@article {2470,
	title = {Data-driven characterization of knee structures using non-negative matrix factorization of 3D multi-echo UTE MRI},
	journal = {NMR in Biomedicine},
	year = {In Press},
	author = {C{\'e}line Smekens and Pieter Van Dyck and Patrick S Fuchs and Jan Sijbers and Thomas Janssens and Ben Jeurissen}
}
@mastersthesis {2465,
	title = {Accurate and precise parameter estimation for diffusion magnetic resonance imaging},
	year = {2026},
	month = {January, 2026},
	type = {PhD thesis},
	abstract = {			
Diffusion magnetic resonance imaging (dMRI) offers a unique way of imaging the human brain noninvasively. By carefully controlling various acquisition parameters, the dMRI signal can be used to probe the movement of water molecules inside biological tissues, revealing important information about tissue structure. However, this signal is subject to noise and other undesired electromagnetic effects that reduce the signal-to-noise ratio. Efficient acquisition schemes and robust estimators are required to obtain accurate and precise tissue maps of the human brain. In this thesis, we have aimed our efforts towards improving the accuracy, precision, generalizability and applicability of several dMRI analysis methods. Constrained spherical deconvolution (CSD) is a popular dMRI technique that can be used to infer the local tissue densities and their orientations in the human brain, which consists of cerebrospinal fluid, anisotropic white matter and isotropic gray matter. This is achieved by densely sampling q-space, the space of q-vectors representing the direction and strength of diffusion weighting. After sampling q-space in multiple shells, tissue densities and orientations can be estimated by deconvolving this multi-shell dMRI signal with the respective response functions for white matter, gray matter and cerebrospinal fluid. These response functions are typically represented using spherical harmonics (SH) basis functions. However, when sampling is nonspherical, either due to inhomogeneous gradients or by design such as Cartesian sampling, the estimated tissue maps suffer from biases. To counter this issue, we adopted a compact response function model that accounts for nonspherical sampling. On multi-shell data, our approach provides fiber orientation density functions and tissue densities indistinguishable from those estimated using SH. On Cartesian data, estimates are on par with those obtained from shell-wise data, significantly broadening the range of data sets analyzable using CSD. In addition, inhomogeneous gradients can be accounted for, resulting in more accurate apparent tissue densities and connectivity metrics. Q-space trajectory imaging is a dMRI technique that uses time-varying q-vectors to sensitize the dMRI signal to microscopic variations in heterogeneous tissues. By modelling the dMRI signal with a diffusion tensor distribution (DTD), this approach allows teasing apart variations in diffusivity from microscopic anisotropy, orientation dispersion, and mixtures of multiple isotropic diffusivities. To improve the estimation of the DTD parameters, we propose an efficient acquisition scheme optimized for the most used QTI-derived microstructural parameters. A constrained iteratively reweighted least squares estimator is used to further improve the bias and precision of the DTD parameters.},
	author = {Jan Morez}
}
@mastersthesis {2438,
	title = {Advancement of Defect Detection and Quantifying Measurement Uncertainty in X-Ray CT Inspection},
	volume = {PhD in Sciences: Physics},
	year = {2026},
	type = {PhD thesis},
	author = {Miroslav Yosifov}
}
@mastersthesis {2433,
	title = {Advances in Monte Carlo simulations for the design of X-ray phase contrast imaging systems},
	volume = {PhD in Science: Physics},
	year = {2026},
	type = {PhD thesis},
	author = {Jonathan Sanctorum}
}
@article {2436,
	title = {Combining magnetic resonance imaging and evoked potentials enhances machine learning prediction of multiple sclerosis disability worsening},
	journal = {Frontiers in Immunology},
