@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}
}
@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}
}
@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}
}
@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}
}
@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 {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.}
}
@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 {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}
}
@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}
}
@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}
}
@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}
}
@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 {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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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 {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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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 {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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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 {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 {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 {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}
}
@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 {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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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.}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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 {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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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 {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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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 {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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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 {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}
}
@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}
}
@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}
}
@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}
}
@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 {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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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.}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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 {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 {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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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 {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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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 {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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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 {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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
