cant and Jan Sijbers}
}
@inproceedings {1669,
	title = {Continuous Digital Laminography},
	booktitle = {6th Conference on Industrial Computed Tomography},
	year = {2016},
	address = {Wels, Austria},
	url = {http://www.ndt.net/article/ctc2016/papers/ICT2016_paper_id42.pdf},
	author = {Jeroen Cant and Gert Behiels and Jan Sijbers}
}
@article {1584,
	title = {Cortical reorganization in an astronaut{\textquoteright}s brain after long-duration spaceflight},
	journal = {Brain Structure and Function},
	volume = {221},
	year = {2016},
	pages = {2873{\textendash}2876},
	doi = {10.1007/s00429-015-1054-3},
	author = {Athena Demertzi and Angelique Van Ombergen and Elena Tomilovskaya and Ben Jeurissen and Ekaterina V. Pechenkova and Carol Di Perri and Liudmila Litvinova and Enrico Amico and Alena Rumshiskaya and Ilya Rukavishnikov and Jan Sijbers and Valentin Sinitsyn and Inessa B. Kozlovskaya and Stefan Sunaert and Paul M Parizel and Paul H Van de Heyning and Steven S L Laureys and Floris L Wuyts}
}
@article {1651,
	title = {D-BRAIN: Anatomically Accurate Simulated Diffusion MRI Brain Data},
	journal = {PlosOne},
	volume = {11},
	number = {3},
	year = {2016},
	pages = {1-23},
	doi = {10.1371/journal.pone.0149778},
	author = {Daniele Perrone and Ben Jeurissen and Jan Aelterman and Timo Roine and Jan Sijbers and Aleksandra Pizurica and Alexander Leemans and Wilfried Philips}
}
@article {1763,
	title = {Denoising of diffusion MRI using random matrix theory},
	journal = {NeuroImage},
	volume = {142},
	year = {2016},
	pages = {384-396},
	doi = {10.1016/j.neuroimage.2016.08.016},
	author = {Jelle Veraart and Dmitry S. Novikov and Christiaens Daan and Ades-Aron, Benjamin and Jan Sijbers and Els Fieremans}
}
@inproceedings {1699,
	title = {Denoising Of Hyperspectral Images Using Shearlet Transform And Fully Constrained Least Squares Unmixing},
	booktitle = {8th workshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing},
	year = {2016},
	month = {08/2016},
	address = { Los Angeles, USA},
	author = {Azam Karami and Rob Heylen and Paul Scheunders}
}
@article {1688,
	title = {Detecting and locating light atoms from high-resolution STEM images: the quest for a single optimal design Ultramicroscopy},
	journal = {Ultramicroscopy},
	volume = {170},
	year = {2016},
	pages = {128-138},
	doi = {http://dx.doi.org/10.1016/j.ultramic.2016.07.014},
	author = {Julie Gonnissen and Annick De Backer and Arnold Jan den Dekker and Jan Sijbers and Sandra Van Aert}
}
@inbook {1673,
	title = {Diffusion Kurtosis Imaging},
	booktitle = { Diffusion Tensor Imaging: a practical handbook},
	year = {2016},
	pages = {407-418},
	publisher = {Springer-Verlag},
	organization = {Springer-Verlag},
	chapter = {Diffusion Kurtosis Imaging},
	address = {New York},
	issn = {ISBN 978-1-4939-3118-7},
	doi = {10.1007/978-1-4939-3118-7},
	author = {Jelle Veraart and Jan Sijbers}
}
@article {1620,
	title = {Diffusion kurtosis imaging probes cortical alterations and white matter pathology following cuprizone-induced demyelination and spontaneous remyelination},
	journal = {NeuroImage},
	volume = {125},
	year = {2016},
	pages = {363{\textendash}377},
	doi = {10.1016/j.neuroimage.2015.10.052},
	author = {Caroline Guglielmetti and Jelle Veraart and Ella Roelant and Zhenhua Mai and Jasmijn Daans and Johan Van Audekerke and Maarten Naeyaert and Greetje Vanhoutte and Rafael  Delgado Y Palacios and Jelle Praet and Els Fieremans and Peter Ponsaerts and Jan Sijbers and Annemie Van Der Linden and Marleen Verhoye}
}
@inproceedings {1652,
	title = {Discrete tomographic reconstruction from deliberately motion blurred X-ray projections},
	booktitle = {6th Conference on Industrial Computed Tomography},
	year = {2016},
	pages = {1-6},
	address = {Wels, Austria},
	author = {Wim Van Aarle and Jeroen Cant and Jan De Beenhouwer and Jan Sijbers}
}
@article {1715,
	title = {A distributed ASTRA Toolbox},
	journal = {Advanced Structural and Chemical Imaging},
	volume = {2},
	year = {2016},
	doi = {10.1186/s40679-016-0032-z},
	author = {Willem Jan Palenstijn and Jeroen B{\'e}dorf and Jan Sijbers and Kees Joost Batenburg}
}
@inproceedings {1619,
	title = {Dynamic flat field correction in X-ray computed tomography},
	booktitle = {Optimess conference},
	year = {2016},
	edition = {Antwerp},
	author = {Vincent Van Nieuwenhove and Jan De Beenhouwer and Jan Sijbers}
}
@inproceedings {1627,
	title = {A dynamic region estimation method for cerebral perfusion CT},
	booktitle = {6th International Conference on Optical Measurement Techniques for Structures and Systems (OPTIMESS)},
	year = {2016},
	pages = {331-342},
	abstract = {In cerebral perfusion computed tomography (PCT), multiple scans of the brain are acquired after an intravenous contrast bolus injection. Therefore, radiation dose is a major issue. 
Recently, methods have been proposed that can reconstruct high quality dynamic (i.e., 4D) images, while keeping the radiation dose limited. These methods typically require an accurate estimate of the dynamic region inside the brain volume, i.e., the region containing tissue/vessels. Conventionally, the dynamic region is indicated manually.
In this work, a method for low-dose cerebral PCT is presented in which the dynamic region is estimated in an automatic way.
Simulation results on two PCT phantoms show that the dynamic region can be accurately estimated, even in a very low-dose regime, which is an important step towards more powerful reconstruction methods for low-dose cerebral PCT.
},
	author = {Van Eyndhoven, Geert and Jan De Beenhouwer and Jan Sijbers}
}
@article {1600,
	title = {Easy implementation of advanced tomography algorithms using the ASTRA toolbox with Spot operators},
	journal = {Numerical Algorithms},
	volume = {71},
	number = {3},
	year = {2016},
	pages = {673-697},
	doi = {10.1007/s11075-015-0016-4},
	author = {Folkert Bleichrodt and Tristan van Leeuwen and Willem Jan Palenstijn and Wim Van Aarle and Jan Sijbers and Kees Joost Batenburg}
}
@article {1892,
	title = {EPR and DFT analysis of biologically relevant chromium(V) complexes with d-glucitol and d-glucose},
	journal = {Journal of Inorganic Biochemistry},
	volume = {162},
	year = {2016},
	pages = {216 - 226},
	keywords = {1, 2-diolato ligands, DFT, ENDOR, EPR, HYSCORE, Oxidochromium(V)},
	issn = {0162-0134},
	doi = {https://doi.org/10.1016/j.jinorgbio.2016.07.012},
	url = {http://www.sciencedirect.com/science/article/pii/S0162013416302136},
	author = {Sabine Van Doorslaer and Quinten Beirinckx and Kevin Nys and Mar{\'\i}a Florencia Mangiameli and Bert Cuypers and Freddy Callens and Henk Vrielinck and Juan Carlos Gonz{\'a}lez}
}
@inproceedings {1696,
	title = {Evaluation of the thickness of hair layer from 3D scans and medical images},
	booktitle = {7th International Conference and Exhibition on 3D Body Scanning Technologies (3DBST)},
	year = {2016},
	edition = {Lugano, Switzerland, 30 November - 1 December},
	author = {Stijn Verwulgen and Jochen Vleugels and Daniel Lacko and Guido De Bruyne and Toon Huysmans}
}
@article {1701,
	title = {Fast and Flexible X-ray Tomography Using the ASTRA Toolbox},
	journal = {Optics Express},
	volume = {24},
	number = {22},
	year = {2016},
	pages = {25129-25147},
	doi = {10.1364/OE.24.025129},
	author = {Wim Van Aarle and Willem Jan Palenstijn and Jeroen Cant and Eline Janssens and Folkert Bleichrodt and Andrei Dabravolski and Jan De Beenhouwer and Kees Joost Batenburg and Jan Sijbers}
}
@article {1665,
	title = {Fast inline inspection by neural network based filtered backprojection: Application to apple inspection},
	journal = {Case Studies in Nondestructive Testing and Evaluation},
	volume = {6},
	year = {2016},
	pages = {14{\textendash}20},
	doi = {10.1016/j.csndt.2016.03.003},
	author = {Eline Janssens and Luis Filipe Alves Pereira and Jan De Beenhouwer and Ing Ren Tsang and Mattias Van Dael and Pieter Verboven and Bart Nicolai and Jan Sijbers}
}
@inproceedings {1663,
	title = {Fast X-ray Computed Tomography via Image Completion},
	booktitle = {6th Conference on Industrial Computed Tomography(iCT)},
	year = {2016},
	pages = {1-5},
	address = {Wels, Austria},
	author = {Luis Filipe Alves Pereira and Eline Janssens and Mattias Van Dael and Pieter Verboven and Bart Nicolai and George D C Cavalcanti and Ing Jyh Tsang and Jan Sijbers}
}
@inproceedings {1695,
	title = {Foot Abnormality Mapping using Statistical Shape Modelling},
	booktitle = {7th International Conference and Exhibition on 3D Body Scanning Technologies (3DBST)},
	year = {2016},
	pages = {70-79},
	edition = {Lugano, Switzerland, 30 November - 1 December},
	doi = {doi:10.15221/16.070},
	author = {Kristina Stankovi{\'c} and Femke Danckaers and Brian G Booth and Fien Burg and Saartje Duerinck and Jan Sijbers and Toon Huysmans}
}
@article {1682,
	title = {Fusion of Hyperspectral and Multispectral Images Using Spectral Unmixing and Sparse Coding},
	journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
	volume = {9},
	year = {2016},
	pages = {2377-2389},
	abstract = {Unlike multispectral (MSI) and panchromatic (PAN) images, generally the spatial resolution of hyperspectral images (HSI) is limited, due to sensor limitations. In many applications, HSI with a high spectral as well as spatial resolution are required. In this paper, a new method for spatial resolution enhancement of a HSI using spectral unmixing and sparse coding (SUSC) is introduced. The proposed method fuses high spectral resolution features from the HSI with high spatial resolution features from an MSI of the same scene. Endmembers are extracted from the HSI by spectral unmixing, and the exact location of the endmembers is obtained from the MSI. This fusion process by using spectral unmixing is formulated as an ill-posed inverse problem which requires a regularization term in order to convert it into a well-posed inverse problem. As a regularizer, we employ sparse coding (SC), for which a dictionary is constructed using high spatial resolution MSI or PAN images from unrelated scenes. The proposed algorithm is applied to real Hyperion and ROSIS datasets. Compared with other state-of-the-art algorithms based on pansharpening, spectral unmixing, and SC methods, the proposed method is shown to significantly increase the spatial resolution while perserving the spectral content of the HSI.},
	issn = {1939-1404},
	doi = {10.1109/JSTARS.2016.2528339},
	author = {Zahara Hashemi Nezhad and Azam Karami and Rob Heylen and Paul Scheunders}
}
@conference {Van_Steenkiste2016-fy,
	title = {High Resolution Diffusion Tensor Reconstruction from Simultaneous Multi-Slice Acquisitions in a Clinically Feasible Scan Time},
	year = {2016},
	pages = {2},
	author = {Gwendolyn Van Steenkiste and Ben Jeurissen and Baete, Steven and Arnold Jan den Dekker and Dirk H J Poot and Boada, Fernando and Jan Sijbers}
}
@article {1681,
	title = {Hyperspectral Image Compression Optimized for Spectral Unmixing},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {pp},
	year = {2016},
	month = {06/2016},
	chapter = {1},
	abstract = {In this paper, we present a new lossy compression method for hyperspectral images that aims to optimally compress in both spatial and spectral domains and simultaneously minimizes the effect of the compression on linear spectral unmixing performance. To achieve this, a nonnegative Tucker decomposition is applied. This decomposition is a function of three dimension parameters. By employing a link between this decomposition and the linear spectral mixing model, an optimization problem is defined to find the optimal parameters by minimizing the root-mean-square error between the abundance matrices of the original and reconstructed data sets. The resulting optimization problem is solved by a particle swarm optimization algorithm. An approximate method for fast estimation of the free parameters is introduced as well. Our simulation results show that, in comparison with well-known state-of-the-art lossy compression methods, an improved compression and spectral unmixing performance of the reconstructed hyperspectral image is obtained. It is noteworthy to mention that the superiority of our method becomes more apparent as the compression ratio grows.},
	issn = {0196-2892},
	doi = {10.1109/TGRS.2016.2574757},
	author = {Azam Karami and Rob Heylen and Paul Scheunders}
}
@article {1685,
	title = {Hyperspectral unmixing with endmember variability via alternating angle minimization},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {54},
	year = {2016},
	pages = {4983-4993},
	author = {Rob Heylen and Alina Zare and Paul Gader and Paul Scheunders}
}
@conference {1642,
	title = {In vivo high resolution diffusion tensor imaging in a clinically acceptable scan time by combining super resolution reconstruction with simultaneous multi-slice acquisition},
	volume = {8},
	number = {P-036},
	year = {2016},
	author = {Gwendolyn Van Steenkiste and Ben Jeurissen and Steven Baete and Arnold Jan den Dekker and Dirk H J Poot and Fernando Boada and Jan Sijbers}
}
@article {1708,
	title = {In-line NDT with X-Ray CT combining sample rotation and translation},
	journal = {NDT \& E International},
	volume = {89},
	year = {2016},
	pages = {89{\textendash}98},
	doi = {10.1016/j.ndteint.2016.09.001},
	author = {Thomas De Schryver and Jelle Dhaene and Manuel Dierick and Boone, M.N. and Eline Janssens and Jan Sijbers and Mattias Van Dael and Pieter Verboven and Bart Nicolai and Luc Van Hoorebeke}
}
@article {1664,
	title = {Integration of TomoPy and the ASTRA toolbox for advanced processing and reconstruction of tomographic synchrotron data},
	journal = {Journal of Synchrotron Radiation},
	volume = {23},
	year = {2016},
	pages = {842-849},
	doi = {10.1107/S1600577516005658},
	author = {Daan Pelt and Doga Gursoy and Willem Jan Palenstijn and Jan Sijbers and Francesco De Carlo and Kees Joost Batenburg}
}
@conference {1741,
	title = {Investigating lattice strain in Au nanodecahedrons},
	year = {2016},
	doi = {10.1002/9783527808465.EMC2016.5519},
	author = {Bart Goris and Jan De Beenhouwer and Annick De Backer and Daniele Zanaga and Kees Joost Batenburg and Ana S{\'a}nchez-Iglesias and Luis M Liz-Marzán and Sandra Van Aert and Jan Sijbers and Van Tendeloo, Gustaaf and Sara Bals}
}
@inproceedings {1667,
	title = {Investigation on Effect of scintillator thickness on Afterglow in Indirect X-ray Detectors},
	booktitle = {6th Conference on Industrial Computed Tomography, Wels, Austria (iCT 2016)},
	year = {2016},
	abstract = {Solid-state scintillation detectors are widely used in modern multi-slice CT systems as well as synchrotron micro-tomography beamlines. Amongst other parameters, the performance of these detectors depends on the thickness of the scintillator. Thicker scintillators result in higher emission intensities, yet the resolution deteriorates as the thickness increases. To achieve a higher scan speed, thicker scintillators are more common. The thickness of scintillators however may influence the afterglow.  In this paper, we investigate the effect of scintillator thickness on the afterglow, using scintillating screens of two different materials (LAG:Ce and Gadox) and different thicknesses.  
Experimental results show that, apart from the scintillator material and excitation condition, the thickness of scintillator has a decisive role on the scintillator decay and particularly on the afterglow.
},
	keywords = {afterglow, flat-panel, image lag, scintillator thickness, synchrotron, x-ray detector},
	url = {http://www.ndt.net/article/ctc2016/papers/ICT2016_paper_id74.pdf},
	author = {Karim Zarei Zefreh and Jan De Beenhouwer and Federica Marone Welford and Jan Sijbers}
}
@article {1748,
	title = {Investigation on the effect of exposure time on scintillator afterglow for ultra-fast tomography acquisition},
	journal = {Journal of Instrumentation},
	volume = {11},
	year = {2016},
	month = {12/2016},
	abstract = {Thanks to the ultra-fast endstation of the TOMCAT beamline, it is possible to do a tomographic scan with a sub-second temporal resolution which allows following dynamic processes in 4D (3D space + time). This ultra- high-rate tomography acquisition, exploiting the distinctive peculiarities of synchrotron radiation, provides nondestructive investigation of many dynamic processes which were not possible in the past. For example a continuous tensile test has been conducted recently in-situ for the first time with a frequency of 20 tomograms per second (20 Hz acquisition frequency). In the ultra-fast endstation a scintillator is used to convert X-ray to visible photons that can be detected by the camera. However, this conversion is not ideal and the scintillator response decays exponentially with afterglow. Afterglow can cause resolution degradation and artifacts (such as ring and band) especially with high rotation speed. On the other hand, to achieve a higher scan speed, thicker scintillators are more common because they result in higher emission intensities that can compensate the short exposure time in fast scans. However, the resolution deteriorates as the scintillator{\textquoteright}s thickness increases and thicker scintillators show higher afterglow. Performing many ultra-fast scans at the TOMCAT beamline with different acquisition rate, we demonstrate how the exposure time effects on the projection data and reconstructed images. Using two different thicknesses of LAG scintillator we also investigate the afterglow artifacts for different acquisition rate and exposure time.},
	keywords = {4DCT, computed tomography, exposure time, scintillator},
	doi = {10.1088/1748-0221/11/12/C12014},
	url = {http://iopscience.iop.org/article/10.1088/1748-0221/11/12/C12014/meta;jsessionid=DE490396BDCED3C5746F4059065713A1.c3.iopscience.cld.iop.org},
	author = {Karim Zarei Zefreh},
	editor = {Federica Marone Welford}
}
@inproceedings {1719,
	title = {Investigation on the effect of exposure time on scintillator afterglow for ultra-fast tomography acquisition},
	booktitle = {18th International Workshop on Radiation Imaging Detectors},
	volume = {11},
	year = {2016},
	pages = {C12014},
	publisher = {IOP Publishing},
	organization = {IOP Publishing},
	address = {Barcelona, Spain},
	doi = {10.1088/1748-0221/11/12/C12014},
	author = {Karim Zarei Zefreh and Federica Marone Welford and Jan Sijbers}
}
@conference {Kenney2016-ju,
	title = {Lateralisation of the arcuate fasciculus in psychosis \& the role in verbal learning \& auditory verbal hallucinations},
	volume = {26},
	year = {2016},
	pages = {S76{\textendash}S77},
	publisher = {Elsevier Science Bv},
	address = {Po Box 211, 1000 Ae Amsterdam, Netherlands},
	author = {Kenney, J and McInerney, S and McPhilemy, G and Najt, P and Scanlon, C and Arndt, S and Scherz, E and Byrne, F and Leemans, A and Ben Jeurissen and Donohoe, G and Hallahan, B and McDonald, C and Cannon, D}
}
@inproceedings {1810,
	title = {Lidar information extraction by attribute filters with partial reconstruction},
	booktitle = {IEEE IGARSS 2016, International Geoscience and Remote Sensing Symposium, pp. 1484-1487, Beijing, July 10-15 },
	year = {2016},
	doi = {10.1109/IGARSS.2016.7729379},
	author = {Wenzhi Liao and M Della Mura and X Huang and Jocelyn Chanussot and S Gautama and Paul Scheunders and Wilfried Philips}
}
@article {1703,
	title = {Local Attenuation Curve Optimization (LACO) framework for high quality perfusion maps in low-dose cerebral perfusion CT},
	journal = {Medical Physics},
	volume = {43},
	year = {2016},
	pages = {6429-6438},
	doi = {10.1118/1.4967263},
	author = {Vincent Van Nieuwenhove and Van Eyndhoven, Geert and Kees Joost Batenburg and Nico Buls and Jaf Vandemeulebroucke and Jan De Beenhouwer and Jan Sijbers}
}
@article {1781,
	title = {Method and system for correcting geometric misalignment during image reconstruction in chest tomosynthesis},
	number = {EP16173767.1},
	year = {2016},
	abstract = {A method to find panel misalignment. Necessary when exact panel position is unknown.},
	issn = {EP16173767.1},
	author = {Jeroen Cant and Jan Sijbers}
}
@conference {Roine2016-je,
	title = {Methodological Considerations on Graph Theoretical Analysis of Structural Brain Networks},
	year = {2016},
	pages = {3437},
	author = {Roine, Timo and Ben Jeurissen and Perrone, Daniele and Jan Aelterman and Philips, Wilfried and Jan Sijbers and Leemans, Alexander}
}
@inproceedings {1736,
	title = {Modeling effects of illumination and plant geometry on leaf reflectance spectra in close-range hyperspectral imaging},
	booktitle = {8th WHISPERS - Evolution in Remote Sensing},
	year = {2016},
	month = {August/2016},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Los Angeles, USA},
	author = {Mohd Shahrimie Mohd Asaari and Puneet Mishra and Stien Mertens and Stijn Dhondt and Nathalie Wuyts and Paul Scheunders}
}
@article {1640,
	title = {A multilinear mixing model for nonlinear spectral unmixing},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {54},
	year = {2016},
	pages = {240-251},
	author = {Rob Heylen and Paul Scheunders}
}
@inproceedings {1653,
	title = {Multi-voxel algorithm for quantitative bi-exponential MRI T1 estimation},
	booktitle = {SPIE Medical Imaging},
	volume = {9784},
	year = {2016},
	pages = {978402},
	address = {San Diego, California, United States of America},
	abstract = {In this work, we propose a joint multi-voxel bi-exponential estimator (JMBE) for quantitative bi-exponential T1 estimation in magnetic resonance imaging, to account for partial volume effects and to yield more accurate results compared to single-voxel bi-exponential estimators (SBEs). Using a numerical brain phantom with voxels containing two tissues, the minimal signal-to-noise ratio (SNR) needed to estimate both T1 values with sufficient accuracy was investigated. Compared to the SBE, and for clinically achievable single-voxel SNRs, the JMBE yields accurate T1 estimates if four or more neighboring voxels are used in the joint estimation framework, in which case it is also efficient.},
	doi = {http://dx.doi.org/10.1117/12.2216831},
	author = {Piet Bladt and Gwendolyn Van Steenkiste and Gabriel Ramos-Llord{\'e}n and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {1647,
	title = {NOVIFAST: A fast non-linear least squares method for accurate and precise estimation of T1 from SPGR signals},
	year = {2016},
	author = {Gabriel Ramos-Llord{\'e}n and Arnold Jan den Dekker and Marcus Bj{\"o}rk and Marleen Verhoye and Jan Sijbers}
}
@inproceedings {1697,
	title = {Optical Solutions for Improving Spatial Resolution of Hyperspectral Sensors},
	booktitle = {8th workshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing},
	year = {2016},
	month = {08/2016},
	address = { Los Angeles, USA},
	author = {Ashkan  Adibi and Azam Karami and Rob Heylen and Paul Scheunders}
}
@article {1730,
	title = {Orthosis},
	number = { WO/2016/181282},
	year = {2016},
	url = {https://www.google.com/patents/WO2016181282A1?cl=en},
	author = {Toon Huysmans and Jan Sijbers and Frederik Verstreken}
}
@article {kenney2016p,
	title = {P. 3.033 Lateralisation of the arcuate fasciculus in psychosis \& the role in verbal learning \& auditory verbal hallucinations},
	journal = {European Neuropsychopharmacology},
	volume = {26},
	year = {2016},
	pages = {S76{\textendash}S77},
	author = {Kenney, J and McInerney, S and McPhilemy, G and Najt, P and Scanlon, C and Arndt, S and Scherz, E and Byrne, F and Alexander Leemans and Ben Jeurissen and G Donohoe and B Hallahan and C McDonald and D Cannon}
}
@inproceedings {1710,
	title = {Pixel clustering for face recognition},
	booktitle = {5th Brazilian Conference on Intelligent Systems},
	year = {2016},
	pages = {121-126},
	address = {Brazil},
	doi = {https://doi.org/10.1109/BRACIS.2016.032},
	author = {Tiago B. A. de Carvalho and Maria A. A. Sibaldo and Ing Ren Tsang and George D C Cavalcanti and Ing Jyh Tsang and Jan Sijbers}
}
@booklet {1709,
	title = {On the problem of uploading pdf files},
	year = {2016},
	author = {Nikolas Garofil}
}
@inproceedings {1573,
	title = {Projection-based polygon estimation in X-ray computed tomography},
	booktitle = {6th International Conference on Optical Measurement Techniques for Structures and Systems (OPTIMESS)},
	year = {2016},
	pages = {41-50},
	author = {Andrei Dabravolski and Jan De Beenhouwer and Jan Sijbers}
}
@article {1674,
	title = {Quantitative 3D analysis of huge nanoparticle assemblies.},
	journal = {Nanoscale},
	volume = {8},
	year = {2016},
	month = {2016 Jan 7},
	pages = {292-9},
	abstract = {Nanoparticle assemblies can be investigated in 3 dimensions using electron tomography. However, it is not straightforward to obtain quantitative information such as the number of particles or their relative position. This becomes particularly difficult when the number of particles increases. We propose a novel approach in which prior information on the shape of the individual particles is exploited. It improves the quality of the reconstruction of these complex assemblies significantly. Moreover, this quantitative Sparse Sphere Reconstruction approach yields directly the number of particles and their position as an output of the reconstruction technique, enabling a detailed 3D analysis of assemblies with as many as 10,000 particles. The approach can also be used to reconstruct objects based on a very limited number of projections, which opens up possibilities to investigate beam sensitive assemblies where previous reconstructions with the available electron tomography techniques failed.},
	issn = {2040-3372},
	doi = {10.1039/c5nr06962a},
	author = {Daniele Zanaga and Folkert Bleichrodt and Thomas Altantzis and Winckelmans, Naomi and Willem Jan Palenstijn and Jan Sijbers and de Nijs, Bart and Marijn A van Huis and Ana S{\'a}nchez-Iglesias and Luis M Liz-Marzán and van Blaaderen, Alfons and Kees Joost Batenburg and Sara Bals and Van Tendeloo, Gustaaf}
}
@conference {1672,
	title = {Robust DKI parameter estimation in case of CSF partial volume effects},
	year = {2016},
	abstract = {Diffusion kurtosis imaging (DKI) suffers from partial volume effects caused by cerebrospinal fluid (CSF). We propose a DKI+CSF model combined with a framework to robustly estimate the DKI parameters. Since the estimation problem is ill-conditioned, a Bayesian estimation approach with a shrinkage prior is incorporated. Both simulation and real data experiments suggest that the use of this prior leads to a more accurate, precise and robust estimation of the DKI+CSF model parameters. Finally, we show that not correcting for the CSF compartment can lead to severe biases in the parameter estimations.},
	author = {Quinten Collier and Arnold Jan den Dekker and Ben Jeurissen and Jan Sijbers}
}
@conference {Collier2016-tu,
	title = {Robust DKI Parameter Estimation in Case of CSF Partial Volume Effects},
	year = {2016},
	pages = {1044},
	author = {Collier, Quinten and Arnold Jan den Dekker and Ben Jeurissen and Jan Sijbers}
}
@conference {1671,
	title = {A robust framework for combined estimation of DKI and CSF partial volume fraction parameters},
	year = {2016},
	abstract = {Diffusion kurtosis imaging (DKI) suffers from partial volume effects caused by cerebrospinal fluid (CSF). We propose a DKI+CSF model combined with a framework to robustly estimate the DKI parameters. Since the estimation problem is ill-conditioned, a Bayesian estimation approach with a shrinkage prior is incorporated. Both simulation and real data experiments suggest that the use of this prior leads to a more accurate, precise and robust estimation of the DKI+CSF model parameters. Finally, we show that not correcting for the CSF compartment can lead to severe biases in the parameter estimations.},
	author = {Quinten Collier and Arnold Jan den Dekker and Ben Jeurissen and Jan Sijbers}
}
@article {1612,
	title = {A segmentation and classification algorithm for online detection of internal disorders in citrus using X-ray radiographs},
	journal = {Postharvest Biology and Technology},
	volume = {112},
	year = {2016},
	pages = {205-214},
	doi = {10.1016/j.postharvbio.2015.09.020},
	author = {Mattias Van Dael and S Lebotsa and E Herremans and Pieter Verboven and Jan Sijbers and U. L. Opara and U. L. Cronje and Bart Nicolai}
}
@article {1704,
	title = {StatSTEM: An efficient approach for accurate and precise model-based quantification of atomic resolution electron microscopy images},
	journal = {Ultramicroscopy},
	volume = {171},
	year = {2016},
	pages = {104{\textendash}116},
	doi = {http://dx.doi.org/10.1016/j.ultramic.2016.08.018},
	author = {Annick De Backer and K.H.W. van den Bos and Wouter Van den Broek and Jan Sijbers and Sandra Van Aert}
}
@mastersthesis {2367,
	title = {Subpixel analysis of hyperspectral remote sensing images},
	year = {2016},
	type = {PhD thesis},
	author = {Awais Muhammad Akhter}
}
@mastersthesis {1713,
	title = {Super-resolution estimation of quantitative MRI parameters},
	volume = {Doctor of Science},
	year = {2016},
	school = {University of Antwerp},
	type = {PhD thesis},
	author = {Gwendolyn Van Steenkiste}
}
@inproceedings {1698,
	title = {Superresolution Of Hyperspectral Images Using Spectral Unmixing And Sparse Regularization},
	booktitle = {IGARSS  2016},
	year = {2016},
	month = {07/2016},
	address = {Beijing China},
	author = {Zahara Hashemi Nezhad and Azam Karami and Rob Heylen and Paul Scheunders}
}
@article {1536,
	title = {Super-resolution reconstruction of diffusion parameters from diffusion-weighted images with different slice orientations},
	journal = {Magnetic Resonance in Medicine},
	volume = {75},
	year = {2016},
	pages = {181-195},
	doi = {10.1002/mrm.25597},
	url = {http://onlinelibrary.wiley.com/doi/10.1002/mrm.25597/abstract},
	author = {Gwendolyn Van Steenkiste and Ben Jeurissen and Jelle Veraart and Arnold Jan den Dekker and Paul M Parizel and Dirk H J Poot and Jan Sijbers}
}
@article {1690,
	title = {T1 relaxometry of crossing fibres in the human brain.},
	journal = {NeuroImage},
	year = {2016},
	month = {07/2016},
	abstract = {A comprehensive tract-based characterisation of white matter should include the ability to quantify myelin and axonal attributes irrespective of the complexity of fibre organisation within the voxel. Recently, a new experimental framework that combines inversion recovery and diffusion MRI, called inversion recovery diffusion tensor imaging (IR-DTI), was introduced and applied in an animal study. IR-DTI provides the ability to assign to each unique fibre population within a voxel a specific value of the longitudinal relaxation time, T1, which is a proxy for myelin content. Here, we apply the IR-DTI approach to the human brain in vivo on 7 healthy subjects for the first time. We demonstrate that the approach is able to measure differential tract properties in crossing fibre areas, reflecting the different myelination of tracts. We also show that tract-specific T1 has less inter-subject variability compared to conventional T1 in areas of crossing fibres, suggesting increased specificity to distinct fibre populations. Finally we show in simulations that changes in myelination selectively affecting one fibre bundle in crossing fibre areas can potentially be detected earlier using IR-DTI.},
	issn = {1095-9572},
	doi = {10.1016/j.neuroimage.2016.07.037},
	author = {Silvia De Santis and Yaniv Assaf and Ben Jeurissen and Derek K. Jones and Roebroeck, Alard}
}
@conference {1765,
	title = {Understanding microstructural deformation of apple tissue from 4D micro-CT imaging},
	year = {2016},
	doi = {10.17660/ActaHortic.2018.1197.2},
	author = {Wang, Z and Seppe Rogge and Mattias Van Dael and Vincent Van Nieuwenhove and Pieter Verboven and Jan Sijbers and Bart Nicolai}
}
@article {1648,
	title = {Unsupervised Retinal Vessel Segmentation Using Combined Filters},
	journal = {Plos One},
	volume = {11},
	number = {2},
	year = {2016},
	pages = {1-21},
	doi = {10.1371/journal.pone.0149943},
	author = {Wendeson S. Oliveira and Joyce Vitor Teixeira and Ing Ren Tsang and George D C Cavalcanti and Jan Sijbers}
}
@conference {1613,
	title = {3D evaluation of clavicle fractures},
	year = {2015},
	author = {Van Tongel, Alexander and Toussaint,, Arnaud and Lieven De Wilde and Toon Huysmans}
}
@conference {1589,
	title = {A 4D CT reconstruction algorithm for fast liquid flow imaging},
	year = {2015},
	abstract = {  The study of fluid flow through solid matter by computed tomography (CT) imaging has a broad range of applications, ranging from oil extraction to scientific research on fluid dynamics. Current techniques are often limited by a low temporal/spatial resolution. In this talk, a new iterative CT reconstruction algorithm for improved temporal/spatial resolution in the imaging of fluid flowing through solid matter is introduced. The proposed algorithm exploits prior knowledge in two ways. Firstly, the time-varying object is assumed to consist of stationary (the solid matter) and dynamic regions (the fluid flow). Secondly, the attenuation of a particular voxel in the dynamic region is modeled by a piecewise constant function over time (i.e., the voxel consists of fluid or air). Experiments on simulation data and on a real neutron tomography dataset demonstrate that the proposed approach can significantly increase the temporal resolution in comparison to conventional algorithms.},
	author = {Van Eyndhoven, Geert and Kees Joost Batenburg and Kazantsev, Daniil and Vincent Van Nieuwenhove and Lee, Peter D. and Katherine J Dobson and Jan Sijbers}
}
@article {1570,
	title = {4D-CT reconstruction with unified spatial-temporal patch-based regularization},
	journal = { Inverse Problems and Imaging},
	volume = {9},
	year = {2015},
	pages = {447-467},
	doi = {10.3934/ipi.2015.9.447},
	author = {Kazantsev, Daniil and Thompson, William M. and Lionheart, William R. B. and Van Eyndhoven, Geert and Kaestner,  Anders P. and Katherine J Dobson and Withers, Philip J. and Lee, Peter D.}
}
@article {1634,
	title = {Abnormal wiring of the connectome in adults with high-functioning autism spectrum disorder},
	journal = {Molecular Autism},
	volume = {6},
	number = {1},
	year = {2015},
	pages = {65},
	abstract = {Recent brain imaging findings suggest that there are widely distributed abnormalities affecting the brain connectivity in individuals with autism spectrum disorder (ASD). Using graph theoretical analysis, it is possible to investigate both global and local properties of brain{\textquoteright}s wiring diagram, i.e., the connectome.
We acquired diffusion-weighted magnetic resonance imaging data from 14 adult males with high-functioning ASD and 19 age-, gender-, and IQ-matched controls. As with diffusion tensor imaging-based tractography, it is not possible to detect complex (e.g., crossing) fiber configurations, present in 60{\textendash}90 \% of white matter voxels; we performed constrained spherical deconvolution-based whole brain tractography. Unweighted and weighted structural brain networks were then reconstructed from these tractography data and analyzed with graph theoretical measures.
In subjects with ASD, global efficiency was significantly decreased both in the unweighted and the weighted networks, normalized characteristic path length was significantly increased in the unweighted networks, and strength was significantly decreased in the weighted networks. In the local analyses, betweenness centrality of the right caudate was significantly increased in the weighted networks, and the strength of the right superior temporal pole was significantly decreased in the unweighted networks in subjects with ASD.
Our findings provide new insights into understanding ASD by showing that the integration of structural brain networks is decreased and that there are abnormalities in the connectivity of the right caudate and right superior temporal pole in subjects with ASD.},
	keywords = {Autism spectrum disorder, Brain networks, Connectivity, Connectome, Diffusion Magnetic Resonance Imaging, Graph theoretical analysis, Tractography, White matter tract},
	issn = {2040-2392},
	doi = {10.1186/s13229-015-0058-4},
	url = {http://dx.doi.org/10.1186/s13229-015-0058-4},
	author = {Roine, Ulrika and Timo Roine and Salmi, Juha and Nieminen-von Wendt, Taina and Tani, Pekka and Lepp{\"a}m{\"a}ki, Sami and Rintahaka, Pertti and Caeyenberghs, Karen and Alexander Leemans and Sams, Mikko}
}
@article {1541,
	title = {An accurate projection model for diffraction image formation and inversion using a polychromatic cone beam},
	journal = {Journal of Applied Crystallography},
	volume = {48},
	year = {2015},
	pages = {334-343},
	author = {Wim Van Aarle and Ludwig, Wolfgang and King, Andrew and Dayakar Penumadu}
}
@proceedings {1906,
	title = {ACIVS 2015, Advanced Concepts for Intelligent Vision Systems},
	volume = {9386},
	year = {2015},
	author = {S Battiato and J Blanc-Talon and G Gallo and Wilfried Philips and D Popescu and Paul Scheunders}
}
@inproceedings {1592,
	title = {Affine deformation correction in cone beam Computed Tomography},
	booktitle = {Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine},
	year = {2015},
	pages = {182-185},
	address = {Newport, Rhode Island, USA},
	abstract = {In Computed Tomography (CT), motion and deformation during the acquisition produce streaks and blurring, known as motion artefacts. In contrast to other deformation correction techniques, this work introduces an efficient algorithm to correct for global affine deformations directly on the cone beam projections. During an experiment, the exact deformation parameters are unknown. These parameters are estimated in the projection domain by minimizing a plane based raw data redundancy criterion. Simulations and experimental data show a reduction of motion artefacts in the reconstructed images.},
	url = {http://fully3d.org/proceedings.html},
	author = {Vincent Van Nieuwenhove and Jan De Beenhouwer and Thomas De Schryver and Luc Van Hoorebeke and Jan Sijbers}
}
@conference {1599,
	title = {Assessing inter-subject variability of white matter response functions used for constrained spherical deconvolution},
	volume = {23},
	year = {2015},
	pages = {2834},
	author = {Ben Jeurissen and Jan Sijbers and Jacques-Donald Tournier}
}
@article {1580,
	title = {The ASTRA Toolbox: a platform for advanced algorithm development in electron tomography},
	journal = {Ultramicroscopy},
	volume = {157},
	year = {2015},
	pages = {35{\textendash}47},
	doi = {10.1016/j.ultramic.2015.05.002},
	author = {Wim Van Aarle and Willem Jan Palenstijn and Jan De Beenhouwer and Thomas Altantzis and Sara Bals and Kees Joost Batenburg and Jan Sijbers}
}
@article {1571,
	title = {Band-specific Shearlet-based Hyperspectral Image Noise Reduction},
	journal = {IEEE Transaction Geosciences and Remote Sensing },
	volume = {53},
	number = {13},
	year = {2015},
	month = {03/2015},
	chapter = {1},
	author = {Azam Karami and Rob Heylen and Paul Scheunders}
}
@article {1582,
	title = {Building a Statistical Shape Model of the Apple from Corresponded Surfaces},
	journal = {Chemical Engineering Transactions},
	volume = {44},
	year = {2015},
	pages = {49-54},
	address = {Milano, Italy},
	abstract = {In this paper, a method for building a 3D statistical shape model of the apple is described. The framework consists of two parts. First, a reference surface is registered to each apple surface, derived from 3D CT scans of apples, of the population to obtain meaningful correspondences between the shapes. In the second part, the corresponded surfaces are used to build a statistical shape model from the population of apples. This model maps out the variability within the population and by adapting the shape model parameters, new, realistic surfaces can be obtained. By parameterizing the surface, an apple can be described with a compact set of basis functions, which has applications in surface fitting description, recognition, or meshing, e.g. for storage simulation. The constructed apple shape model is tested on performance and has proven to be a good representation of the population and can be used in many applications.},
	doi = { DOI:10.3303/CET1544009},
	author = {Femke Danckaers and Toon Huysmans and Mattias Van Dael and Pieter Verboven and Bart Nicolai and Jan Sijbers}
}
@conference {1598,
	title = {Combination of super-resolution reconstruction diffusion tensor imaging and track density imaging reveals song control system connectivity in zebra finches},
	volume = {23},
	year = {2015},
	pages = {2861},
	author = {Gwendolyn Van Steenkiste and Hamaide, Julie and Ben Jeurissen and Dirk H J Poot and Johan Van Audekerke and Jan Sijbers and Marleen Verhoye}
}
@conference {Van_Steenkiste2015-va,
	title = {Combination of super-resolution reconstruction diffusion tensor imaging and track density imaging reveals song control system connectivity in zebra finches},
	year = {2015},
	pages = {2861},
	author = {Gwendolyn Van Steenkiste and Hamaide, Julie and Ben Jeurissen and Dirk H J Poot and Johan Van Audekerke and Jan Sijbers and Verhoye, Marleen}
}
@article {1518,
	title = {Constrained spherical deconvolution-based tractography and tract-based spatial statistics show abnormal microstructural organization in Asperger syndrome},
	journal = {Molecular Autism},
	volume = {6},
	year = {2015},
	pages = {4},
	abstract = {Background: The aim of this study was to investigate potential differences in neural structure in individuals with Asperger syndrome (AS), high-functioning individuals with autism spectrum disorder (ASD). The main symptoms in AS are severe impairments in social interaction, and restricted or repetitive patterns of behavior, interests or activities. Methods: Diffusion weighted magnetic resonance imaging data was acquired for 14 adult males with AS and 19 age, gender and IQ-matched controls. Voxel-wise group differences in fractional anisotropy (FA) were studied with Tract-Based Spatial
Statistics (TBSS). Based on the results of TBSS, a tract-level comparison was performed with constrained spherical deconvolution (CSD)-based tractography, which is able to detect complex (e.g. crossing) fiber configurations. In addition, to investigate the relationship between the microstructural changes and the severity of symptoms, we looked for correlations between FA and Autism Spectrum Quotient (AQ), Empathy Quotient and Systemizing Quotient. Results: TBSS revealed widely distributed local increases in FA bilaterally in individuals with AS, most prominent in the temporal part of superior longitudinal fasciculus, corticospinal tract, splenium of corpus callosum, anterior thalamic radiation, inferior fronto-occipital fasciculus (IFO), posterior thalamic radiation, uncinate fasciculus and inferior longitudinal fasciculus (ILF). CSD-based tractography also showed increases in the FA in multiple tracts. However, only the difference in the left ILF was significant after a Bonferroni correction. These results were not explained by the complexity of microstructural organization, measured using the planar diffusion coefficient. In addition, we found a correlation between AQ and FA in the right IFO in the whole group. Conclusions: Our results suggest that there are local and tract-level abnormalities in WM microstructure in our homogenous and carefully characterized group of adults with AS, most prominent in the left ILF.},
	doi = {10.1186/2040-2392-6-4},
	url = {http://www.molecularautism.com/content/6/1/4},
	author = {Roine, Ulrika and Salmi, Juha and Timo Roine and Nieminen-von Wendt, Taina and Lepp{\"a}m{\"a}ki, Sami and Rintahaka, Pertti and Tani, Pekka and Alexander Leemans and Sams, Mikko}
}
@conference {1624,
	title = {CSF partial volume modeling in diffusion kurtosis imaging: a comparative parameter estimation study},
	year = {2015},
	month = {11/2015},
	doi = {10.3389/conf.fninf.2015.19.00039},
	url = {http://www.frontiersin.org/myfrontiers/events/abstractdetails.aspx?abs_doi=10.3389/conf.fninf.2015.19.00039},
	author = {Quinten Collier and Jelle Veraart and Arnold Jan den Dekker and Ben Jeurissen and Jan Sijbers}
}
@article {1604,
	title = {Diffusion Kurtosis Imaging: a possible MRI biomarker for AD diagnosis?},
	journal = {Journal of Alzheimer{\textquoteright}s Disease},
	volume = {48},
	number = {4},
	year = {2015},
	pages = {937-948},
	doi = {10.3233/JAD-150253},
	author = {Hanna Struyfs and Wim Van Hecke and Jelle Veraart and Jan Sijbers and Sylvie Slaets and Maya De Belder and Laura Wuyts and Benjamin Peters and Kristel Sleegers and Caroline Robberecht and Van Broeckhoven, Christine and Frank De Belder and Paul M Parizel and Sebastiaan Engelborghs}
}
@conference {1594,
	title = {Diffusion kurtosis imaging probes cortical alterations and white matter pathology following cuprizone-induced demyelination and spontaneous remyelination},
	volume = {23},
	number = {4332},
	year = {2015},
	author = {Caroline Guglielmetti and Jelle Veraart and Ella Roelant and Zhenhua Mai and Jasmijn Daans and Johan Van Audekerke and Jelle Praet and Peter Ponsaerts and Jan Sijbers and Annemie Van Der Linden and Marleen Verhoye}
}
@article {1615,
	title = {Dynamic intensity normalization using eigen flat fields in X-ray imaging},
	journal = {Optics Express},
	volume = {23},
	year = {2015},
	pages = {27975-27989},
	abstract = {In X-ray imaging, it is common practice to normalize the acquired projection data with averaged flat fields taken prior to the scan. Unfortunately, due to source instabilities, vibrating beamline components such as the monochromator, time varying detector properties, or other confounding factors, flat fields are often far from stationary, resulting in significant systematic errors in intensity normalization. In this work, a simple and efficient method is proposed to account for dynamically varying flat fields. Through principal component analysis of a set of flat fields, eigen flat fields are computed. A linear combination of the most important eigen flat fields is then used to individually normalize each X-ray projection. Experiments show that the proposed dynamic flat field correction leads to a substantial reduction of systematic errors in projection intensity normalization compared to conventional flat field correction. },
	doi = {10.1364/OE.23.027975},
	author = {Vincent Van Nieuwenhove and Jan De Beenhouwer and Francesco De Carlo and Lucia Mancini and Federica Marone and Jan Sijbers}
}
@article {perrone2015effect,
	title = {The effect of Gibbs ringing artifacts on measures derived from diffusion MRI},
	journal = {NeuroImage},
	volume = {120},
	year = {2015},
	pages = {441{\textendash}455},
	publisher = {Academic Press},
	author = {Daniele Perrone and Jan Aelterman and Pi{\.z}urica, Aleksandra and Ben Jeurissen and Wilfried Philips and Alexander Leemans}
}
@article {1579,
	title = {Employing temporal self-similarity across the entire time domain in computed tomography reconstruction},
	journal = {Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences},
	volume = {373},
	number = {2043},
	year = {2015},
	publisher = {The Royal Society},
	abstract = {There are many cases where one needs to limit the X-ray dose, or the number of projections, or both, for high frame rate (fast) imaging. Normally, it improves temporal resolution but reduces the spatial resolution of the reconstructed data. Fortunately, the redundancy of information in the temporal domain can be employed to improve spatial resolution. In this paper, we propose a novel regularizer for iterative reconstruction of time-lapse computed tomography. The non-local penalty term is driven by the available prior information and employs all available temporal data to improve the spatial resolution of each individual time frame. A high-resolution prior image from the same or a different imaging modality is used to enhance edges which remain stationary throughout the acquisition time while dynamic features tend to be regularized spatially. Effective computational performance together with robust improvement in spatial and temporal resolution makes the proposed method a competitive tool to state-of-the-art techniques.},
	isbn = {1471-2962},
	issn = {1364-503X},
	doi = {10.1098/rsta.2014.0389},
	author = {Kazantsev, Daniil and Van Eyndhoven, Geert and Lionheart, William R. B. and Withers, Philip J. and Katherine J Dobson and McDonald, S. A. and Atwood, R and Lee, Peter D.}
}
@inproceedings {1621,
	title = {Evaluation of 3D Body Shape Predictions Based on Features},
	booktitle = {6th International Conference on 3D Body Scanning Technologies},
	year = {2015},
	pages = {258-265},
	address = {Lugano, Switserland},
	abstract = {The human body comes in many sizes and shapes. For design purposes, it is useful to be able to quickly simulate a virtual mannequin of a customer. A statistical shape model can be used for this purpose, because it describes the main variations of body shape inside the model{\textquoteright}s population. From this model, the specific features of each person in the population are known. Therefore, a mapping between the shape model parameters and specific features can be calculated, which allows adjusting the body shape, in an intuitive way. In this work, we have investigated how accurate a body shape can be predicted based on a set of features and which features are most suitable for this purpose. Height, weight, and hip circumference appeared to be the most suitable features to accurately predict the body shape.},
	keywords = {body features, shape prediction},
	doi = {10.15221/15.258},
	author = {Femke Danckaers and Toon Huysmans and Daniel Lacko and Jan Sijbers}
}
@article {1517,
	title = {Evaluation of an anthropometric shape model of the human scalp},
	journal = {Applied Ergonomics},
	volume = {48},
	year = {2015},
	pages = {70-85},
	doi = {10.1016/j.apergo.2014.11.008},
	author = {Daniel Lacko and Toon Huysmans and Paul M Parizel and Guido De Bruyne and Stijn Verwulgen and Marc M. Van Hulle and Jan Sijbers}
}
@conference {1618,
	title = {A fast 4D CT reconstruction algorithm for affine deforming objects},
	year = {2015},
	edition = {Helsinki},
	author = {Vincent Van Nieuwenhove and Jan De Beenhouwer and Jan Sijbers}
}
@inproceedings {1637,
	title = {A fast alternative for the pixel purity index algorithm},
	booktitle = {IEEE IGARSS 2015, International Geoscience and Remote Sensing Symposium, Milan, Italy, July 26-31},
	year = {2015},
	pages = {1781-1784},
	author = {Rob Heylen and Muhamed Awais Akhter and Paul Scheunders}
}
@article {1593,
	title = {Fast Fourier-based phase unwrapping on the graphics processing unit in real-time imaging applications},
	journal = {Journal of Imaging},
	volume = {1},
	year = {2015},
	pages = {31-44},
	doi = {doi:10.3390/jimaging1010031 },
	author = {Sam Van der Jeught and Jan Sijbers and Joris J. J. Dirckx}
}
@article {1591,
	title = {Fast Neural Network Based X-Ray Tomography of Fruit on a Conveyor Belt},
	journal = {Chemical Engineering Transactions},
	volume = {44},
	year = {2015},
	pages = {181-186},
	address = {Milano, Italy},
	doi = {10.3303/CET1544031},
	author = {Eline Janssens and Daan Pelt and Jan De Beenhouwer and Mattias Van Dael and Pieter Verboven and Bart Nicolai and Jan Sijbers}
}
@inproceedings {1542,
	title = {Filtered backprojection using algebraic filters; Application to biomedical micro-CT data},
	booktitle = {International Symposium on Biomedical Imaging (ISBI)},
	year = {2015},
	pages = {1596-1599},
	author = {Linda Plantagie and Wim Van Aarle and Kees Joost Batenburg and Jan Sijbers}
}
@conference {Van_Ombergen2015-nk,
	title = {A first insight in regional brain changes after parabolic flight: a voxel-based morphometry study},
	year = {2015},
	pages = {1258},
	author = {Angelique Van Ombergen and Ben Jeurissen and Floris Vanhevel and Loeckx, Dirk and Dousset, Vincent and Paul M Parizel and Floris L Wuyts}
}
@mastersthesis {2078,
	title = {Full-field X-ray orientation imaging using convex optimization and a discrete representation of six-dimensional position - orientation space},
	year = {2015},
	type = {PhD thesis},
	author = {Nicola Roberto Vigano}
}
@article {1528,
	title = {A geometric matched filter for hyperspectral target detection and partial unmixing},
	journal = {IEEE Geoscience and Remote Sensing letters},
	volume = {12},
	number = {3},
	year = {2015},
	pages = {661-665},
	author = {Muhamed Awais Akhter and Rob Heylen and Paul Scheunders}
}
@article {1526,
	title = {A geometric unmixing concept for the selection of optimal binary endmember combinations},
	journal = {IEEE Geoscience and Remote Sensing letters},
	volume = {12},
	number = {1},
	year = {2015},
	pages = {82-86},
	author = {Laurent Tits and Rob Heylen and Ben Somers and Paul Scheunders and Pol Coppin}
}
@conference {1597,
	title = {Gibbs ringing removal in diffusion MRI using second order total variation minimization},
	volume = {23},
	year = {2015},
	pages = {2809},
	author = {Jelle Veraart and Florian Knoll and Jan Sijbers and Els Fieremans and Dmitry S. Novikov}
}
@conference {1633,
	title = {High resolution diffusion tensor imaging in a clinically feasible scan time},
	year = {2015},
	doi = {10.3389/conf.fninf.2015.19.00016},
	author = {Gwendolyn Van Steenkiste and Ben Jeurissen and Steven Baete and Arnold Jan den Dekker and Dirk H J Poot and Fernando Boada and Jan Sijbers}
}
@inproceedings {1548,
	title = {High resolution T1 estimation from multiple low resolution magnetic resonance images},
	booktitle = {IEEE International Symposium on Biomedical Imaging (ISBI): From nano to macro},
	volume = {12},
	year = {2015},
	pages = {1036-1039},
	doi = {10.1109/ISBI.2015.7164048},
	author = {Gwendolyn Van Steenkiste and Dirk H J Poot and Ben Jeurissen and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {1660,
	title = {Human In Vivo Myeloarchitecture Using Whole-Brain Diffusion MRI},
	year = {2015},
	pages = {1679},
	address = {Toronto, Ontario, Canada},
	author = {Fernando Calamante and Ben Jeurissen and Robert Elton Smith and Jacques-Donald Tournier and Alan Connelly}
}
@inproceedings {1638,
	title = {Hyperspectral unmixing with projection onto convex sets using distance geometry},
	booktitle = {IEEE IGARSS 2015, International Geoscience and Remote Sensing Symposium, Milan, Italy, July 26-31},
	year = {2015},
	pages = {5059-5062},
	author = {Muhamed Awais Akhter and Rob Heylen and Paul Scheunders}
}
@article {1519,
	title = {Informed constrained spherical deconvolution (iCSD)},
	journal = {Medical Image Analysis},
	volume = {24},
	year = {2015},
	pages = {269{\textendash}281},
	type = {Original research},
	chapter = {269},
	abstract = {Diffusion-weighted (DW) magnetic resonance imaging (MRI) is a noninvasive imaging method, which can be used to investigate neural tracts in the white matter (WM) of the brain. However, the voxel sizes used in DW-MRI are relatively large, making DW-MRI prone to significant partial volume effects (PVE). These PVEs can be caused both by complex (e.g. crossing) WM fiber configurations and non-WM tissue, such as gray matter (GM) and cerebrospinal fluid. High angular resolution diffusion imaging methods have been developed to correctly characterize complex WM fiber configurations, but significant non-WM PVEs are also present in a large proportion of WM voxels.
In constrained spherical deconvolution (CSD), the full fiber orientation distribution function (fODF) is deconvolved from clinically feasible DW data using a response function (RF) representing the signal of a single coherently oriented population of fibers. Non-WM PVEs cause a loss of precision in the detected fiber orientations and an emergence of false peaks in CSD, more prominently in voxels with GM PVEs. We propose a method, informed CSD (iCSD), to improve the estimation of fODFs under non-WM PVEs by modifying the RF to account for non-WM PVEs locally. In practice, the RF is modified based on tissue fractions estimated from high-resolution anatomical data.
Results from simulation and in-vivo bootstrapping experiments demonstrate a significant improvement in the precision of the identified fiber orientations and in the number of false peaks detected under GM PVEs. Probabilistic whole brain tractography shows fiber density is increased in the major WM tracts and decreased in subcortical GM regions. The iCSD method significantly improves the fiber orientation estimation at the WM-GM interface, which is especially important in connectomics, where the connectivity between GM regions is analyzed.},
	keywords = {Connectomics, constrained spherical deconvolution, Diffusion MRI, fiber orientation, gray matter, partial volume effect, Response function, Tractography},
	doi = {10.1016/j.media.2015.01.001},
	url = {http://www.sciencedirect.com/science/article/pii/S1361841515000080},
	author = {Timo Roine and Ben Jeurissen and Daniele Perrone and Jan Aelterman and Wilfried Philips and Alexander Leemans and Jan Sijbers}
}
@conference {1661,
	title = {Inversion Recovery DTI In Vivo at 7T in the Human Brain},
	year = {2015},
	pages = {567},
	address = {Toronto, Ontario, Canada},
	author = {Silvia De Santis and Ben Jeurissen and Derek K. Jones and Yaniv Assaf and Alard Roebroek}
}
@article {roine2015isotropic,
	title = {Isotropic non-white matter partial volume effects in constrained spherical deconvolution},
	journal = {Information-based methods for neuroimaging: analyzing structure, function and dynamics},
	year = {2015},
	pages = {112},
	publisher = {Frontiers Media SA},
	author = {Timo Roine and Ben Jeurissen and Daniele Perrone and Jan Aelterman and Alexander Leemans and Wilfried Philips and Jan Sijbers}
}
@article {1606,
	title = {An iterative CT reconstruction algorithm for fast fluid flow imaging},
	journal = {IEEE Transactions on Image Processing},
	volume = {24},
	year = {2015},
	pages = {4446-4458},
	abstract = {The study of fluid flow through solid matter by computed tomography (CT) imaging has many applications, ranging from petroleum and aquifer engineering to biomedical, manufacturing and environmental research. To avoid motion artifacts, current experiments are often limited to slow fluid flow dynamics. This severely limits the applicability of the technique.