	volume = {17},
	year = {2026},
	doi = {https://doi.org/10.3389/fimmu.2026.1625837},
	author = {Sofie Aerts and Lorin Werthen-Brabants and Hamza Khan and Diana Giraldo and Edward De Brouwer and Lotte Geys and Veronica Popescu and Jan Sijbers and Henry C. Woodruff and Tom Dhaene and Dirk Deschrijver and Bart Van Wijmeersch and Philippe Lambin and Liesbet M. Peeters}
}
@inproceedings {2442,
	title = {Cycloidal Acquisition for Inline Edge Illumination X-ray Phase Contrast Computed Tomography: A Simulation Study},
	booktitle = {15th Conference on Industrial Computed Tomography (iCT)},
	volume = {31},
	year = {2026},
	month = {February},
	address = {Linz, Austria},
	keywords = {computed tomography, edge illumination, inline acquisition, X-ray phase contrast imaging},
	doi = {10.58286/32604},
	url = {https://doi.org/10.58286/32604},
	author = {Verstraete, J. and Francken, N. and Jan Sijbers and Jan De Beenhouwer}
}
@mastersthesis {2437,
	title = {Edge illumination phase contrast imaging  with continuous motion},
	volume = {PhD in Sciences: Physics},
	year = {2026},
	month = {February, 2026},
	type = {PhD thesis},
	author = {Ben Huyge}
}
@article {2423,
	title = {Framework for optimal design of a multi-source rectangular X-ray cargo scanning system},
	journal = {Radiation Physics and Chemistry},
	volume = {239},
	year = {2026},
	pages = {113332},
	doi = {https://doi.org/10.1016/j.radphyschem.2025.113332 },
	author = {Caroline Bossuyt and Arnold Jan den Dekker and Domenico Iuso and Jan De Beenhouwer and Jan Sijbers}
}
@article {2428,
	title = {From Skin to Skeleton: A Statistical Shape Modelling Approach for Predicting Hand and Foot Bony Geometry},
	journal = {Annals of Biomedical Engineering},
	volume = {54},
	year = {2026},
	pages = {284-302},
	doi = {https://doi.org/10.1007/s10439-025-03882-0},
	author = {Kate Duquesne and Molnar Adris and Roel Huysentruyt and Aline Van Oevelen and Jing Li and Jan Sijbers and Wim Van Paepegem and Emmanuel Audenaert}
}
@inproceedings {2447,
	title = {GEO-ASTRA: a toolbox for non-conventional XCT scanning and reconstruction},
	booktitle = { 15th Conference on Industrial Computed Tomography (iCT), 10 - 13 February 2026, Linz, Austria (iCT 2026), e-Journal of Nondestructive Testing},
	volume = {31 (3)},
	year = {2026},
	doi = {https://doi.org/10.58286/32567},
	author = {Joaquim G. Sanctorum and Bjorn De Samber and Pavel Paramonov and Jan De Beenhouwer and Jan Sijbers}
}
@inproceedings {2443,
	title = {Inline Edge Illumination Computed Tomography: A Simulation Study},
	booktitle = {2026 IEEE 23rd Mediterranean Electrotechnical Conference (MELECON)},
	year = {2026},
	address = {Cairo, Egypt},
	keywords = {edge illumination, Industry 4.0, Inline inspection, Non-destructive testing, X-ray phase contrast computed tomography},
	doi = {https://doi.org/10.1109/MELECON64486.2026.11418832},
	author = {Verstraete, J. and Francken, N. and Jan Sijbers and Jan De Beenhouwer}
}
@article {2432,
	title = {Investigating the Influence of Individual Physical Effects on Dimensional Measurement Deviations Using XCT Simulations},
	journal = {Measurement,},
	volume = {263},
	year = {2026},
	doi = {https://doi.org/10.1016/j.measurement.2025.120168},
	author = {Miroslav Yosifov and Thiago Linhares Fernandes and Filippo Zanini and Simone Carmignato and Jan De Beenhouwer and Jan Sijbers and Johann Kastner and Christoph Heinzl}
}
@article {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}
}
@inproceedings {2448,
	title = {Towards X-ray-based inspection of corrosion in maritime infrastructure},
	booktitle = {15th Conference on Industrial Computed Tomography (iCT), 10 - 13 February 2026, Linz, Austria (iCT 2026) Special Issue of e-Journal of Nondestructive Testing (eJNDT) ISSN 1435-4934 },
	volume = {31(3)},
	year = {2026},
	doi = {https://doi.org/10.58286/32583},
	author = {Joaquim G. Sanctorum and Nathan Bekaert and Wikke Witteveen and Geert Potters and Jan Sijbers and Jan De Beenhouwer}
}
@inproceedings {2401,
	title = {Correcting for focal spot drift in edge illumination X-ray phase contrast imaging},