In this paper, a new iterative CT reconstruction algorithm for improved temporal/spatial resolution in the imaging of fluid flow through solid matter is introduced. The proposed algorithm exploits prior knowledge in two ways. Firstly, the time-varying object is assumed to consist of stationary (the solid matter) and dynamic regions (the fluid flow). Secondly, the attenuation curve of a particular voxel in the dynamic region is modeled by a piecewise constant function over time, which is in accordance with the actual advancing fluid/air boundary.

Quantitative and qualitative results on different simulation experiments and a real neutron tomography dataset show that, in comparison state-of-the-art algorithms, the proposed algorithm allows reconstruction from substantially fewer projections per rotation without image quality loss. Therefore, temporal resolution can be substantially increased and thus fluid flow experiments with faster dynamics can be performed.},
	issn = {1057-7149},
	doi = {10.1109/TIP.2015.2466113},
	author = {Van Eyndhoven, Geert and Kees Joost Batenburg and Kazantsev, Daniil and Vincent Van Nieuwenhove and Lee, Peter D. and Katherine J Dobson and Jan Sijbers}
}
@conference {1588,
	title = {An iterative prior knowledge based reconstruction algorithm for increased temporal/spatial resolution in the CT imaging of fluid flow through solid matter},
	year = {2015},
	abstract = {The study of fluid flow through solid matter by computed tomography (CT) imaging has a broad range of applications, e.g., in oil production (the manner in which water displaces oil in rocks determines the fraction of oil that can be recovered) and in scientific research on fluid dynamics (validation of fluid flow models) [1-4]. In this work, a novel iterative computed tomography reconstruction algorithm for improved temporal/spatial resolution in the imaging of fluid flowing through solid matter is introduced. The proposed algorithm exploits prior knowledge in a twofold manner. Firstly, a dynamic reconstruction is generated assuming the existence of stationary regions (the solid matter) and dynamic regions (the fluid flow) throughout the reconstruction domain. This assumption is enforced by sharing iterative updates in the stationary regions over different time frames. Secondly, the fact that a particular voxel in the dynamic region can typically be described by a piecewise constant (PWC) function over time (i.e., the voxel consists of fluid or air) is exploited by estimating a PWC function in a robust manner in all voxels belonging to the dynamic region at intermediate iterations. The proposed reconstruction algorithm was validated on simulation data and on a real neutron tomography dataset (PSI ICON experiment, courtesy of Manchester X-ray Imaging Facility). These experiments demonstrated that the new iterative technique is able to significantly increase the temporal resolution in comparison to more conventional algorithms such as the Simultaneous Iterative Reconstruction Technique (SIRT) or Filtered Backprojection (FBP) [5-6].

[1]	V. Cnudde and M. Boone: High-resolution x-ray computed tomography in geosciences: A review of the current technology and applications. Earth-Science Reviews, vol. 123, pp. 1 {\textendash} 17, 2013
[2]	S. Akin and A. Kovscek: Computed tomography in petroleum engineering research. Geological Society, London, Special Publications, vol. 215, no. 1, pp. 23{\textendash}38, 2003
[3]	O. P. Wennberg, L. Rennan, and R. Basquet: Computed tomography scan imaging of natural open fractures in a porous rock; geometry and fluid flow. Geophysical Prospecting, vol. 57, no. 2, pp. 239{\textendash}249, 2009
[4]	M. Kumar, T. J. Senden, A. P. Sheppard, J. P. Middleton, and M. A. Knackstedt: Visualizing and quantifying the residual phase distribution in core material. Petrophysics, vol. 51, no. 5, p. 323, 2010
[5]	J. Gregor and T. Benson: Computational analysis and improvement of SIRT. IEEE Trans. Med. Imag., vol. 27, no. 7, pp. 918{\textendash}24, 2008
[6]	L. Feldkamp, L. Davis, and J. Kress: Practical cone-beam algorithm. J. Opt. Soc. Am. A, vol. 1, no. 6, pp. 612{\textendash}619, 1984
},
	author = {Van Eyndhoven, Geert and Kees Joost Batenburg and Kazantsev, Daniil and Jan Sijbers}
}
@article {1492,
	title = {Iterative Reweighted Linear Least Squares for Accurate, Fast, and Robust Estimation of Diffusion Magnetic Resonance Parameters},
	journal = {Magnetic Resonance in Medicine},
	volume = {73},
	year = {2015},
	pages = {2174{\textendash}2184},
	abstract = {Purpose: Diffusion-weighted magnetic resonance imaging suffers from physiological noise, such as artifacts caused by motion or system instabilities. Therefore, there is a need for robust diffusion parameter estimation techniques. In the past, several techniques have been proposed, including RESTORE and iRESTORE. However, these techniques are based on nonlinear estimators and are consequently computationally intensive.
Method: In this work, we present a new, robust, iteratively reweighted linear least squares (IRLLS) estimator. IRLLS performs a voxel-wise identification of outliers in diffusion-weighted magnetic resonance images, where it exploits the natural skewness of the data distribution to become more sensitive to both signal hyperintensities and signal dropouts. 
Results: Both simulations and real data experiments were conducted to compare IRLLS with other state-of-the-art techniques. While IRLLS showed no significant loss in accuracy or precision, it proved to be substantially faster than both RESTORE and iRESTORE. In addition, IRLLS proved to be even more robust when considering the overestimation of the noise level or when the signal-to-noise ratio is low. 
Conclusion: The substantially shortened calculation time in combination with the increased robustness and accuracy, make IRLLS a practical and reliable alternative to current state-of-theart
techniques for the robust estimation of diffusion-weighted magnetic resonance parameters.},
	keywords = {diffusion tensor imaging, MRI, outlier detection, robust, weighted linear least squares},
	doi = {10.1002/mrm.25351},
	author = {Quinten Collier and Jelle Veraart and Ben Jeurissen and Arnold Jan den Dekker and Jan Sijbers}
}
@inproceedings {1639,
	title = {Lossy Compression of hyperspectral images optimizing spectral unmixing},
	booktitle = {IEEE IGARSS 2015, International Geoscience and Remote Sensing Symposium, Milan, Italy, July 26-31},
	year = {2015},
	pages = {5031-034},
	author = {Azam Karami and Rob Heylen and Paul Scheunders}
}
@inproceedings {1622,
	title = {Lossy Compression of Hyperspectral Images Optimizing Spectral Unmixing},
	booktitle = {IGARSS  2015},
	year = {2015},
	address = {Milan, Italy},
	author = {Azam Karami and R.Heylen and Paul Scheunders}
}
@article {1611,
	title = {Measuring Lattice Strain in Three Dimensions through Electron Microscopy},
	journal = {Nano Letters},
	volume = {15},
	year = {2015},
	pages = {6996{\textendash}7001},
	doi = {10.1021/acs.nanolett.5b03008},
	author = {Bart Goris and Jan De Beenhouwer and Annick De Backer and Daniele Zanaga and Kees Joost Batenburg and Ana S{\'a}nchez-Iglesias and Luis M Liz-Marzán and Sandra Van Aert and Sara Bals and Jan Sijbers and Van Tendeloo, Gustaaf}
}
@mastersthesis {1631,
	title = {Model-based iterative reconstruction algorithms for computed tomography},
	volume = {PhD in Sciences},
	year = {2015},
	month = {12/2015},
	type = {PhD thesis},
	author = {Van Eyndhoven, Geert}
}
@article {1563,
	title = {Modeling blurring effects due to continuous gantry rotation: application to region of interest tomography},
	journal = {Medical Physics},
	volume = {42},
	year = {2015},
	pages = {2709-2717},
	doi = { http://dx.doi.org/10.1118/1.4914422},
	author = {Jeroen Cant and Willem Jan Palenstijn and Gert Behiels and Jan Sijbers}
}
@article {1540,
	title = {A multi-level preconditioned Krylov method for the efficient solution of algebraic tomographic reconstruction problems},
	journal = {Journal of Computational and Applied Mathematics},
	volume = {238},
	year = {2015},
	pages = {1-16},
	abstract = {Classical iterative methods for tomographic reconstruction include the class of Algebraic Reconstruction Techniques (ART). Convergence of these stationary linear iterative methods is however notably slow. In this paper we propose the use of Krylov solvers for tomographic linear inversion problems. These advanced iterative methods feature fast convergence at the expense of a higher computational cost per iteration, causing them to be generally uncompetitive without the inclusion of a suitable preconditioner. Combining elements from standard multigrid (MG) solvers and the theory of wavelets, a novel wavelet-based multi-level (WMG) preconditioner is introduced, which is shown to significantly speed-up Krylov convergence. The performance of the WMG-preconditioned Krylov method is analyzed through a spectral analysis, and the approach is compared to existing methods like the classical Simultaneous Iterative Reconstruction Technique (SIRT) and unpreconditioned Krylov methods on a 2D tomographic benchmark problem. Numerical experiments are promising, showing the method to be competitive with the classical Algebraic Reconstruction Techniques in terms of convergence speed and overall performance (CPU time) as well as precision of the reconstruction. },
	doi = {doi:10.1016/j.cam.2014.12.044},
	author = {Siegfried Cools and Pieter Ghysels and Wim Van Aarle and Jan Sijbers and Wim Vanroose}
}
@inproceedings {1576,
	title = {Neural Network Based X-Ray Tomography for Fast Inspection of Apples on a Conveyor Belt},
	booktitle = {IEEE International Conference on Image Processing},
	year = {2015},
	month = {Sept 21-27},
	pages = {917-921},
	doi = {10.1109/ICIP.2015.7350933},
	author = {Eline Janssens and Jan De Beenhouwer and Mattias Van Dael and Pieter Verboven and Bart Nicolai and Jan Sijbers}
}
@article {1529,
	title = {Nonlinear unmixing by using different metrics in a linear unmixing chain},
	journal = {IEEE-JSTARS, Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
	year = {2015},
	author = {Rob Heylen and Paul Scheunders and Paul Gader and A. Rangarajan}
}
@inproceedings {1636,
	title = {Nonlinear unmixing with a multilinear mixing model},
	booktitle = {IEEE Whispers 2015, Workshop on Hyperspectral Image and Signal Processing, June 2-5, Tokyo},
	year = {2015},
	author = {Rob Heylen and Paul Scheunders}
}
@article {1587,
	title = {Online Tomato Inspection Using X-Ray Radiographies and 3- Dimensional Shape Models},
	journal = {Chemical Engineering Transactions},
	volume = {44},
	year = {2015},
	pages = {37-42},
	isbn = {978-88-95608-35-8},
	doi = {10.3303/CET1544007},
	author = {Mattias Van Dael and Seppe Rogge and Pieter Verboven and Wouter Saeys and Jan Sijbers and Bart Nicolai}
}
@mastersthesis {1782,
	title = {Optical techniques for real-time morphology measurement of the tympanic membrane},
	year = {2015},
	type = {PhD thesis},
	author = {Sam Van der Jeught}
}
@conference {1561,
	title = {Partial discreteness: a new type of prior knowledge for MRI reconstruction},
	volume = {23},
	year = {2015},
	pages = {3417},
	author = {Gabriel Ramos-Llord{\'e}n and Segers, Hilde and Willem Jan Palenstijn and Arnold Jan den Dekker and Jan Sijbers}
}
@inproceedings {7351081,
	title = {Partially discrete magnetic resonance tomography},
	booktitle = {2015 IEEE International Conference on Image Processing (ICIP)},
	year = {2015},
	month = {Sept},
	pages = {1653-1657},
	keywords = {Bayes methods, Bayesian segmentation, Bayesian segmentation regularization, biomedical MRI, Breast, breast implant MR images, computerised tomography, Discrete tomography, image reconstruction, image representation, image segmentation, Implants, medical image processing, MR angiography images, MR image reconstruction, MRI, partially discrete magnetic resonance tomography, reconstruction, Tomography, TV},
	doi = {10.1109/ICIP.2015.7351081},
	author = {Gabriel Ramos-Llord{\'e}n and Segers, Hilde and Willem Jan Palenstijn and Arnold Jan den Dekker and Jan Sijbers}
}
@inproceedings {VLEUGELS2015,
	title = {Physical Evaluation of an Anthropometric Shape Model of the Human Scalp},
	booktitle = {Proceedings of the 6th International Conference on 3D Body Scanning Technologies, Lugano, Switzerland, 27-28 October},
	year = {2015},
	month = {oct},
	publisher = {Hometrica Consulting},
	organization = {Hometrica Consulting},
	doi = {10.15221/15.161},
	url = {http://dx.doi.org/10.15221/15.161},
	author = {Jochen Vleugels and Daniel Lacko and Guido De Bruyne and Toon Huysmans and Stijn Verwulgen}
}
@article {1496,
	title = {Pore REconstruction and Segmentation (PORES) method for improved porosity quantification of nanoporous materials},
	journal = {Ultramicroscopy},
	volume = {148},
	year = {2015},
	pages = {10-19},
	abstract = {Electron tomography is currently a versatile tool to investigate the connection between the structure and properties of nanomaterials. However, a quantitative interpretation of electron tomography results is still far from straightforward. Especially accurate quantification of pore-space is hampered by artifacts introduced in all steps of the processing chain, i.e., acquisition, reconstruction, segmentation and quantification. Furthermore, most common approaches require subjective manual user input. In this paper, the PORES algorithm ({\textquotedblleft}POre REconstruction and Segmentation{\textquotedblright}) is introduced; it is a tailor-made, integral approach, for the reconstruction, segmentation, and quantification of porous nanomaterials. The PORES processing chain starts by calculating a reconstruction with a nanoporous-specific reconstruction algorithm: the Simultaneous Update of Pore Pixels by iterative REconstruction and Simple Segmentation algorithm (SUPPRESS). It classifies the interior region to the pores during reconstruction, while reconstructing the remaining region by reducing the error with respect to the acquired electron microscopy data. The SUPPRESS reconstruction can be directly plugged into the remaining processing chain of the PORES algorithm, resulting in accurate individual pore quantification and full sample pore statistics. The proposed approach was extensively validated on both simulated and experimental data, indicating its ability to generate accurate statistics of nanoporous materials.},
	doi = {10.1016/j.ultramic.2014.08.008},
	author = {Van Eyndhoven, Geert and Mert Kurttepeli and C. J. {Van Oers} and Pegie Cool and Sara Bals and Kees Joost Batenburg and Jan Sijbers}
}
@conference {jeurissen2015processing,
	title = {Processing multi-shell diffusion MRI data using MRtrix3},
	year = {2015},
	author = {Ben Jeurissen}
}
@conference {Jeurissen2015-ge,
	title = {Processing multi-shell diffusion MRI data using MRtrix3},
	year = {2015},
	publisher = {Frontiers in Neuroinformatics},
	author = {Ben Jeurissen}
}
@article {1626,
	title = {Quantitative 3D analysis of huge nanoparticle assemblies},
	journal = {Nanoscale},
	volume = {8},
	year = {2015},
	pages = {292-299},
	doi = {10.1039/C5NR06962A},
	author = {Daniele Zanaga and Folkert Bleichrodt and Thomas Altantzis and Winckelmans, Naomi and Willem Jan Palenstijn and Jan Sijbers and B. van Nijs and Marijn A van Huis and van Blaaderen, Alfons and Ana S{\'a}nchez-Iglesias and Luis M Liz-Marzán and Kees Joost Batenburg and Sara Bals and Van Tendeloo, Gustaaf}
}
@inproceedings {1590,
	title = {Region based 4D tomographic image reconstruction: application to cardiac X-ray CT},
	booktitle = {IEEE International Conference on Image Processing},
	year = {2015},
	pages = {113 - 117},
	abstract = {X-ray computed tomography (CT) is a powerful tool for noninvasive cardiac imaging. However, radiation dose is a major issue. In this paper, we propose an iterative reconstruction method that reduces the radiation dose without compromising image quality. This is achieved by exploiting prior knowledge in two ways: the reconstructed object is assumed to consist of both stationary and dynamic regions over time and the dynamic region is assumed to have sparse structures after a proper sparsifying space-time transform. Experiments on simulation data and a real micro CT cardiac mouse dataset show that, with comparable image quality, the radiation dose can be substantially reduced compared to conventional acquisition/reconstruction protocols.},
	doi = {10.1109/ICIP.2015.7350770},
	author = {Van Eyndhoven, Geert and Kees Joost Batenburg and Jan Sijbers}
}
@conference {1537,
	title = {Simultaneous group-wise rigid registration and T1 ML estimation for T1 mapping},
	year = {2015},
	author = {Gabriel Ramos-Llord{\'e}n and Arnold Jan den Dekker and Gwendolyn Van Steenkiste and Johan Van Audekerke and Marleen Verhoye and Jan Sijbers}
}
@conference {1537,
	title = {Simultaneous group-wise rigid registration and T1 ML estimation for T1 mapping},
	volume = {23},
	year = {2015},
	pages = {447},
	author = {Gabriel Ramos-Llord{\'e}n and Arnold Jan den Dekker and Gwendolyn Van Steenkiste and Johan Van Audekerke and Marleen Verhoye and Jan Sijbers}
}
@inproceedings {7351386,
	title = {Simultaneous motion correction and T1 estimation in quantitative T1 mapping: An ML restoration approach},
	booktitle = {2015 IEEE International Conference on Image Processing (ICIP)},
	year = {2015},
	month = {Sept},
	pages = {3160-3164},
	keywords = {alignment, Approximation methods, biomedical MRI, image restoration, interpolation effect, Magnetic Resonance Imaging, maximum likelihood approach, maximum likelihood estimation, medical image processing, ML approach, ML restoration approach, motion estimation, motion model parameters, quantitative T1 mapping, registration, relaxometry, Rician channels, Signal to noise ratio, simultaneous motion correction, Standards, T1 estimation, T1 mapping, T1-weighted images, tissue spin-lattice relaxation time, voxel-wise estimation},
	doi = {10.1109/ICIP.2015.7351386},
	author = {Gabriel Ramos-Llord{\'e}n and Arnold Jan den Dekker and Gwendolyn Van Steenkiste and Johan Van Audekerke and Marleen Verhoye and Jan Sijbers}
}
@inproceedings {2043,
	title = {SPIE ProceedingsEnergy calibration of photon counting detectors using x-ray tube potential as a reference for material decomposition applications},
	booktitle = {SPIE Medical ImagingMedical Imaging 2015: Physics of Medical Imaging},
	volume = {9412},
	year = {2015},
	pages = {941214},
	publisher = {SPIE},
	organization = {SPIE},
	address = {Orlando, Florida, United States},
	doi = {10.1117/12.2082979},
	url = {http://proceedings.spiedigitallibrary.org/proceeding.aspx?doi=10.1117/12.2082979},
	author = {Das, Mini and Kandel, Bigyan and Park, Chan Soo and Zhihua Liang},
	editor = {Hoeschen, Christoph and Kontos, Despina and Flohr, Thomas G.}
}
@inproceedings {2044,
	title = {SPIE ProceedingsSingle-step, quantitative x-ray differential phase contrast imaging using spectral detection in a coded aperture setup},
	booktitle = {SPIE Medical ImagingMedical Imaging 2015: Physics of Medical Imaging},
	volume = {9412},
	year = {2015},
	pages = {941252},
	publisher = {SPIE},
	organization = {SPIE},
	address = {Orlando, Florida, United States},
	doi = {10.1117/12.2082977},
	url = {http://proceedings.spiedigitallibrary.org/proceeding.aspx?doi=10.1117/12.2082977},
	author = {Das, Mini and Zhihua Liang},
	editor = {Hoeschen, Christoph and Kontos, Despina and Flohr, Thomas G.}
}
@inproceedings {2042,
	title = {SPIE ProceedingsTowards using eye-tracking data to develop visual-search observers for x-ray breast imaging},
	booktitle = {SPIE Medical ImagingMedical Imaging 2015: Image Perception, Observer Performance, and Technology Assessment},
	volume = {9416},
	year = {2015},
	pages = {94160V},
	publisher = {SPIE},
	organization = {SPIE},
	address = {Orlando, Florida, United States},
	doi = {10.1117/12.2082978},
	url = {http://proceedings.spiedigitallibrary.org/proceeding.aspx?doi=10.1117/12.2082978},
	author = {Jiang, Zhengqiang and Zhihua Liang and Das, Mini and Gifford, Howard C.},
	editor = {Mello-Thoms, Claudia R. and Kupinski, Matthew A.}
}
@conference {1605,
	title = {Statistical shape modeling of the incudomalleolar complex using micro-CT and clinical cone-beam CT},
	year = {2015},
	month = {July},
	address = {Aalborg, Denmark},
	abstract = {Introduction: A large variation in material properties and geometry exists in the human temporal bone. These variations can affect hearing and middle ear sound transmission. Middle ear computer (finite element, FE) models are successfully used to predict sound transmission and its dependence on material properties. The model geometry, however, is mostly based on data of a single sample. Here we use statistical shape models (SSM) to characterize the natural anatomical variations present in the incudomalleolar (IM) complex of humans. SSM can later be used in FE models to study the effect of geometry on sound transmission, or parameters can be fitted to clinical CT data to obtain a patient-specific computer model.

Methods and Materials: In this study we combine data of high resolution micro-CT scans (uCT, 20 um resolution) of 6 human cadaveric temporal bones and clinical cone-beam CT scans (CBCT, 150 um resolution) of 100 patients. First, a dense correspondence between the uCT samples is obtained by pair-wise elasticity modulated registration of a reference sample to each of the remaining samples. A SSM is built from these corresponded scans using principal component analysis (PCA), describing the average shape and the main variations of the middle ear within the uCT population. Next this SSM is fitted to clinical CBCT data by elastic registration with the SSM as shape prior.

Results and conclusions: We will obtain an average geometrical model for malleus, incus and IM complex and characterize the deviations present in the patient population. We will do this by reporting natural variation of size and thickness of malleus head, neck and manubrium, the long and short process of the incus and relative angles in the IM complex.},
	author = {Joris Soons and Femke Danckaers and Toon Huysmans and Jan Sijbers and Jan W. Casselamn and Joris J. J. Dirckx}
}
@article {forde2015structural,
	title = {Structural brain network analysis in families multiply affected with bipolar I disorder},
	journal = {Psychiatry Research: Neuroimaging},
	volume = {234},
	number = {1},
	year = {2015},
	pages = {44{\textendash}51},
	publisher = {Elsevier},
	author = {Forde, Natalie J and O{\textquoteright}Donoghue, Stefani and Scanlon, Cathy and Louise Emsell and Chaddock, Chris and Alexander Leemans and Ben Jeurissen and Gareth J. Barker and Dara M. Cannon and Murray, Robin M and others}
}
@article {1535,
	title = {Subcortical volumetric changes across the adult lifespan: subregional thalamic atrophy accounts for age-related sensorimotor performance declines},
	journal = {Cortex},
	volume = {65},
	year = {2015},
	pages = {128-138},
	doi = { 10.1016/j.cortex.2015.01.003},
	author = {L. Serbruyns and Inge Leunissen and Toon Huysmans and K. Cuypers and R. L. Meesen and P. van Ruitenbeek and Jan Sijbers and S. P. Swinnen}
}
@conference {1520,
	title = {Super-resolution structural connectivity and anatomy of the zebra finch brain },
	year = {2015},
	author = {Gwendolyn Van Steenkiste and Hamaide, Julie and Ben Jeurissen and Dirk H J Poot and Johan Van Audekerke and Jan Sijbers and Marleen Verhoye}
}
@conference {1596,
	title = {Super-resolution T1 mapping: a simulation study},
	volume = {23},
	year = {2015},
	pages = {1679},
	author = {Gwendolyn Van Steenkiste and Dirk H J Poot and Ben Jeurissen and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {1545,
	title = {Theoretical study of the free water elimination model},
	volume = {23},
	year = {2015},
	pages = {2757},
	abstract = {Partial volume effects caused by cerebrospinal fluid are an important issue in diffusion MRI. In this work, we study the free water elimination model by analyzing the Cram{\'e}r-Rao lower bound (CRLB) of its parameters. We show that through optimizing the acquisition protocol by minimizing the trace of the CRLB, a significant gain in the precision of the parameter estimation can be achieved. Moreover, further analysis indicates that regularization and/or constraints are necessary for parameter estimation from voxels with large CSF fractions and/or low FA values. These theoretical findings are confirmed by both simulation and real data experiments.},
	author = {Quinten Collier and Jelle Veraart and Ben Jeurissen and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {Collier2015-pa,
	title = {Theoretical study of the free water elimination model},
	volume = {15},
	year = {2015},
	pages = {2757},
	author = {Collier, Quinten and Veraart, Jelle and Ben Jeurissen and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {1544,
	title = {Theoretical study of the free water elimination model},
	year = {2015},
	pages = {78},
	abstract = {Partial volume effects caused by cerebrospinal fluid are an important issue in diffusion MRI. In this work, we study the free water elimination model by analyzing the Cram{\'e}r-Rao lower bound (CRLB) of its parameters. We show that through optimizing the acquisition protocol by minimizing the trace of the CRLB, a significant gain in the precision of the parameter estimation can be achieved. Moreover, further analysis indicates that regularization and/or constraints are necessary for parameter estimation from voxels with large CSF fractions and/or low FA values. These theoretical findings are confirmed by both simulation and real data experiments.},
	author = {Quinten Collier and Jelle Veraart and Ben Jeurissen and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {1662,
	title = {Time to Move On: An FOD-Based DEC Map to Replace DTI{\textquoteright}s Trademark DEC FA},
	year = {2015},
	pages = {1027},
	author = {Thijs Dhollander and Robert Elton Smith and Jacques-Donald Tournier and Ben Jeurissen and Alan Connelly}
}
@conference {1595,
	title = {Tissue-type segmentation using non-negative matrix factorization of multi-shell diffusion-weighted MRI images},
	volume = {23},
	year = {2015},
	pages = {349},
	author = {Ben Jeurissen and Jacques-Donald Tournier and Jan Sijbers}
}
@article {1998,
	title = {Tomography for in-line product inspection},
	number = {EP3106863A1},
	year = {2015},
	chapter = {EP3106863A1},
	author = {Thomas De Schryver and Manuel Dierick and Luc Van Hoorebeke and Pieter Verboven and Mattias Van Dael and Jan Sijbers}
}
@mastersthesis {1632,
	title = {Towards In Loco X-ray Computed Tomography},
	year = {2015},
	month = {12/2015},
	type = {PhD thesis},
	author = {Andrei Dabravolski}
}
@inproceedings {1635,
	title = {Two-stage fusion of thermal hyperspectral and visible RGB image by PCA and guided filter},
	booktitle = {IEEE Whispers 2015, Workshop on Hyperspectral Image and Signal Processing, June 2-5, Tokyo},
	year = {2015},
	author = {W. Liao and F. Huang and F. v. Coillie and Guy Thoonen and Aleksandra Pizurica and Paul Scheunders and Wilfried Philips}
}
@inproceedings {1499,
	title = {3D imaging of semiconductor components by discrete laminography},
	booktitle = {Stress induced phenomena and reliability in 3D microelectronics: AIP Conference Proceedings},
	volume = {1601},
	year = {2014},
	pages = {168-179},
	doi = { http://dx.doi.org/10.1063/1.4881350},
	author = {Kees Joost Batenburg and Willem Jan Palenstijn and Jan Sijbers}
}
@conference {1607,
	title = {Accurate reconstruction of porous samples in CT},
	year = {2014},
	month = {2014},
	abstract = {Quantification of a sample{\textquoteright}s porosity from computed tomography (CT) images is important in many industrial applications. However, in non-standard acquisition set-ups (e.g., limited data or angular range), the reconstruction and subsequent segmentation of pore-space is challenging. In this paper, a novel algorithm exploiting prior knowledge about the porous structure of the scanned is proposed, improving the quality of both reconstruction and segmentation.},
	author = {Van Eyndhoven, Geert and Kees Joost Batenburg and Jan Sijbers}
}
@article {1424,
	title = {Adaptive zooming in X-ray computed tomography},
	journal = {Journal of X-Ray Science and Technology},
	volume = {22},
	number = {1},
	year = {2014},
	pages = {77-89},
	keywords = {Acquisition geometry, adaptive zooming, computed tomography, prior information},
	doi = {10.3233/XST-130410},
	author = {Andrei Dabravolski and Kees Joost Batenburg and Jan Sijbers}
}
@mastersthesis {2368,
	title = {Advances in unmixing of hyperspectral remote sensing imagery},
	year = {2014},
	type = {PhD thesis},
	author = {Dzevdet Burazerovic}
}
@article {1471,
	title = {Aligning Projection Images from Binary Volumes},
	journal = {Fundamenta Informaticae},
	volume = {135},
	year = {2014},
	pages = {1-22},
	doi = {10.3233/FI-2014-1090},
	author = {Folkert Bleichrodt and Jan De Beenhouwer and Jan Sijbers and Kees Joost Batenburg}
}
@article {1421,
	title = {The anatomy of the clavicle: A Three-dimensional Cadaveric Study},
	journal = {Clinical anatomy},
	volume = {27},
	year = {2014},
	month = {2013 Oct 21},
	pages = {712{\textendash}723},
	abstract = {The clavicle has a complex osteologic structure that makes morphological analysis extremely difficult. A three-dimensional study was conducted to examine the anatomical variations and characteristics of the bone. Sixty-eight human cadaver clavicles were dissected, CAT-scanned, and reconstructed. An automated parameterization and correspondence shape analysis system was developed. A new length, designated as centerline (CL) length, was defined and measured. This length represents the true length of the clavicle. The endpoint length was measured as the distance between two endpoints. The width and curvature were measured in the axial (AX) and frontal (FR) plane and defined along the CL. Next gender and side characteristics and variations were examined. The mean CL length was 159.0 {\textpm} 11.0 mm. The mean endpoint length was 149.4 {\textpm} 10.3 mm, which was statistically significantly shorter than the CL. The male clavicle was significantly longer (166.8 {\textpm} 7.3 mm vs. 151.0 {\textpm} 8.2 mm), wider (14.6 {\textpm} 1.5 mm vs. 12.7 {\textpm} 1.3 mm lateral FR plane, 25.9 {\textpm} 4.1 mm vs. 23.5 {\textpm} 3.0 mm lateral AX plane and 24.7 {\textpm} 2.8 mm vs. 22.8 {\textpm} 2.8 mm medial AX plane), and more curved (10.8 {\textpm} 2.8 mm vs. 8.6 {\textpm} 2.3 mm medial and 10.5 {\textpm} 3.3 mm vs. 9.1 {\textpm} 2.5 mm lateral) than the female one. Left clavicles were significant longer (159.8 {\textpm} 10.9 mm vs. 158.0 {\textpm} 11.2 mm) than right clavicles. A novel three-dimensional system was developed, used and tested in order to explore the anatomical variations and characteristics of the human clavicle. This information, together with the automated system, can be applied to future clavicle populations and to the design of fixation plates for clavicle fractures. Clin. Anat., 2013. {\textcopyright} 2013 Wiley Periodicals, Inc.},
	issn = {1098-2353},
	doi = {10.1002/ca.22288},
	author = {Amit Bernat and Toon Huysmans and Francis Van Glabbeek and Jan Sijbers and Gielen, Jan and Van Tongel, Alexander}
}
@conference {1487,
	title = {An Anthropometric Shape Model for the Design of Sports Helmets},
	year = {2014},
	author = {Daniel Lacko and Toon Huysmans and Annelies Claeskens and Stijn Verwulgen and Peter Aerts and Jan Sijbers and Guido De Bruyne}
}
@inproceedings {1501,
	title = {Anthropometrics 2.0: Enrichment of Classical Anthropometry through Multidisciplinary Collaboration},
	booktitle = {6th International Conference on Engineering \& Product Design Education.},
	year = {2014},
	author = {Stijn Verwulgen and Daniel Lacko and Guido De Bruyne and Femke Danckaers and Naomi Christis and Jan Sijbers and Toon Huysmans}
}
@article {2045,
	title = {Approximated transport-of-intensity equation for coded-aperture x-ray phase-contrast imaging},
	journal = {Optics Letters},
	volume = {39},
	year = {2014},
	month = {Jan-01-2014},
	pages = {5395},
	issn = {0146-9592},
	doi = {10.1364/OL.39.005395},
	url = {https://www.osapublishing.org/abstract.cfm?URI=ol-39-18-5395https://www.osapublishing.org/viewmedia.cfm?URI=ol-39-18-5395\&seq=0},
	author = {Das, Mini and Zhihua Liang}
}
@article {1484,
	title = {Automated correction of improperly rotated diffusion gradient orientations in diffusion-weighted MRI},
	journal = {Medical Image Analysis},
	volume = {18},
	number = {7},
	year = {2014},
	pages = {953-962},
	doi = { 10.1016/j.media.2014.05.012},
	author = {Ben Jeurissen and Alexander Leemans and Jan Sijbers}
}
@inproceedings {1533,
	title = {Automated Social behaviour Recognition At Low Resolution},
	booktitle = {ICPR14, International Conference on Pattern Recognition, Stockholm, Sweden},
	year = {2014},
	author = {Tanmay, Nath and G. Liu and Bassem Hassan and Barbara Weyn and Steve De Backer and Paul Scheunders}
}
@inproceedings {1453,
	title = {Automated Social behaviour Recognition At Low Resolution},
	booktitle = {22nd IEEE - International Conference of Pattern Recognition (ICPR)},
	year = {2014},
	author = {Tanmay Nath and G.Liu and Barbara Weyn and Bassem Hassan and Steve De Backer and Paul Scheunders}
}
@conference {1474,
	title = {Brain Tissue Types Resolved Using Spherical Deconvolution of Multi-Shell Diffusion MRI Data},
	year = {2014},
	month = {May},
	pages = {973},
	address = {Milan, Italy},
	author = {Ben Jeurissen and Jacques-Donald Tournier and Thijs Dhollander and Alan Connelly and Jan Sijbers}
}
@article {1504,
	title = {Combined Estimation of Affine Movement and Reconstruction in  Tomography},
	year = {2014},
	publisher = {3D Materials Science Conference},
	author = {Vincent Van Nieuwenhove and Van Eyndhoven, Geert and Jan De Beenhouwer and Jan Sijbers}
}
@conference {1503,
	title = {Compensation of affine deformations in fan and cone beam projections},
	year = {2014},
	pages = {187-189},
	author = {Vincent Van Nieuwenhove and Van Eyndhoven, Geert and Jan De Beenhouwer and Jan Sijbers}
}
@inproceedings {1500,
	title = {Conveyor belt X-ray CT using Domain Constrained Discrete Tomography},
	booktitle = {Sibgrapi conference on Graphics, Patterns and Images},
	year = {2014},
	pages = {290 - 297},
	doi = {10.1109/SIBGRAPI.2014.21},
	author = {Luis Filipe Alves Pereira and Andrei Dabravolski and Ing Ren Tsang and George D C Cavalcanti and Jan Sijbers}
}
@inproceedings {1455,
	title = {Correspondence Preserving Elastic Surface Registration with Shape Model Prior},
	booktitle = {International Conference of Pattern Recognition},
	volume = {22},
	year = {2014},
	pages = {2143-2148},
	abstract = {In this paper, we describe a framework for surface
registration. The framework consists of a combination of rigid
registration, elasticity modulated registration and the use of a
shape model prior. The main goal in this paper is to mini-
mize the geometric surface registration error while maintain-
ing correspondences. Experiments show improved geometric fit,
correspondence, and timing compared to the current state of
the art. Possible applications of the framework are construction
of correspondences for shape models, reconstruction of missing
parts, and artifact reduction.},
	doi = {10.1109/ICPR.2014.373},
	author = {Femke Danckaers and Toon Huysmans and Daniel Lacko and A. Ledda and Stijn Verwulgen and Van Dongen, Stefan and Jan Sijbers}
}
@article {1476,
	title = {Data distributions in magnetic resonance images: a review},
	journal = {Physica Medica},
	volume = {30},
	year = {2014},
	pages = {725{\textendash}741},
	doi = {http://dx.doi.org/10.1016/j.ejmp.2014.05.002},
	author = {Arnold Jan den Dekker and Jan Sijbers}
}
@article {1525,
	title = {A distance geometric framework for non-linear hyperspectral unmixing},
	journal = {IEEE-JSTARS, Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
	volume = {7},
	number = {6},
	year = {2014},
	pages = {1879-1888},
	author = {Rob Heylen and Paul Scheunders}
}
@article {1418,
	title = {Dynamic Angle Selection in X-ray Computed Tomography},
	journal = {Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms},
	volume = {324},
	year = {2014},
	pages = {17-24},
	keywords = {Angle selection, computed tomography, dynamic imaging, information gain},
	doi = {10.1016/j.nimb.2013.08.077},
	author = {Andrei Dabravolski and Kees Joost Batenburg and Jan Sijbers}
}
@article {1488,
	title = {Evaluation of prominence of straight plates and precontoured clavicle plates using automated plate-to-bone fitting},
	journal = {Acta Orthopaedica Belgica},
	volume = {80},
	year = {2014},
	pages = {301-308},
	author = {Van Tongel, Alexander and Toon Huysmans and Amit Bernat and Iwein Piepers and Jan Sijbers and Francis Van Glabbeek and Lieven De Wilde}
}
@article {1678,
	title = {Filter for Tomographic Reconstructions},
	number = {WO2013011031A1},
	year = {2014},
	chapter = {WO2013011031A1},
	author = {Kees Joost Batenburg and Jan Sijbers and Linda Plantagie}
}
@inproceedings {1531,
	title = {Geometric matched filter for hyperspectral partial unmixing},
	booktitle = {IEEE-Whispers 2014, Workshop on Hyperspectral Image and Signal Processing, Lausanne, Suisse},
	year = {2014},
	author = {Muhamed Awais Akhter and Rob Heylen and Paul Scheunders}
}
@inproceedings {1543,
	title = {GPGPU and MIC in accelerated cluster for remote sensed image processing software},
	booktitle = {Conference on Big Data from Space (BiDS), 12-14 November, ESRIN, Frascati, Italy},
	year = {2014},
	author = {Olivier Melet and Toon Huysmans and Michel Hummel and Pierre-Marie Brunet}
}
@inproceedings {1532,
	title = {Hyperspectral Image Noise Reduction and its Effect on  Spectral Unmixing},
	booktitle = {IEEE-Whispers 2014, Workshop on Hyperspectral Image and Signal Processing, Lausanne, Suisse},
	year = {2014},
	author = {Azam Karami and Rob Heylen and Paul Scheunders}
}
@inproceedings {1569,
	title = {Hyperspectral Image Noise Reduction and its Effect on  Spectral Unmixing},
	booktitle = {IEEE-Whispers 2014, Workshop on Hyperspectral Image and Signal Processing},
	year = {2014},
	address = { Lausanne, Suisse},
	author = {Azam Karami and Rob Heylen and Paul Scheunders}
}
@inproceedings {1534,
	title = {Hyperspectral unmixing using an active set algorithm},
	booktitle = {IEEE ICIP 2014, International Conference on Image Processing, October 27-30, Paris, France },
	year = {2014},
	author = {Rob Heylen and Paul Scheunders}
}
@conference {roine2014ismrm,
	title = {Improving fiber orientation estimation in constrained spherical deconvolution under non-white matter partial volume effects},
	year = {2014},
	month = {05/2014},
	pages = {4492},
	author = {Timo Roine and Ben Jeurissen and Wilfried Philips and Alexander Leemans and Jan Sijbers}
}
@conference {1486,
	title = {Influence of Correspondence Method on Statistical Model Based Shape Prediction},
	year = {2014},
	author = {Christophe Van Dijck and Wirix-Speetjens, Roel and Toon Huysmans and Femke Danckaers and Jan Sijbers and Vander Sloten, Jos}
}
@inproceedings {1485,
	title = {Influence of Correspondence Method on Statistical Model Based Shape Prediction},
	booktitle = {IEEE International Symposium on Biomedical Imaging (ISBI)},
	year = {2014},
	author = {Christophe Van Dijck and Wirix-Speetjens, Roel and Toon Huysmans and Femke Danckaers and Jan Sijbers and Vander Sloten, Jos}
}
@conference {1443,
	title = {Isotropic non-white matter partial volume effects in constrained spherical deconvolution},
	year = {2014},
	author = {Timo Roine and Ben Jeurissen and Wilfried Philips and Alexander Leemans and Jan Sijbers}
}
@article {roine2014frontiers,
	title = {Isotropic non-white matter partial volume effects in constrained spherical deconvolution},
	journal = {Frontiers in Neuroinformatics},
	volume = {8},
	number = {28},
	year = {2014},
	month = {03/2014},
	pages = {1-9},
	publisher = {Frontiers},
	type = {Original Research},
	abstract = {Diffusion-weighted (DW) magnetic resonance imaging (MRI) is a noninvasive imaging method, which can be used to investigate neural tracts in the white matter (WM) of the brain. Significant partial volume effects (PVE) are present in the DW signal due to relatively large voxel sizes. These PVEs can be caused by both non-WM tissue, such as gray matter (GM) and cerebrospinal fluid (CSF), and by multiple nonparallel WM fiber populations. High angular resolution diffusion imaging (HARDI) methods have been developed to correctly characterize complex WM fiber configurations, but to date, many of the HARDI methods do not account for non-WM PVEs. 

In this work, we investigated the isotropic PVEs caused by non-WM tissue in WM voxels on fiber orientations extracted with constrained spherical deconvolution (CSD). Experiments were performed on simulated and real DW-MRI data. In particular, simulations were performed to demonstrate the effects of varying the diffusion weightings, signal-to-noise ratios (SNR), fiber configurations, and tissue fractions. 

Our results show that the presence of non-WM tissue signal causes a decrease in the precision of the detected fiber orientations and an increase in the detection of false peaks in CSD. We estimated 35-50 \% of WM voxels to be affected by non-WM PVEs. For HARDI sequences, which typically have a relatively high degree of diffusion weighting, these adverse effects are most pronounced in voxels with GM PVEs. The non-WM PVEs become severe with 50 \% GM volume for maximum spherical harmonics orders of 8 and below, and already with 25 \% GM volume for higher orders. In addition, a low diffusion weighting or SNR increases the effects. 

The non-WM PVEs may cause problems in connectomics, where reliable fiber tracking at the WM-GM interface is especially important. We suggest acquiring data with high diffusion-weighting 2500-3000 s/mm2, reasonable SNR (~30) and using lower SH orders in GM contaminated regions to minimize the non-WM PVEs in CSD.
},
	keywords = {constrained spherical deconvolution, Diffusion MRI, fiber orientation, gray matter, partial volume effect},
	doi = {10.3389/fninf.2014.00028},
	url = {http://journal.frontiersin.org/Journal/10.3389/fninf.2014.00028/abstract},
	author = {Timo Roine and Ben Jeurissen and Daniele Perrone and Jan Aelterman and Alexander Leemans and Wilfried Philips and Jan Sijbers}
}
@article {1481,
	title = {Iterative bilateral filter for Rician noise reduction in MR images},
	journal = {Signal, Image and Video Processing },
	year = {2014},
	doi = {10.1007/s11760-013-0611-6},
	author = {R. Riji and Jeny Rajan and Jan Sijbers and Madhu S. Nair}
}
@booklet {1538,
	title = {Joint motion correction and estimation for T1 mapping: proof of concept},
	howpublished = {Medical Imaging Summer School 2014, Favignana, Italy},
	year = {2014},
	author = {Gabriel Ramos-Llord{\'e}n and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {1449,
	title = {A local enhancement based tomographic reconstruction technique for radiation exposure reduction in cerebral perfusion computed tomography},
	year = {2014},
	doi = {10.1594/ecr2014/C-0282},
	url = {http://posterng.netkey.at/esr/online_viewing/index.php?module=view_postercoverpage\&task=viewcoverpage\&start=0\&ls=authorlist\&pi=121333\&perid=248156\&num=1\&count=1\&cid=515},
	author = {Van Eyndhoven, Geert and Kees Joost Batenburg and Jan Sijbers}
}
@article {1473,
	title = {A memory efficient method for fully three-dimensional object reconstruction with HAADF STEM Ultramicroscopy},
	journal = {Ultramicroscopy},
	volume = {141},
	year = {2014},
	pages = {22{\textendash}31},
	doi = {http://dx.doi.org/10.1016/j.ultramic.2014.03.008},
	author = {Wouter Van den Broek and A. Rosenauer and Jan Sijbers and Dirk Van Dyck and Sandra Van Aert}
}
@article {1502,
	title = {A Multiresolution Approach to Discrete Tomography Using DART},
	journal = {PLoS ONE},
	volume = {9},
	year = {2014},
	doi = {10.1371/journal.pone.0106090},
	url = {http://www.plosone.org/article/info\%3Adoi\%2F10.1371\%2Fjournal.pone.0106090},
	author = {Andrei Dabravolski and Kees Joost Batenburg and Jan Sijbers}
}
@conference {1457,
	title = {A multiresolution approach to the Discrete Algebraic Reconstruction Technique (DART)},
	year = {2014},
	month = {07/2014},
	keywords = {computed tomography, DART, Discrete tomography, prior information},
	author = {Andrei Dabravolski and Kees Joost Batenburg and Jan Sijbers}
}
@article {1494,
	title = {Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data},
	journal = {NeuroImage},
	volume = {103},
	year = {2014},
	pages = {411{\textendash}426},
	abstract = {Constrained spherical deconvolution (CSD) has become one of the most widely used methods to extract white matter (WM) fibre orientation information from diffusion-weighted MRI (DW-MRI) data, overcoming the crossing fibre limitations inherent in the diffusion tensor model. It is routinely used to obtain high quality fibre orientation distribution function (fODF) estimates and fibre tractograms and is increasingly used to obtain apparent fibre density (AFD) measures. Unfortunately, CSD typically only supports data acquired on a single shell in q-space. With multi-shell data becoming more and more prevalent, there is a growing need for CSD to fully support such data. Furthermore, CSD can only provide high quality fODF estimates in voxels containing WM only. In voxels containing other tissue types such as grey matter (GM) and cerebrospinal fluid (CSF), the WM response function may no longer be appropriate and spherical deconvolution produces unreliable, noisy fODF estimates.

The aim of this study is to incorporate support for multi-shell data into the CSD approach as well as to exploit the unique b-value dependencies of the different tissue types to estimate a multi-tissue ODF. The resulting approach is dubbed multi-shell, multi-tissue CSD (MSMT-CSD) and is compared to the state-of-the-art single-shell, single-tissue CSD (SSST-CSD) approach. Using both simulations and real data, we show that MSMT-CSD can produce reliable WM/GM/CSF volume fraction maps, directly from the DW data, whereas SSST-CSD has a tendency to overestimate the WM volume in voxels containing GM and/or CSF. In addition, compared to SSST-CSD, MSMT-CSD can substantially increase the
precision of the fODF fibre orientations and reduce the presence of spurious fODF peaks in voxels containing GM and/or CSF. Both effects translate into more reliable AFD measures and tractography results with MSMT-CSD compared to SSST-CSD.},
	doi = {10.1016/j.neuroimage.2014.07.061},
	author = {Ben Jeurissen and Jacques-Donald Tournier and Thijs Dhollander and Alan Connelly and Jan Sijbers}
}
@article {1472,
	title = {Neutron radiography and tomography applied to fuel degradation during ramp tests and loss of coolant accident tests in a research reactor},
	journal = {Progress in Nuclear Energy},
	volume = {72},
	year = {2014},
	pages = {55-62},
	doi = {http://dx.doi.org/10.1016/j.pnucene.2013.11.001},
	author = {Hakon Kristian Jenssen and B.C. Oberlander and Jan De Beenhouwer and Jan Sijbers and M. Verwerft}
}
@article {1441,
	title = {A new non local maximum likelihood estimation method for Rician noise reduction in Magnetic Resonance images using the Kolmogorov-Smirnov test},
	journal = {Signal Processing},
	volume = {103},
	year = {2014},
	pages = {16-23},
	doi = {http://dx.doi.org/10.1016/j.sigpro.2013.12.018},
	author = {Jeny Rajan and Arnold Jan den Dekker and Jan Sijbers}
}
@inproceedings {1530,
	title = {Nonlinear unmixing by using non-Euclidean metrics in a linear unmixing chain},
	booktitle = {IEEE-Whispers 2014, Workshop on Hyperspectral Image and Signal Processing, Lausanne, Suisse},
	year = {2014},
	author = {Rob Heylen and Paul Scheunders and A. Rangarajan and Paul Gader}
}
@conference {1559,
	title = {A Novel Method for Realistic DWI Data Generation},
	volume = {22},
	year = {2014},
	pages = {4427},
	address = {Milan, Italy},
	author = {Daniele Perrone and Jan Aelterman and Ben Jeurissen and Aleksandra Pizurica and Wilfried Philips and Jan Sijbers}
}
@article {1497,
	title = {Optimal experimental design for the detection of light atoms from high-resolution scanning transmission electron microscopy images},
	journal = {Applied Physics Letters},
	volume = {105},
	year = {2014},
	doi = {10.1063/1.4892884},
	author = {Julie Gonnissen and Annick De Backer and Arnold Jan den Dekker and G T Martinez and A. Rosenauer and Jan Sijbers and Sandra Van Aert}
}
@conference {1617,
	title = {Post-processing of diffusion-weighted MR data lowers the accuracy of the weighted linear least squares estimator },
	volume = {22},
	year = {2014},
	pages = {2573},
	author = {Jelle Veraart and Jan Sijbers}
}
@article {1480,
	title = {The reconstructed residual error: A novel segmentation evaluation measure for reconstructed images in tomography},
	journal = {Computer Vision and Image Understanding},
	volume = {126},
	year = {2014},
	pages = {28-37},
	doi = {10.1016/j.cviu.2014.05.007},
	author = {Roelandts, Tom and Kees Joost Batenburg and Arnold Jan den Dekker and Jan Sijbers}
}
@article {1547,
	title = {Recursive calibration of the fiber response function for spherical deconvolution of diffusion MRI data.},
	journal = {NeuroImage},
	volume = {86},
	year = {2014},
	month = {2014 Feb 1},
	pages = {67-80},
	abstract = {There is accumulating evidence that at current acquisition resolutions for diffusion-weighted (DW) MRI, the vast majority of white matter voxels contains "crossing fibers", referring to complex fiber configurations in which multiple and distinctly differently oriented fiber populations exist. Spherical deconvolution based techniques are appealing to characterize this DW intra-voxel signal heterogeneity, as they provide a balanced trade-off between constraints on the required hardware performance and acquisition time on the one hand, and the reliability of the reconstructed fiber orientation distribution function (fODF) on the other hand. Recent findings, however, suggest that an inaccurate calibration of the response function (RF), which represents the DW signal profile of a single fiber orientation, can lead to the detection of spurious fODF peaks which, in turn, can have a severe impact on tractography results. Currently, the computation of this RF is either model-based or estimated from selected voxels that have a fractional anisotropy (FA) value above a predefined threshold. For both approaches, however, there are user-defined settings that affect the RF and, consequently, fODF estimation and tractography. Moreover, these settings still rely on the second-rank diffusion tensor, which may not be the appropriate model, especially at high b-values. In this work, we circumvent these issues for RF calibration by excluding "crossing fibers" voxels in a recursive framework. Our approach is evaluated with simulations and applied to in vivo and ex vivo data sets with different acquisition settings. The results demonstrate that with the proposed method the RF can be calibrated in a robust and automated way without needing to define ad-hoc FA threshold settings. Our framework facilitates the use of spherical deconvolution approaches in data sets in which it is not straightforward to define RF settings a priori.},
	keywords = {Adult, Algorithms, Brain, Calibration, diffusion tensor imaging, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Imaging, Three-Dimensional, Male, Nerve Fibers, Myelinated, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity},
	issn = {1095-9572},
	doi = {10.1016/j.neuroimage.2013.07.067},
	author = {Chantal M W Tax and Ben Jeurissen and Vos, Sjoerd B. and Viergever, Max A. and Alexander Leemans}
}
@article {1436,
	title = {Region-based iterative reconstruction of structurally changing objects in CT},
	journal = {IEEE Transactions on Image Processing},
	volume = {23},
	year = {2014},
	pages = {909-919},
	chapter = {909-919},
	doi = {10.1109/TIP.2013.2297024},
	author = {Van Eyndhoven, Geert and Kees Joost Batenburg and Jan Sijbers}
}
@conference {1454,
	title = {Reliable Pore-size Measurements Based on a Procedure Specifically Designed for Electron Tomography Measurements of Nanoporous Samples},
	year = {2014},
	author = {Van Eyndhoven, Geert and Kees Joost Batenburg and Cynthia Van Oers and Mert Kurttepeli and Sara Bals and Pegie Cool and Jan Sijbers}
}
@inproceedings {1521,
	title = {Spectral adaptation of hyperspectral flight lines using VHR contextual information},
	booktitle = {Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International},
	year = {2014},
	pages = {2953-2956},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Quebec City, QC, Canada},
	abstract = {Due to technological constraints, hyperspectral earth observation imagery are often a mosaic of overlapping flight lines collected in different passes over the area of interest. This causes variations in aqcuisition conditions such that the reflected spectrum can vary significantly between these flight lines. Partly, this problem is solved by atmospherical correction, but residual spectral differences often remain. A probabilistic domain adaptation framework based on graph matching using Hidden Markov Random Fields was recently proposed for transforming hyperspectral data from one image to better correspond to the other. This paper investigates the use of scale and angle invariant textural features for improving the performance of the used Hidden Markov Random Field matching framework in the case of hyperspectral flight lines. These textural features are derived from the filtering of VHR optical imagery with a bank of Gabor filters with varying orientation, scale and frequency and subsequently rendering them invariant to scale and frequency by applying the 2D DFT on the filter responses in the scale and frequency space.},
	doi = {10.1109/IGARSS.2014.6947096},
	url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6947096},
	author = {Jan-Pieter Jacobs and Guy Thoonen and Devis Tuia and Gustavo Camps-Valls and Pieter Kempeneers and Paul Scheunders}
}
@article {1527,
	title = {A spectral-unmixing approach to estimate water-mass concentrations in case-II waters},
	journal = {IEEE-JSTARS, Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
	volume = {7},
	number = {8},
	year = {2014},
	author = {Dzevdet Burazerovic and Rob Heylen and Dries Raymaekers and Els Knaeps and Paul Scheunders}
}
@conference {1554,
	title = {Structural brain network analysis in families multiply affected with bipolar 1 disorder},
	year = {2014},
	address = {Athens, Greece},
	author = {Forde, Natalie J and Scanlon, Cathy and Louise Emsell and O{\textquoteright}Donoghue, S and Chaddock, Chris and Alexander Leemans and Ben Jeurissen and Dara M. Cannon and Murray, R M and Colm McDonald}
}
@article {1546,
	title = {Structural neuroimaging correlates of allelic variation of the BDNF val66met polymorphism.},
	journal = {NeuroImage},
	volume = {90},
	year = {2014},
	month = {2014 Apr 15},
	pages = {280-9},
	abstract = {BACKGROUND: The brain-derived neurotrophic factor (BDNF) val66met polymorphism is associated with altered activity dependent secretion of BDNF and a variable influence on brain morphology and cognition. Although a met-dose effect is generally assumed, to date the paucity of met-homozygotes have limited our understanding of the role of the met-allele on brain structure.

METHODS: To investigate this phenomenon, we recruited sixty normal healthy subjects, twenty in each genotypic group (val/val, val/met and met/met). Global and local morphology were assessed using voxel based morphometry and surface reconstruction methods. White matter organisation was also investigated using tract-based spatial statistics and constrained spherical deconvolution tractography.

RESULTS: Morphological analysis revealed an "inverted-U" shaped profile of cortical changes, with val/met heterozygotes most different relative to the two homozygous groups. These results were evident at a global and local level as well as in tractography analysis of white matter fibre bundles.

CONCLUSION: In contrast to our expectations, we found no evidence of a linear met-dose effect on brain structure, rather our results support the view that the heterozygotic BDNF val66met genotype is associated with cortical morphology that is more distinct from the BDNF val66met homozygotes. These results may prove significant in furthering our understanding of the role of the BDNF met-allele in disorders such as Alzheimer{\textquoteright}s disease and depression.},
	keywords = {Adolescent, Adult, Alleles, Brain, Brain-Derived Neurotrophic Factor, diffusion tensor imaging, Female, Genotype, Heterozygote, Homozygote, Humans, Image Processing, Computer-Assisted, Male, Middle Aged, Polymorphism, Single Nucleotide, Young Adult},
	issn = {1095-9572},
	doi = {10.1016/j.neuroimage.2013.12.050},
	author = {Forde, Natalie J and Ronan, Lisa and Suckling, John and Scanlon, Cathy and Neary, Simon and Holleran, Laurena and Alexander Leemans and Tait, Roger and Rua, Catarina and Fletcher, Paul C and Ben Jeurissen and Dodds, Chris M and Miller, Sam R and Bullmore, Edward T and Colm McDonald and Nathan, Pradeep J and Dara M. Cannon}
}
@article {1437,
	title = {Super-resolution for computed tomography based on discrete tomography},
	journal = {IEEE Transactions on Image Processing},
	volume = {23},
	year = {2014},
	pages = {1181 - 1193},
	doi = {10.1109/TIP.2013.2297025},
	author = {Wim Van Aarle and Kees Joost Batenburg and Gert Van Gompel and Elke Van de Casteele and Jan Sijbers}
}
@conference {1482,
	title = {Super-resolution reconstruction of diffusion parameters from multi-oriented diffusion weighted images},
	year = {2014},
	month = {january},
	address = {Maastricht, The Netherlands},
	author = {Gwendolyn Van Steenkiste and Ben Jeurissen and Dirk H J Poot and Paul M Parizel and Jan Sijbers}
}
@conference {1483,
	title = {Super-resolution reconstruction of diffusion parameters from multi-oriented diffusion weighted images},
	year = {2014},
	month = {May},
	address = {Milan, Italy},
	author = {Gwendolyn Van Steenkiste and Ben Jeurissen and Paul M Parizel and Dirk H J Poot and Jan Sijbers}
}
@inproceedings {1495,
	title = {Tomographic image reconstruction from continuous projections},
	booktitle = {The third international conference on image formation in X-ray computed tomography},
	year = {2014},
	month = {06/2014},
	pages = {295-298},
	address = {Salt Lake City, USA},
	author = {Jeroen Cant and Willem Jan Palenstijn and Gert Behiels and Jan Sijbers}
}
@inproceedings {1478,
	title = {Type-2 fuzzy GMMs for robust text-independent speaker verification in noisy environments},
	booktitle = {International Conference of Pattern Recognition},
	year = {2014},
	author = {Hector N. B. Pinheiro and Ing Ren Tsang and George D C Cavalcanti and Ing Jyh Tsang and Jan Sijbers}
}
@proceedings {1907,
	title = {ACIVS 2013, Advanced Concepts for Intelligent Vision Systems},
	volume = {8129},
	year = {2013},
	author = {J Blanc-Talon and A Kazinski and Wilfried Philips and D Popescu and Paul Scheunders}
}
@inproceedings {1386,
	title = {Adaptive Zooming in X-ray Computed Tomography},
	booktitle = {1st International Conference on Tomography of Materials and Structures (ICTMS)},
	year = {2013},
	pages = {5-8},
	address = {Ghent, Belgium},
	author = {Andrei Dabravolski and Kees Joost Batenburg and Jan Sijbers}
}
@article {1343,
	title = {Advanced reconstruction algorithms for electron tomography: from comparison to combination},
	journal = {Ultramicroscopy},
	volume = {127},
	year = {2013},
	pages = {40{\textendash}47},
	author = {Bart Goris and Roelandts, Tom and Kees Joost Batenburg and Hamed Heidari Mezerji and Sara Bals}
}
@article {1392,
	title = {Advances in X-ray diffraction contrast tomography: flexibility in the setup geometry and application to multiphase materials},
	journal = {Journal of Applied Crystallography},
	volume = {46},
	year = {2013},
	month = {04/2013},
	pages = {297 - 311},
	issn = {0021-8898},
	doi = {10.1107/S0021889813002604},
	author = {Reischig, P{\'e}ter and King, Andrew and Nervo, Laura and Vigan{\'o}, Nicola and Guilhem, Yoann and Willem Jan Palenstijn and Kees Joost Batenburg and Preuss, Michael and Ludwig, Wolfgang}
}
@inproceedings {1399,
	title = {An Algebraic Reconstruction Technique for the Study of Local Structural Changes during CT},
	booktitle = {1st International Conference on Tomography of Materials and Structures (ICTMS)},
	year = {2013},
	pages = {137-141},
	abstract = {If an object undergoes structural changes during a CT-scan, artefacts are introduced in the reconstruction. Current methods that compensate for these changes assume a continuous deformation model, which is insufficient to capture the complexity of structural changes. In this paper, an iterative method is proposed that incorporates prior knowledge about the changing region in the reconstruction algorithm, which results in significantly more accurate reconstructions.},
	author = {Van Eyndhoven, Geert and Kees Joost Batenburg and Jan Sijbers}
}
@inproceedings {1650,
	title = {An Alignment Method for Fan Beam Tomography},
	booktitle = {International Conference on Tomography of Materials and Structures (ICTMS)},
	year = {2013},
	pages = {103-107},
	author = {Folkert Bleichrodt and Jan De Beenhouwer and Jan Sijbers and Kees Joost Batenburg}
}
@article {1402,
	title = {Altered diffusion tensor imaging measurements in aged transgenic Huntington disease rats.},
	journal = {Brain structure \& function},
	volume = {218},
	year = {2013},
	month = {2013 May},
	pages = {767-78},
	abstract = {Rodent models of Huntington disease (HD) are valuable tools for investigating HD pathophysiology and evaluating new therapeutic approaches. Non-invasive characterization of HD-related phenotype changes is important for monitoring progression of pathological processes and possible effects of interventions. The first transgenic rat model for HD exhibits progressive late-onset affective, cognitive, and motor impairments, as well as neuropathological features reflecting observations from HD patients. In this report, we contribute to the anatomical phenotyping of this model by comparing high-resolution ex vivo DTI measurements obtained in aged transgenic HD rats and wild-type controls. By region of interest analysis supplemented by voxel-based statistics, we find little evidence of atrophy in basal ganglia regions, but demonstrate altered DTI measurements in the dorsal and ventral striatum, globus pallidus, entopeduncular nucleus, substantia nigra, and hippocampus. These changes are largely compatible with DTI findings in preclinical and clinical HD patients. We confirm earlier reports that HD rats express a moderate neuropathological phenotype, and provide evidence of altered DTI measures in specific HD-related brain regions, in the absence of pronounced morphometric changes.},
	issn = {1863-2661},
	doi = {10.1007/s00429-012-0427-0},
	author = {Antonsen, Bj{\o}rnar T and Yi Jiang and Jelle Veraart and Qu, Hong and Nguyen, Huu Phuc and Jan Sijbers and Von H{\"o}rsten, Stephan and Allan G Johnson and Trygve B Leergaard}
}
@article {1406,
	title = {Alveolar Nerve Unfolding Technique for Synoptic Analysis: Visualization and Quantification of Inferior Alveolar Nerve Tracings in Three-dimensional Cone-Beam Computed Tomography.},
	journal = {The Journal of craniofacial surgery},
	volume = {24},
	year = {2013},
	month = {2013 Jul},
	pages = {e374-7},
	abstract = {The aim of the technique presented here is to visualize the anatomical context of the inferior alveolar nerve (IAN) canal. For 2 cases, cone-beam computed tomography images of the mandible were obtained from patient files together with the manual preoperative IAN canal tracings. For both cases, similar to simulated panoramic images, a two-dimensional image is extracted from a three-dimensional cone-beam computed tomography image. Unlike panoramic images, the unfolding does not follow the general curvature of the mandible but follows the nerve tracing closely and places the traced nerve track on a horizontal central line. Because of the centering of the nerve tracing together with the nerve canal and its surroundings in a two-dimensional representation, the technique (ANUTSA [Alveolar Nerve Unfolding Technique for Synoptic Analysis]) allowed the first case to evidence the adjacency of root tips along the IAN, whereas in the second case the degree of penetration of the IAN by an implant is revealed. The global aspect of the representation through unfolding allowed for the detection of the anomalies and the IAN-penetrating lesion along the IAN canal at a glance.},
	issn = {1536-3732},
	doi = {10.1097/SCS.0b013e3182903013},
	author = {Jacquet, Wolfgang and Nyssen, Edgard and Sun, Yi and De Munter, Stephanie and Jan Sijbers and Politis, Constantinus}
}
@conference {1433,
	title = {Analysis and modeling of isotropic partial volume effects in diffusion MRI},
	volume = {7},
	year = {2013},
	month = {2013},
	doi = {10.3389/conf.fninf.2013.10.00027},
	author = {Timo Roine and Ben Jeurissen and Alexander Leemans and Wilfried Philips and Jan Sijbers}
}
@inproceedings {1403,
	title = {The ASTRA Tomography Toolbox},
	booktitle = {13th International Conference on Computational and Mathematical Methods in Science and Engineering, CMMSE 2013},
	volume = {4},
	year = {2013},
	pages = {1139-1145},
	isbn = {978-84-616-2723-3},
	author = {Willem Jan Palenstijn and Kees Joost Batenburg and Jan Sijbers}
}
@article {1317,
	title = {Classification of heathland vegetation in a hierarchical contextual framework},
	journal = {International Journal of Remote Sensing},
	volume = {34},
	year = {2013},
	month = {2013},
	pages = {96 - 111},
	abstract = {Heathlands in Western Europe have shown dramatic declines over the last century and therefore have been given a high conservation priority in the Habitats Directive of the European Union (EU). Accurate surveying and monitoring of heathland habitats is essential for appropriate conservation management, but the large heterogeneity of vegetation types within habitats as well as the occurrence of similar vegetation across habitat types hinders a straightforward, automated mapping based on aerial images. In such a case, a context-dependent classification algorithm is expected to be superior to traditional classification techniques. This article presents a novel approach to map the conservation status of heathland vegetation by using a hierarchical classification scheme that describes the structural dependencies in the field between the basic vegetation and the land-cover types that habitats are composed of. These dependency relationships are included as contextual information in the classification process, using a tree-structured Markov random field (TS-MRF) technique with a tree that reflects the hierarchy of the classification scheme. Results of this approach for a heathland area in Belgium were compared with results from more conventional classification approaches. Validation of the results showed that the structure of the scheme contained important spatial relationships, which were further reinforced by using the contextual classification strategy, especially for the most detailed level of the classification scheme. Accuracy increased and the classification results were more suitable for visual interpretation.},
	issn = {0143-1161},
	doi = {10.1080/01431161.2012.708061},
	author = {Guy Thoonen and Toon Spanhove and Jeroen Vanden Borre and Paul Scheunders}
}
@article {1369,
	title = {Comprehensive framework for accurate diffusion MRI parameter estimation},
	journal = {Magnetic Resonance in Medicine},
	volume = {81},
	year = {2013},
	pages = {972-984},
	doi = {10.1002/mrm.24529},
	author = {Jelle Veraart and Jeny Rajan and Ron R Peeters and Alexander Leemans and Stefan Sunaert and Jan Sijbers}
}
@article {1379,
	title = {Contextual Subpixel Mapping of Hyperspectral Images Making Use of a High Resolution Color Image},
	journal = {IEEE JSTARS, Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
	volume = {6},
	year = {2013},
	month = {2013},
	pages = {779 - 791},
	abstract = {This paper describes a hyperspectral image classification method to obtain classification maps at a finer resolution than the image{\textquoteright}s original resolution. We assume that a complementary color image of high spatial resolution is available. The proposed methodology consists of a soft classification procedure to obtain landcover fractions, followed by a subpixel mapping of these fractions. While the main contribution of this article is in fact the complete multisource framework for obtaining a subpixel map, the major novelty of this subpixel mapping approach is the inclusion of contextual information, obtained from the color image. Experiments, conducted on two hyperspectral images and one real multi source data set, show excellent results, when compared to classification of the hyperspectral data only. The advantage of the contextual approach, compared to conventional subpixel mapping approaches, is clearly demonstrated.},
	keywords = {fusion, hyperspectral data, spectral unmixing, subpixel mapping, superresolution},
	issn = {1939-1404},
	doi = {10.1109/JSTARS.2012.2236539},
	author = {Zahid Mahmood and Muhamed Awais Akhter and Guy Thoonen and Paul Scheunders}
}
@article {1407,
	title = {DART: a new approach for super-resolution reconstruction of license plates},
	journal = {Journal of Electronic Imaging},
	volume = {22},
	year = {2013},
	chapter = {041111},
	doi = {10.1117/1.JEI.22.4.041111},
	url = {http://electronicimaging.spiedigitallibrary.org/article.aspx?articleid=1733763},
	author = {Karim Zarei Zefreh and Wim Van Aarle and Kees Joost Batenburg and Jan Sijbers}
}
@article {1409,
	title = {Detecting the adjacency effect in hyperspectral imagery with spectral unmixing techniques},
	journal = {IEEE JSTARS, Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
	volume = {6},
	year = {2013},
	pages = {1070-1078},
	author = {Dzevdet Burazerovic and Bert Geens and Rob Heylen and S Sterckx and Paul Scheunders}
}
@article {1377,
	title = {Diffusion kurtosis imaging to detect amyloidosis in an APP/PS1 mouse model for Alzheimer{\textquoteright}s disease},
	journal = {Magnetic Resonance in Medicine},
	volume = {69},
	year = {2013},
	pages = {1115{\textendash}1121},
	doi = {10.1002/mrm.24680},
	author = {Greetje Vanhoutte and S. Pereson and Rafael  Delgado Y Palacios and Pieter-Jan Guns and B. Asselbergh and Jelle Veraart and Jan Sijbers and Marleen Verhoye and Van Broeckhoven, Christine and Annemie Van Der Linden}
}
@conference {1405,
	title = {Discrete Tomography in MRI: a proof of concept},
	volume = {21},
	year = {2013},
	pages = {2662},
	author = {Segers, Hilde and Willem Jan Palenstijn and Kees Joost Batenburg and Jan Sijbers}
}
@article {1404,
	title = {Discrete Tomography in MRI: a Simulation Study},
	journal = {Fundamenta Informaticae},
	volume = {125},
	year = {2013},
	pages = {223-237},
	doi = {10.3233/FI-2013-861},
	author = {Segers, Hilde and Willem Jan Palenstijn and Kees Joost Batenburg and Jan Sijbers}
}
@article {1361,
	title = {Does the use of hormonal contraceptives cause microstructural changes in cerebral white matter? Preliminary results of a DTI and tractography study.},
	journal = {European radiology},
	volume = {23},
	year = {2013},
	month = {2012 Jul 20},
	pages = {57-64},
	abstract = {OBJECTIVE: To evaluate the effect of monophasic combined oral contraceptive pill (COCP) and menstrual cycle phase in healthy young women on white matter (WM) organization using diffusion tensor imaging (DTI). METHODS: Thirty young women were included in the study; 15 women used COCP and 15 women had a natural cycle. All subjects underwent DTI magnetic resonance imaging during the follicular and luteal phase of their cycle, or in different COCP cycle phases. DTI parameters were obtained in different WM structures by performing diffusion tensor fibre tractography. Fractional anisotropy and mean diffusivity were calculated for different WM structures. Hormonal plasma concentrations were measured in peripheral venous blood samples and correlated with the DTI findings. RESULTS: We found a significant difference in mean diffusivity in the fornix between the COCP and the natural cycle group. Mean diffusivity values in the fornix were negatively correlated with luteinizing hormone and estradiol blood concentrations. CONCLUSION: An important part in the limbic system, the fornix, regulates emotional processes. Differences in diffusion parameters in the fornix may contribute to behavioural alternations related to COCP use. This finding also suggests that the use of oral contraceptives needs to be taken into account when designing DTI group studies. KEY POINTS: {\textbullet} Diffusion tensor MRI offers new insights into brain white matter microstructure. {\textbullet} The effects of oral hormonal contraception were examined in young women. {\textbullet} Diffusion tensor images and hormone blood concentrations were evaluated. {\textbullet} Women using hormonal contraception demonstrated higher mean diffusivity in the fornix. {\textbullet} These changes may contribute to behavioural alternations related to contraception use.},
	issn = {1432-1084},
	doi = {10.1007/s00330-012-2572-5},
	author = {De Bondt, Timo and Wim Van Hecke and Jelle Veraart and Alexander Leemans and Jan Sijbers and Stefan Sunaert and Jacquemyn, Yves and Paul M Parizel}
}
@inproceedings {Jacobs13,
	title = {Domain adaptation with Hidden Markov Random Fields},
	booktitle = {Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International},
	year = {2013},
	month = {July},
	pages = {3112-3115},
	address = {Melbourne, VIC, Australia},
	abstract = {In this paper, we propose a method to match multitemporal sequences of hyperspectral images using Hidden Markov Random Fields. Based on the matching of the data manifold, the algorithm matches the reflectance spectra of the classes, thus allowing the reuse of labeled examples acquired on one image to classify the other. This allows valorization of spectra collected in situ to other acquisitions than the one they were acquired for, without user supervision, prior knowledge of the class reflectance in the new domain or global information about atmospheric conditions.},
	keywords = {domain adaptation, graph matching, Hidden Markov Random Fields, Multitemporal classification},
	issn = {2153-6996},
	doi = {10.1109/IGARSS.2013.6723485},
	author = {Jan-Pieter Jacobs and Guy Thoonen and Devis Tuia and Gustavo Camps-Valls and Birgen Haest and Paul Scheunders}
}
@article {1370,
	title = {Dynamic angle selection in binary tomography},
	journal = {Computer Vision and Image Understanding},
	volume = {117},
	year = {2013},
	pages = {306{\textendash}318},
	doi = {http://dx.doi.org/10.1016/j.cviu.2012.07.005},
	author = {Kees Joost Batenburg and Willem Jan Palenstijn and Peter Balazs and Jan Sijbers}
}
@inproceedings {1387,
	title = {Dynamic Angle Selection in X-ray Computed Tomography},
	booktitle = {1st International Conference on Tomography of Materials and Structures (ICTMS)},
	year = {2013},
	pages = {27-30},
	address = {Ghent, Belgium},
	author = {Andrei Dabravolski and Kees Joost Batenburg and Jan Sijbers}
}
@article {1677,
	title = {Dynamic tomography angle selection},
	number = {PCT/EP2012/071421},
	year = {2013},
	chapter = {WO2013064472 A1},
	issn = {US2014307934A1},
	author = {Kees Joost Batenburg and Jan Sijbers}
}
@article {1395,
	title = {Estimation of unknown structure parameters from high-resolution (S)TEM images: what are the limits?},
	journal = {Ultramicroscopy},
	volume = {134},
	year = {2013},
	pages = {34-43},
	doi = {http://dx.doi.org/10.1016/j.ultramic.2013.05.017},
	url = {http://www.sciencedirect.com/science/article/pii/S0304399113001368},
	author = {Arnold Jan den Dekker and Julie Gonnissen and Annick De Backer and Jan Sijbers and Sandra Van Aert}
}
@inproceedings {1423,
	title = {Fast Tomographic Reconstruction from Highly Limited Data Using Artificial Neural Networks},
	booktitle = {1st International Conference on Tomography of Materials and Structures (ICTMS)},
	year = {2013},
	pages = {109-112},
	author = {Daan Pelt and Jan Sijbers and Kees Joost Batenburg}
}
@inproceedings {1523,
	title = {Handling spectral variability with alternating angle minimization},
	booktitle = {IEEE-WHISPERS 2013, Workshop on Hyperspectral Image and Signal Processing, Gainesville, Florida, June 25-28},
	year = {2013},
	author = {Rob Heylen and Paul Scheunders and Paul Gader}
}
@conference {1389,
	title = {How to make sure you are using the correct gradient orientations in your diffusion MRI study?},
	year = {2013},
	month = {April},
	pages = {2047},
	address = {Salt Lake City, USA},
	author = {Ben Jeurissen and Alexander Leemans and Jan Sijbers}
}
@mastersthesis {2369,
	title = {Hyperspectral data fusion for classification and visualization in remote sensing},
	year = {2013},
	type = {PhD thesis},
	author = {Zahid Mahmood}
}
@inproceedings {1508,
	title = {Hyperspectral image subpixel mapping using Getis index},
	booktitle = {IEEE-WHISPERS 2013, Workshop on Hyperspectral Image and Signal Processing, Gainesville, Florida, June 25-28, 2013},
	year = {2013},
	author = {Muhamed Awais Akhter and Zahid Mahmood and Paul Scheunders}
}
@article {1408,
	title = {Hyperspectral intrinsic dimensionality estimation with nearest-neighbor distance ratio{\textquoteright}s},
	journal = {IEEE JSTARS, Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
	volume = {6},
	year = {2013},
	pages = {570-579},
	author = {Rob Heylen and Paul Scheunders}
}
@article {1410,
	title = {Hyperspectral remote sensing data analysis and future challenges},
	journal = {IEEE Geoscience and Remote Sensing Magazine},
	volume = {1},
	year = {2013},
	pages = {6-36},
	author = {Jose Bioucas-Dias and Antonio Plaza and Gustavo Camps-Valls and Paul Scheunders and Nasser Nasrabadi and Jocedlyn Chanussot}
}
@inproceedings {1524,
	title = {Improving the efficiency of MESMA through geometric unmixing principles},
	booktitle = {SPIE Remote Sensing, Drseden, Germany, 23-26 September },
	volume = {8892},
	number = {UNSP 88920Q},
	year = {2013},
	author = {Laurent Tits and Ben Somers and Rob Heylen and Paul Scheunders and Pol Coppin}
}
@article {1412,
	title = {Increased coherence of white matter fiber tract organization in adults with Asperger syndrome: A diffusion tensor imaging study},
	journal = {Autism Research},
	volume = {6},
	year = {2013},
	pages = {642-650},
	chapter = {642},
	doi = {10.1002/aur.1332},
	author = {Roine, Ulrika and Timo Roine and Salmi, Juha and Nieminen-von Wendt, Taina and Lepp{\"a}m{\"a}ki, Sami and Rintahaka, Pertti and Tani, Pekka and Alexander Leemans and Sams, Mikko}
}
@inproceedings {1401,
	title = {Inline 3D X-ray Inspection of Food using Discrete Tomography},
	booktitle = {InsideFood Symposium},
	year = {2013},
	address = {Leuven, Belgium},
	author = {Luis Filipe Alves Pereira and Roelandts, Tom and Jan Sijbers}
}
@article {1348,
	title = {Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging.},
	journal = {Human Brain Mapping},
	volume = {34},
	number = {11},
	year = {2013},
	pages = {2747-66},
	abstract = {It has long been recognized that the diffusion tensor model is inappropriate to characterize complex fiber architecture, causing tensor-derived measures such as the primary eigenvector and fractional anisotropy to be unreliable or misleading in these regions. There is however still debate about the impact of this problem in practice. A recent study using a Bayesian automatic relevance detection (ARD) multicompartment model suggested that a third of white matter (WM) voxels contain crossing fibers, a value that, whilst already significant, is likely to be an underestimate. The aim of this study is to provide more robust estimates of the proportion of affected voxels, the number of fiber orientations within each WM voxel, and the impact on tensor-derived analyses, using large, high-quality diffusion-weighted data sets, with reconstruction parameters optimized specifically for this task. Two reconstruction algorithms were used: constrained spherical deconvolution (CSD), and the ARD method used in the previous study. We estimate the proportion of WM voxels containing crossing fibers to be \~{}90\% (using CSD) and 63\% (using ARD). Both these values are much higher than previously reported, strongly suggesting that the diffusion tensor model is inadequate in the vast majority of WM regions. This has serious implications for downstream processing applications that depend on this model, particularly tractography, and the interpretation of anisotropy and radial/axial diffusivity measures.},
	issn = {1097-0193},
	doi = {10.1002/hbm.22099},
	author = {Ben Jeurissen and Alexander Leemans and Jacques-Donald Tournier and Derek K. Jones and Jan Sijbers}
}
@conference {1426,
	title = {Iterative reweighted linear least squares for the accurate, fast, and robust estimation of diffusion magnetic resonance parameters},
	year = {2013},
	author = {Quinten Collier and Jelle Veraart and Jan Sijbers}
}
@article {1368,
	title = {Limbic and callosal white matter changes in euthymic bipolar I disorder: an advanced diffusion MRI tractography study},
	journal = {Biologicial Psychiatry},
	volume = {73},
	year = {2013},
	pages = {194-201},
	doi = {http://dx.doi.org/10.1016/j.biopsych.2012.09.023},
	author = {Louise Emsell and Alexander Leemans and Camilla Langan and Wim Van Hecke and Gareth J. Barker and Peter McCarthy and Ben Jeurissen and Jan Sijbers and Stefan Sunaert and Dara M. Cannon and Colm McDonald}
}
@mastersthesis {1428,
	title = {Local Prior Knowledge in Tomography},
	volume = {PhD in Sciences: Physics},
	year = {2013},
	month = {11/2013},
	type = {PhD thesis},
	author = {Roelandts, Tom}
}
@inproceedings {1400,
	title = {Localizing DART using the Reconstructed Residual Error},
	booktitle = {1st International Conference on Tomography of Materials and Structures (ICTMS)},
	volume = {Book of Abstracts: Talks},
	year = {2013},
	pages = {113-116},
	address = {Ghent, Belgium},
	author = {Roelandts, Tom and Kees Joost Batenburg and Jan Sijbers}
}
@article {2039,
	title = {MEKS: A program for computation of inclusive jet cross sections at hadron colliders},
	journal = {Computer Physics Communications},
	volume = {184},
	year = {2013},
	month = {Jan-06-2013},
	pages = {1626 - 1642},
	abstract = {EKS is a numerical program that predicts differential cross sections for production of single-inclusive hadronic jets and jet pairs at next-to-leading order (NLO) accuracy in a perturbative QCD calculation. We describe MEKS 1.0, an upgraded EKS program with increased numerical precision, suitable for comparisons to the latest experimental data from the Large Hadron Collider and Tevatron. The program integrates the regularized patron-level matrix elements over the kinematical phase space for production of two and three partons using the VEGAS algorithm. It stores the generated weighted events in finely binned two-dimensional histograms for fast offline analysis. A user interface allows one to customize computation of inclusive jet observables. Results of a benchmark comparison of the MEKS program and the commonly used FastNLO program are also documented.},
	issn = {00104655},
	doi = {10.1016/j.cpc.2013.01.022},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0010465513000611https://api.elsevier.com/content/article/PII:S0010465513000611?httpAccept=text/xmlhttps://api.elsevier.com/content/article/PII:S0010465513000611?httpAccept=text/plain},
	author = {Gao, Jun and Liang, Zhihua and Soper, Davison E. and Lai, Hung-Liang and Nadolsky, Pavel M. and Yuan, C.-P.}
}
@inproceedings {1442,
	title = {Memory access optimization for iterative tomography on many-core architectures},
	booktitle = {The 12th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine},
	year = {2013},
	pages = {364-367},
	author = {Wim Van Aarle and Pieter Ghysels and Jan Sijbers and Wim Vanroose}
}
@conference {1475,
	title = {Misalignment correction for T1 maps using a maximum likelihood estimator approach},
	year = {2013},
	doi = {10.3389},
	author = {Gabriel Ramos-Llord{\'e}n and Jan Sijbers}
}
@inproceedings {Sabino:2013:MCT:2571272.2572228,
	title = {Motion Compensation Techniques in Permutation-Based Video Encryption},
	booktitle = {Proceedings of the 2013 IEEE International Conference on Systems, Man, and Cybernetics},
	series = {SMC {\textquoteright}13},
	year = {2013},
	pages = {1578{\textendash}1581},
	publisher = {IEEE Computer Society},
	organization = {IEEE Computer Society},
	address = {Washington, DC, USA},
	keywords = {motion compensation, multimedia data security, spatial correlation, video coding, Video encryption},
	isbn = {978-1-4799-0652-9},
	doi = {10.1109/SMC.2013.272},
	url = {http://dx.doi.org/10.1109/SMC.2013.272},
	author = {Sabino, Caio C. and Andrade, Lais S. and Ing Ren Tsang and George D C Cavalcanti and Ing Jyh Tsang and Jan Sijbers}
}
@inproceedings {1522,
	title = {Multi-dimensional Pixel Purity Index},
	booktitle = {IEEE-WHISPERS 2013, Workshop on Hyperspectral Image and Signal Processing, Gainesville, Florida, June 25-28},
	year = {2013},
	author = {Rob Heylen and Paul Scheunders}
}
@article {1371,
	title = {Multi-dimensional pixel purity index for convex hull estimation and endmember extraction},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {51},
	year = {2013},
	pages = {4059-4069},
	author = {Rob Heylen and Paul Scheunders}
}
@inproceedings {1439,
	title = {A New Nonlocal Maximum Likelihood Estimation Method for Denoising Magnetic Resonance Images},
	booktitle = {5th International Conference, PReMI 2013, Kolkata, India, December 10-14, 2013. Proceedings},
	volume = {8251},
	year = {2013},
	month = {2013},
	edition = {Lecture Notes in Computer Science},
	doi = {10.1007/978-3-642-45062-4_62},
	author = {Jeny Rajan and Arnold Jan den Dekker and Juntu, Jaber and Jan Sijbers}
}
@mastersthesis {1427,
	title = {Optimal estimation of diffusion MRI parameters},
	volume = {PhD in Sciences: Physics},
	year = {2013},
	month = {10/2013},
	type = {PhD thesis},
	author = {Jelle Veraart}
}
@inproceedings {1422,
	title = {Practical Error Bounds for Binary Tomography},
	booktitle = {1st International Conference on Tomography of Materials and Structures},
	year = {2013},
	pages = {97-100},
	author = {Wagner Fortes and Jan Sijbers and Kees Joost Batenburg}
}
@conference {1557,
	title = {Recursive calibration of the fiber response function for spherical deconvolution diffusion ODF sharpening},
	year = {2013},
	address = {Podstrana, Croatia},
	author = {Chantal M W Tax and Ben Jeurissen and Vos, Sjoerd B. and Viergever, Max A. and Alexander Leemans}
}
@article {1413,
	title = {Reelin Associated With Restricted and Stereotyped Behavior Based on Principal Component Analysis on Autism Diagnostic Interview-Revised},
	journal = {Autism - Open Access},
	volume = {3},
	year = {2013},
	chapter = {107},
	url = {http://www.omicsgroup.org/journals/2165-7890/2165-7890-3-107.pdf},
	author = {Roine, Ulrika and Ripatti, Samuli and Rehnstr{\"o}m, Karola and Timo Roine and Kilpinen, Helena and Surakka, Ida and Wedenoja, Juho and Ylisaukko-oja, Tero and Kempas, Elli and Wessman, Jaana and Moilanen, Irma and Mattila, Marja-Leena and Kielinen, Marko and Jussila, Katja and Suomalainen, Saara and Pulkkinen, Esko and von Wendt, Lennart and Peltonen, Leena}
}
@article {1470,
	title = {Regional gray matter volume differences and sex-hormone correlations as a function of menstrual cycle phase and hormonal contraceptives use.},
	journal = {Brain research},
	volume = {1530},
	year = {2013},
	month = {2013 Sep 12},
	pages = {22-31},
	abstract = {During the menstrual cycle, hormone-driven functional and morphological changes occur in the female brain. The influence of hormonal contraceptives on these changes has received only little attention in the medical literature. The purpose of our study is to measure regional gray matter volume changes as a function of the cycle phase and use of hormonal contraceptives, in relation to blood concentrations of sex hormones. We performed a prospective study in 30 healthy young women; 15 women had a natural menstrual cycle and 15 were using monophasic combined hormonal contraceptives. MRI examinations were acquired at 2 specific time-points in the cycle (follicular and luteal phase). MRI studies included a T1-weighted, isotropic, high-resolution 3-D gradient echo acquisition, for the purpose of performing voxel based morphometry. Peripheral venous blood samples were obtained to determine concentrations of luteinizing hormone (LH), follicle stimulating hormone (FSH), estradiol, and progesterone. We found a highly significant negative correlation of regional gray matter volume in the anterior cingulate cortex with estradiol concentrations. To the best of our knowledge, this result has not been described before, and was only present in the natural cycle group, not in women using hormonal contraceptives. The anterior cingulate cortex is involved in emotion processing and there is literature describing behavioral alternations with changing hormone levels. Our findings provide a structural, morphological basis to support these data. Therefore, we advise neuroscientists to take into account the menstrual cycle phase and use of hormonal contraceptives, in order to avoid obtaining heterogeneous data sets, leading to a significant loss of accuracy and precision.},
	issn = {1872-6240},
	doi = {10.1016/j.brainres.2013.07.034},
	author = {De Bondt, Timo and Jacquemyn, Y and Wim Van Hecke and Jan Sijbers and Stefan Sunaert and Paul M Parizel}
}
@conference {1398,
	title = {Region-Based SIRT algorithm for the reconstrcution of phase bins in dynamic micro-CT},
	year = {2013},
	author = {Van Eyndhoven, Geert and Kees Joost Batenburg and Jan Sijbers}
}
@conference {1555,
	title = {Robust fiber response function estimation for deconvolution based diffusion MRI methods},
	volume = {5},
	year = {2013},
	pages = {55},
	address = {Rotterdam, The Netherlands},
	author = {Chantal M W Tax and Ben Jeurissen and Viergever, Max A. and Alexander Leemans}
}
@conference {1556,
	title = {Robust fiber response function estimation for deconvolution based diffusion MRI methods},
	volume = {21},
	year = {2013},
	pages = {3149},
	address = {Salt Lake City, Utah},
	author = {Chantal M W Tax and Ben Jeurissen and Viergever, Max A. and Alexander Leemans}
}
@article {1347,
	title = {Semi-supervised local discriminant analysis for feature extraction in hyperspectral images},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {51},
	year = {2013},
	pages = {184-198},
	author = {W. Liao and Aleksandra Pizurica and Paul Scheunders and Wilfried Philips and Y Pi}
}
@inproceedings {1509,
	title = {Solving the Hyperspectral Unmixing Problem with Projection Onto Convex Sets},
	booktitle = {21st European Signal Processing Conference (EUSIPCO), September 2013, Marrakech, Morocco},
	year = {2013},
	author = {Rob Heylen and Muhamed Awais Akhter and Paul Scheunders}
}
@article {1431,
	title = {Statistical shape modeling and population analysis of the aortic root of TAVI patients},
	journal = {Journal of Medical Devices},
	volume = {7},
	year = {2013},
	month = {12/2013},
	publisher = {ASME},
	edition = {040925 },
	abstract = {The new transcatheter technique to implant synthetic aortic valves offers a treatment to patients previously considered untreatable. However the majority of patients suffer from leakage alongside the implant. Using a statistical shape model of the anatomy a correlation was discovered between leakage and the shape of the sinuses of valsalva.},
	doi = {10.1115/1.4025904},
	url = {http://medicaldevices.asmedigitalcollection.asme.org/article.aspx?articleid=1767192},
	author = {Bosmans, Bart and Toon Huysmans and Wirix-Speetjens, Roel and Verschueren, Peter and Jan Sijbers and Bosmans, Johan and Vander Sloten, Jos}
}
@article {1396,
	title = {Subchronic memantine induced concurrent functional disconnectivity and altered ultra-structural tissue integrity in the rodent brain: revealed by multimodal MRI.},
	journal = {Psychopharmacology},
	volume = {227},
	year = {2013},
	month = {2013 Jun},
	pages = {479-91},
	abstract = {BACKGROUND: An effective NMDA antagonist imaging model may find key utility in advancing schizophrenia drug discovery research. We investigated effects of subchronic treatment with the NMDA antagonist memantine by using behavioural observation and multimodal MRI.

METHODS: Pharmacological MRI (phMRI) was used to map the neuroanatomical binding sites of memantine after acute and subchronic treatment. Resting state fMRI (rs-fMRI) and diffusion MRI were used to study the changes in functional connectivity (FC) and ultra-structural tissue integrity before and after subchronic memantine treatment. Further corroborating behavioural evidences were documented.

RESULTS: Dose-dependent phMRI activation was observed in the prelimbic cortex following acute doses of memantine. Subchronic treatment revealed significant effects in the hippocampus, cingulate, prelimbic and retrosplenial cortices. Decreases in FC amongst the hippocampal and frontal cortical structures (prelimbic, cingulate) were apparent through rs-fMRI investigation, indicating a loss of connectivity. Diffusion kurtosis MRI showed decreases in fractional anisotropy and mean diffusivity changes, suggesting ultra-structural changes in the hippocampus and cingulate cortex. Limited behavioural assessment suggested that memantine induced behavioural effects comparable to other NMDA antagonists as measured by locomotor hyperactivity and that the effects could be reversed by antipsychotic drugs.