	booktitle = {14th Conference on Industrial Computed Tomography (iCT), 4 - 7 February 2025, Antwerp, Belgium},
	year = {2025},
	doi = {doi.org/10.58286/30746},
	author = {Nicholas Francken and Jonathan Sanctorum and Ben Huyge and Jan Sijbers and Jan De Beenhouwer}
}
@article {2412,
	title = {DAMMER: Direct Adaptive Multi-resolution MEsh Reconstruction from X-ray measurements},
	journal = {IEEE Transactions on Computational Imaging },
	volume = {11},
	year = {2025},
	pages = {926 - 941},
	doi = {10.1109/TCI.2025.3587408},
	author = {Jannes Merckx and Arnold Jan den Dekker and Jan Sijbers and Jan De Beenhouwer}
}
@article {2351,
	title = {Deep Learning-Enhanced Accelerated 2D TSE and 3D Super-Resolution Dixon TSE for Rapid Comprehensive Knee Joint Assessment},
	journal = {Investigative Radiology},
	volume = {60},
	year = {2025},
	pages = {220-233},
	doi = {10.1097/RLI.0000000000001118},
	author = {C{\'e}line Smekens and Quinten Beirinckx and Frederik Bosmans and Floris Vanhevel and Annemie Snoeckx and Jan Sijbers and Ben Jeurissen and Thomas Janssens and Pieter Van Dyck}
}
@article {2383,
	title = {Degradation Detection in Rice Products via Shape Variations in XCT Simulation-Empowered AI},
	journal = {Journal of Nondestructive Evaluation},
	volume = {44},
	year = {2025},
	doi = {https://doi.org/10.1007/s10921-024-01147-9},
	author = {Miroslav Yosifov and Thomas Lang and Virginia Florian and Stefan Gerth and Jan De Beenhouwer and Jan Sijbers and Johann Kastner and Christoph Heinzl}
}
@inproceedings {2391,
	title = {Dual Domain Swin Transformer based Reconstruction method for Sparse-View Computed Tomography},
	booktitle = {iCT 2025 - 14th Conference on Industrial Computed Tomography (iCT), 4 - 7 February 2025, Antwerp, Belgium},
	year = {2025},
	doi = {doi.org/10.58286/30751},
	author = {Jonas Van der Rauwelaert and Caroline Bossuyt and Jan Sijbers}
}
@mastersthesis {2415,
	title = {Edge illumination X-ray phase contrast imaging: Advancements in simulations and inline acquisitions},
	volume = {PhD in Sciences: Physics},
	year = {2025},
	type = {PhD thesis},
	author = {Nicholas Francken}
}
@article {2417,
	title = {Edge illumination X-ray phase contrast imaging with continuous mask motion},
	journal = {Optics Express},
	volume = {33},
	year = {2025},
	pages = {41271-41286},
	doi = {https://doi.org/10.1364/OE.570476},
	author = {Ben Huyge and Nicholas Francken and Joaquim Sanctorum and Jonathan Sanctorum and Jan De Beenhouwer and Jan Sijbers}
}
@article {2385,
	title = {Efficient X-ray dark field contrast simulations using a condensed history approach},
	journal = {Optics Express},
	volume = {33},
	year = {2025},
	month = {01/2025},
	pages = {3562-3576},
	doi = {10.1364/OE.544864},
	url = {https://opg.optica.org/oe/fulltext.cfm?uri=oe-33-2-3562\&id=567367},
	author = {Nicholas Francken and Jonathan Sanctorum and Ben Huyge and Jan Sijbers and Jan De Beenhouwer}
}
@conference {2452,
	title = {Enhancing AdAPTS: Fast Field Evaporation Simulation with Parametric Local Electrodes for Atom Probe Tomography},
	year = {2025},
	abstract = {Atom probe tomography (APT) is a powerful technique for analyzing the composition of materials at the atomic level. However, no reconstruction method currently fully capitalizes on the spatial resolution potential of APT instrumentation. A model of field evaporation, efficiently and accurately describing ion trajectories, remains essential for reconstruction. To address this, we developed AdAPTS, a simulation tool that improves the efficiency of modeling individual evaporations compared to TAPSim. Both tools, however, lack a local electrode model, which influences ion trajectories. Directly incorporating a local electrode into the finite element mesh hinders simulation speed and memory efficiency. To overcome this, we introduce a local electrode feature in AdAPTS that leverages the superposition principle by projecting a solution of the field emitted by the local electrode onto a mesh of the experimental domain, resulting in constant computational overhead instead of recalculating the total electrostatic field in each iteration. The parametric definition of local electrodes allows for precise modeling of electrode geometries, providing a flexible framework for electrode design and potentially enabling more accurate 3D reconstruction. Future work will focus on integrating additional physical models in AdAPTS to expand its capabilities across diverse material systems.},