CONCLUSION: Our findings substantiate the hypothesis that repeated NMDA receptor blockade with nonspecific, noncompetitive NMDA antagonists may lead to functional and ultra-structural alterations, particularly in the hippocampus and cingulate cortex. These changes may underlie the behavioural effects. Furthermore, the present findings underscore the utility and the translational potential of multimodal MR imaging and acute/subchronic memantine model in the search for novel disease-modifying treatments for schizophrenia.},
	issn = {1432-2072},
	doi = {10.1007/s00213-013-2966-3},
	author = {Sekar, S and Jonckers, E and Marleen Verhoye and Willems, R and Jelle Veraart and Johan Van Audekerke and Couto, J and Giugliano, M and Wuyts, K and Dedeurwaerdere, S and Jan Sijbers and Mackie, C and Ver Donck, L and Steckler, T and Annemie Van Der Linden}
}
@conference {1384,
	title = {Super resolution reconstruction from  differently oriented diffusion tensor data sets},
	year = {2013},
	month = {January},
	address = {Rotterdam, The Netherlands},
	author = {Gwendolyn Van Steenkiste and Ben Jeurissen and Jan Sijbers and Dirk H J Poot}
}
@conference {1385,
	title = {Super resolution reconstruction from differently oriented diffusion tensor data sets},
	year = {2013},
	month = {April},
	pages = {3186},
	address = {Salt Lake City, USA},
	author = {Gwendolyn Van Steenkiste and Ben Jeurissen and Jan Sijbers and Dirk H J Poot}
}
@conference {1425,
	title = {Super resolution reconstruction of diffusion tensor parameters from multi-oriented diffusion weighted images},
	year = {2013},
	author = {Gwendolyn Van Steenkiste and Ben Jeurissen and Dirk H J Poot and Jan Sijbers}
}
@article {1328,
	title = {Super-Resolution for Multislice Diffusion Tensor Imaging},
	journal = {Magnetic Resonance in Medicine},
	volume = {69},
	year = {2013},
	pages = {103{\textendash}113},
	abstract = {Diffusion weighted (DW) magnetic resonance images are often recorded with single shot multislice
imaging sequences, because of their short scanning times and robustness to motion. To minimize
noise and acquisition time, images are generally acquired with either anisotropic or isotropic
low resolution voxels, which impedes subsequent posterior image processing and visualization. In
this paper, we propose a super-resolution method for diffusion weighted imaging that combines
anisotropic multislice images to enhance the spatial resolution of diffusion tensor (DT) data. Each
DW image is reconstructed from a set of arbitrarily oriented images with a low through-plane
resolution. The quality of the reconstructed DW images was evaluated by DT metrics and tractography.
Experiments with simulated data, a hardware DTI phantom, as well as in vivo human
brain data were conducted. Our results show a significant increase in spatial resolution of the DT
data while preserving high signal to noise ratio.},
	doi = {10.1002/mrm.24233},
	author = {Dirk H J Poot and Ben Jeurissen and Yannick Bastiaensen and Jelle Veraart and Wim Van Hecke and Paul M Parizel and Jan Sijbers}
}
@article {1419,
	title = {Super-Resolution of License Plate Images using Algebraic Reconstruction Technique},
	journal = {Journal of Image and Graphics},
	volume = {1},
	year = {2013},
	month = {2013},
	pages = {94 - 98},
	publisher = {Engineering and Technology Publishing (ETP)},
	address = {USA},
	abstract = {In this paper, an iterative super-resolution reconstruction method is introduced for license plate recognition. A high-resolution image of the license plate is reconstructed by fusing the information derived from a set of subpixel shifted low-resolution images. The reconstruction problem is formulated as a system of linear equations that is solved by using the simultaneous algebraic reconstruction technique (SIRT). Simulation experiments show that SIRT can reconstruct a HR image with superior quality compared to conventional super-resolution reconstruction methods.},
	keywords = {algebraic reconstruction technique, license plate, reconstruction, SIRT, super-resolution},
	issn = {23013699},
	doi = {10.12720/joig.1.2.94-98},
	url = {http://www.joig.org/index.php?m=content\&c=index\&a=show\&catid=31\&id=41},
	author = {Karim Zarei Zefreh and Wim Van Aarle and Kees Joost Batenburg and Jan Sijbers}
}
@inproceedings {tanmay2013tracking,
	title = {Tracking for Quantifying Social Network of Drosophila Melanogaster},
	booktitle = {15th Computer Analysis of Images and Patterns (CAIP)},
	volume = {8048},
	year = {2013},
	pages = {539{\textendash}545},
	publisher = {Springer},
	organization = {Springer},
	author = {Tanmay Nath and Liu, Guangda and Barbara Weyn and Bassem Hassan and Steve De Backer and Paul Scheunders}
}
@article {1505,
	title = {On using projection onto convex sets for solving the hyperspectral unmixing problem},
	journal = {IEEE Geoscience and Remote Sensing Letters},
	year = {2013},
	author = {Rob Heylen and Muhamed Awais Akhter and Paul Scheunders}
}
@article {1397,
	title = {Weighted linear least squares estimation of diffusion MRI parameters: Strengths, limitations, and pitfalls.},
	journal = {NeuroImage},
	volume = {81},
	year = {2013},
	month = {2013 May 16},
	pages = {335-346},
	abstract = {PURPOSE: Linear least squares estimators are widely used in diffusion MRI for the estimation of diffusion parameters. Although adding proper weights is necessary to increase the precision of these linear estimators, there is no consensus on how to practically define them. In this study, the impact of the commonly used weighting strategies on the accuracy and precision of linear diffusion parameter estimators is evaluated and compared with the nonlinear least squares estimation approach. METHODS: Simulation and real data experiments were done to study the performance of the weighted linear least squares estimators with weights defined by (a) the squares of the respective noisy diffusion-weighted signals; and (b) the squares of the predicted signals, which are reconstructed from a previous estimate of the diffusion model parameters. RESULTS: The negative effect of weighting strategy (a) on the accuracy of the estimator was surprisingly high. Multi-step weighting strategies yield better performance and, in some cases, even outperformed the nonlinear least squares estimator. CONCLUSION: If proper weighting strategies are applied, the weighted linear least squares approach shows high performance characteristics in terms of accuracy/precision and may even be preferred over nonlinear estimation methods.},
	issn = {1095-9572},
	doi = {10.1016/j.neuroimage.2013.05.028},
	author = {Jelle Veraart and Jan Sijbers and Stefan Sunaert and Alexander Leemans and Ben Jeurissen}
}
@conference {1558,
	title = {{\textquotedblleft}The weighted linear least squares for estimating diffusion (kurtosis) tensors: Revisited},
	year = {2013},
	address = {Podstrana, Croatia},
	author = {Jelle Veraart and Jan Sijbers and Stefan Sunaert and Alexander Leemans and Ben Jeurissen}
}
@article {ThoonenHufkensBorreSpanhovepscheund2011,
	title = {Accuracy assessment of contextual classification results for vegetation mapping},
	journal = {International Journal of Applied Earth Observation and Geoinformation},
	volume = {15},
	year = {2012},
	pages = {7 - 15},
	abstract = {A new procedure for quantitatively assessing the geometric accuracy of thematic maps, obtained from classifying hyperspectral remote sensing data, is presented. More specifically, the methodology is aimed at the comparison between results from any of the currently popular contextual classification strategies. The proposed procedure characterises the shapes of all objects in a classified image by defining an appropriate reference and a new quality measure. The results from the proposed procedure are represented in an intuitive way, by means of an error matrix, analogous to the confusion matrix used in traditional thematic accuracy representation. A suitable application for the methodology is vegetation mapping, where lots of closely related and spatially connected land cover types are to be distinguished. Consequently, the procedure is tested on a heathland vegetation mapping problem, related to Natura 2000 habitat monitoring. Object-based mapping and Markov Random Field classification results are compared, showing that the selected Markov Random Fields approach is more suitable for the fine-scale problem at hand, which is confirmed by the proposed procedure.},
	keywords = {Accuracy assessment, Confusion matrix, Contextual classification, Natura 2000},
	issn = {0303-2434},
	doi = {10.1016/j.jag.2011.05.013},
	url = {http://www.sciencedirect.com/science/article/pii/S0303243411000766},
	author = {Guy Thoonen and Koen Hufkens and Jeroen Vanden Borre and Toon Spanhove and Paul Scheunders}
}
@article {1320,
	title = {Accurate segmentation of dense nanoparticles by partially discrete electron tomography},
	journal = {Ultramicroscopy},
	volume = {114},
	year = {2012},
	pages = {96-105},
	author = {Roelandts, Tom and Kees Joost Batenburg and E. Biermans and C. Kubel and Sara Bals and Jan Sijbers}
}
@proceedings {1373,
	title = {ACIVS 2012, Advanced Concepts for Intelligent Vision Systems},
	volume = {7517},
	year = {2012},
	publisher = {Springer},
	author = {J Blanc-Talon and Wilfried Philips and D Popescu and Paul Scheunders and P. Zemcik}
}
@inproceedings {1332,
	title = {An adaptive non local maximum likelihood estimation method for denoising magnetic resonance images},
	booktitle = { IEEE International Symposium on Biomedical Imaging (ISBI)},
	year = {2012},
	author = {Jeny Rajan and Johan Van Audekerke and Annemie Van Der Linden and Marleen Verhoye and Jan Sijbers}
}
@mastersthesis {1339,
	title = {Advances in the quality and resolution improvement of Magnetic Resonance images},
	volume = {PhD in Sciences: Physics},
	year = {2012},
	month = {03/2012},
	type = {PhD thesis},
	author = {Zhenhua Mai}
}
@conference {1341,
	title = {Assessing the implications of complex fiber configurations for DTI metrics in real data sets},
	year = {2012},
	month = {May},
	pages = {3584},
	address = {Melbourne, Australia},
	author = {Ben Jeurissen and Alexander Leemans and Jacques-Donald Tournier and Derek K. Jones and Jan Sijbers}
}
@article {1350,
	title = {Automatic parameter estimation for the Discrete Algebraic Reconstruction Technique (DART)},
	journal = {IEEE Transactions on Image Processing},
	volume = {21},
	year = {2012},
	pages = {4608-4621},
	doi = {10.1109/TIP.2012.2206042},
	author = {Wim Van Aarle and Kees Joost Batenburg and Jan Sijbers}
}
@inproceedings {1352,
	title = {Automatic threshold selection for morphological attribute profiles},
	booktitle = {IEEE IGARSS2012, International Geoscience and Remote Sensing Symposium, Munich, July 22-27},
	year = {2012},
	pages = {4946-4949},
	author = {Zahid Mahmood and Guy Thoonen and Paul Scheunders}
}
@article {1318,
	title = {A Bayesian Restoration Approach for Hyperspectral Images},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {50},
	year = {2012},
	pages = {3453-3462},
	author = {Y. Zhang and A. Duijster and Paul Scheunders}
}
@article {1316,
	title = {Calculation of geodesic distances in non-linear mixing models: demonstration on the generalized bilinear model},
	journal = {IEEE Geoscience and Remote Sensing letters},
	volume = {9},
	year = {2012},
	pages = {644-648},
	author = {Rob Heylen and Paul Scheunders}
}
@inproceedings {1367,
	title = {Combined Motion Estimation and Reconstruction in Tomography},
	booktitle = {12th European Conference on Computer Vision},
	volume = {7583},
	year = {2012},
	pages = {12-21},
	publisher = {Lecture Notes on Computer Science},
	organization = {Lecture Notes on Computer Science},
	address = {Firenze, Italy},
	abstract = {If objects or patients move during a CT scan, reconstructions suffer from severe motion artifacts. Time dependent computed tomography (4DCT) tries to minimize these artifacts by estimating motion and/or reconstruction simultaneously. Most current methods assume a known deformation or a reconstruction without artifacts at a certain time point. This work explores the possibilities of estimating the motion model and reconstruction simultaneously. It does so by modifying the simultaneous iterative reconstruction technique (SIRT) to incorporate motion (trans-SIRT) and uses this method in an optimization routine that computes motion and reconstruction at the same time. Results show that the optimization routine is able to estimate motion accurately, assuming only the type of parametrization for the motion model. Our approach can potentially be extended to more complex motion models.},
	doi = {10.1007/978-3-642-33863-2_2},
	author = {Van Eyndhoven, Geert and Jan Sijbers and Kees Joost Batenburg}
}
@article {CampI.L.N.mverhoyejveraartwvheckeE.S.jsijbersA.avdlinde2011,
	title = {A complementary DTI-histological study in a model of Huntingtons disease},
	journal = {Neurobiology of Aging},
	volume = {33},
	number = {5},
	year = {2012},
	pages = {945-959},
	doi = {http://dx.doi.org/10.1016/j.neurobiolaging.2010.07.001},
	author = {N. Van Camp and Ines Blockx and L. Camon and N. de Vera and Marleen Verhoye and Jelle Veraart and Wim Van Hecke and E. Martinez and Guadelupe S. and Jan Sijbers and A. Planas and Annemie Van Der Linden}
}
@article {1459,
	title = {Compression and Noise Reduction of Hyperspectral Images using Tucker Decomposition and Discrete Wavelet Transform},
	journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
	volume = {5},
	year = {2012},
	chapter = {444},
	author = {Azam Karami and M.Yazdi and G. Mercier}
}
@mastersthesis {1333,
	title = {Contextual classification of hyperspectral remote sensing images - Application in vegetation monitoring},
	year = {2012},
	pages = {156},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp, Belgium},
	author = {Guy Thoonen}
}
@conference {1325,
	title = {Correction of Gibbs ringing in diffusion MRI data using total variation regularization},
	year = {2012},
	month = {January},
	pages = {99},
	keywords = {MRI distortion artefacts},
	author = {Daniele Perrone and Jan Aelterman and Maryna Kudzinava and Jan Sijbers and Aleksandra Pizurica and Wilfried Philips and Alexander Leemans}
}
@article {1304,
	title = {Diffusion kurtosis imaging in the grading of gliomas},
	journal = {Radiology},
	volume = {2},
	number = {263},
	year = {2012},
	pages = {492-501},
	doi = {10.1148/radiol.12110927},
	author = {Van Cauter, Sofie and Jelle Veraart and Jan Sijbers and Ron R Peeters and U. Himmelreich and S. Van Gool and Van Calenbergh, F. and De Vleeschouwer, S. and Wim Van Hecke and Stefan Sunaert}
}
@conference {1366,
	title = {Discrete algebraic reconstruction in MRI: a simulation study},
	volume = {25},
	year = {2012},
	pages = {480-481},
	keywords = {compressed sensing, DART, iterative reconstruction, prior knowledge},
	doi = {10.1007/s10334-012-0324-9},
	author = {Quinten Collier and Segers, Hilde and Jan Sijbers}
}
@conference {1415,
	title = {DTI-based Classification of Affection Status in Asperger Syndrome.},
	year = {2012},
	author = {Roine, Ulrika and Timo Roine and Salmi, Juha and Nieminen-von Wendt, Taina and Lepp{\"a}m{\"a}ki, Sami and Rintahaka, Pertti and Tani, Pekka and Sams, Mikko}
}
@inproceedings {1351,
	title = {Estimating the number of endmembers in hyperspectral imagery with nearest neighbor distances},
	booktitle = {IEEE IGARSS2012, International Geoscience and Remote Sensing Symposium, Munich, July 22-27},
	year = {2012},
	pages = {1377-1380},
	author = {Rob Heylen and Paul Scheunders}
}
@mastersthesis {1378,
	title = {Estimation and removal of noise from single and multiple coil  Magnetic Resonance images},
	year = {2012},
	month = {11/2012},
	type = {PhD Thesis},
	author = {Jeny Rajan}
}
@article {1515,
	title = {Extraction of airways from CT (EXACT{\textquoteright}09).},
	journal = {IEEE Transactions on Medical Imaging},
	volume = {31},
	year = {2012},
	month = {2012 Nov},
	pages = {2093-107},
	abstract = {This paper describes a framework for establishing a reference airway tree segmentation, which was used to quantitatively evaluate fifteen different airway tree extraction algorithms in a standardized manner. Because of the sheer difficulty involved in manually constructing a complete reference standard from scratch, we propose to construct the reference using results from all algorithms that are to be evaluated. We start by subdividing each segmented airway tree into its individual branch segments. Each branch segment is then visually scored by trained observers to determine whether or not it is a correctly segmented part of the airway tree. Finally, the reference airway trees are constructed by taking the union of all correctly extracted branch segments. Fifteen airway tree extraction algorithms from different research groups are evaluated on a diverse set of twenty chest computed tomography (CT) scans of subjects ranging from healthy volunteers to patients with severe pathologies, scanned at different sites, with different CT scanner brands, models, and scanning protocols. Three performance measures covering different aspects of segmentation quality were computed for all participating algorithms. Results from the evaluation showed that no single algorithm could extract more than an average of 74\% of the total length of all branches in the reference standard, indicating substantial differences between the algorithms. A fusion scheme that obtained superior results is presented, demonstrating that there is complementary information provided by the different algorithms and there is still room for further improvements in airway segmentation algorithms.},
	keywords = {Algorithms, Analysis of Variance, Databases, Factual, Humans, Lung, Radiographic Image Enhancement, Tomography, X-Ray Computed, Trachea},
	issn = {1558-254X},
	doi = {10.1109/TMI.2012.2209674},
	author = {Lo, Pechin and van Ginneken, Bram and Reinhardt, Joseph M and Yavarna, Tarunashree and de Jong, Pim A and Irving, Benjamin and Fetita, Catalin and Ortner, Margarete and R{\^o}mulo Pinho and Jan Sijbers and Feuerstein, Marco and Fabija{\'n}ska, Anna and Bauer, Christian and Beichel, Reinhard and Mendoza, Carlos S and Wiemker, Rafael and Lee, Jaesung and Reeves, Anthony P and Born, Silvia and Weinheimer, Oliver and van Rikxoort, Eva M and Tschirren, Juerg and Mori, Ken and Odry, Benjamin and Naidich, David P and Hartmann, Ieneke and Eric A. Hoffman and Prokop, Mathias and Pedersen, Jesper H and de Bruijne, Marleen}
}
@inproceedings {1354,
	title = {A fast geometric algorithm for solving the inversion problem in spectral unmixing},
	booktitle = {IEEE-WHISPERS 2012, Workshop on Hyperspectral Image and Signal Processing, Shanghai, June 4-7},
	year = {2012},
	author = {Rob Heylen and Paul Scheunders}
}
@article {1313,
	title = {Force Feedback to Assist Active Contour Modelling for Tracheal Stenosis Segmentation},
	journal = {Advances in Human-Computer Interaction},
	volume = {2012},
	year = {2012},
	abstract = {Manual segmentation of structures for diagnosis and treatment of various diseases is a very time-consuming procedure. Therefore, some level of automation during the segmentation is desired, as it often significantly reduces the segmentation time. A typical solution is to allow manual interaction to steer the segmentation process, which is known as semiautomatic segmentation. In 2D, such interaction is usually achieved with click-and-drag operations, but in 3D a more sophisticated interface is called for. In this paper, we propose a semi-automatic Active Contour Modelling for the delineation of medical structures in 3D, tomographic images. Interaction is implemented with the employment of a 3D haptic device, which is used to steer the contour deformation towards the correct boundaries. In this way, valuable haptic feedback is provided about the 3D surface and its deformation. Experiments on simulated and real tracheal CT data showed that the proposed technique is an intuitive and effective segmentation mechanism.},
	doi = {doi:10.1155/2012/632498},
	url = {http://www.hindawi.com/journals/ahci/2012/632498/},
	author = {Lode Vanacken and R{\^o}mulo Pinho and Jan Sijbers and Karen Coninx}
}
@conference {1714,
	title = {A framework for markerless alignment with full 3D flexibility},
	year = {2012},
	author = {Jan De Beenhouwer and Willem Jan Palenstijn and Folkert Bleichrodt and Kees Joost Batenburg and Jan Sijbers}
}
@article {Verdoolaegepscheund2011,
	title = {On the geometry of Multivariate Generalized Gaussian models},
	journal = {Journal of Mathematical Imaging and Vision},
	volume = {43},
	year = {2012},
	pages = {180-193},
	author = {G. Verdoolaege and Paul Scheunders}
}
@conference {1340,
	title = {HARDI-based methods for fiber orientation estimation},
	year = {2012},
	note = {Received the Summa Cum Laude Merit Award for the 20th Annual ISMRM meeting.},
	month = {May},
	pages = {3585},
	address = {Melbourne, Australia},
	author = {Ben Jeurissen and Alexander Leemans and Jacques-Donald Tournier and Jan Sijbers}
}
@proceedings {1463,
	title = {Hyperspectral Image Compression Using 3d Discrete Cosine Transform and Support Vector Machine Learning},
	year = {2012},
	address = {Montreal, Canada},
	author = {Azam Karami and S. Beheshti and M.Yazdi}
}
@article {1349,
	title = {Identification and characterization of Huntington related pathology: an in vivo DKI imaging study},
	journal = {NeuroImage},
	volume = {63},
	year = {2012},
	month = {09/2012},
	pages = {653-662},
	doi = {http://dx.doi.org/10.1016/j.neuroimage.2012.06.032},
	author = {Ines Blockx and Marleen Verhoye and Johan Van Audekerke and Irene  Bergwerf and Jack X Kane and Rafael  Delgado Y Palacios and Jelle Veraart and Ben Jeurissen and Kerstin Raber and Von H{\"o}rsten, Stephan and Peter Ponsaerts and Jan Sijbers and Trygve B Leergaard and Annemie Van Der Linden}
}
@mastersthesis {1337,
	title = {Improved analysis of brain connectivity using high angular resolution diffusion MRI},
	volume = {PhD in Sciences},
	year = {2012},
	month = {03/2012},
	pages = {208},
	school = {University of Antwerp},
	type = {PhD thesis},
	keywords = {complex WM architecture, constrained spherical deconvolution, crossing fibers, CSD, Diffusion MRI, diffusion tensor imaging, DTI, HARDI, high angular resolution diffusion imaging, Magnetic Resonance Imaging, MRI, probabilistic tractography, Spherical deconvolution, Tractography, white matter, WM},
	author = {Ben Jeurissen}
}
@article {1516,
	title = {Improved sensitivity to cerebral white matter abnormalities in Alzheimer{\textquoteright}s disease with spherical deconvolution based tractography.},
	journal = {PloS one},
	volume = {7},
	year = {2012},
	month = {2012},
	pages = {e44074},
	abstract = {Diffusion tensor imaging (DTI) based fiber tractography (FT) is the most popular approach for investigating white matter tracts in vivo, despite its inability to reconstruct fiber pathways in regions with "crossing fibers." Recently, constrained spherical deconvolution (CSD) has been developed to mitigate the adverse effects of "crossing fibers" on DTI based FT. Notwithstanding the methodological benefit, the clinical relevance of CSD based FT for the assessment of white matter abnormalities remains unclear. In this work, we evaluated the applicability of a hybrid framework, in which CSD based FT is combined with conventional DTI metrics to assess white matter abnormalities in 25 patients with early Alzheimer{\textquoteright}s disease. Both CSD and DTI based FT were used to reconstruct two white matter tracts: one with regions of "crossing fibers," i.e., the superior longitudinal fasciculus (SLF) and one which contains only one fiber orientation, i.e. the midsagittal section of the corpus callosum (CC). The DTI metrics, fractional anisotropy (FA) and mean diffusivity (MD), obtained from these tracts were related to memory function. Our results show that in the tract with "crossing fibers" the relation between FA/MD and memory was stronger with CSD than with DTI based FT. By contrast, in the fiber bundle where one fiber population predominates, the relation between FA/MD and memory was comparable between both tractography methods. Importantly, these associations were most pronounced after adjustment for the planar diffusion coefficient, a measure reflecting the degree of fiber organization complexity. These findings indicate that compared to conventionally applied DTI based FT, CSD based FT combined with DTI metrics can increase the sensitivity to detect functionally significant white matter abnormalities in tracts with complex white matter architecture.},
	keywords = {Aged, 80 and over, Alzheimer Disease, Cerebrum, Cognition, Corpus Callosum, diffusion tensor imaging, Female, Humans, Male, Memory, Nerve Fibers, Myelinated},
	issn = {1932-6203},
	doi = {10.1371/journal.pone.0044074},
	author = {Reijmer, Yael D and Alexander Leemans and Heringa, Sophie M and Wielaard, Ilse and Ben Jeurissen and Koek, Huiberdina L and Biessels, Geert Jan}
}
@inproceedings {1356,
	title = {Imrpoving PLCA-based score-informed source separation with invertible constant-Q transform},
	booktitle = {EUSIPCO 2012, 20th European Signal Processing Conference, Bucharest, Romania, august 27-31 },
	year = {2012},
	pages = {2634-2638},
	author = {Joachim Ganseman and Paul Scheunders and S. Dixon}
}
@article {1336,
	title = {The influence of complex white matter architecture on the mean diffusivity in diffusion tensor MRI of the human brain},
	journal = {NeuroImage},
	volume = {59},
	year = {2012},
	month = {2/2012},
	pages = {2208 - 2216},
	issn = {10538119},
	doi = {10.1016/j.neuroimage.2011.09.086},
	author = {Vos, Sjoerd B. and Derek K. Jones and Ben Jeurissen and Viergever, Max A. and Alexander Leemans}
}
@inproceedings {1357,
	title = {A jump start for NMF with N-Findr and NNLS},
	booktitle = {DAFx-12, 15-th Int. Conference on Digital Audio Effects, York, UK, september 17-21},
	year = {2012},
	author = {Joachim Ganseman and Paul Scheunders}
}
@article {1309,
	title = {Microstructural changes observed with DKI in a transgenic Huntington rat model: Evidence for abnormal neurodevelopment.},
	journal = {NeuroImage},
	volume = {59},
	number = {2},
	year = {2012},
	month = {2012 Jan 16},
	pages = {957-67},
	abstract = {Huntington Disease (HD) is a fatal neurodegenerative disorder, caused by a mutation in the Huntington gene. Although HD is most often diagnosed in mid-life, the key to its clinical expression may be found during brain maturation. In the present work, we performed in vivo diffusion kurtosis imaging (DKI) in order to study brain microstructure alterations in developing transgenic HD rat pups. Several developing brain regions, relevant for HD pathology (caudate putamen, cortex, corpus callosum, external capsule and anterior commissure anterior), were examined at postnatal days 15 (P15) and 30 (P30), and DKI results were validated with histology. At P15, we observed higher mean (MD) and radial (RD) diffusivity values in the cortex of transgenic HD rat pups. In addition, at the age of P30, lower axial kurtosis (AK) values in the caudate putamen of transgenic HD pups were found. At the level of the external capsule, higher MD values at P15 but lower MD and AD values at P30 were detected. The observed DKI results have been confirmed by myelin basic protein immunohistochemistry, which revealed a reduced fiber staining as well as less ordered fibers in transgenic HD rat pups. These results indicate that neuronal development in young transgenic HD rat pups occurs differently compared to controls and that the presence of mutant huntingtin has an influence on postnatal brain development. In this context, various diffusivity parameters estimated by the DKI model are a powerful tool to assess changes in tissue microstructure and detect developmental changes in young transgenic HD rat pups.},
	issn = {1095-9572},
	doi = {10.1016/j.neuroimage.2011.08.062},
	author = {Ines Blockx and G. De Groof and Marleen Verhoye and Johan Van Audekerke and Kerstin Raber and Dirk H J Poot and Jan Sijbers and Osmand, Alexander P and Von H{\"o}rsten, Stephan and Annemie Van Der Linden}
}
@article {ThoonenMahmoodPeeterspscheund2012,
	title = {Multisource classification of color and hyperspectral images using color attribute profiles and composite decision fusion},
	journal = {Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of},
	volume = {5},
	number = {2},
	year = {2012},
	pages = {510 - 521},
	abstract = {In this work, we treat the problem of combined classification of a high spatial resolution color image and a lower spatial resolution hyperspectral image of the same scene. The problem is particularly challenging, since we aim for classification maps at the spatial resolution of the color image. Contextual information is obtained from the color image by introducing Color Attribute Profiles (CAPs). Instead of treating the {\textquoteleft}R{\textquoteright}, {\textquoteleft}G{\textquoteright}, and {\textquoteleft}B{\textquoteright} bands separately, the color image is transformed into CIE-Lab space. In this color space, attribute profiles are extracted from the {\textquoteleft}L{\textquoteright} band, which corresponds to the Luminance, while the {\textquoteleft}a{\textquoteright} and {\textquoteleft}b{\textquoteright} bands, which contain the color information, are kept intact, and the resulting images are transformed back into RGB space. The spectral information is obtained from the hyperspectral image. A Composite Decision Fusion (CDF) strategy is proposed, combining a state-of-the-art kernel-based decision fusion technique with the popular composite kernel classification approach. Experiments are conducted, using simulated data and a real multisource dataset containing airborne hyperspectral data and orthophotographic data from a suburban area in Belgium. These experiments show that our CAPs perform well with respect to other approaches for extracting attribute profiles from high resolution color images, and that the proposed CDF strategy produces meaningful results with respect to concatenation and the highlighted state-of-the-art approaches for combining multisource data.},
	keywords = {Color, image classification, morphological operations, multiresolution techniques, multisensor systems},
	issn = {1939-1404},
	doi = {10.1109/JSTARS.2011.2168317},
	author = {Guy Thoonen and Zahid Mahmood and Stijn Peeters and Paul Scheunders}
}
@article {1438,
	title = {Nonlocal maximum likelihood estimation method for denoising multiple-coil magnetic resonance images.},
	journal = {Magnetic resonance imaging},
	volume = {30},
	year = {2012},
	month = {2012 Dec},
	pages = {1512-8},
	abstract = {Effective denoising is vital for proper analysis and accurate quantitative measurements from magnetic resonance (MR) images. Even though many methods were proposed to denoise MR images, only few deal with the estimation of true signal from MR images acquired with phased-array coils. If the magnitude data from phased array coils are reconstructed as the root sum of squares, in the absence of noise correlations and subsampling, the data is assumed to follow a non central-χ distribution. However, when the k-space is subsampled to increase the acquisition speed (as in GRAPPA like methods), noise becomes spatially varying. In this note, we propose a method to denoise multiple-coil acquired MR images. Both the non central-χ distribution and the spatially varying nature of the noise is taken into account in the proposed method. Experiments were conducted on both simulated and real data sets to validate and to demonstrate the effectiveness of the proposed method.},
	keywords = {Algorithms, Animals, Brain, Brain Mapping, Computer Simulation, Fourier Analysis, Image Processing, Computer-Assisted, Likelihood Functions, Magnetic Resonance Imaging, Mice, Models, Statistical, Normal Distribution, Signal-To-Noise Ratio, Stochastic Processes},
	issn = {1873-5894},
	doi = {10.1016/j.mri.2012.04.021},
	author = {Jeny Rajan and Jelle Veraart and Johan Van Audekerke and Marleen Verhoye and Jan Sijbers}
}
@article {1344,
	title = {Nonlocal maximum likelihood estimation method for denoising multiple-coil magnetic resonance images},
	journal = {Magnetic Resonance Imaging},
	volume = {30},
	year = {2012},
	pages = {1512-1518},
	doi = {10.1016/j.mri.2012.04.021},
	author = {Jeny Rajan and Jelle Veraart and Johan Van Audekerke and Marleen Verhoye and Jan Sijbers}
}
@conference {1388,
	title = {Optimization of Compressed Sensing/RARE Combining Acquisition Schemes},
	volume = {25},
	year = {2012},
	pages = {513-514},
	doi = {10.1007/s10334-012-0324-9},
	author = {Maarten Naeyaert and Jan Aelterman and Johan Van Audekerke and Kasper Claes and Annemie Van Der Linden and Jan Sijbers and Marleen Verhoye}
}
@inproceedings {1299,
	title = {Quantitative evaluation of ASiR image quality: an adaptive statistical iterative reconstruction technique},
	booktitle = {SPIE Medical Imaging},
	volume = {8313},
	year = {2012},
	month = {02/2012},
	abstract = {Adaptive statistical iterative reconstruction (ASiR) is a new algorithm used in medical X-ray imaging. It combines the standard FBP and the strength of iterative reconstruction by including a noise model in the reconstruction scheme. In this paper the effect of ASiR on the contrast to noise ratio (CNR) is studied using a low contrast phantom (Catphan). The experiments were done at different voltages and currents. The results show reduced noise and increased contrast for the ASiR reconstructions compared to FBP. For the same CNR the images from ASiR are obtained using 60\% less current, leading to a 60\% dose reduction.},
	doi = {doi:10.1117/12.911283},
	author = {Elke Van de Casteele and Paul M Parizel and Jan Sijbers}
}
@conference {1430,
	title = {Segmentation of MR Images by k-space distance minimization: a proof of concept},
	volume = {25},
	year = {2012},
	pages = {63-64},
	author = {Segers, Hilde and Willem Jan Palenstijn and Kees Joost Batenburg and Jan Sijbers}
}
@inproceedings {1360,
	title = {Spatial Variations in Reconstruction Methods for CT},
	booktitle = {The Second International Conference on Image Formation in X-Ray Computed Tomography (CT Meeting)},
	year = {2012},
	month = {07/2012},
	pages = {170-173},
	author = {Linda Plantagie and Willem Jan Palenstijn and Jan Sijbers and Kees Joost Batenburg}
}
@conference {1365,
	title = {Statistical shape modelling in support of user-centred BCI headset design},
	year = {2012},
	author = {Daniel Lacko and Toon Huysmans and Stijn Verwulgen and Paul M Parizel and Marc M. Van Hulle and Jan Sijbers}
}
@inproceedings {1507,
	title = {Subpixel mapping of hyperspectral data using high resolution color images},
	booktitle = {IEEE-WHISPERS 2012, Workshop on Hyperspectral Image and Signal Processing, Shanghai, June 4-7},
	year = {2012},
	author = {Zahid Mahmood and Guy Thoonen and Muhamed Awais Akhter and Paul Scheunders}
}
@article {1393,
	title = {Through the Looking-Glass, and What the Quadratic Camera Found There},
	journal = {The Mathematical Intelligencer},
	volume = {34},
	year = {2012},
	month = {9/2012},
	pages = {30 - 34},
	issn = {0343-6993},
	doi = {10.1007/s00283-012-9309-9},
	author = {de Smit, Bart and McClure, Mark and Willem Jan Palenstijn and Sparling, E. Isaac and Wagon, Stan}
}
@mastersthesis {1327,
	title = {Tomographic segmentation and discrete tomography for quantitative analysis of transmission tomography data},
	year = {2012},
	pages = {190},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp, Belgium},
	author = {Wim Van Aarle}
}
@inproceedings {1353,
	title = {Unmixing for detection and quantification of adjacency effects},
	booktitle = {IEEE IGARSS2012, International Geoscience and Remote Sensing Symposium, Munich, July 22-27},
	year = {2012},
	pages = {3090-3093},
	author = {Dzevdet Burazerovic and Bert Geens and Rob Heylen and S Sterckx and Paul Scheunders}
}
@inproceedings {1358,
	title = {Visualizing the Segmentation Error of a Tomogram using the Residual Projection Error},
	booktitle = {The Second International Conference on Image Formation in X-Ray Computed Tomography (CT Meeting)},
	year = {2012},
	pages = {293-296},
	address = {Salt Lake City, UT, USA},
	author = {Roelandts, Tom and Kees Joost Batenburg and Jan Sijbers}
}
@inbook {1257,
	title = {Wavelet-based Multi/Hyperspectral Image Restoration and Fusion},
	booktitle = {Signal and Image Processing for Remote Sensing},
	year = {2012},
	pages = {505-523},
	publisher = {Taylor and Francis},
	organization = {Taylor and Francis},
	chapter = {25},
	author = {Paul Scheunders and A. Duijster and Y. Zhang},
	editor = {C.H. Chen}
}
@proceedings {1235,
	title = {ACIVS 2011, Advanced Concepts for Intelligent Vision Systems},
	volume = {6915},
	year = {2011},
	publisher = {Springer},
	author = {J Blanc-Talon and Wilfried Philips and D Popescu and R. Kleihorst and Paul Scheunders}
}
@article {rpinhoTournoyjsijbers2011,
	title = {Assessment and Stenting of Tracheal Stenosis using Deformable Shape Models},
	journal = {Medical Image Analysis},
	volume = {15},
	number = {2},
	year = {2011},
	pages = {250-266},
	abstract = {This work presents a decision support system for the assessment of tracheal stenosis. In the proposed method, a statistical shape model of healthy tracheas is registered to a 3D CT image of a patient with tracheal stenosis. The registration yields an estimation of the shape of the patient{\textquoteright}s trachea as if stenosis was not present. From this point, the extent and the severity of the stenosis is assessed and stent parameters are obtained automatically. The method was extensively evaluated on simulation as well on real data and the results showed that it is accurate and fast enough to be used in the clinical setting.},
	issn = {1361-8423},
	doi = {http://dx.doi.org/doi:10.1016/j.media.2010.12.001},
	author = {R{\^o}mulo Pinho and Kurt G. Tournoy and Jan Sijbers}
}
@article {zmaiR.jbatenbumverhoyepscheundjsijbers2011,
	title = {Bias field reduction by localized Lloyd-Max quantization},
	journal = {Magnetic Resonance Imaging},
	volume = {29},
	year = {2011},
	pages = {536-545},
	doi = {http://dx.doi.org/10.1016/j.mri.2010.10.015},
	author = {Zhenhua Mai and Rudolf Hanel and Kees Joost Batenburg and Marleen Verhoye and Paul Scheunders and Jan Sijbers}
}
@inproceedings {1298,
	title = {Bone segmentation using discrete tomography},
	booktitle = {Micro-CT User Meeting},
	year = {2011},
	pages = {178-183},
	isbn = {9789081678100},
	issn = {2033-8031},
	author = {Elke Van de Casteele and Kees Joost Batenburg and Salmon, Phil and Jan Sijbers}
}
@inproceedings {WitteThoonenpscheundPizuricaPhilips2011,
	title = {Classification of multi-source images using color mathematical morphological profiles},
	booktitle = {Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE International},
	year = {2011},
	pages = {3919 - 3922},
	address = {Vancouver, BC, Canada},
	abstract = {In the remote sensing domain data from many different sources are often available. Each of these data sources are characterized by their own sensor- and platform-specific properties, i.e. spectral range, or spatial and spectral resolution. In this paper we consider a low spatial, but high spectral resolution satellite image, together with its high spatial resolution RGB color image, e.g. obtained by UAV. Spatial features are extracted from the color image by combining the three color bands R, G and B, ordering these color vectors, and presenting color mathematical morphological profiles accordingly. This way the spatial information contained in the correlation between the different bands is completely taken into account and thus also totally preserved in the feature extraction. In a classification experiment these color morphological profiles are combined with the spectral features of the hyperspectral image, and we show that the spatial characterization of the color image is improved.},
	keywords = {Classification, Color, Morphological profiles, Multisource images, remote sensing},
	isbn = {978-1-4577-1003-2},
	issn = {2153-6996},
	doi = {10.1109/IGARSS.2011.6050088},
	url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=6050088\&isnumber=6048881},
	author = {Val{\'e}rie De Witte and Guy Thoonen and Paul Scheunders and Aleksandra Pizurica and Wilfried Philips}
}
@inbook {1263,
	title = {Classification of Soft Tissue Tumors by Machine Learning Algorithms},
	booktitle = {Soft Tissue Tumors},
	year = {2011},
	publisher = {InTech},
	organization = {InTech},
	issn = {978-953-307-862-5},
	author = {Juntu, Jaber and Arthur M. De Schepper and Pieter Van Dyck and Dirk Van Dyck and Jan L Gielen and Paul M Parizel and Jan Sijbers}
}
@article {jveraartwvheckejsijbers2011,
	title = {Constrained Maximum Likelihood Estimation of the Diffusion Kurtosis Tensor Using a Rician Noise Model},
	journal = {Magnetic Resonance in Medicine},
	volume = {66},
	number = {3},
	year = {2011},
	pages = {678-686},
	doi = {http://dx.doi.org/doi:10.1002/mrm.22835},
	author = {Jelle Veraart and Wim Van Hecke and Jan Sijbers}
}
@conference {jveraartwvheckedpootjsijbers2011,
	title = {Constrained maximum likelihood estimator for more accurate diffusion kurtosis tensor estimates},
	year = {2011},
	month = {January},
	address = {Hoeven, The Netherlands},
	author = {Jelle Veraart and Wim Van Hecke and Dirk H J Poot and Jan Sijbers}
}
@conference {1551,
	title = {Constrained spherical deconvolution based tractography and cognition in Alzheimer{\textquoteright}s disease},
	year = {2011},
	address = {Qu{\'e}bec, Canada},
	author = {Reijmer, Yael D and Alexander Leemans and Heringa, Sophie M and Wielaard, Ilse and Ben Jeurissen and Koek, Huiberdina L and Biessels, Geert Jan}
}
@conference {1552,
	title = {Constrained spherical deconvolution based tractography and cognition in Alzheimer{\textquoteright}s disease},
	year = {2011},
	address = {Paris, France},
	author = {Reijmer, Yael D and Alexander Leemans and Heringa, Sophie M and Wielaard, Ilse and Ben Jeurissen and Koek, Huiberdina L and Biessels, Geert Jan}
}
@conference {1549,
	title = {Constrained spherical deconvolution based tractography and cognition in Alzheimer{\textquoteright}s disease},
	volume = {5},
	year = {2011},
	address = {Lille, France},
	author = {Reijmer, Yael D and Alexander Leemans and Heringa, Sophie M and Wielaard, Ilse and Ben Jeurissen and Koek, Huiberdina L and Biessels, Geert Jan}
}
@conference {bjeurissM.aleemansjsijbers2011,
	title = {Correction of DWI gradient orientations using registration techniques},
	year = {2011},
	month = {January},
	author = {Ben Jeurissen and Maarten Naeyaert and Alexander Leemans and Jan Sijbers}
}
@article {jbatenbujsijbers2011,
	title = {DART: A practical reconstruction algorithm for discrete tomography},
	journal = {IEEE Transactions on Image Processing},
	volume = {20},
	number = {9},
	year = {2011},
	pages = {2542-2553},
	doi = {10.1109/TIP.2011.2131661},
	author = {Kees Joost Batenburg and Jan Sijbers}
}
@conference {1245,
	title = {Denoising magnitude MRI using an adaptive NLML method},
	year = {2011},
	pages = {383},
	address = {Leipzig, Germay},
	author = {Jeny Rajan and Johan Van Audekerke and Marleen Verhoye and Annemie Van Der Linden and Jan Sijbers}
}
@conference {Kudzinava_2011_ESMRMB,
	title = {Denoising of DKI images: effect on feasibility and accuracy of kurtosis parameters},
	year = {2011},
	month = {October},
	pages = {236},
	address = {Leipzig, Germany},
	abstract = {Diffusion Kurtosis Imaging (DKI) is a new MRI technique, which quantifies non-Gaussianity of water diffusion. DKI requires high b-values, resulting in a low SNR of acquired diffusion weighted images (DWIs). Generally, kurtosis tensors are estimated from these DWIs using Weighted Least Squares (WLS) or Maximum Likelihood (ML) methods. In this work, we apply the Restricted Local Maximum Likelihood (RLML) denoising algorithm to remove noise from DWIs prior to WLS estimation. Then, we compare the RLML+WLS approach to the WLS and ML methods in terms of feasibility, that is the percentage of estimated tensors that satisfy certain DKI model constraints; and in terms of accuracy of calculated values of Mean Kurtosis.},
	keywords = {denoising, diffusion kurtosis imaging, maximum likelihood estimation},
	author = {Maryna Kudzinava and Jeny Rajan and Jan Sijbers}
}
@conference {1306,
	title = {Denoising SENSE reconstructed MR images},
	year = {2011},
	author = {Jeny Rajan and Jan Sijbers}
}
@conference {Kudzinava_2011_WMSG,
	title = {Diffusion and kurtosis parameters in white matter of premature newborns},
	year = {2011},
	month = {August},
	pages = {21},
	address = {Reykjavik, Iceland},
	abstract = {Diffusion Tensor Imaging (DTI) is often used to study brain maturation processes and white matter (WM) injuries in premature newborns. However, DTI assumes that water diffusion in biological tissues is free. In reality, there is always structural hindrance in WM, which only increases with age due to myelination. Diffusional Kurtosis Imaging (DKI) relaxes the assumption of free diffusion and allows estimation of conventional diffusion parameters plus new kurtosis measures, which are related to tissue complexity. In this work, we present and discuss the first statistics of DKI parameters in WM of two prematurely born infants.},
	keywords = {anisotropy, diffusion, kurtosis, premature newborns, white matter},
	author = {Maryna Kudzinava and A. Plaisier and J. Dudink and Jan Sijbers}
}
@conference {R.jveraartG.H.mverhoyeP.jsijbersavdlinde2011,
	title = {Diffusion kurtosis abnormalities in pre-symptomatic (alpha)-synycleunopathy mouse model},
	year = {2011},
	month = {January},
	address = {Hoeven, The Netherlands},
	author = {Rafael  Delgado Y Palacios and Jelle Veraart and Greetje Vanhoutte and H. Schell and Marleen Verhoye and P. Kahle and Jan Sijbers and Annemie Van Der Linden}
}
@conference {1550,
	title = {Diffusion tensor imaging and cognition in Alzheimer{\textquoteright}s disease: the influence of crossing fibers},
	year = {2011},
	address = {Lunteren, The Netherlands},
	author = {Wielaard, Ilse and Reijmer, Yael D and Alexander Leemans and Heringa, Sophie M and Ben Jeurissen and Koek, Huiberdina L and Biessels, Geert Jan}
}
@conference {1307,
	title = {Distortion correction of DKI data: affine approach},
	year = {2011},
	month = {December},
	abstract = {In this work, the performance of FMRIB{\textquoteright}s Linear Registration Tool (FLIRT) is evaluated with respect to motion and distortion correction of diffusion kurtosis imaging (DKI) data. FLIRT is tested on two DKI datasets heavily distorted by motion and eddy currents artefacts. Six different criteria are used for evaluation. Our results show that distortion correction using FLIRT substantially improves the original data. However, being an affine registration tool, FLIRT cannot cope with the amount of non-linear distortion present in DKI datasets. Hence, we conclude that, for correction of DKI data, non-linear registration techniques are more suitable.},
	keywords = {medical imaging},
	author = {Maryna Kudzinava and Alexander Leemans and Wilfried Philips and Daniele Perrone and Jan Aelterman and J. Dudink and Jan Sijbers}
}
@article {wvheckealeemansC.jveraartjsijbersS.2011,
	title = {The effect of template selection on diffusion tensor voxel based analysis results},
	journal = {NeuroImage},
	volume = {55},
	number = {2},
	year = {2011},
	pages = {566-573},
	author = {Wim Van Hecke and Alexander Leemans and Caroline A Sage and Jelle Veraart and Jan Sijbers and Stefan Sunaert}
}
@inproceedings {1429,
	title = {Efficient parameter estimation for discrete tomography using adaptive modeling},
	booktitle = {11th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine},
	year = {2011},
	month = {07/2011},
	author = {Wim Van Aarle and K. Crombecq and I. Couckuyt and Kees Joost Batenburg and Jan Sijbers}
}
@article {Mahmoodpscheund2011,
	title = {Enhanced visualization of hyperspectral images},
	journal = {IEEE Geoscience and Remote Sensing letters},
	volume = {8},
	number = {5},
	year = {2011},
	pages = {869-873},
	author = {Zahid Mahmood and Paul Scheunders}
}
@conference {1331,
	title = {An extended NLML method for denoising non-central chi distributed data - application to parallel MRI},
	year = {2011},
	pages = {41},
	author = {Jeny Rajan and Johan Van Audekerke and Jelle Veraart and Marleen Verhoye and Jan Sijbers}
}
@inproceedings {1942,
	title = {Feasibility and advantages of diffusion weighted imaging atlas construction in Q-space},
	booktitle = {MICCAI 2011: Medical Image Computing and Computer-Assisted Intervention},
	year = {2011},
	pages = {166-173},
	author = {Thijs Dhollander and Jelle Veraart and Wim Van Hecke and F. Maes and Stefan Sunaert and Jan Sijbers and Paul Suetens}
}
@conference {M.J.bjeurissP.J.K.J.aleemans2011,
	title = {Fiber architecture of the female pelvic floor: An exploratory investigation using different diffusion MRI tractography algorithms},
	year = {2011},
	month = {May},
	address = {Montreal, Canada},
	author = {Martijn Froeling and Strijkers G. J. and Ben Jeurissen and van der Paardt M. P. and J. Stoker and K. Nicolay and A. J. Nederveen and Alexander Leemans}
}
@article {HeylenBurazerovicpscheund2011,
	title = {Fully constrained least-squares spectral unmixing by simplex projection},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {49},
	year = {2011},
	pages = {4112-4122},
	author = {Rob Heylen and Dzevdet Burazerovic and Paul Scheunders}
}
@article {Verdoolaegepscheund2011,
	title = {Geodesics on the Manifold of Multivariate Generalized Gaussian Distributions With an Application to Multicomponent Texture Discrimination},
	journal = {International Journal of Computer Vision},
	volume = {95},
	number = {3},
	year = {2011},
	pages = {265-286},
	author = {G. Verdoolaege and Paul Scheunders}
}
@conference {1308,
	title = {Gibbs artifact suppression for DT-MRI data},
	year = {2011},
	month = {December},
	keywords = {medical imaging},
	author = {Daniele Perrone and Jan Aelterman and Maryna Kudzinava and Jan Sijbers and Aleksandra Pizurica and Wilfried Philips and Alexander Leemans}
}
@proceedings {1464,
	title = {Hyperspectral Image Compression based on Tucker Decomposition and Wavelet Transform},
	year = {2011},
	address = {Lisbon, Portugal},
	author = {Azam Karami and M.Yazdi and G. Mercier}
}
@article {gvgompelSlambrouckDefrisejbatenbuMeyjsijbersNuyts2011,
	title = {Iterative correction of beam hardening artifacts in CT},
	journal = {Medical Physics},
	volume = {38},
	number = {1},
	year = {2011},
	month = {July},
	pages = {36-49},
	doi = {http://dx.doi.org/10.1118/1.3577758},
	author = {Gert Van Gompel and Van Slambrouck, Katrien and M. Defrise and Kees Joost Batenburg and Johan De Mey and Jan Sijbers and Johan Nuyts}
}
@article {R.AdriaanKimmverhoyedpootJoukeJ.HjsijbersOavdlinde2011,
	title = {Magnetic resonance imaging and spectroscopy reveal differential hippocampal changes in anhedonic and resilient subtypes of the chronic mild stress rat model},
	journal = {Biological psychiatry},
	volume = {70},
	number = {5},
	year = {2011},
	pages = {449-457},
	author = {Rafael  Delgado Y Palacios and Campo Adriaan and Henningsen Kim and Marleen Verhoye and Dirk H J Poot and Dijkstra Jouke and Johan Van Audekerke and H Benveniste and Jan Sijbers and O. Wiborg and Annemie Van Der Linden}
}
@article {jrajanbjeurissmverhoyeJ.jsijbers2011,
	title = {Maximum likelihood estimation based denoising of magnetic resonance images using restricted local neighborhoods},
	journal = {Physics in Medicine and Biology},
	volume = {56},
	number = {16},
	year = {2011},
	pages = {5221-5234},
	doi = {doi:10.1088/0031-9155/56/16/009},
	author = {Jeny Rajan and Ben Jeurissen and Marleen Verhoye and Johan Van Audekerke and Jan Sijbers}
}
@inproceedings {jrajanmverhoyejsijbers2011,
	title = {A maximum likelihood estimation method for denoising magnitude MRI using restricted local neighborhood},
	booktitle = {SPIE Medical Imaging},
	volume = {7962},
	year = {2011},
	publisher = {SPIE},
	organization = {SPIE},
	author = {Jeny Rajan and Marleen Verhoye and Jan Sijbers},
	editor = {B.M. Dawant and D.R. Haynor}
}
@conference {S.jveraartjsijbersU.R.S.wvhecke2011,
	title = {Mean Kurtosis: a new potential biomarker for brain tumor grading},
	year = {2011},
	month = {January},
	address = {Hoeven, The Netherlands},
	author = {Van Cauter, Sofie and Jelle Veraart and Jan Sijbers and U. Himmelreich and Ron R Peeters and S. Van Gool and Wim Van Hecke}
}
@conference {1303,
	title = {Mediolateral shape and curvature analysis of the clavicle},
	year = {2011},
	month = {June},
	author = {Van Tongel, Alexander and Toon Huysmans and Amit Bernat and Jan Sijbers and Francis Van Glabbeek}
}
@article {jveraartdpootwvheckeBlockxavdlindemverhoyejsijbers2011,
	title = {More accurate estimation of diffusion tensor parameters using diffusion kurtosis imaging},
	journal = {Magnetic Resonance in Medicine},
	volume = {65},
	number = {1},
	year = {2011},
	month = {January},
	pages = {138-145},
	doi = {10.1002/mrm.22603},
	author = {Jelle Veraart and Dirk H J Poot and Wim Van Hecke and Ines Blockx and Annemie Van Der Linden and Marleen Verhoye and Jan Sijbers}
}
@inproceedings {MahmoodWittepscheund2011,
	title = {Multi-source image classification using color attribute profiles},
	booktitle = {3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)},
	year = {2011},
	pages = {1 - 4},
	address = {Lisbon, Portugal},
	abstract = {This work introduces a method to extract attribute profiles from RGB color images with high spatial resolution, for instance images acquired from Unmanned Aerial Vehicles (UAV). The resulting Color Attribute Profiles (CAP) are intended to improve the classification of low spatial resolution hyperspectral images by merging the attribute features with the spectral features of the hyperspectral image. Instead of treating the R, G and B bands separately, the color image is transformed into CIE-Lab space. In this color space, attribute profiles are extracted from the {\textquoteleft}L{\textquoteright} band, while the {\textquoteleft}a{\textquoteright} and {\textquoteleft}b{\textquoteright} bands are kept intact, and the resulting images are transformed back into RGB space. In our experiments, classification results using this methodology are compared to classification results using other strategies for extracting attribute profiles in CIE-Lab space, as well as regular grayscale attribute profiles.},
	keywords = {Attributes, Classification, Color, Hyperspectral, Morphology},
	isbn = {978-1-4577-2202-8},
	issn = {2158-6268},
	doi = {10.1109/WHISPERS.2011.6080859},
	url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=6080859\&isnumber=6080842},
	author = {Zahid Mahmood and Guy Thoonen and Val{\'e}rie De Witte and Paul Scheunders}
}
@article {1458,
	title = {Noise Reduction of Hyperspectral Images Using Kernel Nonnegative Tucker Decomposition},
	journal = {IEEE Journal of Selected Topics in Signal Processing},
	volume = {5},
	year = {2011},
	chapter = {487},
	author = {Azam Karami and M.Yazdi and A Zolghadre}
}
@inproceedings {Heylenpscheund2011,
	title = {Non-linear fully-constrained spectral unmixing},
	booktitle = {IEEE IGARSS2011, IEEE International Geoscience and Remote Sensing Symposium, Vancouver, July 25-29},
	year = {2011},
	author = {Rob Heylen and Paul Scheunders}
}
@article {HeylenBurazerovicpscheund2011,
	title = {Nonlinear spectral unmixing by geodesic simplex volume maximization},
	journal = {IEEE Journal of Selected Topics in Signal Processing},
	volume = {5},
	number = {3},
	year = {2011},
	pages = {534-542},
	author = {Rob Heylen and Dzevdet Burazerovic and Paul Scheunders}
}
@article {wvaarlejbatenbujsijbers2011,
	title = {Optimal threshold selection for segmentation of dense homogeneous objects in tomographic reconstructions},
	journal = {IEEE Transactions on Medical Imaging},
	volume = {30},
	number = {4},
	year = {2011},
	pages = {980-989},
	doi = {http://dx.doi.org/doi:10.1109/TMI.2010.2104328},
	author = {Wim Van Aarle and Kees Joost Batenburg and Jan Sijbers}
}
@inproceedings {Kudzinava_2011_ISBI,
	title = {Optimized Workflow for Diffusion Kurtosis Imaging of Newborns},
	booktitle = {ISBI, 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro},
	year = {2011},
	month = {April},
	pages = {922-926},
	address = {Chicago, USA},
	abstract = {Diffusional kurtosis imaging (DKI) is a recently proposed extension of the conventional DTI model. It has been shown to offer more sensitive characterization of neural tissues than DTI. So far, DKI has only been applied to adult human and small animal studies, but not yet to human newborns. In this work, we present an optimized workflow for the acquisition and processing of DKI images of newborns. First, optimal set of diffusion weighting gradients for DKI studies of newborn subjects is proposed. Optimized gradients allow to estimate DKI parameters with the highest precision. Next, preprocessing and segmentation of the DKI data is considered, including motion correction, eddy currents suppression, intensity modulation and gradients reorientation. Finally, statistics of estimated diffusion and kurtosis parameters for different neonatal brain tissues are calculated.},
	keywords = {diffusion and kurtosis parameters, DKI, motion  correction, newborns, optimal gradients, precision},
	doi = {10.1109/ISBI.2011.5872554},
	url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=5872554},
	author = {Maryna Kudzinava and Dirk H J Poot and A. Plaisier and Jan Sijbers}
}
@inproceedings {1194,
	title = {PDART: A Partially Discrete Algorithm for the Reconstruction of Dense Particles},
	booktitle = {11th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully 3D)},
	year = {2011},
	pages = {448-451},
	address = {Potsdam, Germany},
	author = {Roelandts, Tom and Kees Joost Batenburg and Jan Sijbers}
}
@article {1249,
	title = {Performance improvements for iterative electron tomography reconstruction using graphics processing units (GPUs)},
	journal = {Journal of structural biology},
	volume = {176},
	year = {2011},
	month = {2011 Nov},
	pages = {250-253},
	abstract = {Iterative reconstruction algorithms are becoming increasingly important in electron tomography of biological samples. These algorithms, however, impose major computational demands. Parallelization must be employed to maintain acceptable running times. Graphics Processing Units (GPUs) have been demonstrated to be highly cost-effective for carrying out these computations with a high degree of parallelism. In a recent paper by Xu et al. (2010), a GPU implementation strategy was presented that obtains a speedup of an order of magnitude over a previously proposed GPU-based electron tomography implementation. In this technical note, we demonstrate that by making alternative design decisions in the GPU implementation, an additional speedup can be obtained, again of an order of magnitude. By carefully considering memory access locality when dividing the workload among blocks of threads, the GPU{\textquoteright}s cache is used more efficiently, making more effective use of the available memory bandwidth.},
	issn = {1095-8657},
	doi = {10.1016/j.jsb.2011.07.017},
	author = {Willem Jan Palenstijn and Kees Joost Batenburg and Jan Sijbers}
}
@article {jveraartB.T.wvheckeI.bjeurissY.avdlindeA.mverhoyejsijbers2011,
	title = {Population-averaged diffusion tensor imaging atlas of the Sprague Dawley rat brain},
	journal = {NeuroImage},
	volume = {58},
	year = {2011},
	pages = {975-983},
	doi = {10.1016/j.neuroimage.2011.06.063},
	author = {Jelle Veraart and Trygve B Leergaard and Antonsen, Bj{\o}rnar T and Wim Van Hecke and Ines Blockx and Ben Jeurissen and Yi Jiang and Annemie Van Der Linden and Allan G Johnson and Marleen Verhoye and Jan Sijbers}
}
@article {1258,
	title = {Probabilistic fiber tracking using the residual bootstrap with constrained spherical deconvolution},
	journal = {Human Brain Mapping},
	volume = {32},
	year = {2011},
	month = {March, 2011},
	pages = {461 - 479},
	keywords = {Diffusion MRI, Spherical deconvolution, Tractography},
	doi = {10.1002/hbm.21032},
	author = {Ben Jeurissen and Alexander Leemans and Derek K. Jones and Jacques-Donald Tournier and Jan Sijbers}
}
@article {P.M.A.S.bjeurissJ.A.M.K.F.T.J.-F.C.2011,
	title = {Quantitative Evaluation of 10 Tractography Algorithms on a Realistic Diffusion MR Phantom},
	journal = {NeuroImage},
	volume = {56},
	number = {1},
	year = {2011},
	month = {May},
	pages = {220-234},
	author = {Pierre Fillard and Maxime Descoteaux and Alvina Goh and Sylvain Gouttard and Ben Jeurissen and James Malcolm and Alonso Ramirez-Manzanares and Marco Reisert and Ken Sakaie and Fatima Tensaouti and Ting-Shou Yo and Jean-Fran{\c c}ois Mangin and Cyril Poupon}
}
@conference {bjeurissM.aleemansjsijbers2011,
	title = {Registration based correction of DWI gradient orientations},
	year = {2011},
	month = {May},
	address = {Montreal, Canada},
	author = {Ben Jeurissen and Maarten Naeyaert and Alexander Leemans and Jan Sijbers}
}
@conference {1256,
	title = {Robust edge directed interpolation of diffusion weighted MR images},
	year = {2011},
	pages = {382},
	author = {Zhenhua Mai and Jeny Rajan and Marleen Verhoye and Jan Sijbers}
}
@conference {zmaijrajanmverhoyejsijbers2011,
	title = {Robust Edge-directed Interpolation: Application to Diffusion MR Images},
	year = {2011},
	month = {May},
	author = {Zhenhua Mai and Jeny Rajan and Marleen Verhoye and Jan Sijbers}
}
@article {1261,
	title = {Robust edge-directed interpolation of magnetic resonance images},
	journal = {Physics in medicine and biology},
	volume = {56},
	year = {2011},
	pages = {7287-7303},
	author = {Zhenhua Mai and Jeny Rajan and Marleen Verhoye and Jan Sijbers}
}
@article {jbatenbuwvaarlejsijbers2011,
	title = {A Semi-Automatic Algorithm for Grey Level Estimation in Tomography},
	journal = {Pattern Recognition Letters},
	volume = {32},
	number = {9},
	year = {2011},
	month = {July},
	pages = {1395-1405},
	author = {Kees Joost Batenburg and Wim Van Aarle and Jan Sijbers}
}
@inproceedings {Heylenpscheund2011,
	title = {Spectral unmixing using distance geometry},
	booktitle = {IEEE-WHISPERS 2011, Workshop on Hperspectral Image and Signal Processing, Lisbon, Portugal, 6-9 June},
	year = {2011},
	author = {Rob Heylen and Paul Scheunders}
}
@article {1269,
	title = {Successive Cambia: A Developmental Oddity or an Adaptive Structure?},
	journal = {PLoS ONE},
	volume = {6},
	year = {2011},
	month = {1/2011},
	pages = {e16558},
	doi = {10.1371/journal.pone.0016558},
	author = {Robert, Elisabeth M. R. and Schmitz, Nele and Boeren, Ilse and Driessens, Tess and Herremans, Kristof and Johan De Mey and Elke Van de Casteele and Beeckman, Hans and Koedam, Nico},
	editor = {Peer, Wendy}
}
@inproceedings {BurazerovicHeylenpscheund2011,
	title = {Towards streaming hyperspectral endmember extraction},
	booktitle = {IEEE IGARSS2011, IEEE International Geoscience and Remote Sensing Symposium , Vancouver, July 25-29},
	year = {2011},
	author = {Dzevdet Burazerovic and Rob Heylen and Paul Scheunders}
}
@inproceedings {1414,
	title = {Training Simulator for Flotation Process Operators},
	booktitle = {18th IFAC World Congress},
	year = {2011},
	url = {http://www.nt.ntnu.no/users/skoge/prost/proceedings/ifac11-proceedings/data/html/papers/2171.pdf},
	author = {Timo Roine and Kaartinen, Jani and Lamberg, Pertti}
}
@conference {1204,
	title = {Ultra-High Resolution Electron Tomography for Materials Science: A Roadmap},
	year = {2011},
	address = {Nashville, TN, United States},
	doi = {http://dx.doi.org/10.1017/S143192761100554X},
	author = {Kees Joost Batenburg and Sara Bals and Sandra Van Aert and Roelandts, Tom and Jan Sijbers}
}
@conference {1553,
	title = {White Matter Tract Deficits in Schizophrenia},
	volume = {7},
	year = {2011},
	address = {Dublin, Ireland},
	author = {Forde, Natalie J and Ellison-Wright, I and Nathan, Pradeep J and Zaman, R and Dudas, R and Agius, M and Fernandez-Egea, E and Alexander Leemans and Ben Jeurissen and Scanlon, Cathy and Colm McDonald and Dara M. Cannon}
}
@inproceedings {wvaarlegvgompeljbatenbuevdcastejsijbers2010,
	title = {A 3-dimensional discrete tomography approach for superresolution micro-CT images: application to foams},
	booktitle = {The first international conference on image formation in X-ray computed tomography},
	year = {2010},
	month = {June},
	pages = {45-48},
	author = {Wim Van Aarle and Gert Van Gompel and Kees Joost Batenburg and Elke Van de Casteele and Jan Sijbers},
	editor = {Fr{\'e}d{\'e}ric Noo}
}
@proceedings {1236,
	title = {ACIVS 2010, Advanced Concepts for Intelligent Vision Systems},
	volume = {6474 \& 6475},
	year = {2010},
	publisher = {Springer},
	author = {J Blanc-Talon and D. Bone and Wilfried Philips and D Popescu and Paul Scheunders}
}
@article {1267,
	title = {Analysis of micro computed tomography images; a look inside historic enamelled metal objects},
	journal = {Applied Physics A},
	volume = {98},
	year = {2010},
	month = {2/2010},
	pages = {385 - 392},
	issn = {0947-8396},
	doi = {10.1007/s00339-009-5394-9},
	author = {Annemie Van Der Linden and Elke Van de Casteele and Thomas, Mienke Simon and Vos, Annemie and Janssen, Elsje and Janssens, Koen}
}
@article {thuysmanjsijbersVerdonk2010,
	title = {Automatic Construction of Correspondences for Tubular Surfaces},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {32},
	number = {4},
	year = {2010},
	pages = {636-651},
	author = {Toon Huysmans and Jan Sijbers and B. Verdonk}
}
@proceedings {1466,
	title = {Best Rank-r Tensor Selection Using Genetic Algorithm for Better Noise Reduction and Compression of Hyperspectral Images},
	year = {2010},
	author = {Azam Karami and M.Yazdi and A Zolghadre}
}
@article {wvheckeAleemanssdbackerBjeurisspmparizejsijbers2010,
	title = {Comparing isotropic and anisotropic smoothing for voxel-based DTI analyses: A simulation study},
	journal = {Human Brain Mapping},
	volume = {31},
	number = {1},
	year = {2010},
	pages = {98-114},
	author = {Wim Van Hecke and Alexander Leemans and Steve De Backer and Ben Jeurissen and Paul M Parizel and Jan Sijbers}
}
@article {1460,
	title = {Compression and Noise Reduction of Hyperspectral Images Using Hybrid Genetic Algorithm and Nonnegative Tucker Decomposition},
	journal = {International Journal of Information studies },
	volume = {2},
	number = {10},
	year = {2010},
	chapter = {207},
	author = {Azam Karami and M.Yazdi and A Zolghadre}
}
@inbook {rpinhoTournoyjsijbers2010,
	title = {Computer-Aided Assessment and Stenting of Tracheal Stenosis},
	booktitle = {Computer Aided Diagnosis of Lung Imaging},
	year = {2010},
	author = {R{\^o}mulo Pinho and Kurt G. Tournoy and Jan Sijbers},
	editor = {Ayman El-Baz}
}
@conference {jveraartAntonsenBlockxwvheckeJiangJohnsonavdlindeLeergaardmverhoyejsijbers2010,
	title = {Construction of a population based diffusion tensor image atlas of the Sprague Dawley rat brain},
	year = {2010},
	month = {May},
	address = {Stockholm, Sweden},
	author = {Jelle Veraart and Antonsen, Bj{\o}rnar T and Ines Blockx and Wim Van Hecke and Yi Jiang and Allan G Johnson and Annemie Van Der Linden and Trygve B Leergaard and Marleen Verhoye and Jan Sijbers}
}
@article {wvheckeNagelsaleemansVandervlietjsijbersParizel2010,
	title = {Correlation of cognitive dysfunction and diffusion tensor MRI measures in patients with mild and moderate multiple sclerosis},
	journal = {Journal of magnetic resonance imaging},
	volume = {31},
	number = {6},
	year = {2010},
	month = {May},
	pages = {1492-1498},
	author = {Wim Van Hecke and Guy Nagels and Alexander Leemans and Everhard Vandervliet and Jan Sijbers and Paul M Parizel}
}
@conference {bjeurissaleemansdkjonesjdtournierjsijbers2010,
	title = {Counting the number of fiber orientations in diffusion MRI voxels using constrained spherical deconvolution},
	year = {2010},
	month = {January},
	address = {Utrecht, The Netherlands},
	author = {Ben Jeurissen and Alexander Leemans and Derek K. Jones and Jacques-Donald Tournier and Jan Sijbers}
}
@article {jbatenbujsijbersH.F.E.2010,
	title = {DART: A Robust Algorithm for Fast Reconstruction of 3D Grain Maps},
	journal = {Journal of Applied Crystallography},
	volume = {43},
	year = {2010},
	pages = {1464-1473},
	doi = {https://doi.org/10.1107/S0021889810034114},
	author = {Kees Joost Batenburg and Jan Sijbers and H.F. Poulsen and E. Knudsen}
}
@mastersthesis {1284,
	title = {A Decision Support System for the Assessment and Stenting of Tracheal Stenosis},
	year = {2010},
	month = {18/11/2010},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	abstract = {This thesis sets forth a decision support system that proposes a method for automatic assessment of tracheal stenosis and prediction of stent length and diameter from chest CT scans. The main idea behind the proposed method is to estimate the shape of the trachea of a patient as if stenosis were not present. This shape can be used the by the physician for surgery planning and is the basis for the automatic assessment of the stenosis and the prediction of patient-specific stents.
},
	author = {R{\^o}mulo Pinho}
}
@inproceedings {rpinhoTournoyGosselinjsijbers2010,
	title = {A Decision Support System for the Treatment of Tracheal Stenosis},
	booktitle = {Proc. of Workshop on Discrete Geometry and Mathematical Morphology (WADGMM)},
	year = {2010},
	month = {August},
	pages = {72-76},
	publisher = {IAPR},
	organization = {IAPR},
	address = {Istanbul},
	author = {R{\^o}mulo Pinho and Kurt G. Tournoy and Robert Gosselin and Jan Sijbers}
}
@article {zmaimverhoyeavdlindejsijbers2010,
	title = {Diffusion tensor image up-sampling: a registration-based approach},
	journal = {Magnetic resonance Imaging},
	volume = {28},
	number = {10},
	year = {2010},
	month = {December},
	pages = {1497-1506},
	doi = {10.1016/j.mri.2010.06.018},
	author = {Zhenhua Mai and Marleen Verhoye and Annemie Van Der Linden and Jan Sijbers}
}
@inproceedings {zmaimverhoyejsijbers2010,
	title = {Diffusion Tensor Images Edge-Directed Interpolation},
	booktitle = {International Symposium on Biomedical Imaging 2010},
	year = {2010},
	month = {April},
	pages = {732-735},
	author = {Zhenhua Mai and Marleen Verhoye and Jan Sijbers}
}
@inproceedings {gvgompeljbatenbuevdcastewvaarlejsijbers2010,
	title = {A discrete tomography approach for superresolution micro-CT images},
	booktitle = {IEEE International Symposium on Biomedical Imaging},
	year = {2010},
	month = {April},
	pages = {816-819},
	address = {Rotterdam},
	author = {Gert Van Gompel and Kees Joost Batenburg and Elke Van de Casteele and Wim Van Aarle and Jan Sijbers}
}
@inproceedings {5652813,
	title = {Enhanced visualization of hyperspectral images},
	booktitle = {Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International},
	year = {2010},
	month = {july},
	pages = {991 -994},
	keywords = {color matching, data visualisation, geophysical image processing, gradient methods, human vision system, hyperspectral image, hyperspectral visualization algorithm, image colour analysis, image matching, image resolution, multiband gradient information, multiresolution framework, multiscale fundamental form representation, wavelet transforms, wavelets},
	issn = {2153-6996},
	doi = {10.1109/IGARSS.2010.5652813},
	author = {Zahid Mahmood and Paul Scheunders}
}
@conference {bjeurissaleemansdkjonesjdtournierjsijbers2010,
	title = {Estimating the number of fiber orientations in diffusion MRI voxels: a constrained spherical deconvolution study},
	year = {2010},
	month = {May},
	address = {Stockholm, Sweden},
	author = {Ben Jeurissen and Alexander Leemans and Derek K. Jones and Jacques-Donald Tournier and Jan Sijbers}
}
@inproceedings {GansemanMysoreScheundersAbel2010,
	title = {Evaluation of a score-informed source separation system},
	booktitle = {Proc. 11th International Society for Music Information Retrieval Conference (ISMIR 2010), Utrecht, the Netherlands, August 2010},
	year = {2010},
	pages = {219-224},
	author = {Joachim Ganseman and Gautham J. Mysore and Paul Scheunders and Jonathan S. Abel}
}
@inproceedings {1319,
	title = {General and Efficient Super-Resolution method for Multi-Slice MRI},
	booktitle = {Medical Image Computing and Computer Assisted Intervention},
	volume = {13},
	year = {2010},
	pages = {615-622},
	doi = {10.1007/978-3-642-15705-9_75},
	author = {Dirk H J Poot and V. Van Meir and Jan Sijbers}
}
@inproceedings {HeylenBurazerovicScheunders2010,
	title = {A graph-based method for non-linear unmixing of hyperspectral imagery},
	booktitle = {IEEE IGARSS2010, IEEE International Geoscience and Remote Sensing Symposium, Honolulu, Haway, July 25-30},
	year = {2010},
	pages = {197-200},
	author = {Rob Heylen and Dzevdet Burazerovic and Paul Scheunders}
}
@inproceedings {Thoonen10,
	title = {Habitat mapping and quality assessment of heathlands using a modified kernel-based reclassification technique},
	booktitle = {Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International},
	year = {2010},
	month = {July},
	pages = {2707 - 2710},
	address = {Honolulu, HI, USA},
	abstract = {This article presents a method for acquiring habitat maps, intended for monitoring and evaluating the conservation status of heathland vegetation, starting from thematic land cover maps. The procedure is a modified kernel-based reclassification technique, that fits into a complete habitat quality assessment framework. Part one of the procedure shifts a small square kernel over the land cover map and assigns a habitat type to each position that complies with a single set of expert rules, related to the land cover composition in that position. Part two fills the gaps, by assigning a habitat type to any of the map positions that don{\textquoteright}t conform to any of the rules, or to more than one set of rules, by using a distance measure. The technique is tested on real data from a heathland site and shows some promising results.},
	keywords = {Belgium, environmental factors, geophysical image processing, geophysical techniques, habitat mapping, heathland vegetation, image classification, Kalmthoutse Heide, kernel-based reclassification technique, quality assessment, terrain mapping, thematic land cover maps, vegetation mapping},
	isbn = {978-1-4244-9565-8},
	issn = {2153-6996},
	doi = {10.1109/IGARSS.2010.5649240},
	author = {Guy Thoonen and Toon Spanhove and Haest, B. and Jeroen Vanden Borre and Paul Scheunders}
}
@article {Hufkens10,
	title = {Habitat reporting of a heathland site: Classification probabilities as additional information, a case study},
	journal = {Ecological Informatics},
	volume = {5},
	number = {4},
	year = {2010},
	pages = {248 - 255},
	abstract = {Heathlands are man-made habitats and their decline during the last century can be contributed to shifts in both agricultural and management practices as well as to hydrological and atmospheric changes. As a result, many heathland sites, including the Kalmthoutse Heide in Belgium, were included in the European Natura 2000 program, a network of protected areas across the European Union. To assure an accurate mapping of the Kalmthoutse Heide and other Natura 2000 sites in Belgium a classification framework for habitat status reporting with remote sensing data and in particular high resolution hyperspectral imagery was started. In this study we propose a simple and fast context based method for mapping heathland heterogeneity using the intermediate, otherwise redundant, classification probabilities as generated by a hard classification algorithm. Our study proved to be successful in using intermediate classification probabilities as a valuable source of ecological information. The delineated areas have been shown to be statistically sound and robust compared to a neutral model. The technique is not limited to a particular hard classification technique and can easily be adopted into current vegetation monitoring efforts. The resulting maps provided accessible maps which can support management of the protected site and enhance the accuracy of EU reportage as required by the habitat directive.},
	keywords = {Heterogeneity, Hyperspectral, Patch, remote sensing, Uncertainty, Vegetation},
	issn = {1574-9541},
	doi = {10.1016/j.ecoinf.2009.09.002},
	url = {http://www.sciencedirect.com/science/article/pii/S1574954109000740},
	author = {Koen Hufkens and Guy Thoonen and Jeroen Vanden Borre and Paul Scheunders and Reinhart Ceulemans}
}
@proceedings {1465,
	title = {Hyperspectral Image Compression Based on Tucker Decomposition and Discrete Cosine Transform},
	year = {2010},
	author = {Azam Karami and M.Yazdi and A Zolghadre}
}
@article {dpootwpintjenmverhoyeavdlindejsijbers2010,
	title = {Improved B0 field map estimation for high field EPI},
	journal = {Magnetic Resonance Imaging},
	volume = {28},
	number = {3},
	year = {2010},
	month = {April},
	pages = {441-450},
	author = {Dirk H J Poot and W. Pintjens and Marleen Verhoye and Annemie Van Der Linden and Jan Sijbers}
}
@inproceedings {1322,
	title = {Iterative correction of beam hardening artifacts in CT},
	booktitle = {The First International Conference on Image Formation in X-Ray Computed Tomography},
	year = {2010},
	month = {06/2010},
	author = {Van Slambrouck, Katrien and Gert Van Gompel and M. Defrise and Kees Joost Batenburg and Jan Sijbers and Johan Nuyts}
}
@article {jjuntujsijberssdbackerjrajandvandyck2010,
	title = {A Machine Learning Study of Several Classifiers Trained with Texture Analysis Features to Differentiate Benign from Malignant Soft Tissue Tumors in T1-MRI Images},
	journal = {Journal of Magnetic Resonance Imaging},
	volume = {31},
	year = {2010},
	pages = {680{\textendash}689},
	doi = {10.1002/jmri.22095},
	author = {Juntu, Jaber and Jan Sijbers and Steve De Backer and Jeny Rajan and Dirk Van Dyck}
}
@article {1196,
	title = {Method for Mapping Tubular Surfaces to a Cylinder},
	number = {PCT/EP2010/057882},
	year = {2010},
	month = {01/2015},
	edition = {G06T 17/20 (2006.01), A61B 1/00 (2006.01)},
	abstract = {The present invention relates to the field of cylindrical surface parameterization such as colon flattening. Methods are provided for parameterizing tubular surfaces onto a cylinder, wherein the length of said cylinder is modified so that parameterization distortion is reduced.},
	url = {https://www.google.com/patents/US8933932},
	author = {Toon Huysmans and Jan Sijbers}
}
@conference {jveraartwvheckedpootBlockxavdlindemverhoyejsijbers2010,
	title = {A more accurate and b-value independent estimation of diffusion parameters using Diffusion Kurtosis Imaging,},
	year = {2010},
	month = {May},
	address = {Stockholm, Sweden},
	author = {Jelle Veraart and Wim Van Hecke and Dirk H J Poot and Ines Blockx and Annemie Van Der Linden and Marleen Verhoye and Jan Sijbers}
}
@conference {jveraartwvheckedpootBlockxavdlindemverhoyejsijbers2010,
	title = {A more accurate and b-value independent estimation of diffusion parameters using Diffusion Kurtosis Imaging},
	year = {2010},
	month = {January},
	pages = {10},
	address = {Utrecht, the Netherlands},
	author = {Jelle Veraart and Wim Van Hecke and Dirk H J Poot and Ines Blockx and Annemie Van Der Linden and Marleen Verhoye and Jan Sijbers}
}
@article {1479,
	title = {Morphologic and functional changes in the unilateral 6-hydroxydopamine lesion rat model for Parkinson{\textquoteright}s disease discerned with microSPECT and quantitative MRI.},
	journal = {Magnetic Resonance Materials in Physics, Biology and Medicine},
	volume = {23},
	year = {2010},
	month = {04/2010},
	pages = {65-75},
	abstract = {OBJECT: In the present study, we aimed to evaluate the impact of neurodegeneration of the nigrostriatal tract in a rodent model of Parkinson{\textquoteright}s disease on the different MR contrasts (T(2), T(1), CBF and CBV) measured in the striatum.