	url = {https://ge.iitm.ac.in/apt\&m-2025/schedule.html},
	author = {Julian L{\"u}ken and Claudia Fleischmann and Jan Sijbers and Jan De Beenhouwer}
}
@inproceedings {2450,
	title = {Exact reconstruction for helical cone-beam X-ray CT using the ASTRA toolbox},
	booktitle = {10th International Symposium on Digital Industrial Radiology and Computed Tomography, 1 {\textendash} 3 July 2025 in Paris, France (DIR 2025)},
	volume = {30(8)},
	year = {2025},
	url = {https://www.ndt.net/?id=31452},
	author = {Pavel Paramonov and Joaquim G. Sanctorum and Femke Danckaers and Jan De Beenhouwer and Jan Sijbers}
}
@article {2405,
	title = {Gratings for multiresolution edge illumination X-ray phase contrast imaging: concept and simulation},
	journal = {Optics Express},
	volume = {33},
	year = {2025},
	pages = {17886-17902},
	doi = {10.1364/OE.550252},
	author = {Pieter-Jan Vanthienen and Nicholas Francken and Jonathan Sanctorum and Jan Sijbers and Jan De Beenhouwer}
}
@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}
}
@mastersthesis {2430,
	title = {Improved Diagnosis and Prognosis of Mild Traumatic Brain Injury via Harmonised Diffusion MRI},
	year = {2025},
	type = {PhD thesis},
	author = {Maira Siqueira Pinto}
}
@inproceedings {2400,
	title = {In-depth imaging of in-vitro tissue models using electrical impedance tomography},
	booktitle = {iCT 2025 - 14th Conference on Industrial Computed Tomography},
	volume = {4 - 7 February},
	year = {2025},
	doi = {doi.org/10.58286/30763},
	author = {Jian Deng and Babu Linkoon P Meenaketan and Saeedeh Ebrahimi Takalloo and Yoke Chin Chai and Tom Torfs and Dries Braeken and Jan De Beenhouwer and Jan Sijbers}
}
@article {2426,
	title = {Leveraging Hand-Crafted Radiomics on Multicenter FLAIR MRI for Predicting Disability Worsening in People with Multiple Sclerosis},
	journal = {Front. Neurosci.},
	volume = {19},
	year = {2025},
	doi = {https://doi.org/10.3389/fnins.2025.1610401},
	author = {Hamza Khan and Diana Giraldo and Henry C. Woodruff and Lorin Werthen-Brabants and Shruti Atul Mali and Sina Amirrajab and Edward De Brouwer and Veronica Popescu and Bart Van Wijmeersch and Oliver Gerlach and Jan Sijbers and Liesbet M. Peeters and Philippe Lambin}
}
@article {2399,
	title = {Loss of the Ubiquitin-Associated Domain of sqstm1/p62 in Zebrafish Causes a Phenotype Resembling Paget{\textquoteright}s Disease of Bone},
	journal = {Calcified Tissue International},
	volume = {116},
	year = {2025},
	pages = {1-15},
	author = {Yentl Huybrechts and Rapha{\"e}l De Ridder and Dylan Bergen and Bjorn De Samber and Eveline Boudin and Francesca Tonelli and Dries Knapen and Lucia Vergauwen and Dorien Schepers and Evelien Van Dijck and Qiao Tong and Anja Verhulst and Jan De Beenhouwer and Jan Sijbers and Chrissy Hammond and Antonella Forlino and Geert Mortier and Paul Coucke and P Eckhard Witten and Ronald Young Kwon and Andy Willaert and Gretl Hendrickx and Wim Van Hul}
}
@article {2388,
	title = {Multi-contrast benchmarking of edge illumination Monte Carlo simulations using virtual gratings},
	journal = {Journal of Applied Physics},
	volume = {137},
	year = {2025},
	pages = {104904},
	doi = {10.1063/5.0244152},
	author = {Jonathan Sanctorum and Jan Sijbers and Jan De Beenhouwer}
}
@article {2404,
	title = {A novel Soft Tissue-Integrated Kinematic Solver for skeletal motion: Validation and applications},
	journal = {Computer Methods and Programs in Biomedicine},
	volume = {265},
	year = {2025},
	chapter = {108766},
	doi = {https://doi.org/10.1016/j.cmpb.2025.108766},
	author = {Kate Duquesne and Aline Van Oevelen and Jan Sijbers and Wim Van Paepegem and Emmanuel Audenaert}