MATERIAL AND METHODS: Animals were injected with 6-hydroxydopamine (6OHDA) in the substantia nigra resulting in massive loss of nigrostriatal neurons and hence dopamine depletion in the ipsilateral striatum. Using 7T MRI imaging, we have quantified T(2), T(1), CBF and CBV in the striata of 6OHDA and control rats. To validate the lesion size, behavioral testing, dopamine transporter muSPECT and tyrosine hydroxylase staining were performed.

RESULTS: No significant differences were demonstrated in the absolute MRI values between 6OHDA animals and controls; however, 6OHDA animals showed significant striatal asymmetry for all MRI parameters in contrast to controls.

CONCLUSIONS: These PD-related asymmetry ratios might be the result of counteracting changes in both intact and affected striatum and allowed us to diagnose PD lesions. As lateralization is known to occur also in PD patients and might be expected in transgenic PD models as well, we propose that MR-derived asymmetry ratios in the striatum might be a useful tool for in vivo phenotyping of animal models of PD.},
	keywords = {Animals, Corpus Striatum, Disease Models, Animal, Female, Magnetic Resonance Imaging, Oxidopamine, Parkinsonian Disorders, Positron-Emission Tomography, Rats, Rats, Wistar, Reproducibility of Results, Sensitivity and Specificity},
	issn = {1352-8661},
	doi = {10.1007/s10334-010-0198-7},
	author = {N. Van Camp and Vreys, Ruth and Koen Van Laere and E. Lauwers and Dirk Beque and Marleen Verhoye and Casteels, Cindy and Alfons Verbruggen and Zeger Debyser and Luc Mortelmans and Jan Sijbers and Johan Nuyts and Veerle Baekelandt and Annemie Van Der Linden}
}
@article {1189,
	title = {Noise measurement from magnitude MRI using local estimates of variance and skewness.},
	journal = {Physics in medicine and biology},
	volume = {55},
	year = {2010},
	month = {2010 Aug 21},
	pages = {N441-9},
	abstract = {In this note, we address the estimation of the noise level in magnitude magnetic resonance (MR) images in the absence of background data. Most of the methods proposed earlier exploit the Rayleigh distributed background region in MR images to estimate the noise level. These methods, however, cannot be used for images where no background information is available. In this note, we propose two different approaches for noise level estimation in the absence of the image background. The first method is based on the local estimation of the noise variance using maximum likelihood estimation and the second method is based on the local estimation of the skewness of the magnitude data distribution. Experimental results on synthetic and real MR image datasets show that the proposed estimators accurately estimate the noise level in a magnitude MR image, even without background data.},
	keywords = {Algorithms, Artifacts, Brain, Data Interpretation, Statistical, Fourier Analysis, Humans, Image Processing, Computer-Assisted, Likelihood Functions, Magnetic Resonance Imaging, Models, Statistical, Myocardium, Normal Distribution, Reproducibility of Results},
	issn = {1361-6560},
	doi = {10.1088/0031-9155/55/16/N02},
	author = {Jeny Rajan and Dirk H J Poot and Juntu, Jaber and Jan Sijbers}
}
@inproceedings {Heylenpscheund2010,
	title = {Nonlinear barycentric dimensionality reduction},
	booktitle = {IEEE ICIP10, IEEE International Conference on Image Processing, Hong Kong, september 26-29},
	year = {2010},
	pages = {1341-1344},
	author = {Rob Heylen and Paul Scheunders}
}
@inproceedings {jveraartwvheckeBlockxavdlindemverhoyejsijbers2010,
	title = {Non-Rigid coregistration of diffusion kurtosis data},
	booktitle = {Biomedical Imaging: From Nano to Macro, 2010 IEEE International Symposium on},
	year = {2010},
	month = {April},
	pages = {392-395},
	address = {Rotterdam, the Netherlands},
	author = {Jelle Veraart and Wim Van Hecke and Ines Blockx and Annemie Van Der Linden and Marleen Verhoye and Jan Sijbers}
}
@inproceedings {Haest10,
	title = {An object-based approach to quantity and quality assessment of heathland habitats in the framework of NATURA 2000 using hyperspectral airborne AHS images.},
	booktitle = {Proceedings of GEOBIA 2010, the Geographic Object-Based Image Analysis Conference},
	volume = {XXXVIII-4/C7},
	year = {2010},
	month = {July},
	abstract = {Straightforward mapping of detailed heathland habitat patches and their quality using remote sensing is hampered by (1) the intrinsic property of a high heterogeneity in habitat species composition (i.e. high intra-variability), and (2) the occurrence of the same species in multiple habitat types (i.e. low inter-variability). Mapping accuracy of detailed habitat objects can however be improved by using an advanced approach that specifically takes into account and exploits these inherent patch characteristics. To demonstrate the idea, we developed and applied a multi-step mapping framework on a protected semi-natural heathland area in the north of Belgium. The method consecutively consists of (1) a 4-level hierarchical land cover classification of hyperspectral airborne AHS image data, and (2) a kernel-based structural re-classification algorithm in combination with habitat patch object composition definitions. Detailed land cover composition data were collected in 1325 field plots. Multi-variate analysis (Wards clustering; TWINSPAN) of these data led to the design of meaningful land cover classes in a dedicated classification scheme. Subsequently, the data were used as reference for the classification of hyperspectral AHS image data. Linear Discriminant Analysis in combination with Sequential-Floating-Forward-Selection (SFFS-LDA) was applied to classify the hyperspectral images. Classification accuracies of these maps are in the order of 74-93\% (Kappa= 0.81-0.92) depending on the classification detail. To subsequently obtain habitat patch (object) maps, the land cover classifications were used as input for a kernel-based spatial re-classification process, in combination with a rule-set that relates specific Natura 2000 habitats with a composition range of the land cover classes. The resulting habitat patch maps illustrate the methodologys potential for detailed heathland habitat characterization using hyperspectral image data, and hence contribute to the improved mapping and understanding of heathland habitat, essential for the EU member states reporting obligations under the Habitats Directive.},
	keywords = {Application, Classification, Contextual, Ecosystem, Hyper spectral, Landscape, Object, Vegetation},
	url = {http://www.isprs.org/proceedings/XXXVIII/4-C7/papers\%20proceedings/Haest_211_An_object-based_approach_to_quantity_and_quality_assessment_of_heath_land_habitats.pdf},
	author = {Haest, B. and Guy Thoonen and Jeroen Vanden Borre and Toon Spanhove and Stephanie Delalieux and L. Bertels and Kooistra, L. and C A M{\"u}cher and Paul Scheunders}
}
@article {dpootajdendekAchtenmverhoyejsijbers2010,
	title = {Optimal experimental design for Diffusion Kurtosis Imaging},
	journal = {IEEE Transactions on Medical Imaging},
	volume = {29},
	number = {3},
	year = {2010},
	pages = {819-829},
	doi = {http://dx.doi.org/10.1109/TMI.2009.2037915},
	author = {Dirk H J Poot and Arnold Jan den Dekker and Eric Achten and Marleen Verhoye and Jan Sijbers}
}
@conference {1203,
	title = {Partially Discrete Tomography for the Reconstruction of Dense Particles},
	year = {2010},
	pages = {I7.19},
	address = {Rio de Janeiro, Brazil},
	author = {Roelandts, Tom and Kees Joost Batenburg and E. Biermans and Sara Bals and Jan Sijbers}
}
@inproceedings {1568,
	title = {Projection and backprojection in tomography: design choices and considerations},
	booktitle = {Workshop on Applications of Discrete Geometry and Mathematical Morphology},
	year = {2010},
	pages = {106-110},
	author = {Kees Joost Batenburg and Willem Jan Palenstijn and Jan Sijbers}
}
@inproceedings {DBLP:conf/iciar/RajanPJS10,
	title = {Segmentation Based Noise Variance Estimation from Background MRI Data},
	booktitle = {ICIAR },
	volume = {6111},
	year = {2010},
	month = {2010},
	pages = {62-70},
	publisher = {Springer LNCS},
	organization = {Springer LNCS},
	address = {Porto, Portugal},
	author = {Jeny Rajan and Dirk H J Poot and Juntu, Jaber and Jan Sijbers}
}
@inproceedings {GansemanMysoreScheundersAbel2010,
	title = {Source separation by score synthesis},
	booktitle = {Proc. International Computer Music Conference (ICMC 2010), New York, NY, June 1-5},
	year = {2010},
	pages = {462-465},
	author = {Joachim Ganseman and Gautham J. Mysore and Paul Scheunders and Jonathan S. Abel}
}
@inproceedings {DBLP:conf/interspeech/NamMGLA10,
	title = {A super-resolution spectrogram using coupled PLCA},
	booktitle = {INTERSPEECH},
	year = {2010},
	pages = {1696-1699},
	address = {Makuhari, Japan},
	author = {Juhan Nam and Gautham J. Mysore and Joachim Ganseman and Kyogu Lee and Jonathan S. Abel}
}
@inproceedings {Thoonen10b,
	title = {Using patch metrics as validation for contextual classification of heathland vegetation.},
	booktitle = {Proceedings of GEOBIA 2010, the Geographic Object-Based Image Analysis Conference},
	volume = {XXXVIII-4/C7},
	year = {2010},
	month = {July},
	abstract = {This article presents a method to assess the accuracy of vegetation maps for which contextual information has been included in the classification process. It is well known that land use classification may benefit from combining spatial and spectral information. Consequently, many classification techniques incorporating spatial information have been implemented. To compare various contextual classification techniques, the shape of vegetation patches, in the spatially enhanced maps, are statistically linked to their counterparts in the spectral classification result, on which these spatial enhancements are applied. To this end, measures for the change in shape of patches are introduced. The shape of any patch is characterized by the edges between the patch and its neighbors. Therefore, patch shape can be represented by an edge map in which each pixel gets the value of the number of classes that are different from the class label of the central pixel in a four-adjacency neighborhood. Rather than defining a single metric for the edge map difference, an error matrix is used to depict not only how many edges have changed with respect to the reference, but also by how much they have changed. The method is tested on contextual classification results of heathland vegetation.},
	keywords = {Accuracy, Classification, Contextual, Hierarchical, Hyper spectral, Quality, Spatial, Vegetation},
	url = {http://www.isprs.org/proceedings/XXXVIII/4-C7/papers\%20proceedings/Thoonen_44_UsingPatchMetricsAsValidationForContextualClassificationOfHeathlandVegetation.pdf},
	author = {Guy Thoonen and Koen Hufkens and Jeroen Vanden Borre and Paul Scheunders}
}
@article {1268,
	title = {X-ray micro tomography and image analysis as complementary methods for morphological characterization and coating thickness measurement of coated particles},
	journal = {Advanced Powder Technology},
	volume = {21},
	year = {2010},
	month = {11/2010},
	pages = {663 - 675},
	issn = {09218831},
	doi = {10.1016/j.apt.2010.08.002},
	author = {Perfetti, Giacomo and Elke Van de Casteele and Rieger, Bernd and Wildeboer, Willem J. and Meesters, Gabrie M.H.}
}
@article {jbatenbuBalsjsijbersKubelMidgleyHernandezKaiserEncinaCoronadoTendeloo2009,
	title = {3D imaging of nanomaterials by discrete tomography},
	journal = {Ultramicroscopy},
	volume = {109},
	year = {2009},
	pages = {730-740},
	doi = {10.1016/j.ultramic.2009.01.009},
	author = {Kees Joost Batenburg and Sara Bals and Jan Sijbers and C. Kubel and P.A. Midgley and J.C. Hernandez and U. Kaiser and E.R. Encina and E.A. Coronado and Van Tendeloo, Gustaaf}
}
@proceedings {1237,
	title = {ACIVS 2009, Advanced Concepts for Intelligent Vision Systems},
	volume = {5807},
	year = {2009},
	publisher = {Springer},
	author = {J Blanc-Talon and Wilfried Philips and D Popescu and Paul Scheunders}
}
@article {jbatenbujsijbers2009,
	title = {Adaptive thresholding of tomograms by projection distance minimization},
	journal = {Pattern Recognition},
	volume = {42},
	number = {10},
	year = {2009},
	month = {April},
	pages = {2297-2305},
	doi = {10.1016/j.patcog.2008.11.027},
	author = {Kees Joost Batenburg and Jan Sijbers}
}
@mastersthesis {1290,
	title = {Advances in the reconstruction and statistical processing of Magnetic Resonance images},
	volume = {PhD in Sciences: Physics},
	year = {2009},
	type = {PhD Thesis},
	author = {Dirk H J Poot}
}
@inproceedings {Thoonen09,
	title = {Assessing the quality of heathland vegetation by classification of hyperspectral data using spatial information},
	booktitle = {Geoscience and Remote Sensing Symposium,2009 IEEE International,IGARSS 2009},
	volume = {4},
	year = {2009},
	month = {July},
	pages = {IV-330 - IV-333},
	address = {Cape Town, South Africa},
	abstract = {This article deals with a method for acquiring vegetation maps, suitable for monitoring and evaluating the conservation status of heathland vegetation from hyperspectral data. The applied method is a recursive supervised segmentation algorithm based on a Tree-structured Markov Random Field (TS-MRF), capable of incorporating structural dependencies in the classification process. To this end, a tree structure is used that is built upon structural dependencies that are present in the field. The classification results from this TS-MRF with extended tree are compared to pixel-based classification results, results from a simple smoothing post-processing, and the result from the original binary TS-MRF technique.},
	keywords = {geophysical signal processing, heathland vegetation conservation, heathland vegetation quality, hyperspectral data classification, image segmentation, Markov processes, pixel based classification comparison, recursive supervised segmentation algorithm, smoothing post processing comparison, spatial information, tree structured Markov random field, trees (mathematics), TS-MRF, vegetation mapping, vegetation maps},
	isbn = {978-1-4244-3394-0},
	doi = {10.1109/IGARSS.2009.5417380},
	author = {Guy Thoonen and Jeroen Vanden Borre and Steve De Backer and Paul Scheunders}
}
@inproceedings {rpinhoTournoyGosselinjsijbers2009,
	title = {Assessment of Tracheal Stenosis Using Active Shape Models of Healthy Tracheas: A Surface Registration Study},
	booktitle = {Proceedings of 2nd International Workshop on Pulmonary Image Analysis},
	year = {2009},
	month = {September},
	pages = {125-136},
	author = {R{\^o}mulo Pinho and Kurt G. Tournoy and Robert Gosselin and Jan Sijbers}
}
@inproceedings {pscheund2009,
	title = {Bayesian techniques for multi/hyperspectral image processing},
	booktitle = {4th IEEE Conference on Industrial Electronics and Applications, Xi{\textquoteright}an, China, May 25-27},
	year = {2009},
	pages = {1-10},
	author = {Paul Scheunders}
}
@inproceedings {aduijstepscheund2009,
	title = {A combined hyperspectral image restoration and fusion approach},
	booktitle = {IEEE International Geoscience and Remote Sensing Symposium, Cape Town, South Africa, July, 13-17},
	number = {3},
	year = {2009},
	pages = {995-998},
	author = {Y. Zhang and A. Duijster and Paul Scheunders}
}
@article {wvheckejsijberssdbackerdpootParizelaleemans2009,
	title = {On the construction of a ground truth framework for evaluating voxel-based diffusion tensor MRI analysis methods},
	journal = {NeuroImage},
	volume = {46},
	number = {3},
	year = {2009},
	month = {July},
	pages = {692-707},
	doi = {10.1016/j.neuroimage.2008.07.006},
	author = {Wim Van Hecke and Jan Sijbers and Steve De Backer and Dirk H J Poot and Paul M Parizel and Alexander Leemans}
}
@inproceedings {jrajanbjeurissjsijbersK.2009,
	title = {Denoising Magnetic Resonance Images using Fourth Order Complex Diffusion},
	booktitle = {13th International Machine Vision and Image Processing Conference},
	year = {2009},
	pages = {123-127},
	address = {Dublin, Ireland},
	author = {Jeny Rajan and Ben Jeurissen and Jan Sijbers and Keizer Kannan}
}
@inproceedings {1417,
	title = {Development of a Machine Vision System to Monitor a Grinding Mill Prototype},
	booktitle = {12th European Symposium on Comminution and Classification (ESCC 2009)},
	year = {2009},
	author = {Timo Roine and Pietil{\"a}, Janne and Kaartinen, Jani and Blanz, Peter and Rantala, Pertti}
}
@conference {BlockxmverhoyeGroofAudekerkeRaberdpootjsijbersHorstenavdlinde2009,
	title = {Diffusion Kurtosis Imaging (DKI) reveals an early phenotype (P30) in a transgenic rat model for Huntington{\textquoteright}s disease},
	volume = {2009},
	year = {2009},
	month = {April},
	pages = {359},
	author = {Ines Blockx and Marleen Verhoye and G. De Groof and Johan Van Audekerke and Kerstin Raber and Dirk H J Poot and Jan Sijbers and S. von Horsten and Annemie Van Der Linden}
}
@inproceedings {zmaimverhoyeavdlindejsijbers2009,
	title = {Diffusion Tensor Images Upsampling: a Registration-based Approach},
	booktitle = {13th International Machine Vision and Image Processing Conference},
	volume = {13},
	year = {2009},
	month = {September},
	pages = {36-40},
	address = {Dublin, Ireland},
	author = {Zhenhua Mai and Marleen Verhoye and Annemie Van Der Linden and Jan Sijbers}
}
@article {1376,
	title = {A diffusion tensor imaging group study of the spinal cord in multiple sclerosis patients with and without T2 spinal cord lesions.},
	journal = {Journal of magnetic resonance imaging : JMRI},
	volume = {30},
	year = {2009},
	month = {2009 Jul},
	pages = {25-34},
	abstract = {PURPOSE: To examine the T(2)-normal appearing spinal cord of patients with multiple sclerosis (MS) using diffusion tensor imaging.

MATERIALS AND METHODS: Diffusion tensor images of the spinal cord were acquired from 21 healthy subjects, 11 MS patients with spinal cord lesions, and 10 MS patients without spinal cord lesions on the T(2)-weighted MR images. Different diffusion measures were evaluated using both a region of interest (ROI) -based and a diffusion tensor tractography-based segmentation approach.

RESULTS: It was observed that the FA, the transverse diffusivity lambda(perpendicular), and the ratio of the longitudinal and transverse diffusivities (lambda(parallel)/lambda (perpendicular)) were significantly lower in the spinal cord of MS patients with spinal cord lesions compared with the control subjects using both the ROI method (P = 0.014, P = 0.028, and P = 0.039, respectively) and the tractography-based approach (P = 0.006, P = 0.037, and P = 0.012, respectively). For both image analysis methods, the FA and the lambda (parallel)/lambda (perpendicular) values were significantly different between the control group and the MS patient group without T(2) spinal cord lesions (P = 0.013).

CONCLUSION: Our results suggest that the spinal cord may still be affected by MS, even when lesions are not detected on a conventional MR scan. In addition, we demonstrated that diffusion tensor tractography is a robust tool to analyze the spinal cord of MS patients.},
	keywords = {Adult, Analysis of Variance, anisotropy, Diffusion Magnetic Resonance Imaging, Female, Humans, Image Processing, Computer-Assisted, Male, Multiple Sclerosis, Observer Variation, Reproducibility of Results, Spinal Cord, Spinal Cord Diseases, Thoracic Vertebrae},
	issn = {1053-1807},
	doi = {10.1002/jmri.21817},
	author = {Wim Van Hecke and Guy Nagels and Emonds, Griet and Alexander Leemans and Jan Sijbers and van Goethem, Johan and Paul M Parizel}
}
@article {1375,
	title = {Diffusion tensor imaging in a rat model of Parkinson{\textquoteright}s disease after lesioning of the nigrostriatal tract.},
	journal = {NMR in biomedicine},
	volume = {22},
	year = {2009},
	month = {2009 Aug},
	pages = {697-706},
	abstract = {Parkinson{\textquoteright}s disease (PD) is characterised by degeneration of the nigrostrial connection causing dramatic changes in the dopaminergic pathway underlying clinical pathology. Till now, no MRI tools were available to follow up any specific PD-related neurodegeneration. However, recently, diffusion tensor imaging (DTI) has received considerable attention as a new and potential in vivo diagnostic tool for various neurodegenerative diseases. To assess this in PD, we performed DTI in the acute 6-hydroxydopamine (6-OHDA) rat model of PD to evaluate diffusion properties in the degenerating nigrostriatal pathway and its connecting structures. Injection of a neurotoxin in the striatum causes retrograde neurodegeneration of the nigrostriatal tract, and selective degeneration of nigral neurons. The advantage of this model is that the lesion size is well controllable by the injected dose of the toxin. The degree of functional impairment was evaluated in vivo using the amphetamine rotation test and microPET imaging of the dopamine transporter (DAT). Despite a nearly complete lesion of the nigrostriatal tract, DTI changes were limited to the ipsilateral substantia nigra (SN). In this study we demonstrate, using voxel-based statistics (VBS), an increase in fractional anisotropy (FA), whereas all eigenvalues were significantly decreased. VBS enabled us to visualise neurodegeneration of a cluster of neurons but failed to detect degeneration of more diffuse microstructures such as the nigrostriatal fibres or the dopaminergic endings in the striatum. VBS without a priori information proved to be better than manual segmentation of brain structures as it does not suffer from volume averaging and is not susceptible to erroneous segmentations of brain regions that show very little contrast on MRI images such as SN.},
	keywords = {Animals, Behavior, Animal, Diffusion Magnetic Resonance Imaging, Disease Models, Animal, Female, Imaging, Three-Dimensional, Immunohistochemistry, Parkinson Disease, Positron-Emission Tomography, Rats, Rats, Wistar, Substantia Nigra},
	issn = {1099-1492},
	doi = {10.1002/nbm.1381},
	author = {N. Van Camp and Ines Blockx and Marleen Verhoye and Casteels, Cindy and Coun, Frea and Alexander Leemans and Jan Sijbers and Veerle Baekelandt and Koen Van Laere and Annemie Van Der Linden}
}
@conference {PalaciosmverhoyeAudekerkedpootjsijbersWiborgavdlinde2009,
	title = {DKI visualizes hippocampal alterations in the chronic mild stress ratmodel},
	volume = {2009},
	year = {2009},
	month = {April},
	pages = {744},
	author = {Rafael  Delgado Y Palacios and Marleen Verhoye and Johan Van Audekerke and Dirk H J Poot and Jan Sijbers and O. Wiborg and Annemie Van Der Linden}
}
@article {HufkensCeulemanspscheund2009,
	title = {Ecotones in vegetation ecology: methodology and definitions revisited},
	journal = {Ecological Research},
	volume = {24},
	year = {2009},
	pages = {977-986},
	author = {Koen Hufkens and Reinhart Ceulemans and Paul Scheunders}
}
@inproceedings {svdmaarjbatenbujsijbers2009,
	title = {Experiences with Cell-BE and GPU for tomography},
	booktitle = {Embedded COmputer Systems: Architectures, Modeling, and Simulation - 9th International Workshop, SAMOS 2009 - Proceedings},
	year = {2009},
	month = {July},
	pages = {298-307},
	publisher = {Springer-Verlag Berlin Heidelberg},
	organization = {Springer-Verlag Berlin Heidelberg},
	author = {S. van der Maar and Kees Joost Batenburg and Jan Sijbers},
	editor = {Koen Bertels and Stephan Wong}
}
@conference {aleemansbjeurissjsijbersdkjones2009,
	title = {ExploreDTI: A Graphical Toolbox for Processing, Analyzing, and Visualizing Diffusion MR Data},
	year = {2009},
	month = {April},
	address = {Honolulu, USA},
	author = {Alexander Leemans and Ben Jeurissen and Jan Sijbers and Derek K. Jones}
}
@conference {BernatthuysmanGlabbeekjsijbersrpinhoGielen2009,
	title = {Exploring the Clavicle: Morphometric Differences Using a 3D Model},
	year = {2009},
	month = {February},
	address = {Las Vegas,Nevada},
	author = {Amit Bernat and Toon Huysmans and Francis Van Glabbeek and Jan Sijbers and R{\^o}mulo Pinho and Jan L Gielen}
}
@inproceedings {bjeurissaleemansjdtournierjsijbers2009,
	title = {Fiber Tracking on the {\textquoteright}Fiber Cup Phantom{\textquoteright} using Constrained Spherical Deconvolution},
	booktitle = {MICCAI workshop on Diffusion Modelling and the Fiber Cup (DMFC{\textquoteright}09)},
	year = {2009},
	address = {London, United Kingdom},
	author = {Ben Jeurissen and Alexander Leemans and Jacques-Donald Tournier and Jan Sijbers}
}
@article {jbatenbujsijbers2009,
	title = {Generic iterative subset algorithms for discrete tomography},
	journal = {Discrete Applied Mathematics},
	volume = {157},
	number = {3},
	year = {2009},
	pages = {438-451},
	doi = {https://doi.org/10.1016/j.dam.2008.05.033},
	author = {Kees Joost Batenburg and Jan Sijbers}
}
@article {PostnovSchutterjsijbersKarperienClerck2009,
	title = {Glucocorticoid-Induced Osteoporosis in Growing Mice Is Not Prevented by Simultaneous Intermittent PTH Treatment},
	journal = {Calcified Tissue International},
	volume = {85},
	number = {6},
	year = {2009},
	month = {December},
	pages = {530-537},
	doi = {10.1007/s00223-009-9301-3},
	author = {A. Postnov and T. De Schutter and Jan Sijbers and M. Karperien and N. De Clerck}
}
@inproceedings {jbatenbuwvaarlejsijbers2009,
	title = {Grey Level Estimation for Discrete Tomography},
	booktitle = {Discrete Geometry for Computer Imaging},
	series = {Lecture Notes in Computer Science},
	volume = {5810},
	year = {2009},
	month = {September},
	pages = {517-529},
	publisher = {Springer},
	organization = {Springer},
	doi = {https://doi.org/10.1007/978-3-642-04397-0_44},
	author = {Kees Joost Batenburg and Wim Van Aarle and Jan Sijbers},
	editor = {Srecko Brlek}
}
@inproceedings {Zhangaduijstepscheund2009,
	title = {A Hyperspectral Image Restoration technique},
	booktitle = {IEEE International Conference on Image Processing, Cairo, Egypt, November 7-11},
	year = {2009},
	pages = {2873-2876},
	author = {Y. Zhang and A. Duijster and Paul Scheunders}
}
@article {1266,
	title = {Imaging of voids due to deformation in alloy steel using micro-focus X-ray beam},
	journal = {Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms},
	volume = {267},
	year = {2009},
	month = {10/2009},
	pages = {3488 - 3490},
	issn = {0168583X},
	doi = {10.1016/j.nimb.2009.07.005},
	author = {Gupta, C. and Elke Van de Casteele and Chakravartty, J.K.}
}
@conference {jveraartBlockxwvheckemverhoyeavdlindejsijbers2009,
	title = {Improved non rigid coregistration of diffusion kurtosis images by incorporating diffusion kurtosis tensor information},
	year = {2009},
	month = {October},
	pages = {36},
	address = {Antalya, Turkey},
	author = {Jelle Veraart and Ines Blockx and Wim Van Hecke and Marleen Verhoye and Annemie Van Der Linden and Jan Sijbers}
}
@mastersthesis {1271,
	title = {Improved Processing for Diffusion Tensor Magnetic Resonance Images for Coregistration, Atlas Construction, and Voxel Based Analysis},
	year = {2009},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	author = {Wim Van Hecke}
}
@article {ajdendekdpootBosjsijbers2009,
	title = {Likelihood based hypothesis tests for brain activation detection from MRI data disturbed by colored noise: a simulation study},
	journal = {IEEE Transactions on Medical Imaging},
	volume = {28},
	number = {2},
	year = {2009},
	month = {February},
	pages = {287-296},
	author = {Arnold Jan den Dekker and Dirk H J Poot and R. Bos and Jan Sijbers}
}
@inproceedings {1416,
	title = {Machine Vision of Flotation Froths with a Rapid-Prototyping Platform},
	booktitle = {IFAC Workshop on Automation in Mining, Mineral and Metal Industry (IFACMMM2009)},
	year = {2009},
	author = {Kaartinen, Jani and H{\"a}t{\"o}nen, Jari and Timo Roine}
}
@article {Zhangsdbackerpscheund2009,
	title = {Noise-resistant wavelet-based Bayesian fusion of multispectral and hyperspectral images},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {47},
	number = {11},
	year = {2009},
	pages = {3834-3843},
	author = {Y. Zhang and Steve De Backer and Paul Scheunders}
}
@article {jbatenbujsijbers2009,
	title = {Optimal Threshold Selection for Tomogram Segmentation by Projection Distance Minimization},
	journal = {IEEE Transactions on Medical Imaging},
	volume = {28},
	number = {5},
	year = {2009},
	month = {June},
	pages = {676-686},
	doi = {10.1109/TMI.2008.2010437},
	author = {Kees Joost Batenburg and Jan Sijbers}
}
@conference {dpootajdendekmverhoyeBlockxAudekerkeavdlindejsijbers2009,
	title = {Optimizing the Diffusion Weighting Gradients for Diffusion-Kurtosis Imaging},
	volume = {2009},
	year = {2009},
	month = {April},
	pages = {1394},
	author = {Dirk H J Poot and Arnold Jan den Dekker and Marleen Verhoye and Ines Blockx and Johan Van Audekerke and Annemie Van Der Linden and Jan Sijbers}
}
@article {1614,
	title = {Osteologic exploration of the clavicle: a new approach},
	journal = {The FASEB Journal},
	volume = {23},
	year = {2009},
	edition = { (1_MeetingAbstracts)},
	chapter = {LB9},
	abstract = {Introduction Clavicles have a complex osteologic structure which makes a morphometric analysis extremely difficult. Our analysis shows the exact measurements and variations of the clavicle.

Materials and Methods 90 clavicles were dissected, CAT scanned and reconstructed. All measurements were automatically performed. The length and curvatures were calculated around the central line and for each cross-section the average, sagittal and axial diameter was calculated.

Results The average length is 163{\textpm}11 mm. For the length, there is a 9\% difference between the gender and 1.2\% between the left and right clavicle. Between the genders there is a volume difference of 36\%. The extremities show the biggest diameter, this decrease as approaching to the inflexion point, which is the smallest average diameter. In the axial view the acromial curvature is shorter and more curved than the sternal one. In males, the maximum acromial curvature has a difference of 18\% and the maximum sternal curvature a difference of 4\% compared with the females. In the coronal view there is a concave curvature with a maximum of 6 mm. In females the acromial end bends more posteroinferiorly.

Discussion This is the first 3D analysis performed on the clavicle. Females have a smaller clavicle with a shorter and less curved acromial curvature and a posteroinferior bending. The right clavicle is slightly shorter, thicker and more robust.},
	author = {Hilde Elisa Bortier and Amit Bernat and Toon Huysmans and Francis Van Glabbeek and Jan Sijbers and R{\^o}mulo Pinho and Gielen, Jan and Guy Hubens}
}
@mastersthesis {1172,
	title = {Parameterization and Correspondence for Improved Modeling, Analysis, and Visualization of Tubular Surfaces.},
	year = {2009},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	author = {Toon Huysmans}
}
@conference {dpootajdendekjsijbers2009,
	title = {Pearson Set of Distributions as Improved Signal Model for Diffusion Kurtosis Imaging},
	year = {2009},
	month = {April},
	pages = {1383},
	publisher = {ISMRM},
	author = {Dirk H J Poot and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {bjeurissaleemansjdtournierjsijbers2009,
	title = {Probabilistic Fiber Tracking using the Residual Bootstrap with Constrained Spherical Deconvolution MRI},
	year = {2009},
	month = {April},
	address = {Honolulu, USA},
	author = {Ben Jeurissen and Alexander Leemans and Jacques-Donald Tournier and Jan Sijbers}
}
@article {1374,
	title = {Quantitative diffusion tensor imaging in amyotrophic lateral sclerosis: revisited.},
	journal = {Human brain mapping},
	volume = {30},
	year = {2009},
	month = {2009 Nov},
	pages = {3657-75},
	abstract = {Voxel-based analyses (VBA) are increasingly being used to detect white matter abnormalities with diffusion tensor imaging (DTI) in different types of pathologies. However, the validity, specificity, and sensitivity of statistical inferences of group differences to a large extent depend on the quality of the spatial normalization of the DTI images. Using high-dimensional nonrigid coregistration techniques that are able to align both the spatial and orientational diffusion information and incorporate appropriate templates that contain this complete DT information may improve this quality. Alternatively, a hybrid technique such as tract-based spatial statistics (TBSS) may improve the reliability of the statistical results by generating voxel-wise statistics without the need for perfect image alignment and spatial smoothing. In this study, we have used (1) a coregistration algorithm that was optimized for coregistration of DTI data and (2) a population-based DTI atlas to reanalyze our previously published VBA, which compared the fractional anisotropy and mean diffusivity maps of patients with amyotrophic lateral sclerosis (ALS) with those of healthy controls. Additionally, we performed a complementary TBSS analysis to improve our understanding and interpretation of the VBA results. We demonstrate that, as the overall variance of the diffusion properties is lowered after normalizing the DTI data with such recently developed techniques (VBA using our own optimized high-dimensional nonrigid coregistration and TBSS), more reliable voxel-wise statistical results can be obtained than had previously been possible, with our VBA and TBSS yielding very similar results. This study provides support for the view of ALS as a multisystem disease, in which the entire frontotemporal lobe is implicated.},
	keywords = {Adult, Aged, Algorithms, Amyotrophic Lateral Sclerosis, anisotropy, Brain, Brain Mapping, Case-Control Studies, diffusion tensor imaging, Female, Humans, Image Processing, Computer-Assisted, Male, Middle Aged, Reproducibility of Results, Sensitivity and Specificity, Statistics as Topic},
	issn = {1097-0193},
	doi = {10.1002/hbm.20794},
	author = {Caroline A Sage and Wim Van Hecke and Ron R Peeters and Jan Sijbers and Robberecht, Wim and Paul M Parizel and Marchal, Guy and Alexander Leemans and Stefan Sunaert}
}
@article {gvgompelDefrisejbatenbu2009,
	title = {Reconstruction of a uniform star object from interior x-ray data: uniqueness, stability and algorithm},
	journal = {Inverse Problems},
	volume = {25},
	number = {65010},
	year = {2009},
	month = {June},
	doi = {10.1088/0266-5611/25/6/065010},
	author = {Gert Van Gompel and M. Defrise and Kees Joost Batenburg}
}
@inproceedings {rpinhoLuyckxjsijbers2009,
	title = {Robust Region Growing Based Intrathoracic Airway Tree Segmentation},
	booktitle = {Proceedings of 2nd International Workshop on Pulmonary Image Analysis},
	year = {2009},
	month = {September},
	pages = {261-271},
	author = {R{\^o}mulo Pinho and Sten Luyckx and Jan Sijbers}
}
@inproceedings {Driesen09,
	title = {Spatial hyperspectral image classification by prior segmentation},
	booktitle = {Geoscience and Remote Sensing Symposium,2009 IEEE International,IGARSS 2009},
	volume = {3},
	year = {2009},
	month = {July},
	pages = {III-709 - III-712},
	address = {Cape Town, South Africa},
	abstract = {In this paper, we propose a technique to incorporate spatial features in the classification of hyperspectral data by means of a prior segmentation of the dataset. The key idea of the technique is that each pixel is not classified individually, but that the regions obtained from the prior segmentation are classified as a whole. The proposed technique is validated on a hyperspectral dataset of a heathland area in Belgium. Experimental results show that we can achieve larger and spatially smoothed regions, while the overall classification success rate is comparable to the pure spectral classification results.},
	keywords = {Belgium, geophysical image processing, heathland area, hyperspectral data classification, image classification, image segmentation, prior segmentation, remote sensing, spatial hyperspectral image classification, spatially smoothed regions, spectral classification},
	isbn = {978-1-4244-3394-0},
	doi = {10.1109/IGARSS.2009.5417861},
	author = {J. Driesen and Guy Thoonen and Paul Scheunders}
}
@inproceedings {1297,
	title = {Three-dimensional monitoring of physical weathering in limestone with X-ray computed microtomography (micro-CT)},
	booktitle = {12th Euroseminar on Microscopy Applied to Building Materials (EMABM)},
	year = {2009},
	author = {Boone, M.A. and De Kock, T. and Dewanckele, J. and Cnudde, Veerle and Boone, M.N. and Van Loo, D. and Elke Van de Casteele and De Schutter, G. and Jacobs, P.}
}
@article {wvdbroekVerbeeckSchryverssdbackerpscheund2009,
	title = {Tomographic Spectroscopic Imaging; an experimental proof of concept},
	journal = {Ultramicroscopy},
	volume = {109},
	year = {2009},
	pages = {296-303},
	author = {Wouter Van den Broek and Jo Verbeeck and D. Schryvers and Steve De Backer and Paul Scheunders}
}
@mastersthesis {1270,
	title = {Towards accurate image reconstruction from truncated X-ray CT projections},
	year = {2009},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	author = {Gert Van Gompel}
}
@inproceedings {Gansemanpscheundwdhaes2009,
	title = {Using XML-formatted scores in real-time applications},
	booktitle = {10-th International Society for Music Information Retrieval Conference, pp. 663-668, Kobe, Japan, October 26-30},
	year = {2009},
	pages = {663-668},
	author = {Joachim Ganseman and Paul Scheunders and W. D{\textquoteright}haes}
}
@article {HufkenspscheundCeulemans2009,
	title = {Validation of the sigmoid wave curve fitting algorithm on a forest-tundra ecotone in the Northwest Territories, Canada},
	journal = {Ecological Informatics},
	volume = {4},
	year = {2009},
	pages = {1-7},
	author = {Koen Hufkens and Paul Scheunders and Reinhart Ceulemans}
}
@inproceedings {VerdoolaegeLambrechtspscheund2009,
	title = {Wavelet-Based colour texture retrieval using the KL Divergence between bivariate Generalized Gaussian Models},
	booktitle = {IEEE International Conference on Image Processing, Cairo, Egypt, November 7-11},
	year = {2009},
	pages = {265-268},
	author = {G. Verdoolaege and M. Lambrechts and Paul Scheunders}
}
@article {aduijstepscheundsdbacker2009,
	title = {Wavelet-Based EM Algorithm for Multispectral-Image Restoration},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {47},
	number = {11},
	year = {2009},
	month = {November},
	pages = {3892-3898},
	author = {A. Duijster and Paul Scheunders and Steve De Backer}
}
@proceedings {1238,
	title = {ACIVS 2008, Advanced Concepts for Intelligent Vision Systems},
	volume = {5259},
	year = {2008},
	author = {J Blanc-Talon and S. Bourenanne and Wilfried Philips and D Popescu and Paul Scheunders}
}
@inproceedings {svdmaarjbatenbujsijbers2008,
	title = {Algebraic tomographic reconstruction on the GPU},
	booktitle = {Li{\`e}ge Image Days 2008: Medical Imaging},
	year = {2008},
	month = {March},
	author = {S. van der Maar and Kees Joost Batenburg and Jan Sijbers}
}
@inproceedings {jbatenbujsijbers2008,
	title = {Automatic local thresholding of tomographic reconstructions based on the projection data},
	booktitle = {SPIE Medical Imaging},
	volume = {6913},
	year = {2008},
	month = {February},
	pages = {69132},
	address = {San Diego, CA, USA},
	author = {Kees Joost Batenburg and Jan Sijbers},
	editor = {Jiang Hsieh, Ehsan Samei}
}
@inproceedings {Zhangsdbackerpscheund2008,
	title = {Bayesian fusion of multispectral and hyperspectral image in wavelet domain},
	booktitle = {IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2008, Boston, United States, 6-11 July},
	year = {2008},
	pages = {69-72},
	publisher = {IEEE International},
	organization = {IEEE International},
	author = {Y. Zhang and Steve De Backer and Paul Scheunders},
	editor = {t.b.a.}
}
@conference {bjeurissaleemansjdtournierjsijbers2008,
	title = {Bootstrap methods for estimating uncertainty in Constrained Spherical Deconvolution fiber orientations},
	year = {2008},
	month = {May},
	pages = {3324},
	address = {Toronto, Canada},
	author = {Ben Jeurissen and Alexander Leemans and Jacques-Donald Tournier and Jan Sijbers}
}
@conference {bjeurissaleemansjdtournierjsijbers2008,
	title = {Can residual bootstrap reliably estimate uncertainty in fiber orientation obtained by spherical deconvolution from diffusion-weighted MRI?},
	volume = {41},
	year = {2008},
	month = {June},
	address = {Melbourne, Australia},
	author = {Ben Jeurissen and Alexander Leemans and Jacques-Donald Tournier and Jan Sijbers}
}
@conference {1493,
	title = {Computed tomography on all scales using the ASTRA toolbox},
	year = {2008},
	author = {Wim Van Aarle and S. van der Maar and Kees Joost Batenburg and Jan Sijbers}
}
@inproceedings {wvheckealeemanssdbackerParizeljsijbers2008,
	title = {On the construction of a ground truth methodology to evaluate VBM analysis results of diffusion tensor images},
	booktitle = {European Society for Magnetic Resonance in Medicine and Biology},
	year = {2008},
	month = {October},
	address = {Valencia, Spain},
	author = {Wim Van Hecke and Alexander Leemans and Steve De Backer and Paul M Parizel and Jan Sijbers}
}
@article {wvheckejsijbersDagostinoMaessdbackerVandervlietParizelaleemans2008,
	title = {On the construction of an inter-subject diffusion tensor magnetic resonance atlas of the healthy human brain},
	journal = {NeuroImage},
	volume = {43},
	number = {1},
	year = {2008},
	pages = {69-80},
	doi = {0.1016/j.neuroimage.2008.07.006},
	author = {Wim Van Hecke and Jan Sijbers and E. Dagostino and F. Maes and Steve De Backer and Everhard Vandervliet and Paul M Parizel and Alexander Leemans}
}
@article {sdbackerPizuricaHuysmansPhilipspscheund2008,
	title = {Denoising of Multicomponent Images Using Wavelet Least-Squares Estimators},
	journal = {Image and Vision Computing},
	volume = {26},
	number = {7},
	year = {2008},
	month = {July},
	pages = {1038-1051},
	author = {Steve De Backer and Aleksandra Pizurica and B. Huysmans and Wilfried Philips and Paul Scheunders}
}
@inproceedings {wvheckejsijbersGoethemParizel2008,
	title = {Diffusion tensor tractography reveals white matter alterations in the normal appearing spinal cord of Multiple Sclerosis patients},
	booktitle = {European Society for Magnetic Resonance in Medicine and Biology},
	year = {2008},
	month = {October},
	address = {Valencia, Spain},
	author = {Wim Van Hecke and Jan Sijbers and J. Van Goethem and Paul M Parizel}
}
@article {HufkensCeulemanspscheund2008,
	title = {Estimating the ecotone width in patchy ecotones using a sigmoid wave approach},
	journal = {Ecological Informatics},
	volume = {3},
	year = {2008},
	pages = {97-104},
	author = {Koen Hufkens and Reinhart Ceulemans and Paul Scheunders}
}
@inproceedings {bjeurissaleemansjdtournierjsijbers2008,
	title = {Estimation of Uncertainty in Constrained Spherical Deconvolution Fiber Orientations},
	booktitle = {5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro},
	year = {2008},
	pages = {907-910},
	address = {Paris, France},
	author = {Ben Jeurissen and Alexander Leemans and Jacques-Donald Tournier and Jan Sijbers}
}
@inproceedings {wvheckealeemansAgostinosdbackerVandervlietParizeljsijbers2008,
	title = {The evaluation of a population based diffusion tensor image atlas using a ground truth method},
	booktitle = {Proceedings of SPIE Medical Imaging},
	volume = {6914},
	year = {2008},
	month = {February},
	address = {San Diego, USA},
	author = {Wim Van Hecke and Alexander Leemans and E. Dagostino and Steve De Backer and Everhard Vandervliet and Paul M Parizel and Jan Sijbers}
}
@inproceedings {1295,
	title = {Evaluation of conservation treatments for archeological waterlogged wooden artifacts},
	booktitle = {9th International Conference on NDT of Art},
	year = {2008},
	author = {Bugani, Simone and Cloetens, Peter and Colombini, Maria Perla and Giachi, Gianna and Janssens, Koen and Modugno, Francesca and Morselli, Luciano and Elke Van de Casteele}
}
@inproceedings {dpootjsijbersajdendek2008,
	title = {An exploration of spatial similarities in temporal noise spectra in fMRI measurements},
	booktitle = {Proceedings of SPIE Medical Imaging 2008},
	volume = {6914},
	year = {2008},
	month = {February},
	pages = {69142},
	address = {San Diego, CA, USA},
	author = {Dirk H J Poot and Jan Sijbers and Arnold Jan den Dekker}
}
@inproceedings {Haneljbatenbujsijbers2008,
	title = {Fast bias field estimation by localized Lloyd-Max quantization},
	booktitle = {SPIE Medical Imaging},
	year = {2008},
	month = {February},
	address = {San Diego, CA, USA},
	author = {Rudolf Hanel and Kees Joost Batenburg and Jan Sijbers}
}
@conference {1382,
	title = {FASTRA: Fast Tomographic Reconstructions Using an Eight GPU Desktop Super Computer},
	year = {2008},
	address = {San Jose, California, USA},
	author = {S. van der Maar and Kees Joost Batenburg and Toon Huysmans and Jan Sijbers}
}
@conference {bjeurissaleemansefieremajsijbers2008,
	title = {Fiber Tractography on a Crossing Fiber Phantom using Constrained Spherical Deconvolution MRI},
	year = {2008},
	address = {Li{\`e}ge, Belgium},
	author = {Ben Jeurissen and Alexander Leemans and Els Fieremans and Jan Sijbers}
}
@conference {thuysmanBernatrpinhojsijbersGlabbeeckParizelBortier2008,
	title = {A Framework for Morphometric Analysis of Long Bones: Application to the Human Clavicle},
	year = {2008},
	month = {March},
	author = {Toon Huysmans and Amit Bernat and R{\^o}mulo Pinho and Jan Sijbers and Francis Van Glabbeek and Paul M Parizel and H. Bortier}
}
@article {BoumansVignalasmolderjsijbersmverhoyeAudekerkeavdlinde2008,
	title = {Functional magnetic resonance imaging in zebra finch discerns the neural substrate involved in segregation of conspecific song from background noise},
	journal = {Journal of Neurophysiology},
	volume = {99},
	year = {2008},
	month = {November},
	pages = {931-938},
	author = {T. Boumans and C. Vignal and Alain Smolders and Jan Sijbers and Marleen Verhoye and Johan Van Audekerke and Annemie Van Der Linden}
}
@conference {wvheckealeemanssdbackerVandervlietjsijberspmparize2008,
	title = {A Ground Truth Analysis of the Preservation of Diffusion Tensor Information in a Population Specific Atlas},
	year = {2008},
	month = {May},
	address = {Toronto, Canada},
	author = {Wim Van Hecke and Alexander Leemans and Steve De Backer and Everhard Vandervliet and Jan Sijbers and Paul M Parizel}
}
@conference {wpintjendpootmverhoyeavdlindejsijbers2008,
	title = {Improved EPI Correction: Upgrading An Ultrafast Imaging Technique},
	year = {2008},
	month = {March},
	author = {W. Pintjens and Dirk H J Poot and Marleen Verhoye and Annemie Van Der Linden and Jan Sijbers}
}
@article {1265,
	title = {Investigating morphological changes in treated vs. untreated stone building materials by x-ray micro-CT},
	journal = {Analytical and Bioanalytical Chemistry},
	volume = {391},
	year = {2008},
	month = {6/2008},
	pages = {1343 - 1350},
	issn = {1618-2642},
	doi = {10.1007/s00216-008-1946-7},
	author = {Bugani, Simone and Camaiti, Mara and Morselli, Luciano and Elke Van de Casteele and Janssens, Koen}
}
@conference {gvgompeljbatenbuDefrisejsijbers2008,
	title = {An iterative post-reconstruction method for beam hardening reduction without prior information},
	year = {2008},
	month = {March},
	author = {Gert Van Gompel and Kees Joost Batenburg and M. Defrise and Jan Sijbers}
}
@conference {thuysmanBernatGlabbeekjsijbersGielenBortier2008,
	title = {Left-Right and Gender Analysis of The Human Clavicle, A Problem in Developing an Anatomical Plate?},
	year = {2008},
	month = {September},
	pages = {101},
	address = {Brugge, Belgium},
	author = {Toon Huysmans and Amit Bernat and Francis Van Glabbeek and Jan Sijbers and Jan L Gielen and H. Bortier}
}
@article {KempeneersZarco-TejadaNorthsdbackerDelalieuxSepulcre-CantoMoralesAardtSagardoyCoppinpscheund2008,
	title = {Model inversion for chlorophyll estimation in open canopies from hyperspectral imagery},
	journal = {International Journal of Remote Sensing},
	volume = {29},
	number = {17},
	year = {2008},
	pages = {5093-5111},
	author = {Pieter Kempeneers and P.J. Zarco-Tejada and P.R.J. North and Steve De Backer and Stephanie Delalieux and G. Sepulcre-Canto and F. Morales and Jan van Aardt and Ruth Sagardoy and P. Coppin and Paul Scheunders}
}
@inproceedings {BernatthuysmanGlabbeekjsijbersBortierGielen2008,
	title = {Morphometric Study of the Human Clavicle for the Development of an Anatomical Plate},
	booktitle = {54th Annual Meeting of the Orthopaedic Research Society},
	year = {2008},
	month = {March},
	address = {San Francisco, Ca, United States},
	author = {Amit Bernat and Toon Huysmans and Francis Van Glabbeek and Jan Sijbers and H. Bortier and Jan L Gielen}
}
@article {sdbackerCornelissenLemeireNuydensMeertSchelkenspscheund2008,
	title = {Mosiacing of Fibered Fluorescence Microscopy Video},
	journal = {Lecture notes in Computer Science},
	volume = {5259},
	year = {2008},
	month = {October},
	pages = {915-923},
	author = {Steve De Backer and F. Cornelissen and J. Lemeire and R. Nuydens and T. Meert and P. Schelkens and Paul Scheunders}
}
@article {jdriesenpscheund2008,
	title = {A Multicomponent Image Segmentation Framework},
	journal = {Lecture Notes in Computer Science},
	volume = {5259},
	year = {2008},
	pages = {589-600},
	author = {J. Driesen and Paul Scheunders}
}
@inproceedings {Verdoolaegesdbackerpscheund2008,
	title = {Multiscale colour texture retrieval using the geodesic distance between multivariate generalized Gaussian models},
	booktitle = {IEEE, International Conference on Image Processing, San Diego, CA, October 12-15},
	year = {2008},
	pages = {169-172},
	author = {G. Verdoolaege and Steve De Backer and Paul Scheunders}
}
@inproceedings {dpootjsijbersajdendek2008,
	title = {Optimizing the Diffusion Kurtosis imaging acquisition},
	booktitle = {European Society for Magnetic Resonance in Medicine and Biology},
	year = {2008},
	month = {October},
	address = {Valencia, Spain},
	author = {Dirk H J Poot and Jan Sijbers and Arnold Jan den Dekker}
}
@inproceedings {gvgompelDefrisejbatenbu2008,
	title = {Reconstruction of a uniform star object from interior X-ray data},
	booktitle = {Nuclear Science Symposium Conference Record, NSS08, IEEE},
	number = {2},
	year = {2008},
	month = {October},
	pages = {4115-4119},
	address = {Dresden, Germany},
	author = {Gert Van Gompel and M. Defrise and Kees Joost Batenburg}
}
@inproceedings {rpinhoBatenburgjsijbers2008,
	title = {Seeing Through the Window: Pre-fetching Strategies for Out-of-core Image Processing Algorithms},
	booktitle = {Proceedings of SPIE Medical Imaging},
	volume = {6919},
	year = {2008},
	month = {February},
	publisher = {SPIE},
	organization = {SPIE},
	address = {San Diego, CA, USA},
	doi = {doi:10.1117/12.769423},
	author = {R{\^o}mulo Pinho and Kees Joost Batenburg and Jan Sijbers}
}
@inproceedings {Thoonen08,
	title = {Spatial Classification of Hyperspectral Data of Dune Vegetation along the Belgian Coast},
	booktitle = {Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008. IEEE International},
	volume = {3},
	year = {2008},
	month = {July},
	pages = {III-483 - III-486},
	address = {Boston, MA, USA},
	abstract = {This work evaluates a classification method, including spatial information, for dune vegetation along the Belgian coastline. The used method is a recursive supervised segmentation algorithm based on a tree-structured Markov Random Field. This technique describes a K-ary field as a sequence of binary Markov Random Fields, each of which is represented by a node in the tree. The obtained classification results were compared to results with the same data set, for a purely spectral classification and a spectral classification, followed by spatial smoothing.},
	keywords = {airborne data, airborne radar, Belgian Coast, binary Markov Random Field, dune environment, dune vegetation mapping, Europe, hyperspectral data, image classification, image segmentation, Markov processes, spatial classification, spatial smoothing, supervised segmentation algorithm, tree-structured Markov Random Field, trees (mathematics), TS-MRF model, vegetation mapping},
	isbn = {978-1-4244-2807-6},
	doi = {10.1109/IGARSS.2008.4779389},
	author = {Guy Thoonen and Steve De Backer and S. Provoost and Pieter Kempeneers and Paul Scheunders}
}
@inproceedings {wpintjendpootmverhoyeavdlindejsijbers2008,
	title = {Susceptibility correction for improved tractography using high field DT-EPI},
	booktitle = {Proceedings of SPIE Medical Imaging},
	volume = {6914},
	year = {2008},
	month = {February},
	address = {San Diego, USA},
	author = {W. Pintjens and Dirk H J Poot and Marleen Verhoye and Annemie Van Der Linden and Jan Sijbers}
}
@inproceedings {wvaarlejbatenbujsijbers2008,
	title = {Threshold Selection for Segmentation of Dense Objects in Tomograms},
	booktitle = {International Symposium on Visual Computing},
	series = {Lecture Notes on Computer Science},
	volume = {5358},
	year = {2008},
	month = {December},
	pages = {700-709},
	publisher = {Springer-Verlag},
	organization = {Springer-Verlag},
	address = {Berlin Heidelberg},
	author = {Wim Van Aarle and Kees Joost Batenburg and Jan Sijbers},
	editor = {G. Bebis}
}
@inproceedings {rpinhothuysmanVosjsijbers2008,
	title = {Tracheal Stent Prediction Using Statistical Deformable Models of Tubular Shapes},
	booktitle = {Proceedings of SPIE Medical Imaging},
	year = {2008},
	month = {February},
	publisher = {SPIE},
	organization = {SPIE},
	address = {San Diego, CA, USA},
	doi = {http://dx.doi.org/10.1117/12.770237},
	author = {R{\^o}mulo Pinho and Toon Huysmans and W. Vos and Jan Sijbers}
}
@inproceedings {rpinhothuysmanVosjsijbers2008,
	title = {Tracheal Stent Prediction Using Statistical Deformable Models of Healthy Tracheas},
	booktitle = {Liege Image Days 2008: Medical Imaging},
	year = {2008},
	month = {March},
	author = {R{\^o}mulo Pinho and Toon Huysmans and W. Vos and Jan Sijbers}
}
@article {wvheckealeemansjsijbersVandervlietGoethemParizel2008,
	title = {A tracking based DTI segmentation method for the detection of diffusion-related changes of the cervical spinal cord with aging},
	journal = {Journal of Magnetic Resonance Imaging},
	volume = {27},
	number = {5},
	year = {2008},
	month = {March},
	pages = {978-991},
	doi = {10.1002/jmri.21338},
	author = {Wim Van Hecke and Alexander Leemans and Jan Sijbers and Everhard Vandervliet and J. Van Goethem and Paul M Parizel}
}
@inproceedings {pscheundwdhaes2008,
	title = {Using MusicXML in real-time applications},
	booktitle = {Proceedings of the 34th Euromicro conference on Software Engineering and Advanced Applications, Parma, Italy, September 3-5},
	year = {2008},
	pages = {29-30},
	author = {Joachim Ganseman and Paul Scheunders and W. D{\textquoteright}haes}
}
@inproceedings {Gansemanpscheundwdhaes2008,
	title = {Using XQUERY on MusicXML databases for musicological analysis},
	booktitle = {ISMIR 2008, 9-th International Conference on Music Information Retrieval, Philadephia, USA, 14-18 september},
	year = {2008},
	pages = {433-438},
	author = {Joachim Ganseman and Paul Scheunders and W. D{\textquoteright}haes}
}
@inproceedings {aduijstesdbackerpscheund2008,
	title = {Wavelet-based Multispectral Image Restoration},
	booktitle = {IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium},
	volume = {3},
	year = {2008},
	pages = {79-82},
	publisher = {IEEE International},
	organization = {IEEE International},
	author = {A. Duijster and Steve De Backer and Paul Scheunders}
}
@inproceedings {1294,
	title = {X-ray computed tomography as a non-destructive tool for stone conservation},
	booktitle = {9th International Conference on NDT of Art},
	year = {2008},
	author = {Bugani, Simone and Camaiti, Mara and Morselli, Luciano and Elke Van de Casteele and Cloetens, Peter and Janssens, Koen}
}
@conference {1296,
	title = {X-ray microtomography as an imaging tool in cultural heritage},
	year = {2008},
	author = {Elke Van de Casteele and Bugani, Simone and Annemie Van Der Linden and Janssens, Koen}
}
@proceedings {-Blanc-TalonPhilipsPopescupscheund2007,
	title = {ACIVS 2007, Advanced Concepts for Intelligent Vision Systems},
	volume = {4678},
	year = {2007},
	pages = {1-1095},
	publisher = {Springer},
	author = {J Blanc-Talon and Wilfried Philips and D Popescu and Paul Scheunders}
}
@article {jsijbersdpootajdendekwpintjen2007,
	title = {Automatic estimation of the noise variance from the histogram of a magnetic resonance image},
	journal = {Physics in Medicine and Biology},
	volume = {52},
	number = {5},
	year = {2007},
	month = {February},
	pages = {1335-1348},
	doi = {10.1088/0031-9155/52/5/009},
	author = {Jan Sijbers and Dirk H J Poot and Arnold Jan den Dekker and W. Pintjens}
}
@inproceedings {Batenburgjsijbers2007,
	title = {Automatic multiple threshold scheme for segmentation of tomograms},
	booktitle = {Proceedings of SPIE Medical Imaging: Physics of Medical Imaging},
	year = {2007},
	month = {February},
	address = {San Diego, CA, USA},
	author = {Kees Joost Batenburg and Jan Sijbers},
	editor = {Michael J. Flynn, Jiang Hsieh}
}
@inproceedings {pscheundsdbackerPizuricaHuysmansPhilips2007,
	title = {Bayesian Wavelet-based Denoising of Multicomponent Images},
	booktitle = {Proceedings Wavelet Applications in Industrial Processing V, part of SPIE Optics East, Boston, MA, 9-12 september, Vol. 6763-OK (12 pages)},
	year = {2007},
	author = {Paul Scheunders and Steve De Backer and Aleksandra Pizurica and B. Huysmans and Wilfried Philips}
}
@inproceedings {Juntu:2007:CST:1710523.1710591,
	title = {Classification of soft tissue tumors in MRI images using kernel PCA and regularized least square classifier},
	booktitle = {Proceedings of the Fourth IASTED International Conference on Signal Processing, Pattern Recognition, and Applications},
	series = {SPPRA {\textquoteright}07},
	year = {2007},
	pages = {362{\textendash}367},
	publisher = {ACTA Press},
	organization = {ACTA Press},
	address = {Anaheim, CA, USA},
	keywords = {Fourier space, kernel PCA, MRI, RLSC, soft tissue tumors},
	isbn = {978-0-88986-647-8},
	url = {http://dl.acm.org/citation.cfm?id=1710523.1710591},
	author = {Juntu, Jaber and Jan Sijbers and Dirk Van Dyck}
}
@article {zmaithuysmanjsijbers2007,
	title = {Colon Visualization Using Cylindrical Parameterization},
	journal = {Lecture Notes in Computer Science},
	volume = {4678},
	year = {2007},
	pages = {607-615},
	author = {Zhenhua Mai and Toon Huysmans and Jan Sijbers}
}
@inproceedings {wvheckeDagostinoaleemansParizelMaesjsijbers2007,
	title = {On the construction of a healthy brain inter-subject diffusion tensor image and tractography atlas},
	booktitle = {32nd Congress of the Europea Society of NeuroRadiolog},
	year = {2007},
	pages = {146},
	author = {Wim Van Hecke and E. Dagostino and Alexander Leemans and Paul M Parizel and F. Maes and Jan Sijbers}
}
@inproceedings {jbatenbujsijbers2007,
	title = {DART: a fast heuristic algebraic reconstruction algorithm for discrete tomography},
	booktitle = {Proceedings of the IEEE International Conference on Image Processing (ICIP)},
	year = {2007},
	month = {September},
	address = {San Antonio, Texas, USA},
	doi = {10.1109/ICIP.2007.4379972},
	author = {Kees Joost Batenburg and Jan Sijbers}
}
@mastersthesis {1291,
	title = {Data-driven methods for the analysis of time-resolved mental chronometry fMRI data sets},
	year = {2007},
	type = {PhD thesis},
	author = {Alain Smolders}
}
@inproceedings {wvheckealeemansBrabanderLaridonClaeysCeulemansGoethemParizeljsijbers2007,
	title = {Diffusion Tensor Fiber Tracking Reveals Probst Bundles in Patients with Agenesis of the Corpus Callosum},
	booktitle = {32nd Congress of the Europea Society of NeuroRadiolog},
	year = {2007},
	pages = {135},
	author = {Wim Van Hecke and Alexander Leemans and N. De Brabander and A. Laridon and K. Claeys and B. Ceulemans and J. Van Goethem and Paul M Parizel and Jan Sijbers}
}
@inproceedings {Parizelwvhecke2007,
	title = {Diffusion Tensor Imaging: Is it really useful?},
	booktitle = {32nd Congress of the Europea Society of NeuroRadiolog},
	year = {2007},
	author = {Paul M Parizel and Wim Van Hecke}
}
@inproceedings {wvheckealeemansjsijbersParizelGoethem2007,
	title = {Diffusion Tensor Tractoghraphy Reaveals Spinal Cord Alterations in Patients with Multiple Sclerosis},
	booktitle = {32nd Congress of the Europea Society of NeuroRadiolog},
	year = {2007},
	pages = {129},
	author = {Wim Van Hecke and Alexander Leemans and Jan Sijbers and Paul M Parizel and J. Van Goethem}
}
@article {asmolderMartinoStaerenpscheundjsijbersGoebelFormisano2007,
	title = {Dissecting cognitive stages with time-resolved fMRI data: a comparison of fuzzy clustering and independent component analysis},
	journal = {Magnetic Resonance Imaging},
	volume = {25},
	number = {6},
	year = {2007},
	month = {July},
	pages = {860-868},
	doi = {doi:10.1016/j.mri.2007.02.018},
	author = {Alain Smolders and F. De Martino and N. Staeren and Paul Scheunders and Jan Sijbers and R. Goebel and E. Formisano}
}
@inproceedings {PeersmanVanhoenackerBrysDyckStamVerstraeteBloemHeymanHerendaeljsijbersSchepper2007,
	title = {Imaging features of Ewing{\textquoteright}s sarcoma},
	booktitle = {International Skeletal Society 2007, 34th annual ISS Radiology Refresher Course},
	year = {2007},
	month = {October},
	pages = {22},
	address = {Budapest},
	author = {B. Peersman and F. M. Vanhoenacker and P. Brys and Pieter Van Dyck and M. Stam and K. L. Verstraete and J. L. Bloem and S. Heyman and B. Van Herendael and Jan Sijbers and A. M. De Schepper}
}
@article {ParizelRompaeyLoockwvheckeGoethemaleemansjsijbers2007,
	title = {Influence of User-Defined Parameters on Diffusion Tensor Tractography of the Corticospinal Tract},
	journal = {The Neuroradiology Journal},
	number = {20},
	year = {2007},
	pages = {139-147},
	author = {Paul M Parizel and V. Van Rompaey and R. Van Loock and Wim Van Hecke and J. Van Goethem and Alexander Leemans and Jan Sijbers}
}
@mastersthesis {1345,
	title = {Information Extraction from Hyperspectral Images Applied to Vegetation},
	volume = {doctor in physics},
	year = {2007},
	type = {PhD thesis},
	author = {Pieter Kempeneers}
}
@article {1264,
	title = {Investigation on porosity changes of Lecce stone due to conservation treatments by means of x-ray nano- and improved micro-computed tomography: preliminary results},
	journal = {X-Ray Spectrometry},
	volume = {36},
	year = {2007},
	month = {09/2007},
	pages = {316 - 320},
	issn = {00498246},
	doi = {10.1002/(ISSN)1097-453910.1002/xrs.v36:510.1002/xrs.976},
	author = {Bugani, Simone and Camaiti, Mara and Morselli, Luciano and Elke Van de Casteele and Janssens, Koen}
}
@inproceedings {aleemanswvheckeLebelWalkerjsijbersBeaulieu2007,
	title = {A model based approach for voxelwise analysis of multi-subject diffusion tensor data},
	booktitle = {Joint Annual Meeting ISMRM-ESMRMB},
	year = {2007},
	address = {Berlin},
	author = {Alexander Leemans and Wim Van Hecke and C. Lebel and L. Walker and Jan Sijbers and C. Beaulieu}
}
@article {aduijstesdbackerpscheund2007,
	title = {Multicomponent image restoration, an experimental study},
	journal = {Lecture Notes in Computer Science},
	volume = {4633},
	year = {2007},
	pages = {58-68},
	author = {A. Duijster and Steve De Backer and Paul Scheunders}
}
@article {wvheckealeemansD{\textquoteright}AgostinosdbackerVandervlietParizeljsijbers2007,
	title = {Nonrigid Coregistration of Diffusion Tensor Images Using a Viscous Fluid Model and Mutual Information},
	journal = {IEEE Transactions on Medical Imaging},
	volume = {26},
	number = {11},
	year = {2007},
	pages = {1598-1612},
	author = {Wim Van Hecke and Alexander Leemans and E. Dagostino and Steve De Backer and Everhard Vandervliet and Paul M Parizel and Jan Sijbers}
}
@inproceedings {wvheckealeemansVandervlietParizeljsijbers2007,
	title = {An optimized tensor orientation strategy for non-rigid alignment of DT-MRI data},
	booktitle = {Joint Annual Meeting ISMRM-ESMRMB},
	year = {2007},
	address = {Berlin},
	author = {Wim Van Hecke and Alexander Leemans and Everhard Vandervliet and Paul M Parizel and Jan Sijbers}
}
@article {BalsjbatenbuVerbeeckjsijbersTendeloo2007,
	title = {Quantitative three-dimensional reconstruction of catalyst particles for bamboo-like carbon nanotubes},
	journal = {Nano Letters},
	volume = {7},
	number = {12},
	year = {2007},
	pages = {3669-3674},
	doi = {10.1021/nl071899m},
	author = {Sara Bals and Kees Joost Batenburg and Jo Verbeeck and Jan Sijbers and Van Tendeloo, Gustaaf}
}
@inproceedings {rpinhojsijbersthuysman2007,
	title = {Segmentation of The Human Trachea Using Deformable Statistical Models of Tubular Shapes},
	booktitle = {Proceedings of Advanced Concepts for Intelligent Vision Systems},
	series = {Lecture Notes in Computer Science},
	volume = {4678},
	year = {2007},
	month = {August},
	pages = {531-542},
	doi = {http://dx.doi.org/10.1007/978-3-540-74607-2_48},
	author = {R{\^o}mulo Pinho and Jan Sijbers and Toon Huysmans}
}
@inproceedings {CampthuysmanmverhoyeGaljartjsijbersavdlinde2007,
	title = {Statistical shape analysis on 3D MRI of the ventricular system of the Cyln2/Rsn double knock-out mice},
	booktitle = {Proceedings of the International Society for Magnetic Resonance in Medicine},
	year = {2007},
	pages = {1869},
	address = {Berlin, Germany},
	author = {N. Van Camp and Toon Huysmans and Marleen Verhoye and N. Galjart and Jan Sijbers and Annemie Van Der Linden}
}
@inproceedings {Meirthuysmanjsijbersavdlinde2007,
	title = {Statistical shape and position analysis on 3D structural MRI data of a motor region involved in vocal behavior of songbirds},
	booktitle = {Proceedings of the International Society for Magnetic Resonance in Medicine},
	year = {2007},
	pages = {431},
	address = {Berlin, Germany},
	author = {V. Van Meir and Toon Huysmans and Jan Sijbers and Annemie Van Der Linden}
}
@article {1462,
	title = {Voltage Security Enhancement and Congestion Management via STATCOM \& IPFC Using Artificial Intelligence},
	journal = {Iranian Journal of Science and Technology},
	volume = {1},
	number = {12},
	year = {2007},
	chapter = {289},
	author = {Azam Karami and M.Rashidinejad and A. A. Gharaveisi}
}
@article {pscheundsdbacker2007,
	title = {Wavelet denoising of multicomponent images, using Gaussian Scale Mixture models and a noise-free image as priors},
	journal = {IEEE Transactions on Image Processing},
	volume = {16},
	number = {7},
	year = {2007},
	month = {July},
	pages = {1865-1872},
	author = {Paul Scheunders and Steve De Backer}
}
@inproceedings {aduijstesdbackerpscheund2007,
	title = {Wavelet-based Multicomponent Image Restoration},
	booktitle = {Wavelet Applications in Industrial Processing V, part of SPIE Optics East, Boston, MA, United States, September 9-12},
	series = {Presented at the Society of Photo-Optical Instrumentation Engineers (SPIE) Conference},
	volume = {6763},
	year = {2007},
	month = {October},
	author = {A. Duijster and Steve De Backer and Paul Scheunders},
	editor = {F. Truchetet and O. Laligant}
}
@inproceedings {1293,
	title = {X-ray microtomography as a non-destructive tool for stone characterization in a conservation study},
	booktitle = {4th ICNDT Hellenic Society},
	year = {2007},
	author = {Elke Van de Casteele and Bugani, Simone and Camaiti, Mara and Morselli, Luciano and Janssens, Koen}
}
@proceedings {-Blanc-TalonPhilipsPopescupscheund2006,
	title = {ACIVS 2006, Advanced Concepts for Intelligent Vision Systems},
	volume = {4179},
	year = {2006},
	pages = {1-1224},
	publisher = {Springer},
	author = {J Blanc-Talon and Wilfried Philips and D Popescu and Paul Scheunders}
}
@article {wvdbroekVerbeecksdbackerpscheundSchryvers2006,
	title = {Acquisition of the EELS data cube by tomographic reconstruction},
	journal = {Ultramicroscopy},
	volume = {106},
	number = {3},
	year = {2006},
	month = {March},
	pages = {269-276},
	author = {Wouter Van den Broek and Jo Verbeeck and Steve De Backer and Paul Scheunders and D. Schryvers}
}
@proceedings {1468,
	title = {Application of RGA to Optimal Choice and Allocation of UPFC for Voltage Security Enhancement in Deregulated Power System},
	year = {2006},
	author = {Azam Karami and M.Rashidinejad and A. A. Gharaveisi}
}
@inproceedings {dpootjsijbersDekkerwpintjen2006,
	title = {Automatic estimation of the noise variance from the histogram of a magnetic resonance image},
	booktitle = {IEEE/EBMS Benelux Symposium proceedings},
	year = {2006},
	month = {December},
	pages = {135-138},
	publisher = {IEEE/EMBS},
	organization = {IEEE/EMBS},
	author = {Dirk H J Poot and Jan Sijbers and Arnold Jan den Dekker and W. Pintjens}
}
@inproceedings {Batenburgjsijbersevdcaste2006,
	title = {Automatic multiple threshold scheme for segmentation of tomograms},
	booktitle = {Proceedings of the Biomedical Engineering IEEE/EMBS Benelux Symposium},
	volume = {2},
	year = {2006},
	month = {December},
	pages = {143-146},
	address = {Brussels, Belgium},
	author = {Kees Joost Batenburg and Jan Sijbers and Elke Van de Casteele}
}
@inproceedings {VandervlietNagelsHeineckewvheckealeemansjsijbersParizel2006,
	title = {On the cause and mechanisms of the negative BOLD response in fMRI},
	booktitle = {23rd Annual Scientific Meeting of the European Society for Magnetic Resonance in Medicine and Biology},
	year = {2006},
	pages = {308},
	address = {Warsaw, Poland},
	author = {Everhard Vandervliet and Guy Nagels and A. Heinecke and Wim Van Hecke and Alexander Leemans and Jan Sijbers and Paul M Parizel}
}
@inproceedings {KempeneerssdbackerZarco-TejadaDelalieuxSepulcre-Cant{\'o}MoralesSagardoyAardtCoppinpscheund2006,
	title = {Chlorophyll retrieval from canopy reflectance over orchards using hyperspectral techniques},
	booktitle = {2nd International Symposium on Recent Advances in Quantitative Remote Sensing, 25-29 September 2006, Torrent (Valencia), Spain},
	year = {2006},
	author = {Pieter Kempeneers and Steve De Backer and P.J. Zarco-Tejada and Stephanie Delalieux and G. Sepulcre-Cant{\'o} and F. Morales and Ruth Sagardoy and Jan van Aardt and P. Coppin and Paul Scheunders}
}
@inproceedings {wpintjenjsijbersmverhoyeAudekerkeavdlinde2006,
	title = {A Combined Correction Scheme For EPI Distortions at 7 Tesla},
	booktitle = {Belgian Day on Biomedical Engineering - IEEE/EMBS Benelux Symposium},
	year = {2006},
	pages = {147-150},
	address = {Brussels, Belgium},
	author = {W. Pintjens and Jan Sijbers and Marleen Verhoye and Johan Van Audekerke and Annemie Van Der Linden}
}
@inproceedings {wvheckealeemansjsijbersParizelGoethem2006,
	title = {A comparison of diffusion tensor analysis methods for detecting age-related changes of the normal appearing spinal cord},
	booktitle = {23rd Annual Scientific Meeting of the European Society for Magnetic Resonance in Medicine and Biology},
	year = {2006},
	pages = {293-294},
	address = {Warsaw, Poland},
	author = {Wim Van Hecke and Alexander Leemans and Jan Sijbers and Paul M Parizel and J. Van Goethem}
}
@inproceedings {gvgompelDefrisedvandyck2006,
	title = {Consistent Extrapolation of Truncated 2D CT Projections},
	booktitle = {Micro tomography 3D},
	year = {2006},
	month = {September},
	address = {Gasthuisberg, Leuven, Belgium},
	author = {Gert Van Gompel and M. Defrise and Dirk Van Dyck}
}
@article {1461,
	title = {Coordination of UPFC \& SVC for Voltage Security Enhancement},
	journal = {WSEAS Transactions on Circuits and Systems},
	volume = {5},
	number = {6},
	year = {2006},
	chapter = {740},
	author = {Azam Karami and M.Rashidinejad and A. A. Gharaveisi}
}
@inproceedings {wvheckealeemansBrabanderLaridonCeulemansJParizel2006,
	title = {Diffusion tensor fiber tracking in patients with agenesis of the corpus callosum},
	booktitle = {23rd Annual Scientific Meeting of the European Society for Magnetic Resonance in Medicine and Biology},
	year = {2006},
	pages = {34-35},
	address = {Warsaw, Poland},
	author = {Wim Van Hecke and Alexander Leemans and N. De Brabander and A. Laridon and B. Ceulemans and Jan Sijbers and Paul M Parizel}
}
@inproceedings {Batenburgjsijbers2006,
	title = {Discrete tomography from micro-CT data: application to the mouse trabecular bone structure},
	booktitle = {Proceedings of SPIE Medical Imaging: Physics of Medical Imaging},
	volume = {6142},
	year = {2006},
	month = {February},
	pages = {1325-1335},
	address = {San Diego, CA, USA},
	doi = {https://doi.org/10.1117/12.652603},
	author = {Kees Joost Batenburg and Jan Sijbers},
	editor = {Michael J. Flynn, Jiang Hsieh}
}
@inproceedings {wvheckealeemansjsijbersParizelGoethem2006,
	title = {DTI of normal appearing spinal cord in elderly},
	booktitle = {44th Annual Meeting of the American Society of Neuroradiology},
	year = {2006},
	month = {May},
	pages = {380-381},
	address = {San Diego, USA},
	author = {Wim Van Hecke and Alexander Leemans and Jan Sijbers and Paul M Parizel and J. Van Goethem}
}
@inproceedings {rpinhojsijbersVos2006,
	title = {Efficient approaches to intrathoracic airway tree segmentations},
	booktitle = {Proceedings of the Biomedical Engineering IEEE/EMBS Benelux Symposium},
	volume = {2},
	year = {2006},
	month = {December},
	pages = {151-154},
	address = {Brussels, Belgium},
	author = {R{\^o}mulo Pinho and Jan Sijbers and W. Vos}
}
@inproceedings {gvgompelDefrisedvandyck2006,
	title = {Elliptical Extrapolation of Truncated 2D CT Projections using Helgason-Ludwig consistency conditions},
	booktitle = {SPIE Medical Imaging: Physics of Medical Imaging},
	series = {Proceedings of the SPIE},
	volume = {6142},
	year = {2006},
	month = {February},
	pages = {1408-1417},
	address = {San Diego, Californa, USA},
	author = {Gert Van Gompel and M. Defrise and Dirk Van Dyck},
	editor = {Flynn, Michael J.; Hsieh, Jiang.}
}
@inproceedings {sdbackerpscheund2006,
	title = {Enhancement of fMRI image series with the aid of an anatomical image},
	booktitle = {ISBI{\textquoteright}06, IEEE International Symposium on Biomedical Imaging, Arlington, Virginia, april 6-9},
	year = {2006},
	pages = {1052-1055},
	author = {Steve De Backer and Paul Scheunders}
}
@inproceedings {dpootjsijbersajdendekBos2006,
	title = {Estimation of the noise variance from the background histogram mode of an MR image},
	booktitle = {Proceedings of SPS-DARTS 2006 (The second annual IEEE BENELUX/DSP Valley Signal Processing Symposium)},
	year = {2006},
	month = {March},
	pages = {159-162},
	address = {Antwerp, Belgium},
	author = {Dirk H J Poot and Jan Sijbers and Arnold Jan den Dekker and R. Bos}
}
@inproceedings {dpootjsijbersajdendekBos2006,
	title = {Estimation of the noise variance from the background histogram mode of an MR image},
	booktitle = {Proceedings of the 25th Benelux Meeting on Systems and Control},
	series = {TuM06-5},
	year = {2006},
	month = {March},
	address = {Heeze, The Netherlands},
	author = {Dirk H J Poot and Jan Sijbers and Arnold Jan den Dekker and R. Bos}
}
@inproceedings {NagelsVandervlietwvheckeEngelborghshoogheCrasParizelDeyn2006,
	title = {fMRI during PASAT and PVSAT in mild MS, moderate MS and normal volunteers.},
	booktitle = {22nd Congress of the European Committee for},
	year = {2006},
	address = {Madrid, Spain},
	author = {Guy Nagels and Everhard Vandervliet and Wim Van Hecke and Sebastiaan Engelborghs and M.B. D hooghe and P. Cras and Paul M Parizel and P.P. De Deyn}
}
@article {thuysmanjsijbersVanpouckeVerdonk2006,
	title = {Improved Shape Modeling of Tubular Objects Using Cylindrical Parameterization},
	journal = {Lecture Notes in Computer Science},
	volume = {4091},
	year = {2006},
	pages = {84-91},
	author = {Toon Huysmans and Jan Sijbers and F. Vanpoucke and B. Verdonk}
}
@article {GroofmverhoyeMeirTindemansaleemansavdlinde2006,
	title = {In vivo diffusion tensor imaging (DTI) of brain subdivisions and vocal pathways in songbirds},
	journal = {NeuroImage},
	volume = {29},
	year = {2006},
	pages = {754-763},
	author = {G. De Groof and Marleen Verhoye and V. Van Meir and I. Tindemans and Alexander Leemans and Annemie Van Der Linden}
}
@conference {1300,
	title = {Investigation on Lecce stone porosity by means of micro and nano X-ray tomography},
	year = {2006},
	author = {Bugani, Simone and Camaiti, Mara and Morselli, Luciano and Janssens, Koen and Elke Van de Casteele}
}
@mastersthesis {1274,
	title = {Modeling and Processing of Diffusion Tensor Magnetic Resonance Images for Improved Analysis of Brain Connectivity},
	volume = {PhD in Sciences: Physics},
	year = {2006},
	author = {Alexander Leemans}
}
@mastersthesis {1273,
	title = {Modeling and Processing of Diffusion Tensor Magnetic Resonance Images for Improved Analysis of Brain Connectivity},
	year = {2006},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	author = {Alexander Leemans}
}
@inproceedings {wvheckealeemanssdbackerVandervlietParizelAgostinojsijbers2006,
	title = {Multi-channel coregistration of diffusion tensor images based on a viscous fluid model},
	booktitle = {Belgian Day on Biomedical Engineering - IEEE/EMBS Benelux Symposium},
	year = {2006},
	pages = {139-142},
	address = {Brussels, Belgium},
	author = {Wim Van Hecke and Alexander Leemans and Steve De Backer and Everhard Vandervliet and Paul M Parizel and E. Dagostino and Jan Sijbers}
}
@inproceedings {BertelsthuysmanjsijbersVerdonkParizel2006,
	title = {Multi-scale registration of spherical parametrizations of the human cortex},
	booktitle = {23rd Annual Scientific Meeting of the European Society for Magnetic Resonance in Medicine and Biology},
	year = {2006},
	pages = {89},
	address = {Warsaw, Poland},
	author = {J. Bertels and Toon Huysmans and Jan Sijbers and B. Verdonk and Paul M Parizel}
}
@inproceedings {aleemansjsijberssdbackerVandervlietParizel2006,
	title = {Multiscale white matter fiber tract coregistration: a new feature-based approach to align diffusion tensor data},
	booktitle = {14th Scientific Meeting - International Society for Magnetic Resonance in Medicine},
	year = {2006},
	pages = {437},
	address = {Seattle, USA},
	author = {Alexander Leemans and Jan Sijbers and Steve De Backer and Everhard Vandervliet and Paul M Parizel}
}
@article {aleemansjsijberssdbackerVandervlietParizel2006,
	title = {Multiscale white matter fiber tract coregistration: a new feature-based approach to align diffusion tensor data},
	journal = {Magnetic Resonance in Medicine},
	volume = {55},
	number = {6},
	year = {2006},
	pages = {1414-1423},
	author = {Alexander Leemans and Jan Sijbers and Steve De Backer and Everhard Vandervliet and Paul M Parizel}
}
@inproceedings {wvheckealeemanssdbackerVandervlietParizeljsijbersAgostino2006,
	title = {Non-rigid coregistration of diffusion tensor images using a viscous fluid model.},
	booktitle = {23rd Annual Scientific Meeting of the European Society for Magnetic Resonance in Medicine and Biology},
	year = {2006},
	pages = {191-192},
	address = {Warsaw, Poland},
	author = {Wim Van Hecke and Alexander Leemans and Steve De Backer and Everhard Vandervliet and Paul M Parizel and Jan Sijbers and E. Dagostino}
}
@proceedings {1467,
	title = {Optimal Location of STATCOM for Voltage Security Enhancement via Artificial Intelligent},
	year = {2006},
	author = {Azam Karami and M.Rashidinejad and A. A. Gharaveisi}
}
@inproceedings {Groofmverhoyealeemansjsijbersavdlinde2006,
	title = {Paired voxel-wise statistical mapping of in vivo Diffusion Tensor Imaging (DTI) data to assess the seasonal neuronal plasticity in the brain of a songbird},
	booktitle = {1st Annual Meeting - European Society of Molecular Imaging},
	year = {2006},
	address = {Paris, France},
	author = {G. De Groof and Marleen Verhoye and Alexander Leemans and Jan Sijbers and Annemie Van Der Linden}
}
@inproceedings {slamenswdhaes2006,
	title = {Parameter Optimizations Methods for the EDS Model},
	booktitle = {2006 IEEE International Conference on Acoustics, Speech and Signal Processing},
	year = {2006},
	month = {May},
	address = {Toulouse, France},
	author = {S. Lamens and W. D{\textquoteright}haes}
}
@inproceedings {wvheckealeemansjsijbersParizelGoethem2006,
	title = {A preliminary study of diffusion tensor imaging and tractography of the spinal cord in elderly},
	booktitle = {21th Annual Symposium - Belgian Hospital Physicists Association},
	year = {2006},
	pages = {61},
	address = {Ghent, Belgium},
	author = {Wim Van Hecke and Alexander Leemans and Jan Sijbers and Paul M Parizel and J. Van Goethem}
}
@mastersthesis {1272,
	title = {Reconstructie van Transversaal Getrunceerde Cone Beam Projecties in Micro-Tomografie},
	year = {2006},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	author = {G. Tisson}
}
@inproceedings {jsijbersajdendekdpootBosmverhoyeCampavdlinde2006,
	title = {Robust estimation of the noise variance from background MR data},
	booktitle = {Proceedings of SPIE Medical Imaging: Image Processing},
	volume = {6144},
	year = {2006},
	month = {February},
	pages = {2018-2028},
	address = {San Diego, CA, USA},
	author = {Jan Sijbers and Arnold Jan den Dekker and Dirk H J Poot and R. Bos and Marleen Verhoye and N. Van Camp and Annemie Van Der Linden}
}
@inproceedings {thuysmanBernatjsijbersParizelGlabbeekVerdonk2006,
	title = {Shape Analysis of the Human Clavicle for the Development of a Set of osteosynthesis Plates},
	booktitle = {Belgian Day on Biomedical Engineering - IEEE/EMBS Benelux Symposium},
	year = {2006},
	month = {December},
	pages = {128-129},
	address = {Brussels, Belgium},
	author = {Toon Huysmans and Amit Bernat and Jan Sijbers and Paul M Parizel and Francis Van Glabbeek and B. Verdonk}
}
@inproceedings {thuysmanjsijbersVerdonk2006,
	title = {Statistical shape models for tubular objects},
	booktitle = {Proceedings of IEEE BENELUX/DSP Valley Signal Processing Symposium (SPS-DARTS)},
	year = {2006},
	month = {March},
	pages = {155-158},
	address = {Antwerp, Belgium},
	author = {Toon Huysmans and Jan Sijbers and B. Verdonk}
}
@inproceedings {KempeneerssdbackerTejadaDelalieuxCantoIribasAardtCoppinpscheund2006,
	title = {Stress detection in orchards with hyperspectral remote sensing data},
	booktitle = {SPIE Remote Sensing, Stockholm, Sweden, september 11-14},
	volume = {6359},
	number = {38},
	year = {2006},
	month = {September},
	author = {Pieter Kempeneers and Steve De Backer and P J Zarco Tejada and Stephanie Delalieux and G Sepulcre Canto and F Morales Iribas and Jan van Aardt and P. Coppin and Paul Scheunders}
}
@inproceedings {wpintjenjsijbersmverhoyeAudekerkeLinden2006,
	title = {A total correction scheme for EPI distortions at high field},
	booktitle = {22th Annual Scientific Meeting - European Society for Magnetic Resonance in Medicine and Biology},
	year = {2006},
	pages = {328-329},
	address = {Warsaw, Poland},
	author = {W. Pintjens and Jan Sijbers and Marleen Verhoye and Johan Van Audekerke and Annemie Van Der Linden}
}
@inproceedings {Batenburgjsijbers2006,
	title = {Trabecular bone reconstruction from micro-CT data using discrete tomography},
	booktitle = {Proceedings of SPS-DARTS 2006 (The second annual IEEE BENELUX/DSP Valley Signal Processing Symposium)},
	year = {2006},
	month = {March},
	pages = {65-69},
	address = {Antwerp, Belgium},
	author = {Kees Joost Batenburg and Jan Sijbers}
}
@inproceedings {Groofmverhoyealeemansavdlinde2006,
	title = {Using diffusion tensor imaging (DTI) to assess the neuronal plasticity in the brain of a songbird},
	booktitle = {XXeme Congres du Groupement d{\textquoteright}Etudes de Resonance Magnetique},
	year = {2006},
	address = {Blankenberge, Belgium},
	author = {G. De Groof and Marleen Verhoye and Alexander Leemans and Annemie Van Der Linden}
}
@inproceedings {Groofmverhoyealeemansavdlinde2006,
	title = {Using diffusion tensor imaging (DTI) to assess the neuronal plasticity and neuroconnectivity in the brain of a songbird},
	booktitle = {Molecular \& Cellular Basis of Neuroconnectivity},
	year = {2006},
	address = {Leuven, Belgium},
	author = {G. De Groof and Marleen Verhoye and Alexander Leemans and Annemie Van Der Linden}
}
@proceedings {1469,
	title = {Voltage Security Enhancement by Optimal FACTS Location via RGA},
	year = {2006},
	author = {Azam Karami and M.Rashidinejad and A. A. Gharaveisi}
}
@inproceedings {jdriesenpscheund2006,
	title = {Wavelet based segmentation of multi-component images},
	booktitle = {IEEE BENELUX/DSP Valley Signal Processing Symposium (SPS-DARTS) March 28-29, Antwerp, Belgium},
	year = {2006},
	pages = {151-154},
	author = {J. Driesen and Paul Scheunders}
}
@inproceedings {pscheundsdbacker2006,
	title = {Wavelet Denoising of multicomponent images using a Gaussian Scale Mixture model},
	booktitle = {ICPR{\textquoteright}06, International Conference on Pattern Recognition, Hong Kong, august 20-24},
	volume = {3},
	year = {2006},
	pages = {754-757},
	publisher = {IEEE Computer Society},
	organization = {IEEE Computer Society},
	author = {Paul Scheunders and Steve De Backer}
}
@inproceedings {pscheundsdbacker2006,
	title = {Wavelet Denoising of multicomponent images using a noise-free image},
	booktitle = {ICIP{\textquoteright}06, IEEE International Conference on Image Processing, Atlanta, Georgia, October 8-11},
	year = {2006},
	pages = {2619-2622},
	publisher = {IEEE Signal Processing Society},
	organization = {IEEE Signal Processing Society},
	author = {Paul Scheunders and Steve De Backer}
}
@inproceedings {CampBlockxCamonVeramverhoyealeemansMartinezjsijbersPlanasavdlinde2006,
	title = {White and grey matters changes in a rat model for Huntington{\textquoteright}s disease discerned with in vivo DTI},
	booktitle = {23rd Annual Scientific Meeting of the European Society for Magnetic Resonance in Medicine and Biology},
	year = {2006},
	pages = {210-211},
	address = {Warsaw, Poland},
	author = {N. Van Camp and Ines Blockx and L. Camon and N. de Vera and Marleen Verhoye and Alexander Leemans and E. Martinez and Jan Sijbers and A. Planas and Annemie Van Der Linden}
}
@proceedings {-Blanc-TalonPhilipsPopescupscheund2005,
	title = {ACIVS 2005, Advanced Concepts for Intelligent Vision Systems},
	volume = {3708},
	year = {2005},
	pages = {1-725},
	publisher = {Springer},
	author = {J Blanc-Talon and Wilfried Philips and D Popescu and Paul Scheunders}
}
@inbook {ajdendekjsijbers2005,
	title = {Advanced Image Processing in Magnetic Resonance Imaging},
	booktitle = {Series: Signal Processing and Communications},
	volume = {27},
	year = {2005},
	note = {ISBN: 0824725425},
	month = {October},
	pages = {85-143},
	publisher = {Marcel Dekker},
	organization = {Marcel Dekker},
	chapter = {4},
	author = {Arnold Jan den Dekker and Jan Sijbers},
	editor = {L. Landini}
}
@article {aleemansjsijberssdbackerVandervlietParizel2005,
	title = {Affine Coregistration of Diffusion Tensor Magnetic Resonance Images Using Mutual Information},
	journal = {Lecture Notes in Computer Science},
	volume = {3708},
	year = {2005},
	pages = {523-530},
	author = {Alexander Leemans and Jan Sijbers and Steve De Backer and Everhard Vandervliet and Paul M Parizel}
}
@article {sdbackerKempeneersDebruynpscheund2005,
	title = {A Band Selection Technique for Spectral Classification},
	journal = {IEEE Geoscience and Remote Sensing Letters},
	volume = {2},
	number = {3},
	year = {2005},
	month = {July},
	pages = {319-323},
	author = {Steve De Backer and Pieter Kempeneers and W. Debruyn and Paul Scheunders}
}
@inproceedings {jjuntujsijbersdvandyckGielen2005,
	title = {Bias Field Correction for MRI Images},
	booktitle = {Proceedings of the 4th International Conference on Computer Recognition Systems (CORES05)},
	series = {Advances in Computer Science},
	year = {2005},
	month = {May},
	pages = {543-551},
	publisher = {Springer},
	organization = {Springer},
	address = {Rydzyna Castle, Poland},
	author = {Juntu, Jaber and Jan Sijbers and Dirk Van Dyck and Jan L Gielen},
	editor = {Marek Kurzynsji and Edward Puchala and Michal Wozniak and Andrzej Zolnierek}
}
@inproceedings {ajdendekjsijbersBosasmolder2005,
	title = {Brain activation detection from functional magnetic resonance imaging data using likelihood based hypothesis tests},
	booktitle = {Abstracts of the 24th Benelux Meeting on Systems and Control},
	year = {2005},
	month = {March},
	address = {Houffalize, Belgium},
	author = {Arnold Jan den Dekker and Jan Sijbers and R. Bos and Alain Smolders}
}
@article {ParizelGoethemHauweSalgadoVandervlietaleemans2005,
	title = {Characterization of brain tumors},
	journal = {NeuroRadiology},
	volume = {47},
	year = {2005},
	pages = {80-83},
	author = {Paul M Parizel and J. Van Goethem and L. van den Hauwe and R. Salgado and Everhard Vandervliet and Alexander Leemans}
}
@inproceedings {DelputtealeemansFieremansDeeneDAsselerLemahieuAchtenjsijbersWalle2005,
	title = {Density Regularized Fiber Tractography of the Brain White Matter using Diffusion Tensor MRI},
	booktitle = {13th Scientific Meeting - International Society for Magnetic Resonance in Medicine},
	year = {2005},
	pages = {1309},
	address = {Miami, USA},
	author = {S. Delputte and Alexander Leemans and Els Fieremans and Y. De Deene and Y. D{\textquoteright}Asseler and I. Lemahieu and Eric Achten and Jan Sijbers and R. Van de Walle}
}
@inproceedings {Groofmverhoyealeemansavdlinde2005,
	title = {DTI parameters: Fractional Anisotropy, Radial and Axial Diffusivity reveal seasonal neuroplasticity in the adult songbird brain},
	booktitle = {4th annual meeting of the Society of Molecular Imaging},
	year = {2005},
	address = {Keulen, Germany},
	author = {G. De Groof and Marleen Verhoye and Alexander Leemans and Annemie Van Der Linden}
}
@inproceedings {aleemanssdbackerjsijbersVandervlietParizel2005,
	title = {End point clustering for diffusion tensor white matter fiber bundle tractography},
	booktitle = {22th Annual Scientific Meeting - European Society for Magnetic Resonance in Medicine and Biology},
	year = {2005},
	pages = {129-130},
	address = {Basle, Switzerland},
	author = {Alexander Leemans and Steve De Backer and Jan Sijbers and Everhard Vandervliet and Paul M Parizel}
}
@inproceedings {aleemansjsijbersmverhoyeavdlinde2005,
	title = {Entropy-based coregistration for DT-MR images using an efficient tensor shape preserving reorientation strategy},
	booktitle = {13th Scientific Meeting - International Society for Magnetic Resonance in Medicine},
	year = {2005},
	pages = {227},
	address = {Miami, USA},
	author = {Alexander Leemans and Jan Sijbers and Marleen Verhoye and Annemie Van Der Linden}
}
@article {jsijbersajdendek2005,
	title = {Generalized likelihood Ratio tests for complex fMRI data: a simulation study},
	journal = {IEEE Transactions on Medical Imaging},
	volume = {24},
	number = {5},
	year = {2005},
	month = {May},
	pages = {604-611},
	doi = {10.1109/TMI.2005.844075},
	author = {Jan Sijbers and Arnold Jan den Dekker}
}
@article {KempeneerssdbackerDebruynpscheund2005,
	title = {Generic Wavelet-Based Hyperspectral Classification Applied to Vegetation Stress Detection},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {43},
	year = {2005},
	month = {March},
	pages = {610-614},
	author = {Pieter Kempeneers and Steve De Backer and W. Debruyn and Paul Scheunders}
}
@inproceedings {aleemansjsijbersParizel2005,
	title = {A Graphical Toolbox for Exploratory Diffusion Tensor Imaging and Fiber Tractography},
	booktitle = {14th Annual Meeting - Section for Magnetic Resonance Technologists},
	year = {2005},
	address = {Miami, USA},
	author = {Alexander Leemans and Jan Sijbers and Paul M Parizel}
}
@inproceedings {KempeneerssdbackerDebruynProvoostpscheund2005,
	title = {Hyperspectral classification applied to the Belgian coastline},
	booktitle = {SPIE Remote Sensing, Bruges, Belgium, 19-22 september},
	volume = {5982},
	number = {15},
	year = {2005},
	author = {Pieter Kempeneers and Steve De Backer and W. Debruyn and S. Provoost and Paul Scheunders}
}
@article {ajdendekjsijbers2005,
	title = {Implications of the Rician distribution for fMRI generalized likelihood ratio tests},
	journal = {Magnetic Resonance Imaging},
	volume = {23},
	number = {9},
	year = {2005},
	pages = {953-959},
	doi = {10.1016/j.mri.2005.07.008},
	author = {Arnold Jan den Dekker and Jan Sijbers}
}
@inproceedings {CampLaereVreysmverhoyeLauwersBequeMaesVerbruggenDebyserMortelmansjsijbersNuytsBaekelandtavdlinde2005,
	title = {In vivo multimodal (MRI, SPECT) imaging of the 6-OHDA rat model for Parkinson{\textquoteright}s disease correlated with behavior and histology},
	booktitle = {Belgian Society of Neuroscience},
	year = {2005},
	month = {May},
	author = {N. Van Camp and Koen Van Laere and Vreys, Ruth and Marleen Verhoye and E. Lauwers and Dirk Beque and F. Maes and Alfons Verbruggen and Zeger Debyser and Luc Mortelmans and Jan Sijbers and Johan Nuyts and Veerle Baekelandt and Annemie Van Der Linden}
}
@inproceedings {CampLaereVreysmverhoyeLauwersBequeMaesVerbruggenDebyzerMortelmansjsijbersNuytsBaekelandtavdlinde2005,
	title = {In vivo multimodal (MRI,SPECT) imaging of the 6-OHDA rat model for Parkinson{\textquoteright}s disease correlated with behavior and histology},
	booktitle = {XXIInd International Symposium on Cerebral Blood Flow, Metabolism and Function and VIIth International Conference of Quantification of Brain Function with PET},
	year = {2005},
	month = {June},
	address = {Vrije Universiteit, Amsterdam, the Netherlands},
	author = {N. Van Camp and Koen Van Laere and Vreys, Ruth and Marleen Verhoye and E. Lauwers and Dirk Beque and F. Maes and Alfons Verbruggen and Zeger Debyser and Luc Mortelmans and Jan Sijbers and Johan Nuyts and Veerle Baekelandt and Annemie Van Der Linden}
}
@inproceedings {GroofmverhoyeMeirTindemansaleemansavdlinde2005,
	title = {In Vivo Visualization of the Neuroanatomy and Brain Connectivity of Starling Brain Through Diffusion Tensor Imaging},
	booktitle = {6th Bi-Annual Meeting {\textendash} Belgian Society for Neuroscience},
	year = {2005},
	address = {Brussels, Belgium},
	author = {G. De Groof and Marleen Verhoye and V. Van Meir and I. Tindemans and Alexander Leemans and Annemie Van Der Linden}
}
@article {jsijbersajdendekBos2005,
	title = {A likelihood ratio test for functional MRI data analysis to account for colored noise},
	journal = {Lecture Notes in Computer Science},
	volume = {3708},
	year = {2005},
	month = {September},
	pages = {538-546},
	author = {Jan Sijbers and Arnold Jan den Dekker and R. Bos}
}
@article {aleemansjsijbersmverhoyeavdlindedvandyck2005,
	title = {Mathematical Framework for Simulating Diffusion Tensor MR Neural Fiber Bundles},
	journal = {Magnetic Resonance in Medicine},
	volume = {53},
	year = {2005},
	pages = {944-953},
	author = {Alexander Leemans and Jan Sijbers and Marleen Verhoye and Annemie Van Der Linden and Dirk Van Dyck}
}
@inproceedings {aleemansjsijbersmverhoyeavdlinde2005,
	title = {Optimized Fiber Tractography based on Diffusion Tensor Magnetic Resonance Simulations},
	booktitle = {20th Annual Symposium - Belgian Hospital Physicists Association},
	year = {2005},
	address = {Namur, Belgium},
	author = {Alexander Leemans and Jan Sijbers and Marleen Verhoye and Annemie Van Der Linden}
}
@article {thuysmanjsijbersVerdonk2005,
	title = {Parameterization of tubular surfaces on the cylinder},
	journal = {Journal of the Winter School of Computer Graphics},
	volume = {13},
	number = {3},
	year = {2005},
	pages = {97-104},
	author = {Toon Huysmans and Jan Sijbers and B. Verdonk}
}
@inproceedings {KempeneersSterckxDebruynParkRuddicksdbackerpscheund2005,
	title = {Retrieval of oceanic constituents from ocean color using simulated annealing},
	booktitle = {IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2005, Seoul, Korea, 25-29 July},
	volume = {8},
	year = {2005},
	pages = {5651-5654},
	author = {Pieter Kempeneers and S Sterckx and W. Debruyn and Y. Park and K. Ruddick and Steve De Backer and Paul Scheunders}
}
@inproceedings {Groofmverhoyealeemansavdlinde2005,
	title = {Seasonal changes in neuronal connectivity in the songbird brain discerned by repeated in vivo DTI},
	booktitle = {13th Scientific Meeting - International Society for Magnetic Resonance in Medicine},
	year = {2005},
	pages = {715},
	address = {Miami, USA},
	author = {G. De Groof and Marleen Verhoye and Alexander Leemans and Annemie Van Der Linden}
}
@article {MeirBoumansGroofAudekerkeasmolderpscheundjsijbersmverhoyeBalthazartavdlinde2005,
	title = {Spatiotemporal properties of the BOLD response in the songbirds auditory circuit during a variety of listening tasks},
	journal = {NeuroImage},
	volume = {25},
	year = {2005},
	pages = {1242-1255},
	author = {V. Van Meir and T. Boumans and G. De Groof and Johan Van Audekerke and Alain Smolders and Paul Scheunders and Jan Sijbers and Marleen Verhoye and J. Balthazart and Annemie Van Der Linden}
}
@inproceedings {aleemanssdbackerjsijbersVandervlietParizel2005,
	title = {TRACT: Tissue Relative Anisotropy based Curvature Thresholding for deterministic MR diffusion tensor tractography},
	booktitle = {22th Annual Scientific Meeting - European Society for Magnetic Resonance in Medicine and Biology},
	year = {2005},
	pages = {289},
	address = {Basle, Switzerland},
	author = {Alexander Leemans and Steve De Backer and Jan Sijbers and Everhard Vandervliet and Paul M Parizel}
}
@inproceedings {Groofmverhoyealeemansavdlinde2005,
	title = {Using diffusion tensor imaging (DTI) to assess the neuronal plasticity in the brain of a songbird},
	booktitle = {4th Annual Symposium {\textendash} Young Belgian Magnetic Resonance Scientists},
	year = {2005},
	address = {Brussels, Belgium},
	author = {G. De Groof and Marleen Verhoye and Alexander Leemans and Annemie Van Der Linden}
}
@inproceedings {jdriesenpscheund2005,
	title = {Wavelet based segmentation of multivalued images},
	booktitle = {SPIE Optics East, 23-26 October, Boston, Massachusetts USA},
	volume = {6001},
	number = {2},
	year = {2005},
	pages = {13-22},
	author = {J. Driesen and Paul Scheunders}
}
@inproceedings {PizuricaHuysmanspscheundPhilips2005,
	title = {Wavelet domain denoising of multispectral remote sensing imagery adapted to the local spatial and spectral context},
	booktitle = {IEEE International Geoscience and Remote Sensing Symp. IGARSS 2005, Seoul, Korea, 25-29 July},
	volume = {6},
	year = {2005},
	pages = {4260-4263},
	address = {Seoul, Korea},
	author = {Aleksandra Pizurica and B. Huysmans and Paul Scheunders and Wilfried Philips}
}
@inproceedings {PizuricaPhilipspscheund2005,
	title = {Wavelet domain denoising of single-band and multi-band images adapted to the probability of the presence of features of interes},
	booktitle = {SPIE Wavelets XI, San Diego, California, USA, 31 July {\textendash} 4 Aug},
	volume = {5914},
	year = {2005},
	pages = {508-521},
	author = {Aleksandra Pizurica and Wilfried Philips and Paul Scheunders}
}
@inproceedings {pscheundsdbacker2005,
	title = {Wavelet-based enhancement of remote sensing and biomedical image series using an auxiliary image},
	booktitle = {SPIE Optics East, 23-26 October, Boston, Massachusetts USA},
	volume = {6001},
	number = {5},
	year = {2005},
	pages = {41-52},
	author = {Paul Scheunders and Steve De Backer}
}
@inproceedings {gtissonpscheunddvandyck2004,
	title = {3D region of interest X-ray CT for geometric magnification from multiresolution acquisitions},
	booktitle = {Proc. ISBI04, IEEE International Symposium on Biomedical Imaging, 15-18 april 2004, Arlington, VA},
	year = {2004},
	pages = {567-570},
	author = {G. Tisson and Paul Scheunders and Dirk Van Dyck}
}
@inproceedings {ajdendekjsijbers2004,
	title = {Brain activation detection from magnitude fMRI data using a generalized likelihood ratio test},
	booktitle = {Abstracts of the 23rd Benelux Meeting on Systems and Control},
	year = {2004},
	month = {March},
	address = {Helvoirt, The Netherlands},
	author = {Arnold Jan den Dekker and Jan Sijbers}
}
@inproceedings {sdbackerKempeneersDebruynpscheund2004,
	title = {Classification of Dune Vegetation from Remotely Sensed Hyperspectral Images},
	booktitle = {Image Analysis and Recognition Proc. of International Conference on Image Analysis and Recognition},
	series = {Lecture Notes in Computer Science},
	year = {2004},
	pages = {497-503},
	publisher = {Springer},
	organization = {Springer},
	address = {Porto, Portugal},
	author = {Steve De Backer and Pieter Kempeneers and W. Debruyn and Paul Scheunders},
	editor = {A. Campilho and M. Kamel}
}
@inproceedings {KempeneerssdbackerDerondeBertelsDebruynpscheund2004,
	title = {Classifying hyperspectral airborne imagery for vegetation survey along coastlines},
	booktitle = {IEEE International Geoscience and Remote Sensing Symposium, IGARSS, Anchorage, USA, 20-24 september},
	volume = {2},
	year = {2004},
	pages = {1475-1478},
	publisher = {IEEE International},
	organization = {IEEE International},
	address = {Anchorage, USA},
	author = {Pieter Kempeneers and Steve De Backer and B. Deronde and L. Bertels and W. Debruyn and Paul Scheunders}
}
@inproceedings {asmolderpscheundjsijbersMeirmverhoyeavdlinde2004,
	title = {Clustering of hemodynamic response functions from fMRI-data of a starling},
	booktitle = {13th European Microscopy Congress},
	year = {2004},
	month = {January},
	address = {Antwerp, Belgium},
	author = {Alain Smolders and Paul Scheunders and Jan Sijbers and V. Van Meir and Marleen Verhoye and Annemie Van Der Linden}
}
@inproceedings {jsijbersajdendek2004,
	title = {Construction of a likelihood ratio test for magnitude fMRI data},
	booktitle = {19th Annual Symposium of the Belgian Hospital Physicists Association},
	year = {2004},
	month = {January},
	author = {Jan Sijbers and Arnold Jan den Dekker}
}
@inproceedings {jsijbersCampaleemansajdendekmverhoyeavdlinde2004,
	title = {Coregistration of Micro-MRI, microCT and microPET},
	booktitle = {Workshop on non Invasive 3D Microscopy},
	year = {2004},
	month = {August},
	pages = {16},
	address = {University of Antwerp, Belgium},
	author = {Jan Sijbers and N. Van Camp and Alexander Leemans and Arnold Jan den Dekker and Marleen Verhoye and Annemie Van Der Linden}
}
@inproceedings {ajdendekjsijbers2004,
	title = {Detection of brain activation from magnitude fMRI data using a generalized likelihood ratio test},
	booktitle = {Proceedings of the 12th European Signal Processing Conference},
	year = {2004},
	month = {September},
	pages = {233-236},
	publisher = {ISBN 3-200-00165-8},
	organization = {ISBN 3-200-00165-8},
	address = {Vienna, Austria},
	author = {Arnold Jan den Dekker and Jan Sijbers},
	editor = {F. Hlawatsch and B. Wistawel}
}
@inproceedings {evdcastedvandyckjsijberseraman2004,
	title = {The effect of beam hardening on MTF measurements in X-ray microtomography},
	booktitle = {19th Annual Symposium of the Belgian Hospital Physicists Association},
	year = {2004},
	month = {January},
	author = {Elke Van de Casteele and Dirk Van Dyck and Jan Sijbers and Erik Raman}
}
@inproceedings {evdcastedvandyckjsijberseraman2004,
	title = {Effect of beam hardening on resolution in x-ray microtomography},
	booktitle = {SPIE Medical Imaging: Image Processing},
	volume = {5370},
	year = {2004},
	month = {February},
	pages = {2089-2096},
	address = {San Diego CA, USA},
	author = {Elke Van de Casteele and Dirk Van Dyck and Jan Sijbers and Erik Raman},
	editor = {Milan Sonka}
}
@inproceedings {aleemansjsijbersmverhoyeavdlinde2004,
	title = {Experimental Evaluation of Synthetic DT-MRI Models},
	booktitle = {21th Annual Scientific Meeting - European Society for Magnetic Resonance in Medicine and Biology},
	year = {2004},
	pages = {40-41},
	address = {Copenhagen, Denmark},
	author = {Alexander Leemans and Jan Sijbers and Marleen Verhoye and Annemie Van Der Linden}
}
@inproceedings {MeirBoumansGroofAudekerkeasmolderpscheundjsijbersmverhoyeBalthazartavdlinde2004,
	title = {Functional Magnetic resonance Imaging meets animal vocal learner},
	booktitle = {Third One-Day Symposium of Young Belgian Magnetic Resonance Scientists},
	year = {2004},
	month = {November},
	address = {Brussels, Belgium},
	author = {V. Van Meir and T. Boumans and G. De Groof and Johan Van Audekerke and Alain Smolders and Paul Scheunders and Jan Sijbers and Marleen Verhoye and J. Balthazart and Annemie Van Der Linden}
}
@inproceedings {jsijbersajdendek2004,
	title = {Generalized likelihood ratio test for complex fMRI data},
	booktitle = {SPIE Medical Imaging: Physiology, Function, and Structure from Medical Images},
	volume = {5369},
	year = {2004},
	month = {February},
	pages = {652-663},
	address = {San Diego, California, USA},
	author = {Jan Sijbers and Arnold Jan den Dekker},
	editor = {Amir. A. Amini}
}
@inproceedings {aleemansjsijbersmverhoyeavdlindedvandyck2004,
	title = {A Geometric Color Scheme for Visualizing Diffusion Tensor Magnetic Resonance Fiber Pathways},
	booktitle = {19th Annual Symposium - Belgian Hospital Physicists Association},
	year = {2004},
	address = {Brussels, Belgium},
	author = {Alexander Leemans and Jan Sijbers and Marleen Verhoye and Annemie Van Der Linden and Dirk Van Dyck}
}
@inproceedings {wdhaes2004,
	title = {A highly optimized method for computing amplitudes over a windowed short time signal: From O(K^2N) to O(N log(N))},
	booktitle = {Proceedings of the IEEE Benelux Signal Processing Symposium},
	year = {2004},
	address = {Hilvarenbeek, the Netherlands},
	author = {W. D{\textquoteright}haes}
}
@inproceedings {wdhaes2004,
	title = {A highly optimized nonlinear least squares technique for sinusoidal analysis: From O(K^2N) to O(N log(N))},
	booktitle = {Proceedings of the 116th Audio Engineering Society Convention (AES)},
	year = {2004},
	month = {May},
	publisher = {Audio Engineering Society (AES)},
	organization = {Audio Engineering Society (AES)},
	address = {Berlin, Germany},
	author = {W. D{\textquoteright}haes}
}
@inproceedings {CampmverhoyealeemansPostnovBequeEyndenNuytsLauwersVerbruggenDebyzerBaekelandtClerckjsijbersLaereavdlinde2004,
	title = {In vivo multimodal imaging of a rat model for Parkinson{\textquoteright}s disease: high resolution micro-MRI, micro-SPECT and micro-CT},
	booktitle = {21th Annual Scientific Meeting - European Society for Magnetic Resonance in Medicine and Biology},
	year = {2004},
	pages = {22-23},
	address = {Copenhagen, Denmark},
	author = {N. Van Camp and Marleen Verhoye and Alexander Leemans and A. Postnov and Dirk Beque and J. Van den Eynden and Johan Nuyts and E. Lauwers and Alfons Verbruggen and Zeger Debyser and Veerle Baekelandt and N. De Clerck and Jan Sijbers and Koen Van Laere and Annemie Van Der Linden}
}
@inproceedings {mverhoyeGroofMeirTindemansaleemansavdlinde2004,
	title = {In vivo neuroanatomy of the songbird brain, visualized through diffusion tensor imaging},
	booktitle = {21th Annual Scientific Meeting - European Society for Magnetic Resonance in Medicine and Biology},
	year = {2004},
	pages = {161},
	address = {Copenhagen, Denmark},
	author = {Marleen Verhoye and G. De Groof and V. Van Meir and I. Tindemans and Alexander Leemans and Annemie Van Der Linden}
}
@inproceedings {mverhoyeGroofMeirTindemansaleemansavdlinde2004,
	title = {In vivo visualization of the neuroanatomy and brain connectivity of starling brain through diffusion tensor imaging},
	booktitle = {34rd Annual Meeting of the Society for Neuroscience},
	year = {2004},
	address = {San Diego, USA},
	author = {Marleen Verhoye and G. De Groof and V. Van Meir and I. Tindemans and Alexander Leemans and Annemie Van Der Linden}
}
@inproceedings {aleemansjsijberswvdbroekYang2004,
	title = {An Interactive Curvature Based Rigid-body Image Registration Technique: an Application to EFTEM},
	booktitle = {13th European Microscopy Congress},
	year = {2004},
	address = {Antwerp, Belgium},
	author = {Alexander Leemans and Jan Sijbers and Wouter Van den Broek and Z. Yang}
}
@inproceedings {pscheundjdriesen2004,
	title = {Least squares interband denoising of color and multispectral images},
	booktitle = {Proc. ICIP2004, IEEE International Conference on Image Processing, 24-27 october, Singapore},
	year = {2004},
	pages = {985-988},
	author = {Paul Scheunders and J. Driesen}
}
@inproceedings {aleemansjsijbersmverhoyeavdlinde2004,
	title = {A Library of 3D synthetic DT-MRI models for testing White Matter fiber Tractography Algorithms},
	booktitle = {21th Annual Scientific Meeting - European Society for Magnetic Resonance in Medicine and Biology},
	year = {2004},
	pages = {42},
	address = {Copenhagen, Denmark},
	author = {Alexander Leemans and Jan Sijbers and Marleen Verhoye and Annemie Van Der Linden}
}
@article {jsijbersajdendek2004,
	title = {Maximum Likelihood estimation of signal amplitude and noise variance from MR data},
	journal = {Magnetic Resonance in Medicine},
	volume = {51},
	number = {3},
	year = {2004},
	pages = {586-594},
	doi = {10.1002/mrm.10728},
	author = {Jan Sijbers and Arnold Jan den Dekker}
}
@inproceedings {thuysmanjsijbers2004,
	title = {Mesh smoothing through multiscale anisotropic diffusion of geometry images},
	booktitle = {13th European Microscopy Congress (EMC)},
	year = {2004},
	month = {August},
	address = {Antwerp, Belgium},
	author = {Toon Huysmans and Jan Sijbers}
}
@mastersthesis {1289,
	title = {Model-based approach for Beam Hardening Correction and Resolution Measurements in Microtomography},
	year = {2004},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	author = {Elke Van de Casteele}
}
@article {evdcastedvandyckjsijberseraman2004,
	title = {A model-based correction method for beam hardening artefacts in X-ray microtomography},
	journal = {Journal of X-ray science and technology},
	volume = {12},
	number = {1},
	year = {2004},
	pages = {53-57},
	author = {Elke Van de Casteele and Dirk Van Dyck and Jan Sijbers and Erik Raman}
}
@inproceedings {PizuricapscheundPhilips2004,
	title = {Multiresolution multispectral image denoising based on probability of presence of features of interest},
	booktitle = {Advanced Concepts in Image and Vision Systems, Brussels},
	year = {2004},
	pages = {357-364},
	author = {Aleksandra Pizurica and Paul Scheunders and Wilfried Philips}
}
@inproceedings {aleemansjsijbers2004,
	title = {Multiresolutional rigid-body registration for space curves},
	booktitle = {IEEE Advanced Concepts for Intelligent Vision Systems},
	year = {2004},
	pages = {215-221},
	address = {Brussels, Belgium},
	author = {Alexander Leemans and Jan Sijbers}
}
@inproceedings {gvgompeldvandyckgtissonjsijbers2004,
	title = {A new algorithm for Region Of Interest Tomography},
	booktitle = {19th Annual Symposium of the Belgian Hospital Physicists Association},
	year = {2004},
	month = {January},
	author = {Gert Van Gompel and Dirk Van Dyck and G. Tisson and Jan Sijbers}
}
@inproceedings {gvgompelgtissondvandyckjsijbers2004,
	title = {A new algorithm for Region Of Interest Tomography},
	booktitle = {SPIE Medical Imaging: Image Processing},
	volume = {127},
	year = {2004},
	month = {February},
	pages = {2105-2113},
	address = {San Diego, California, USA},
	doi = {10.1117/12.536685},
	author = {Gert Van Gompel and G. Tisson and Dirk Van Dyck and Jan Sijbers},
	editor = {Milan Sonka}
}
@inproceedings {jsijbersajdendek2004,
	title = {The performance of generalized likelihood ratio tests for complex functional MRI data in the presence of phase model misspecification},
	booktitle = {European Society of Magnetic Resonance in Medicine (ESMRMB)},
	year = {2004},
	month = {September},
	pages = {96},
	address = {Copenhagen, Denmark},
	author = {Jan Sijbers and Arnold Jan den Dekker}
}
@inproceedings {jsijbersPostnov2004,
	title = {Reduction of ring artifacts in high resolution micro-CT images},
	booktitle = {Advanced Concepts for Intelligent Vision Systems (ACIVS)},
	year = {2004},
	month = {September},
	address = {Brussels, Belgium},
	author = {Jan Sijbers and A. Postnov}
}
@article {jsijbersPostnov2004,
	title = {Reduction of ring artifacts in high resolution micro-CT reconstructions},
	journal = {Physics in Medicine and Biology},
	volume = {49},
	number = {14},
	year = {2004},
	pages = {247-253},
	doi = {doi:10.1088/0031-9155/49/14/N06},
	author = {Jan Sijbers and A. Postnov}
}
@article {CuytjsijbersVerdonkdvandyck2004,
	title = {Region and Contour Identification of Physical Objects},
	journal = {Applied Numerical Analysis Computational Mathematics},
	volume = {1},
	number = {3},
	year = {2004},
	pages = {343-352},
	doi = {10.1002/anac.200410002},
	author = {A. Cuyt and Jan Sijbers and B. Verdonk and Dirk Van Dyck}
}
@inproceedings {gtissonpscheunddvandyck2004,
	title = {ROI Cone-Beam CT on a Circular Orbit for Geometric Magnification Using Reprojection},
	booktitle = {Proc. IEEE Medical Imaging Conference, Rome, 16-22 october},
	year = {2004},
	author = {G. Tisson and Paul Scheunders and Dirk Van Dyck}
}
@inproceedings {KempeneerssdbackerDelalieuxSterckxDebruynCoppinpscheund2004,
	title = {Upscaling of spectroradiometer data for stress detection in orchards with remote sensing},
	booktitle = {SPIE Remote Sensing, Gran Canaria, 13-16 september},
	volume = {127},
	year = {2004},
	pages = {37-45},
	publisher = {SPIE},
	organization = {SPIE},
	address = {Maspalomas, Spain},
	author = {Pieter Kempeneers and Steve De Backer and Stephanie Delalieux and S Sterckx and W. Debruyn and P. Coppin and Paul Scheunders},
	editor = {Manfred Owe, Guido D{\textquoteright}Urso, Ben T. Gouweleeuw, Anne M. Jochum}
}
@article {HungpscheundPhamSuColeman2004,
	title = {Using Intelligent Optimization Techniques in the K-means Algorithm for Multispectral Image Classification},
	journal = {International Journal of Fuzzy Systems},
	volume = {6},
	number = {3},
	year = {2004},
	pages = {107-117},
	author = {C.C. Hung and Paul Scheunders and M. Pham and M.C. Su and T. Coleman}
}
@mastersthesis {1285,
	title = {On the wavefront aberrations of the human eye and the search for their origins},
	year = {2004},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	author = {Jos Rozema}
}
@inproceedings {jdriesenpscheund2004,
	title = {Wavelet based Filter Array Demosaicking},
	booktitle = {Proc. ICIP2004, IEEE International Conference on Image Processing, 24-27 october, Singapore},
	year = {2004},
	pages = {3311-3314},
	author = {J. Driesen and Paul Scheunders}
}
@inproceedings {sdbackerKempeneersDebruynpscheund2004,
	title = {Wavelet Based Hyperspectral Data Analysis for Vegetation Stress Classification},
	booktitle = {Proc. of Advanced Concepts for Intelligent Vision Systems},
	year = {2004},
	pages = {387-391},
	address = {Brussels, Belgium},
	author = {Steve De Backer and Pieter Kempeneers and W. Debruyn and Paul Scheunders}
}
@article {pscheund2004,
	title = {Wavelet thresholding of multivalued images},
	journal = {IEEE Transactions on Image Processing},
	volume = {13},
	number = {4},
	year = {2004},
	pages = {475-483},
	author = {Paul Scheunders}
}
@inproceedings {aleemansjsijbersmverhoyeavdlindedvandyck2004,
	title = {White Matter Fiber Bundle Coregistration for Diffusion Tensor Magnetic Resonance Tractography},
	booktitle = {13th Annual Meeting - Section for Magnetic Resonance Technologists},
	year = {2004},
	address = {Kyoto, Japan},
	author = {Alexander Leemans and Jan Sijbers and Marleen Verhoye and Annemie Van Der Linden and Dirk Van Dyck}
}
@inproceedings {1292,
	title = {A bimodal energy model for correcting beam hardening artefacts in X-ray tomography},
	booktitle = {IEEE 29th Annual Northeast Bioengineering Conference2003 IEEE 29th Annual Proceedings of Bioengineering Conference},
	year = {2003},
	pages = {57 - 58},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Newark, NJ, USA},
	doi = {10.1109/NEBC.2003.1215990},
	author = {Elke Van de Casteele and Dirk Van Dyck and Jan Sijbers and Erik Raman}
}
@inproceedings {slivensAnthonisMahypscheund2003,
	title = {Cross-media tonal mapping model obtained from psychometric experiments},
	booktitle = {Proc. SPIE 5008: Color Imaging, Device- Independent Color, Color Hardcopy, and Graphic Arts VII , Santa Clara, CA},
	year = {2003},
	pages = {14-23},
	author = {S. Livens and A. Anthonis and M. Mahy and Paul Scheunders}
}
@inproceedings {pscheund2003,
	title = {Denoising of multispectral images using wavelet thresholding},
	booktitle = {Proc. SPIE conference on Image and Signal Processing for Remote Sensing IX, part of the International Symposium on Remote Sensing},
	year = {2003},
	month = {September},
	pages = {28-35},
	address = {Barcelona, Spain},
	author = {Paul Scheunders}
}
@inproceedings {wdhaesxrodet2003,
	title = {Discrete Cepstrum Coefficients as Perceptual Features},
	booktitle = {International Computer Music Conference (ICMC)},
	year = {2003},
	author = {W. D{\textquoteright}haes and X Rodet}
}
@inproceedings {WaarsingaleemansDayEderveendvandyckBuelensClerckSasovWeinans2003,
	title = {Effects of Growth and OVX in the Tibia of Individual Rats: an in-vivo Micro-CT Study},
	booktitle = {49th Annual Meeting - Orthopaedic Research Society},
	year = {2003},
	address = {New Orleans, LA, USA},
	author = {J. H. Waarsing and Alexander Leemans and J. Day and A. G. H. Ederveen and Dirk Van Dyck and E. Buelens and N. De Clerck and A. Sasov and H. Weinans}
}
@mastersthesis {1286,
	title = {Feature Selection and Classifier Ensembles: A Study on Hyperspectral Remote Sensing Data},
	year = {2003},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	author = {Shixin Yu}
}
@inproceedings {SterckxCoppinsdbackerDebruynKempeneersMeulemanNackaertsReusenpscheund2003,
	title = {Information extraction techniques for monitoring of stress symptoms in orchards},
	booktitle = {Proc. Earsel 2003, Imaging Spectroscopy workshop Oberpfaffenhofen},
	year = {2003},
	pages = {278-283},
	author = {S Sterckx and P. Coppin and Steve De Backer and W. Debruyn and Pieter Kempeneers and K. Meuleman and K. Nackaerts and I. Reusen and Paul Scheunders}
}
@inbook {jsijbersavdlinde2003,
	title = {Magnetic Resonance Imaging, In: Encyclopedia of Optical Engineering},
	year = {2003},
	note = {ISBN: 0-8247-4258-3},
	pages = {1237-1258},
	publisher = {Marcel Dekker},
	organization = {Marcel Dekker},
	author = {Jan Sijbers and Annemie Van Der Linden}
}
@inproceedings {jsijbersajdendek2003,
	title = {Mapping a polyhedron onto a sphere: application to Fourier descriptors},
	booktitle = {22nd Benelux Meeting on Systems and Control},
	year = {2003},
	month = {March},
	address = {Lommel, Belgium},
	author = {Jan Sijbers and Arnold Jan den Dekker}
}
@inproceedings {ajdendekjsijbers2003,
	title = {Maximum Likelihood estimation of signal amplitude and noise variance},
	booktitle = {13th IFAC Symposium on System Identification (SYSID-2003)},
	year = {2003},
	month = {August},
	pages = {126-131},
	address = {Rotterdam, The Netherlands},
	author = {Arnold Jan den Dekker and Jan Sijbers}
}
@inproceedings {evdcastedvandyckjsijberseraman2003,
	title = {A model-based correction method for beam-hardening artifacts in X-ray tomography},
	booktitle = {22nd Benelux Meeting on Systems and Control},
	year = {2003},
	month = {March},
	address = {Lommel, Belgium},
	author = {Elke Van de Casteele and Dirk Van Dyck and Jan Sijbers and Erik Raman}
}
@inproceedings {evdcastedvandyckjsijberseraman2003,
	title = {Modelling of Beam hardening in micro CT},
	booktitle = {IEEE International Symposium on Biomedical Imaging},
	year = {2003},
	month = {July},
	pages = {57-58},
	address = {Washington DC, USA},
	author = {Elke Van de Casteele and Dirk Van Dyck and Jan Sijbers and Erik Raman}
}
@inproceedings {wdhaesxrodet2003,
	title = {A New Estimation Technique for Determining the Control Parameters of a physical model of a trumpet},
	booktitle = {6th International Conference on Digital Audio Effects (DAFx03), London, England},
	year = {2003},
	month = {September},
	author = {W. D{\textquoteright}haes and X Rodet}
}
@inproceedings {MeulemanCoppinsdbackerDebruynNackaertspscheundStercks2003,
	title = {Optimal hyperspectral indicators for stress detection in orchards},
	booktitle = {Proc. Earsel 2003, Imaging Spectroscopy workshop Oberpfaffenhofen},
	year = {2003},
	pages = {534-541},
	author = {K. Meuleman and P. Coppin and Steve De Backer and W. Debruyn and K. Nackaerts and Paul Scheunders and S Sterckx}
}
@article {pscheund2003,
	title = {An orthogonal wavelet representation of multivalued images},
	journal = {IEEE Transactions on Image Processing},
	volume = {12},
	number = {6},
	year = {2003},
	pages = {718-725},
	author = {Paul Scheunders}
}
@article {wdhaesdvandyckxrodet2003,
	title = {PCA-based Branch and Bound Search Algorithms for Computing K Nearest Neighbors},
	journal = {Pattern Recognition Letters},
	volume = {24},
	year = {2003},
	pages = {1437-1451},
	author = {W. D{\textquoteright}haes and Dirk Van Dyck and X Rodet}
}
@inproceedings {aleemansjsijbersmverhoyeavdlindedvandyck2003,
	title = {A Simulated Phantom for Diffusion Tensor Fiber Tracking},
	booktitle = {IEEE Advanced Concepts for Intelligent Vision Systems},
	year = {2003},
	pages = {281-285},
	address = {Ghent University, Belgium},
	author = {Alexander Leemans and Jan Sijbers and Marleen Verhoye and Annemie Van Der Linden and Dirk Van Dyck}
}
@inproceedings {aleemansjsijbersmverhoyeavdlindedvandyck2003,
	title = {Simulating Neuronal Fiber Bundles for DT-MRI Tractography},
	booktitle = {20th Annual Scientific Meeting - European Society for Magnetic Resonance in Medicine and Biology},
	year = {2003},
	pages = {278},
	address = {Rotterdam, The Netherlands},
	author = {Alexander Leemans and Jan Sijbers and Marleen Verhoye and Annemie Van Der Linden and Dirk Van Dyck}
}
@inproceedings {KempeneerssdbackerDebruynpscheund2003,
	title = {Wavelet Based Feature Extraction for Hyperspectral Vegetation Monitoring},
	booktitle = {SPIE International Symposium on Remote Sensing, Image and Signal Processing for Remote Sensing IX},
	year = {2003},
	pages = {297-305},
	address = {Barcelona, Spain},
	author = {Pieter Kempeneers and Steve De Backer and W. Debruyn and Paul Scheunders},
	editor = {L. Bruzzone}
}
@inbook {pscheund2003,
	title = {A wavelet representation of multispectral images; in: Frontiers of Remote Sensing Information Processing},
	year = {2003},
	pages = {197-224},
	publisher = {World Scientific},
	organization = {World Scientific},
	chapter = {9},
	author = {Paul Scheunders},
	editor = {C.H. Chen}
}
@inproceedings {pscheund2003,
	title = {Wavelet representations of multivalued images},
	booktitle = {Wavelet Applications in Industrial Processing, part of the International Symposium on Photonics Technologies for Robotics, Automation, and Manufacturing , 27-31 October 2003, Providence, RI USA},
	year = {2003},
	pages = {203-215},
	author = {Paul Scheunders}
}
@inproceedings {pscheund2003,
	title = {Wavelet thresholding of multispectral images},
	booktitle = {Proc. ACIVS03, Andvanced Concepts for intelligent vision systems , Ghent},
	year = {2003},
	pages = {255-259},
	author = {Paul Scheunders}
}
@inproceedings {jsijbersCeulemansdvandyck2002,
	title = {Algorithm for the computation of 3D Fourier descriptors},
	booktitle = {16th International Conference on Pattern Recognition},
	year = {2002},
	month = {August},
	pages = {790-793},
	address = {Quebec, Canada},
	author = {Jan Sijbers and T. Ceulemans and Dirk Van Dyck}
}
@inproceedings {wdhaesdvandyckxrodet2002,
	title = {Control Parameter Estimation for a Physical Model of a Trumpet Using Pattern Recognition},
	booktitle = {IEEE Workshop on Model Based Processing and Coding of Audio (MPCA), Leuven, Belgium},
	year = {2002},
	month = {November},
	author = {W. D{\textquoteright}haes and Dirk Van Dyck and X Rodet}
}
@inproceedings {jsijbersdvandyck2002,
	title = {Efficient algorithm for the computation of 3D Fourier descriptors},
	booktitle = {1st International Symposium on 3D Data Processing Visualization and Transmission},
	year = {2002},
	month = {June},
	pages = {640-643},
	address = {Padua, Italy},
	author = {Jan Sijbers and Dirk Van Dyck}
}
@inproceedings {wdhaesdvandyckxrodet2002,
	title = {An efficient branch and bound seach algorithm for computing K nearest neighbors in a multidimensional vector space},
	booktitle = {Signal Processing, Pattern Recognition and Applications (SPPRA), Crete, Greece},
	year = {2002},
	month = {June},
	author = {W. D{\textquoteright}haes and Dirk Van Dyck and X Rodet}
}
@inproceedings {wdhaesdvandyckxrodet2002,
	title = {An efficient branch and bound seach algorithm for computing K nearest neighbors in a multidimensional vector space},
	booktitle = {IEEE Advanced Concepts for Intelligent Vision Systems (ACIVS), Gent, Belgium},
	year = {2002},
	month = {September},
	author = {W. D{\textquoteright}haes and Dirk Van Dyck and X Rodet}
}
@article {evdcastedvandyckjsijberseraman2002,
	title = {An energy-based beam hardening model in tomography},
	journal = {Physics in Medicine and Biology},
	volume = {47},
	number = {23},
	year = {2002},
	pages = {4181-4190},
	author = {Elke Van de Casteele and Dirk Van Dyck and Jan Sijbers and Erik Raman}
}
@article {Yusdbackerpscheund2002,
	title = {Genetic feature selection combined with composite fuzzy nearest neighbor classifiers for hyperspectral satellite imagery},
	journal = {Pattern Recognition Letters},
	volume = {23},
	number = {1},
	year = {2002},
	pages = {183-190},
	author = {Shixin Yu and Steve De Backer and Paul Scheunders}
}
@inproceedings {pscheundjsijbers2002,
	title = {Multiscale watershed segmentation of multivalued images},
	booktitle = {Proc. ICPR02, International Conference on Pattern Recognition},
	volume = {3},
	year = {2002},
	pages = {855-858},
	address = {Quebec, Canada},
	author = {Paul Scheunders and Jan Sijbers}
}
@article {pscheund2002,
	title = {A multivalued image wavelet representation based on multiscale fundamental forms},
	journal = {IEEE Transactions on Image Processing},
	volume = {11},
	number = {5},
	year = {2002},
	pages = {568-575},
	author = {Paul Scheunders}
}
@inproceedings {pscheund2002,
	title = {An orthogonal wavelet representation of multivalued images},
	booktitle = {Proc. ACIVS02, Andvanced Concepts for intelligent vision systems, Ghent},
	year = {2002},
	author = {Paul Scheunders}
}
@inproceedings {wdhaesxrodet2002,
	title = {Physical Constraints for the Control of a Physical Model of a Trumpet},
	booktitle = {International Conference on Digital Audio Effects (DAFx), Hamburg, Germany},
	year = {2002},
	month = {September},
	author = {W. D{\textquoteright}haes and X Rodet}
}
@mastersthesis {1278,
	title = {Unsupervised Pattern Recognition - Dimensionality Reduction and Classification},
	year = {2002},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	author = {Steve De Backer}
}
@inproceedings {pscheund2002,
	title = {Wavelet-based enhancement and denoising using multiscale structure tensor},
	booktitle = {Proceedings of the IEEE International Conference on Image Processing},
	number = {3},
	year = {2002},
	pages = {569-572},
	address = {Rochester NY},
	author = {Paul Scheunders}
}
@inproceedings {wdhaesxrodet2001,
	title = {Automatic Estimation of Control Parameters: An Instance-Based Learning Approach},
	booktitle = {International Computer Music Conference (ICMC), Havana, Cuba},
	year = {2001},
	month = {September},
	author = {W. D{\textquoteright}haes and X Rodet}
}
@inproceedings {Yupscheund2001,
	title = {Feature selection for high-dimensional remote sensing data by a maximum entropy principal based optimization},
	booktitle = {Proc. Geoscience and Remote Sensing Symposium},
	year = {2001},
	pages = {3303-3305},
	author = {Shixin Yu and Paul Scheunders}
}
@article {pscheundsdbacker2001,
	title = {Fusion and merging of multispectral images using multiscale fundamental forms},
	journal = {Journal of the Optical Society of America A},
	volume = {18},
	number = {10},
	year = {2001},
	pages = {2468-2477},
	author = {Paul Scheunders and Steve De Backer}
}
@inproceedings {Yupscheund2001,
	title = {Fuzzy markov chains approach to feature selection for high dimensional remote sensing data},
	booktitle = {Geoscience and Remote Sensing Symposium},
	year = {2001},
	pages = {3306-3308},
	author = {Shixin Yu and Paul Scheunders}
}
@mastersthesis {1279,
	title = {Infotree - A study of its foundations},
	year = {2001},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	author = {Bart De Boeck}
}
@article {pscheund2001,
	title = {Local mapping for multispectral image visualisation},
	journal = {Image and Vision Computing},
	volume = {19},
	number = {13},
	year = {2001},
	pages = {971-978},
	author = {Paul Scheunders}
}
@inproceedings {pscheundjsijbers2001,
	title = {Multiscale anisotropic filtering of color images},
	booktitle = {Proceedings of the IEEE International Conference on Image Processing},
	volume = {3},
	year = {2001},
	pages = {170-173},
	address = {Thessaloniki, Greece},
	author = {Paul Scheunders and Jan Sijbers}
}
@inproceedings {pscheund2001,
	title = {Multiscale fundamental forms: a multimodal image wavelet representation},
	booktitle = {Proc. ICIAP, International Conference on Image Analysis and Processing, Palermo, Italy},
	year = {2001},
	pages = {179-184},
	author = {Paul Scheunders}
}
@inproceedings {pscheundsdbacker2001,
	title = {Multispectral image fusion and merging using multiscale fundamental forms},
	booktitle = {Proc. ICIP01, IEEE International Conference on Image Processing},
	year = {2001},
	pages = {902-905},
	address = {Thessaloniki, Greece},
	author = {Paul Scheunders and Steve De Backer}
}
@inproceedings {pscheund2001,
	title = {Multivalued image segmentation based on first fundamental form},
	booktitle = {Proc. ICIAP, International Conference on Image Analysis and Processing, Palermo, Italy},
	year = {2001},
	pages = {185-190},
	author = {Paul Scheunders}
}
@article {sdbackerpscheund2001,
	title = {Texture segmentation by frequency-sensitive elliptical competitive learning},
	journal = {Image and Vision Computing},
	volume = {19},
	number = {9},
	year = {2001},
	pages = {639-648},
	author = {Steve De Backer and Paul Scheunders}
}
@article {gvdwouweWeynpscheundJacobMarckdvandyck2000,
	title = {Automated chromatin-texture based diagnosis of carcinoma nuclei},
	journal = {Journal of Microscopy},
	volume = {197},
	year = {2000},
	pages = {25-35},
	author = {G. Van de Wouwer and Barbara Weyn and Paul Scheunders and W. Jacob and E. Van Marck and Dirk Van Dyck}
}
@inproceedings {jsijbersVanrumsteHoeyBoonmverhoyeavdlindedvandyck2000,
	title = {Automatic detection of EEG electrode markers on 3D MR data},
	booktitle = {SPIE Medical Imaging: Image Processing},
	volume = {3979},
	year = {2000},
	month = {February},
	pages = {1476-1481},
	address = {San Diego CA, USA},
	author = {Jan Sijbers and B. Vanrumste and G. Van Hoey and P. Boon and Marleen Verhoye and Annemie Van Der Linden and Dirk Van Dyck},
	editor = {Kenneth M. Hanson}
}
@inproceedings {jsijbersMichielsAudekerkemverhoyeavdlinde2000,
	title = {Automatic EEG signal restoration during simultaneous EEG/MR acquisitions},
	booktitle = {SPIE Medical Imaging: Image Processing},
	volume = {3979},
	year = {2000},
	month = {February},
	pages = {1482-1491},
	address = {San Diego, California, USA},
	author = {Jan Sijbers and I. Michiels and Johan Van Audekerke and Marleen Verhoye and Annemie Van Der Linden},
	editor = {Kenneth M. Hanson}
}
@article {jsijbersVanrumsteHoeyBoonmverhoyeavdlindedvandyck2000,
	title = {Automatic localization of EEG electrode markers within 3D MR data},
	journal = {Magnetic Resonance Imaging},
	volume = {18},
	number = {4},
	year = {2000},
	pages = {485-488},
	doi = {https://doi.org/10.1016/S0730-725X(00)00121-1},
	author = {Jan Sijbers and B. Vanrumste and G. Van Hoey and P. Boon and Marleen Verhoye and Annemie Van Der Linden and Dirk Van Dyck}
}
@inproceedings {mverhoyeMichielsjsijbersEelenPeetersAudekerkeDHoogheDeynavdlinde2000,
	title = {Changes during pentetrazol-induced epilepsy in rat recorded by simultaneous EEG/MRI at 7T},
	booktitle = {SPIE Medical Imaging},
	volume = {3978},
	year = {2000},
	month = {February},
	pages = {485-493},
	address = {San Diego, USA},
	author = {Marleen Verhoye and I. Michiels and Jan Sijbers and J. Eelen and Ron R Peeters and Johan Van Audekerke and R. Dhooghe and P.P. De Deyn and Annemie Van Der Linden},
	editor = {Chin-Tu Chen, Anne V. Clough}
}
@inproceedings {bdboeckpscheunddvandyck2000,
	title = {From inductive inference to the fundamental equations of measuerement},
	booktitle = {Proc. First International Conference on Complex Systems},
	year = {2000},
	pages = {115-122},
	publisher = {Perseus Books},
	organization = {Perseus Books},
	author = {Bart De Boeck and Paul Scheunders and Dirk Van Dyck},
	editor = {Y. Bar-Yam}
}
@inproceedings {Yupscheundsdbacker2000,
	title = {Genetic Feature selection combined with composite fuzzy nearest neighbor classifers for high-dimensional remote sensing data},
	booktitle = {Proc. IEEE Intern. Conf. on Systems, Man \& Cybernetics, October 8-11, Nashville,TN},
	year = {2000},
	pages = {1912-1916},
	author = {Shixin Yu and Paul Scheunders and Steve De Backer}
}
@inproceedings {Yusdbackerpscheund2000,
	title = {Genetic feature selection combined with fuzzy kNN for hyperspectral satellite imagery},
	booktitle = {Geoscience and Remote Sensing Symposium, 2000. Proceedings. IGARSS 2000. IEEE 2000 International},
	year = {2000},
	pages = {702-704},
	author = {Shixin Yu and Steve De Backer and Paul Scheunders}
}
@inproceedings {Yusdbackerpscheund2000,
	title = {Genetic Feature Selection Combined with Fuzzy K-NN for Hyperspectral Satellite Imagery},
	booktitle = {Intelligent Techniques and Soft Computing in Nuclear Science and Engineering},
	year = {2000},
	pages = {281-288},
	publisher = {World Scientific},
	organization = {World Scientific},
	author = {Shixin Yu and Steve De Backer and Paul Scheunders},
	editor = {d. Ruan, H.A. Abderrahim, P. D{\textquoteright}Hondt, E. Kerre}
}
@inproceedings {pscheund2000,
	title = {Multiscale Edge Representation Applied to Image Fusion},
	booktitle = {Proc. Wavelet Applications in Signal and Image Processing VIII},
	year = {2000},
	pages = {894-901},
	author = {Paul Scheunders}
}
@inproceedings {pscheund2000,
	title = {Multispectral image fusion using local mapping techniques},
	booktitle = {Proc. ICPR2000, International Conference on Pattern Recognition, Barcelona, Spain, september 3-7},
	year = {2000},
	pages = {311-314},
	author = {Paul Scheunders}
}
@inproceedings {mverhoyeCampPeetersjsijbersAudekerkeavdlinde2000,
	title = {On-line simultaneous BOLD/EEG measurements of functional activity during pentylenetetrazol induced epilepsy in the rat},
	booktitle = {16th annual meeting of the European Society for Magnetic Resonance in Medicine and Biology (ESMRMB00)},
	year = {2000},
	month = {September},
	address = {Paris, France},
	author = {Marleen Verhoye and N. Van Camp and Ron R Peeters and Jan Sijbers and Johan Van Audekerke and Annemie Van Der Linden}
}
@mastersthesis {1288,
	title = {Pattern Recognition and Complex Systems},
	year = {2000},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	author = {Ing Ren Tsang}
}
@mastersthesis {1287,
	title = {Pattern Recognition, Neighborhood Codes, and Lattice Animals},
	year = {2000},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	author = {Ing Jyh Tsang}
}
@article {jsijbersAudekerkemverhoyeavdlindedvandyck2000,
	title = {Reduction of ECG and gradient related artifacts in simultaneously recorded human EEG/MRI data},
	journal = {Magnetic Resonance Imaging},
	volume = {18},
	number = {7},
	year = {2000},
	pages = {881-886},
	doi = {10.1016/S0730-725X(00)00178-8},
	author = {Jan Sijbers and Johan Van Audekerke and Marleen Verhoye and Annemie Van Der Linden and Dirk Van Dyck}
}
@inproceedings {jsijbersMichielsAudekerkemverhoyeavdlindedvandyck2000,
	title = {Restoration of EEG signals distorted during simultaneous MR acquisitions},
	booktitle = {Proceedings of the International Society of Magnetic Resonance in Medicine},
	year = {2000},
	month = {April},
	pages = {118},
	address = {Denver CO, USA},
	author = {Jan Sijbers and I. Michiels and Johan Van Audekerke and Marleen Verhoye and Annemie Van Der Linden and Dirk Van Dyck}
}
@article {ijtsangirtsangbdboeckdvandyck2000,
	title = {Scaling and critical probability for cluster size and lattice animals diversity on randomly occupied square lattices},
	journal = {Journal of Physics A: Mathematical and General},
	volume = {33},
	number = {14},
	year = {2000},
	pages = {2739-2754},
	author = {Ing Jyh Tsang and Ing Ren Tsang and Bart De Boeck and Dirk Van Dyck}
}
@inproceedings {VanhouttemverhoyePeetersjsijbersEelenDHoogheDeynavdlinde2000,
	title = {Simultaneously recording of EEG/fMRI to study neurophysiological changes in rat brain during pentetrazol induced epileptic seizures},
	booktitle = {Federation of European Neuroscience (FENS) Millennium Meeting},
	volume = {12},
	year = {2000},
	month = {June},
	pages = {107},
	address = {Brighton, UK},
	author = {Greetje Vanhoutte and Marleen Verhoye and Ron R Peeters and Jan Sijbers and J. Eelen and R. Dhooghe and P.P. De Deyn and Annemie Van Der Linden}
}
@article {AudekerkemverhoyePeetersjsijbersavdlinde2000,
	title = {Special designed RF-antenna with integrated non-invasive carbon electrodes for simultaneous MRI and EEG acquisition at 7T},
	journal = {Magnetic Resonance Imaging},
	volume = {18},
	number = {7},
	year = {2000},
	pages = {887-891},
	doi = {https://doi.org/10.1016/S0730-725X(00)00172-7},
	author = {Johan Van Audekerke and Marleen Verhoye and Ron R Peeters and Jan Sijbers and Annemie Van Der Linden}
}
@article {WeynTjalamgvdwouweDaelepscheundJacobMarckdvandyck2000,
	title = {Validation of nuclear texture density, morphometry and tissue syntactic structure analysis as prognosticators of cervical carcinoma},
	journal = {Analytical and Quantitative Cytology and Histology},
	volume = {22},
	number = {5},
	year = {2000},
	pages = {373-382},
	author = {Barbara Weyn and W. Tjalam and G. Van de Wouwer and A. Van Daele and Paul Scheunders and W. Jacob and E. Van Marck and Dirk Van Dyck}
}
@article {gvdwouwepscheund2000,
	title = {Wavelet-based texture classification},
	journal = {Recent Research in the Development of Pattern Recognition},
	volume = {1},
	year = {2000},
	pages = {77-87},
	author = {G. Van de Wouwer and Paul Scheunders}
}
@inproceedings {jsijbersajdendekmverhoyeavdlindedvandyck1999,
	title = {Adaptive anisotropic noise filtering for magnitude MR data},
	booktitle = {Proceedings of SPIE Medical Imaging},
	volume = {3661},
	year = {1999},
	month = {February},
	pages = {1418-1425},
	address = {San Diego, USA},
	author = {Jan Sijbers and Arnold Jan den Dekker and Marleen Verhoye and Annemie Van Der Linden and Dirk Van Dyck},
	editor = {Kenneth M. Hanson}
}
@article {jsijbersajdendekmverhoyeavdlindedvandyck1999,
	title = {Adaptive anisotropic noise filtering for magnitude MR data},
	journal = {Magnetic Resonance Imaging},
	volume = {17},
	number = {10},
	year = {1999},
	pages = {1533-1539},
	doi = {10.1016/S0730-725X(99)00088-0},
	author = {Jan Sijbers and Arnold Jan den Dekker and Marleen Verhoye and Annemie Van Der Linden and Dirk Van Dyck}
}
@inproceedings {jsijbersMichielsmverhoyeAudekerkeavdlindedvandyck1999,
	title = {Automatic, adaptive filter for MRI related artefacts in simultaneously recorded EEG/MRI data},
	booktitle = {Third Meeting Belgian Society for Neuroscience},
	number = {31},
	year = {1999},
	month = {May},
	author = {Jan Sijbers and I. Michiels and Marleen Verhoye and Johan Van Audekerke and Annemie Van Der Linden and Dirk Van Dyck}
}
@article {irtsangijtsang1999,
	title = {Cluster size diversity, percolation, and complex systems},
	journal = {Physical Review E},
	volume = {60},
	number = {3},
	year = {1999},
	pages = {2684-2698},
	author = {Ing Ren Tsang and Ing Jyh Tsang}
}
@article {sdbackerpscheund1999,
	title = {A competitive elliptical clustering algorithm},
	journal = {Pattern Recognition Letters},
	volume = {20},
	number = {11},
	year = {1999},
	pages = {1141-1147},
	author = {Steve De Backer and Paul Scheunders}
}
@article {WeyngvdwouweSamirDaelepscheundMarckJacob1999,
	title = {Computer-assisted differential diagnosis of malignant mesothelioma based on syntactic structure analysis},
	journal = {Cytometry},
	volume = {35},
	year = {1999},
	pages = {23-29},
	author = {Barbara Weyn and G. Van de Wouwer and G.K. Samir and A. Van Daele and Paul Scheunders and E. Van Marck and W. Jacob}
}
@article {pscheundsdbacker1999,
	title = {High-dimensional clustering using frequency sensitive competitive learning},
	journal = {Pattern Recognition},
	volume = {32},
	number = {2},
	year = {1999},
	pages = {193-202},
	author = {Paul Scheunders and Steve De Backer}
}
@article {ajdendekjsijbersdvandyck1999,
	title = {How to optimize the design of a quantitative HREM experiment so as to attain the highest precision},
	journal = {Journal of Microscopy},
	volume = {194},
	number = {1},
	year = {1999},
	pages = {95-104},
	author = {Arnold Jan den Dekker and Jan Sijbers and Dirk Van Dyck}
}
@article {ijtsangirtsangdvandyck1999,
	title = {Image processing using neighbourhood coding},
	journal = {Pattern Recognition Letters},
	volume = {20},
	year = {1999},
	pages = {1279-1286},
	author = {Ing Jyh Tsang and Ing Ren Tsang and Dirk Van Dyck}
}
@inproceedings {mverhoyejsijbersFransenKooySorianoWillemsavdlinde1999,
	title = {In vivo neuro MRI microscopy of L1 knockout mice demonstrated similarities with human crash syndrome},
	booktitle = {Belgian Society for Neuroscience},
	year = {1999},
	month = {May},
	address = {Brussels},
	author = {Marleen Verhoye and Jan Sijbers and Erik Fransen and R.F. Kooy and P. Soriano and P. J. Willems and Annemie Van Der Linden}
}
@article {ebettensdvandyckajdendekjsijbersBos1999,
	title = {Model-based two-object resolution from observations having counting statistics},
	journal = {Ultramicroscopy},
	volume = {77},
	number = {1},
	year = {1999},
	pages = {37-48},
	doi = {https://doi.org/10.1016/S0304-3991(99)00006-6},
	author = {E. Bettens and Dirk Van Dyck and Arnold Jan den Dekker and Jan Sijbers and A. van den Bos}
}
@article {KooyReyniersmverhoyejsijbersCrasOostraWillemsavdlinde1999,
	title = {Neuroanatomy of the fragile X knockout mouse brain studied using in vivo high resolution Magnetic Resonance Imaging (MRI)},
	journal = {European Journal of Human Genetics},
	volume = {7},
	year = {1999},
	pages = {526-532},
	author = {R.F. Kooy and E. Reyniers and Marleen Verhoye and Jan Sijbers and P. Cras and B. A. Oostra and P. J. Willems and Annemie Van Der Linden}
}
@inproceedings {AudekerkejsijbersMichielsmverhoyePeetersavdlinde1999,
	title = {Novel design of an RF-antenna with integrated EEG electrodes for combined EEG and fMRI in animals},
	booktitle = {16th annual meeting: Magnetic Resonance Materials in Physics, Biology, and Medicine},
	year = {1999},
	month = {September},
	address = {Sevilla, Spain},
	author = {Johan Van Audekerke and Jan Sijbers and I. Michiels and Marleen Verhoye and Ron R Peeters and Annemie Van Der Linden}
}
@article {jsijbersajdendekeramandvandyck1999,
	title = {Parameter estimation from magnitude MR images},
	journal = {International Journal of Imaging Systems and Technology},
	volume = {10},
	number = {2},
	year = {1999},
	pages = {109-114},
	doi = {10.1002/(SICI)1098-1098(1999)10:2<109::AID-IMA2>3.0.CO;2-R},
	author = {Jan Sijbers and Arnold Jan den Dekker and Erik Raman and Dirk Van Dyck}
}
@mastersthesis {1277,
	title = {Peak Characterization using Parameter Estimation Methods},
	year = {1999},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	author = {E. Bettens}
}
@inproceedings {ajdendekjsijbersdvandyck1999,
	title = {Quantitative HREM: viewpoints on resolution, precision, and experimental design},
	booktitle = {Acta Cryst. A55 Supplement, Abstract M11.OE.OO5},
	year = {1999},
	month = {August},
	address = {IUCR Glasgow, Scotland},
	author = {Arnold Jan den Dekker and Jan Sijbers and Dirk Van Dyck}
}
@inproceedings {jsijbersMichielsmverhoyeAudekerkeavdlindedvandyck1999,
	title = {Restoration of EEG signals distorted during simultaneous MR acquisitions},
	booktitle = {annual meeting: Magnetic Resonance Materials in Physics, Biology, and Medicine},
	year = {1999},
	month = {September},
	pages = {212},
	address = {Sevilla, Spain},
	author = {Jan Sijbers and I. Michiels and Marleen Verhoye and Johan Van Audekerke and Annemie Van Der Linden and Dirk Van Dyck}
}
@article {jsijbersMichielsmverhoyeAudekerkeavdlindedvandyck1999,
	title = {Restoration of MR induced artifacts in simultaneously recorded MR/EEG data},
	journal = {Magnetic Resonance Imaging},
	volume = {17},
	number = {9},
	year = {1999},
	pages = {1383-1393},
	doi = {https://doi.org/10.1016/S0730-725X(99)00096-X},
	author = {Jan Sijbers and I. Michiels and Marleen Verhoye and Johan Van Audekerke and Annemie Van Der Linden and Dirk Van Dyck}
}
@inproceedings {MichielsjsijbersEelenmverhoyeDHoogheNagelsDeynavdlinde1999,
	title = {Simultaneous EEG and MRI in an animal model for generalized epilepsy induced in the MRI instrument},
	booktitle = {Third Meeting of the Belgian Society for Neuroscience},
	number = {25},
	year = {1999},
	month = {May},
	address = {Brussels, Belgium},
	author = {I. Michiels and Jan Sijbers and J. Eelen and Marleen Verhoye and R. Dhooghe and Guy Nagels and P.P. De Deyn and Annemie Van Der Linden}
}
@inproceedings {MichielsjsijbersEelenmverhoyeDHoogheNagelsDeynavdlinde1999,
	title = {Simultaneous EEG and MRI in an animal model for generalized epilepsy induced in the MRI instrument},
	booktitle = {16th annual meeting: Magnetic Resonance Materials in Physics, Biology, and Medicine},
	year = {1999},
	month = {September},
	address = {Sevilla, Spain},
	author = {I. Michiels and Jan Sijbers and J. Eelen and Marleen Verhoye and R. Dhooghe and Guy Nagels and P.P. De Deyn and Annemie Van Der Linden}
}
@article {gvdwouwepscheunddvandyck1999,
	title = {Statistical texture characterization from discrete wavelet representations},
	journal = {IEEE Transactions on Image Processing},
	volume = {8},
	number = {4},
	year = {1999},
	pages = {592-598},
	author = {G. Van de Wouwer and Paul Scheunders and Dirk Van Dyck}
}
@inproceedings {sdbackerpscheund1999,
	title = {Texture segmentation by frequency-sensitive elliptical competitive learning},
	booktitle = {Proc. ICIAP99, International Conference on Image Analysis and Processing , Venice, Italy, september 27-29},
	year = {1999},
	pages = {64-69},
	author = {Steve De Backer and Paul Scheunders}
}
@article {dvandyckebettensjsijbersBeeckBosajdendekJansenZandbergen1999,
	title = {Towards quantitative structure determination through electron holographic methods},
	journal = {Materials Characterization},
	volume = {42},
	number = {4},
	year = {1999},
	pages = {265-281},
	doi = {https://doi.org/10.1016/S1044-5803(99)00020-0},
	author = {Dirk Van Dyck and E. Bettens and Jan Sijbers and M. Op de Beeck and A. van den Bos and Arnold Jan den Dekker and J. Jansen and H. Zandbergen}
}
@article {WeyngvdwouweKoprowskiDaeleDhaenepscheundJacobMarck1999,
	title = {Value of morphometry, texture analysis, densitometry and histometry in the differential diagnosis and prognosis of malignant mesothelioma},
	journal = {Journal of Pathology},
	volume = {4},
	number = {189},
	year = {1999},
	pages = {581-589},
	author = {Barbara Weyn and G. Van de Wouwer and M. Koprowski and A. Van Daele and K. Dhaene and Paul Scheunders and W. Jacob and E. Van Marck}
}
@article {gvdwouwepscheundslivensdvandyck1999,
	title = {Wavelet correlation signatures for color texture characterization},
	journal = {pattern Recognition},
	volume = {32},
	number = {3},
	year = {1999},
	pages = {443-451},
	author = {G. Van de Wouwer and Paul Scheunders and S. Livens and Dirk Van Dyck}
}
@article {WeyngvdwouweDaelepscheunddvandyckMarckJacob1998,
	title = {Automated breast tumour diagnosis and grading based on wavelet chromatin texture description},
	journal = {Cytometry},
	volume = {33},
	year = {1998},
	pages = {32-40},
	author = {Barbara Weyn and G. Van de Wouwer and A. Van Daele and Paul Scheunders and Dirk Van Dyck and E. Van Marck and W. Jacob}
}
@inproceedings {dmeersmpscheunddvandyck1998,
	title = {Classification of microcalcifications using texture-based features},
	booktitle = {Digital Mammography},
	year = {1998},
	pages = {233-236},
	author = {D. Meersman and Paul Scheunders and Dirk Van Dyck},
	editor = {N Karssemeijer and L. Van Erning}
}
@inproceedings {dmeersmpscheunddvandyck1998,
	title = {Detection of microcalcifications using non-linear filtering},
	booktitle = {Proc. EUSIPCO{\textquoteright}98, European Signal Processing Conference},
	year = {1998},
	pages = {2465-2468},
	author = {D. Meersman and Paul Scheunders and Dirk Van Dyck}
}
@inproceedings {dvandyckajdendekjsijbersebettens1998,
	title = {Dose Limited Resolution},
	booktitle = {Proceedings Microscopy and Microanalysis},
	volume = {2},
	number = {2},
	year = {1998},
	month = {July},
	pages = {802-803},
	address = {Atlanta, Georgia, U.S.A},
	author = {Dirk Van Dyck and Arnold Jan den Dekker and Jan Sijbers and E. Bettens}
}
@mastersthesis {1302,
	title = {Estimation of Signal and Noise from Magnitude Magnetic Resonance Images},
	year = {1998},
	type = {PhD thesis},
	author = {Jan Sijbers}
}
@inproceedings {jsijbersajdendekdvandyckeraman1998,
	title = {Estimation of signal and noise from Rician distributed data},
	booktitle = {Proceedings of the IASTED International Conference on Signal Processing and Communications},
	year = {1998},
	month = {February},
	pages = {140-143},
	address = {Canary Islands, Spain},
	author = {Jan Sijbers and Arnold Jan den Dekker and Dirk Van Dyck and Erik Raman}
}
@article {jsijbersajdendekAudekerkemverhoyedvandyck1998,
	title = {Estimation of the noise in magnitude MR images},
	journal = {Magnetic Resonance Imaging},
	volume = {16},
	number = {1},
	year = {1998},
	pages = {87-90},
	doi = {10.1016/S0730-725X(97)00199-9},
	author = {Jan Sijbers and Arnold Jan den Dekker and Johan Van Audekerke and Marleen Verhoye and Dirk Van Dyck}
}
@article {Wolfpscheund1998,
	title = {Evaluation of the swimming activity of Daphnia magna by image analysis after administration of sublethal Cadmium concentrations},
	journal = {Comparative Biochemistry and Physiology A},
	volume = {120},
	year = {1998},
	pages = {99-105},
	author = {G. Wolf and Paul Scheunders}
}
@inproceedings {HungColemanpscheund1998,
	title = {The genetic algorithm approach and K-means clustering: their role in unsupervised training in image classification},
	booktitle = {Proc. IASTED International Conf. On Computer Graphics and Imaging , Halifax, Canada, june 1-3},
	year = {1998},
	pages = {103-106},
	author = {C.C. Hung and T. Coleman and Paul Scheunders}
}
@inproceedings {ajdendekjsijbersdvandyck1998,
	title = {How to design an HREM experiment so as to attain the highest precision?},
	booktitle = {ICEM14: 14th International Congress on Electron Microscopy},
	volume = {1},
	year = {1998},
	month = {September},
	pages = {621-622},
	address = {Cancun, Mexico},
	author = {Arnold Jan den Dekker and Jan Sijbers and Dirk Van Dyck}
}
@mastersthesis {1281,
	title = {Image Analysis for Material Characterisation},
	year = {1998},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	author = {S. Livens}
}
@inproceedings {AudekerkemverhoyejsijbersDeVoogdSmuldersNewmanavdlinde1998,
	title = {Imaging birds ... in a bird cage: In vivo MRI microscopy of the canary brain at 7T},
	booktitle = {Proceedings of International Society for Magnetic Resonance in Medicine: Sixth Annual Meeting},
	volume = {2},
	number = {2},
	year = {1998},
	month = {April},
	pages = {162-163},
	author = {Johan Van Audekerke and Marleen Verhoye and Jan Sijbers and T. J. DeVoogd and T. Smulders and S. W. Newman and Annemie Van Der Linden}
}
@article {mverhoyeavdlindeAudekerkejsijbersEensBalthazart1998,
	title = {Imaging birds in a bird cage: in-vivo FSE 3D MRI of bird brain},
	journal = {MAGMA: Magnetic Resonance Materials in Physics, Biology and Medicine},
	volume = {6},
	number = {1},
	year = {1998},
	month = {August},
	pages = {22-27},
	author = {Marleen Verhoye and Annemie Van Der Linden and Johan Van Audekerke and Jan Sijbers and M. Eens and J. Balthazart}
}
@inproceedings {mverhoyejsijbersKooyReyniersFransenOostraWillemsavdlinde1998,
	title = {In vivo assessment of cerebellum volume in transgenic Fragile X Knockout mice using MRI microscopy at 7T},
	booktitle = {Proceedings of the International Society for Magnetic Resonance in Medicine},
	year = {1998},
	month = {April},
	address = {Sydney, Australia},
	author = {Marleen Verhoye and Jan Sijbers and R.F. Kooy and E. Reyniers and Erik Fransen and B. A. Oostra and P. J. Willems and Annemie Van Der Linden}
}
@inproceedings {mverhoyejsijbersFransenKooySorianoWillemsavdlinde1998,
	title = {In vivo neuro MRI micrsocopy of L1 Knockout mice demonstrated similarities with human CRASH syndrome},
	booktitle = {Sixth Scientific Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine},
	number = {1371},
	year = {1998},
	month = {April},
	address = {Sydney, Australia},
	author = {Marleen Verhoye and Jan Sijbers and Erik Fransen and R.F. Kooy and P. Soriano and P. J. Willems and Annemie Van Der Linden}
}
@article {pscheund1998,
	title = {Joint quantization and error-diffusion of color images using competitive learning},
	journal = {Journal of the IEE Proceedings, Vision, Image and Signal Processing},
	volume = {14},
	number = {2},
	year = {1998},
	pages = {137-140},
	author = {Paul Scheunders}
}
@article {FransenDhoogheCampmverhoyejsijbersReyniersSorianoKamiguchiWillemsenKoekoekZeeuwDeynavdlindeLemmonKooyWillems1998,
	title = {L1 knockout mice show dilated ventricles, vermis hypoplasia and impaired exploration patterns},
	journal = {Human Molecular Genetics},
	volume = {7},
	number = {6},
	year = {1998},
	pages = {999-1009},
	doi = {10.1093/hmg/7.6.999},
	author = {Erik Fransen and R. Dhooghe and G. Van Camp and Marleen Verhoye and Jan Sijbers and E. Reyniers and P. Soriano and H. Kamiguchi and R. Willemsen and K.E. Koekoek and C.I. De Zeeuw and P.P. De Deyn and Annemie Van Der Linden and V. Lemmon and R.F. Kooy and P. J. Willems}
}
@article {jsijbersajdendekpscheunddvandyck1998,
	title = {Maximum Likelihood estimation of Rician distribution parameters},
	journal = {IEEE Transactions on Medical Imaging},
	volume = {17},
	number = {3},
	year = {1998},
	pages = {357-361},
	doi = {10.1109/42.712125},
	author = {Jan Sijbers and Arnold Jan den Dekker and Paul Scheunders and Dirk Van Dyck}
}
@inproceedings {ajdendekjsijbersmverhoyedvandyck1998,
	title = {Maximum Likelihood estimation of the signal component magnitude in phase contrast MR images},
	booktitle = {Proceedings of SPIE Medical Imaging},
	volume = {3338},
	year = {1998},
	month = {February},
	pages = {408-415},
	address = {San Diego, California, USA},
	author = {Arnold Jan den Dekker and Jan Sijbers and Marleen Verhoye and Dirk Van Dyck}
}
@inproceedings {mverhoyejsijbersKooyReyniersFransenOostraWillemsavdlinde1998,
	title = {MRI as a tool to study brain structure from mouse models of mental retardation},
	booktitle = {Proceedings of SPIE Medical Imaging},
	volume = {3337},
	year = {1998},
	month = {February},
	pages = {314-320},
	address = {San Diego, CA, USA},
	author = {Marleen Verhoye and Jan Sijbers and R.F. Kooy and E. Reyniers and Erik Fransen and B. A. Oostra and P. J. Willems and Annemie Van Der Linden},
	editor = {Eric A. Hoffman}
}
@inproceedings {KooyReyniersmverhoyejsijbersOostraWillemsavdlinde1998,
	title = {Neuroanatomy of the fragile X knockout mouse brain studied using high-resolution MRI},
	booktitle = {48th Annual Meeting of the American Society of Human Genetics},
	year = {1998},
	month = {October},
	pages = {1918},
	address = {Denver, CO (USA)},
	author = {R.F. Kooy and E. Reyniers and Marleen Verhoye and Jan Sijbers and B. A. Oostra and P. J. Willems and Annemie Van Der Linden}
}
@article {sdbackerNaudpscheund1998,
	title = {Non-linear Dimensionality Reduction Techniques for Unsupervised Feature Extraction},
	journal = {Pattern Recognition Letters},
	volume = {19},
	year = {1998},
	pages = {711-720},
	author = {Steve De Backer and A. Naud and Paul Scheunders}
}
@article {pscheundsdbackerNaud1998,
	title = {Non-linear mapping for feature extraction},
	journal = {Lecture Notes in Computer Science},
	volume = {1451},
	year = {1998},
	pages = {823-830},
	author = {Paul Scheunders and Steve De Backer and A. Naud}
}
@article {Baillieuxpscheund1998,
	title = {On-line determination of the velocity of simultaneously moving organisms by image analysis for the detection of sublethal toxicity},
	journal = {Water Research},
	volume = {32},
	number = {4},
	year = {1998},
	pages = {1027-1034},
	author = {M. Baillieux and Paul Scheunders}
}
@inproceedings {jsijbersajdendekmverhoyedvandyck1998,
	title = {Optimal estimation of T2 maps from magnitude MR data},
	booktitle = {Proceedings of SPIE Medical Imaging},
	volume = {3338},
	year = {1998},
	month = {February},
	pages = {384-390},
	address = {San Diego, CA, USA},
	author = {Jan Sijbers and Arnold Jan den Dekker and Marleen Verhoye and Dirk Van Dyck},
	editor = {Kenneth M. Hanson}
}
@article {pjedrasijgarciabdboeckdvandyck1998,
	title = {Optimal filtering versus regularization methods in the Fourier precompensation based proximity neurocorrection in electron beam lithography},
	journal = {Microelectronic Engineering},
	volume = {41},
	year = {1998},
	pages = {195-198},
	author = {P. Jedrasik and J. Garcia and Bart De Boeck and Dirk Van Dyck}
}
@inproceedings {ajdendekjsijbersdvandyck1998,
	title = {Optimizing the design of an HREM experiment so as to attain the highest resolution},
	booktitle = {Proceedings of FEMMS98: Frontiers of Electron Microscopy in Material Science},
	year = {1998},
	month = {April},
	address = {Kloster Irsee, Germany},
	author = {Arnold Jan den Dekker and Jan Sijbers and Dirk Van Dyck}
}
@inproceedings {ajdendekjsijbersdvandyck1998,
	title = {Optimizing the setting of an electron microscope for highest resolution using statistical parameter estimation theory},
	booktitle = {Workshop: Towards Atomic Resolution Analysis 98},
	year = {1998},
	month = {September},
	address = {Port Ludlow, WA, U.S.A},
	author = {Arnold Jan den Dekker and Jan Sijbers and Dirk Van Dyck}
}
@inproceedings {irtsangijtsangpscheunddvandyck1998,
	title = {Pattern Recognition using neighborhood coding},
	booktitle = {Proc. CVPRIP{\textquoteright}98, Intern. Workshop on Computer Vision, Pattern Recognition and Image Processing , North Carolina, october 23-28},
	year = {1998},
	pages = {250-253},
	author = {Ing Ren Tsang and Ing Jyh Tsang and Paul Scheunders and Dirk Van Dyck}
}
@inproceedings {gvdwouwepscheunddvandyck1998,
	title = {Rotation-Invariant Texture Characterization using Isotropic Wavelet Frames},
	booktitle = {Proc. ICPR98, International Conference on Pattern Recognition , Brisbane, Australia, 17-20 august},
	year = {1998},
	pages = {814-816},
	author = {G. Van de Wouwer and Paul Scheunders and Dirk Van Dyck}
}
@mastersthesis {1275,
	title = {Signal and noise estimation from magnetic resonance images },
	year = {1998},
	author = {Jan Sijbers}
}
@inproceedings {slivensRoostpscheunddvandyck1998,
	title = {Tabular silver bromide micro-crystal characterisation using optical microscopy and colour-image analysis},
	booktitle = {Proc. ICPS98, International Conference on Imaging Science , Antwerp, Belgium, 7-11/9},
	year = {1998},
	author = {S. Livens and C. Van Roost and Paul Scheunders and Dirk Van Dyck}
}
@inproceedings {ebettensajdendekjsijbersdvandyck1998,
	title = {Ultimate resolution in the framework of parameter estimation},
	booktitle = {IASTED International Conference - Signal and Image Processing (SIP{\textquoteright}98)},
	year = {1998},
	month = {October},
	pages = {229-233},
	address = {Las Vegas, Nevada, USA},
	author = {E. Bettens and Arnold Jan den Dekker and Jan Sijbers and Dirk Van Dyck}
}
@inproceedings {HungColemanpscheund1998,
	title = {Using genetic differential competitive learning for unsupervised training in multispectral image classification systems},
	booktitle = {Proceedings IEEE International Conference on Systems, Man, and Cybernetics , San Diego, California, October 11-14},
	year = {1998},
	pages = {4482-4485},
	author = {C.C. Hung and T. Coleman and Paul Scheunders}
}
@article {pscheundslivensgvdwouweVautrotdvandyck1998,
	title = {Wavelet-based texture analysis},
	journal = {Intern. Journal on Computer Science and Information Management},
	volume = {1},
	number = {2},
	year = {1998},
	pages = {22-34},
	author = {Paul Scheunders and S. Livens and G. Van de Wouwer and P. Vautrot and Dirk Van Dyck}
}
@inproceedings {KooyReyniersDHoogheDeynavdlindemverhoyejsijbersOostraWillems1997,
	title = {Characterization of the Fragile X knockout mouse},
	booktitle = {Abstracts of the second annual meeting of the Belgian society of neuroscience},
	year = {1997},
	month = {May},
	address = {Brussels, Belgium},
	author = {R.F. Kooy and E. Reyniers and R. Dhooghe and P.P. De Deyn and Annemie Van Der Linden and Marleen Verhoye and Jan Sijbers and B. A. Oostra and P. J. Willems}
}
@inproceedings {gvdwouweslivenspscheunddvandyck1997,
	title = {Color texture classification by wavelet energy-correlation signatures},
	booktitle = {Proc. ICIAP Intern. Conf. On Computer Analysis and Image Processing , Florence, Italy, 17-19 september},
	year = {1997},
	pages = {327-334},
	author = {G. Van de Wouwer and S. Livens and Paul Scheunders and Dirk Van Dyck}
}
@article {pscheund1997,
	title = {Comparison of clustering algorithms applied to colour image quantization},
	journal = {Pattern Recognition Letters},
	volume = {18},
	number = {11},
	year = {1997},
	pages = {1379-1384},
	author = {Paul Scheunders}
}
@article {ebettenspscheunddvandyckMoensOsta1997,
	title = {Computer Analysis of Two-Dimensional Electrophoresis Gels : A New Segmentation and Modeling Algorithm},
	journal = {Electrophoresis},
	volume = {18},
	year = {1997},
	pages = {792-798},
	doi = {10.1002/elps.1150180523},
	author = {E. Bettens and Paul Scheunders and Dirk Van Dyck and L. Moens and P. Van Osta}
}
@article {dvandyckebettensjsijbersBeeckajdendekBos1997,
	title = {From High Resolution Image to Atomic Structure: how fare are we?},
	journal = {Scanning Microscopy, Special Issue on Image Processing},
	volume = {11},
	year = {1997},
	note = {ISSN: 0891-7035},
	pages = {467-478},
	author = {Dirk Van Dyck and E. Bettens and Jan Sijbers and M. Op de Beeck and Arnold Jan den Dekker and A. van den Bos}
}
@article {pscheund1997,
	title = {A genetic c-means clustering algorithm applied to color image quantization},
	journal = {Pattern Recognition},
	volume = {30},
	number = {6},
	year = {1997},
	pages = {859-866},
	author = {Paul Scheunders}
}
@inproceedings {slivensRoostpscheunddvandyck1997,
	title = {Granulometric segmentation using a gradient convergence map},
	booktitle = {Proc. Scandinavian Conference on Image Analysis , Lappeenranta, Finland, 9-11/6},
	year = {1997},
	pages = {389-396},
	author = {S. Livens and C. Van Roost and Paul Scheunders and Dirk Van Dyck}
}
@inproceedings {mverhoyejsijberspscheunddvandyckReyniersKooyWillemsCrasOostraavdlinde1997,
	title = {In vivo assessment of cerebellum volume in transgenic fragile X knockout mice using MTI microscopy at 7T},
	booktitle = {Proceedings of the European Society for Magnetic Resonance in Medicine and Biology: 14th annual meeting},
	volume = {2},
	number = {2},
	year = {1997},
	month = {September},
	pages = {45-46},
	address = {Brussels, Belgium},
	author = {Marleen Verhoye and Jan Sijbers and Paul Scheunders and Dirk Van Dyck and E. Reyniers and R.F. Kooy and P. J. Willems and P. Cras and B. A. Oostra and Annemie Van Der Linden}
}
@article {dvandyckjgarcia1997,
	title = {INFOTREE: A new generic method for supervised pattern},
	journal = {Pattern Recognition Letters},
	volume = {18},
	year = {1997},
	pages = {1211-1217},
	author = {Dirk Van Dyck and J. Garcia}
}
@inproceedings {pscheundsdbacker1997,
	title = {Joint quantization and error-diffusion of color images using competitive learning},
	booktitle = {Proc. IEEE Conference on Image Processing , Santa Barbara, 26-29 october},
	year = {1997},
	pages = {811-814},
	author = {Paul Scheunders and Steve De Backer}
}
@inproceedings {KooyReyniersmverhoyejsijbersFransenCampCrasOostraavdlindeWillems1997,
	title = {MRI as a tool to study brain structure from mouse models of mental retardation},
	booktitle = {Abstracts of the 8th international workshop on Fragile X syndrome and X-linked mental retardation},
	year = {1997},
	month = {August},
	address = {Picton, Ontario, Canada},
	author = {R.F. Kooy and E. Reyniers and Marleen Verhoye and Jan Sijbers and Erik Fransen and C. Van Camp and P. Cras and B. A. Oostra and Annemie Van Der Linden and P. J. Willems}
}
@inproceedings {jsijberspscheunddvandyckeraman1997,
	title = {Noise quantification prior to image restoration},
	booktitle = {Meeting of the Dutch Society for Pattern Recognition and Image Processing},
	year = {1997},
	month = {May},
	address = {Best, The Netherlands},
	author = {Jan Sijbers and Paul Scheunders and Dirk Van Dyck and Erik Raman}
}
@article {denDekker:97,
	title = {Resolution: a survey},
	journal = {J. Opt. Soc. Am. A},
	volume = {14},
	number = {3},
	year = {1997},
	month = {Mar},
	pages = {547{\textendash}557},
	publisher = {OSA},
	abstract = {Past and present approaches to the concept of optical resolution are reviewed.},
	doi = {10.1364/JOSAA.14.000547},
	url = {http://josaa.osa.org/abstract.cfm?URI=josaa-14-3-547},
	author = {Arnold Jan den Dekker and A. van den Bos}
}
@inbook {dvandyckebettensjsijbersajdendekBosBeeckJansenZandbergen1997,
	title = {Resolving atoms: what do we have? what do we want?},
	booktitle = {Institute of Physics Conference Series},
	volume = {153},
	number = {3},
	year = {1997},
	month = {December},
	pages = {95-100},
	publisher = {Institute of Physics Ltd},
	organization = {Institute of Physics Ltd},
	address = {Cambridge, UK},
	author = {Dirk Van Dyck and E. Bettens and Jan Sijbers and Arnold Jan den Dekker and A. van den Bos and M. Op de Beeck and J. Jansen and H. Zandbergen},
	editor = {J. M. Rodenburg}
}
@inproceedings {gvdwouweVautrotpscheundslivensdvandyckBonnet1997,
	title = {Rotation-invariant texture segmentation using continuous wavelets},
	booktitle = {Proc. 2-nd IEEE Symposium on applications of time-frequency and time-scale methods , Coventry, UK, 27-29/9},
	year = {1997},
	author = {G. Van de Wouwer and P. Vautrot and Paul Scheunders and S. Livens and Dirk Van Dyck and N. Bonnet}
}
@inproceedings {jsijbersajdendekpscheunderamandvandyck1997,
	title = {Unbiased signal estimation in magnitude MR images},
	booktitle = {Proceedings of the European Society for Magnetic Resonance in Medicine and Biology},
	volume = {2},
	number = {2},
	year = {1997},
	month = {September},
	pages = {174},
	address = {Brussels, Belgium},
	author = {Jan Sijbers and Arnold Jan den Dekker and Paul Scheunders and Erik Raman and Dirk Van Dyck}
}
@article {gvdwouwepscheunddvandyckWuytsHeyning1997,
	title = {Voice recognition from spectrograms: a wavelet based approach},
	journal = {Fractals},
	volume = {5},
	year = {1997},
	pages = {165-172},
	author = {G. Van de Wouwer and Paul Scheunders and Dirk Van Dyck and Floris L Wuyts and Paul H Van de Heyning}
}
@article {jsijbersmverhoyepscheundavdlindedvandyckeraman1997,
	title = {Watershed based segmentation of 3D MR data for volume quantization},
	journal = {Magnetic Resonance Imaging},
	volume = {15},
	number = {6},
	year = {1997},
	pages = {679-688},
	doi = {10.1016/S0730-725X(97)00033-7},
	author = {Jan Sijbers and Marleen Verhoye and Paul Scheunders and Annemie Van Der Linden and Dirk Van Dyck and Erik Raman}
}
@inproceedings {slivenspscheundgvdwouweVautrotdvandyck1997,
	title = {Wavelets for texture analysis, an overview},
	booktitle = {Proc. IEE International Conference on Image Processing and Applications , Dublin, 14-17 july},
	year = {1997},
	pages = {581-585},
	author = {S. Livens and Paul Scheunders and G. Van de Wouwer and P. Vautrot and Dirk Van Dyck}
}
@inproceedings {ebettenspscheundjsijbersdvandyckMoens1996,
	title = {Automatic segmentation and modeling of two-dimensional electrophoresis gels},
	booktitle = {Proceedings of IEEE International Conference on Image Processing},
	volume = {2},
	year = {1996},
	month = {September},
	pages = {665-668},
	address = {Lausanne, Switserland},
	author = {E. Bettens and Paul Scheunders and Jan Sijbers and Dirk Van Dyck and L. Moens}
}
@inproceedings {dmeersmpscheunddvandyck1996,
	title = {Detection of microcalcifications using neural networks},
	booktitle = {Digital Mammography},
	series = {ICS},
	year = {1996},
	pages = {287-290},
	author = {D. Meersman and Paul Scheunders and Dirk Van Dyck},
	editor = {K. Doi}
}
@inproceedings {pscheund1996,
	title = {A genetic approach towards optimal color image quantization},
	booktitle = {Proc. ICIP{\textquoteright}96, IEEE Internat. Conference on Image Processing , III, Lausanne, september 16-19},
	year = {1996},
	pages = {1031-1034},
	author = {Paul Scheunders}
}
@article {pscheund1996,
	title = {A genetic Lloyd-Max image quantization algorithm},
	journal = {Pattern Recognition Letters},
	volume = {17},
	number = {5},
	year = {1996},
	pages = {547-556},
	author = {Paul Scheunders}
}
@inproceedings {mverhoyeavdlindejsijberspscheunddvandyckReyniersKooyWillemsCrasOostra1996,
	title = {High resolution MRI study of the cerebellum of mice as a function of age, in a mouse model for fragile X mental retardation},
	booktitle = {Proceedings of the European Society for Magnetic Resonance in Medicine and Biology},
	volume = {2},
	number = {2},
	year = {1996},
	month = {September},
	pages = {168},
	address = {Prague, Czech Republic},
	author = {Marleen Verhoye and Annemie Van Der Linden and Jan Sijbers and Paul Scheunders and Dirk Van Dyck and E. Reyniers and R.F. Kooy and P. J. Willems and P. Cras and B. A. Oostra}
}
@inproceedings {pscheundHoveslivens1996,
	title = {On the local optimality of image quantizers},
	booktitle = {Proc. ICPR{\textquoteright}96 IEEE Internat. Conference on Pattern Recognition,, D, pp. 664-668, Vienna, august 25-30},
	year = {1996},
	pages = {664-668},
	author = {Paul Scheunders and H. Van Hove and S. Livens}
}
@article {jsijberspscheundBonnetdvandyckeraman1996,
	title = {Quantification and improvement of the signal-to-noise ratio in a magnetic resonance image acquisition procedure},
	journal = {Magnetic Resonance Imaging},
	volume = {14},
	number = {10},
	year = {1996},
	pages = {1157-1163},
	doi = {10.1016/S0730-725X(96)00219-6},
	author = {Jan Sijbers and Paul Scheunders and N. Bonnet and Dirk Van Dyck and Erik Raman}
}
@inproceedings {jsijbersmverhoyepscheundavdlindeAudekerkedvandyckeraman1996,
	title = {Segmentation Scheme for Volume Quantization from 3D Microscopic MR images},
	booktitle = {Proceedings of the Joint Meeting NVvM \& BVM},
	year = {1996},
	month = {December},
	pages = {168-169},
	address = {Gent},
	author = {Jan Sijbers and Marleen Verhoye and Paul Scheunders and Annemie Van Der Linden and Johan Van Audekerke and Dirk Van Dyck and Erik Raman}
}
@inproceedings {jsijbersavdlindepscheundAudekerkeeramandvandyck1996,
	title = {Semi-automatic mouse cerebellum extraction from 3D magnetic resonance data},
	booktitle = {Proceedings of the International Society for Magnetic Resonance in Medicine},
	volume = {3},
	year = {1996},
	month = {April},
	pages = {1585},
	address = {New York, USA},
	author = {Jan Sijbers and Annemie Van Der Linden and Paul Scheunders and Johan Van Audekerke and Erik Raman and Dirk Van Dyck}
}
@article {slivenspscheundgvdwouwedvandyckSmetsWinkelmansBogaerts1996,
	title = {A texture analysis approach to corrosion image classification},
	journal = {Microscopy, Microanalysis, Microstructures},
	volume = {7},
	year = {1996},
	pages = {1-10},
	author = {S. Livens and Paul Scheunders and G. Van de Wouwer and Dirk Van Dyck and H. Smets and J. Winkelmans and W. Bogaerts}
}
@inproceedings {gvdwouwepscheunddvandyckWuytsHeyning1996,
	title = {Voice classification by wavelet transforms and fuzzy interpreted LVQ networks},
	booktitle = {Proceedings International Symposium on Soft Computing and Intelligent Industrial Automation, Reading, UK, March 26-28},
	year = {1996},
	pages = {171-177},
	author = {G. Van de Wouwer and Paul Scheunders and Dirk Van Dyck and Floris L Wuyts and Paul H Van de Heyning}
}
@inproceedings {jsijbersavdlindepscheundAudekerkedvandyckeraman1996,
	title = {Volume quantization of the mouse cerebellum by semi-automatic 3D segmentation of MR images},
	booktitle = {Proceedings of SPIE Medical Imaging},
	volume = {2710},
	year = {1996},
	month = {February},
	pages = {553-560},
	address = {Newport Beach CA, USA},
	author = {Jan Sijbers and Annemie Van Der Linden and Paul Scheunders and Johan Van Audekerke and Dirk Van Dyck and Erik Raman},
	editor = {H. Loew and Kenneth M. Hanson}
}
@inproceedings {gvdwouwepscheunddvandyckBodtWuytsHeyning1996,
	title = {Wavelet-FILVQ classifier for speech analysis},
	booktitle = {Proc. ICPR{\textquoteright}96 Internat. Conference on Pattern Recognition , D, pp. 214-218, Vienna, august 25-30},
	year = {1996},
	pages = {214-218},
	author = {G. Van de Wouwer and Paul Scheunders and Dirk Van Dyck and M. De Bodt and Floris L Wuyts and Paul H Van de Heyning}
}
@article {slivenspscheundgvdwouwedvandyckSmetsWinkelmansBogaerts1995,
	title = {Classification of corrosion images by wavelet signatures and LVQ networks},
	journal = {Lecture Notes in Computer Science},
	volume = {970},
	year = {1995},
	pages = {538-543},
	author = {S. Livens and Paul Scheunders and G. Van de Wouwer and Dirk Van Dyck and H. Smets and J. Winkelmans and W. Bogaerts}
}
@inproceedings {pscheund1995,
	title = {Genetic optimal quantization of gray-level and color images},
	booktitle = {Proceedings ACCV{\textquoteright}95, Second Asian Conference on Computer Vision, Singapore, 5-8 december},
	number = {2},
	year = {1995},
	pages = {94-98},
	author = {Paul Scheunders}
}
@article {pscheundClerckWille1994,
	title = {Computerized image storage and analysis of contracting cardiac myocytes},
	journal = {Journal of Computer Assisted Microscopy},
	volume = {6},
	number = {2},
	year = {1994},
	pages = {77-83},
	author = {Paul Scheunders and N. De Clerck and J. Wille}
}
@inproceedings {jsijberspscheunddvandyckeraman1994,
	title = {Proceedings of the Royal Microscopical Society},
	booktitle = {Optimization of the SNR in NMR images using image sequences},
	volume = {29},
	number = {4},
	year = {1994},
	month = {September},
	pages = {232},
	address = {London, UK},
	author = {Jan Sijbers and Paul Scheunders and Dirk Van Dyck and Erik Raman}
}
@inproceedings {jsijberspscheunddvandyckeraman1994,
	title = {The use of two NMR realizations for determining and improving of the image SNR},
	booktitle = {Proceedings of the Joint Meeting of the Belgian and Dutch Societies for Electron Microscopy},
	year = {1994},
	month = {May},
	pages = {65},
	address = {Arnhem, Papendal, The Netherlands},
	author = {Jan Sijbers and Paul Scheunders and Dirk Van Dyck and Erik Raman}
}
@inproceedings {jsijberseramanAudekerkeMalflietLindenVerhoyeCloeck1993,
	title = {The construction of a gradient set},
	booktitle = {4th Meeting of the FGWO/FRSM Contact Group Biomedical MR},
	year = {1993},
	pages = {5},
	address = {Louvain-la-Neuve, Belgium},
	author = {Jan Sijbers and Erik Raman and Johan Van Audekerke and W. Malfliet and Annemie Van Der Linden and Marleen Verhoye and C. Cloeck}
}