}
@article {2407,
	title = {A PHASE-CONTRAST X-RAY IMAGING SYSTEM FOR OBTAINING A DARK-FIELD IMAGE AND A METHOD THEREFOR},
	number = {EP 4 351 425 B1},
	year = {2025},
	author = {Jan De Beenhouwer and Jan Sijbers}
}
@inproceedings {2392,
	title = {Proximal Neural Networks based reconstruction for few-view CT applications},
	booktitle = {14th Conference on Industrial Computed Tomography (iCT), 4 - 7 February 2025, Antwerp, Belgium},
	year = {2025},
	author = {H. Le and Caroline Bossuyt and Julie Escoda and Marius Costin and Jan De Beenhouwer and Jan Sijbers}
}
@article {2384,
	title = {Proximity Adjusted Centroid Mapping for Accurate Detection of Nuclei in Dense 3D Cell Systems},
	journal = {Computers in Biology and Medicine},
	volume = {185},
	year = {2025},
	pages = {109561},
	doi = {https://doi.org/10.1016/j.compbiomed.2024.109561},
	author = {Tim Van De Looverbosch and Sarah De Beuckeleer and Frederik De Smet and Jan Sijbers and Winnok De Vos}
}
@mastersthesis {2416,
	title = {Shaping the future: 3D knee morphology analysis and predictive models for data-driven patient selection},
	volume = {PhD in Medicine and Health Sciences},
	year = {2025},
	type = {PhD thesis},
	author = {Jonas Grammens}
}
@inproceedings {2393,
	title = {Single X-ray Projection Material Decomposition using a Mesh Projector},
	booktitle = {14th Conference on Industrial Computed Tomography (iCT), 4 - 7 February 2025, Antwerp, Belgium (iCT 2025)},
	year = {2025},
	author = {Fleur Linsen and Domenico Iuso and Jan Sijbers}
}
@article {2425,
	title = {Superimposing Synthetic Defects into Real XCT Data and Segmentation-Based Comparison for Advanced Probability of Detection Evaluation},
	journal = { Journal of Nondestructive Evaluation },
	volume = {44},
	year = {2025},
	doi = {https://doi.org/10.1007/s10921-025-01262-1},
	author = {Miroslav Yosifov and Bernhard Fr{\"o}hler and Jan Sijbers and Jan De Beenhouwer and Johann Kastner and Christoph Heinzl}
}
@article {2411,
	title = {SwinIR-based Dual-Domain Reconstruction for Sparse-View Computed Tomography},
	journal = {Journal of Nondestructive Evaluation},
	volume = {44},
	year = {2025},
	doi = {https://doi.org/10.1007/s10921-025-01244-3},
	author = {Jonas Van der Rauwelaert and Caroline Bossuyt and Stijn Verleden and Jan Sijbers}
}
@mastersthesis {2414,
	title = {Towards adaptive multi-resolution edge illumination X-ray phase contrast imaging},
	volume = {PhD in Sciences: Physics},
	year = {2025},
	type = {PhD thesis},
	author = {Pieter-Jan Vanthienen}
}
@inproceedings {2418,
	title = {About rapid phase-wrapping in terahertz phase contrast imaging},
	booktitle = {SPIE},
	volume = {13141},
	year = {2024},
	pages = {69-73},
	abstract = {Terahertz (THz) imaging has emerged as a powerful technique in diverse fields, ranging from medical diagnostics to material characterization. Due to its lower frequency, compared to x-ray-based techniques, THz imaging provides an alternative view into the internal structures of samples composed of low-density materials. Notably, phase contrast imaging is advantageous as it generally exhibits reduced distortion from refraction and reflection losses compared to attenuation contrast. However, in the course of THz phase measurements, a fresh issue arises. Due to the relatively wide beam shape of THz imaging combined with rapid phase wrapping, a new type of artifact emerges, posing a significant hindrance to the accuracy and reliability of the imaging process. This paper explores the intricate interplay between beam shape dynamics and phase-wrapping mechanisms in THz imaging systems, unraveling the source of the artifacts that arise from this phenomenon.},
	doi = {https://doi.org/10.1117/12.3027665},
	url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/13141/131410F/About-rapid-phase-wrapping-in-terahertz-phase-contrast-imaging/10.1117/12.3027665.short},
	author = {Lars-Paul Lumbeeck and Pavel Paramonov and Jan Sijbers and Jan De Beenhouwer}
}
@conference {2379,
	title = {AdAPTS: An Adaptive Atom Probe Tomography Simulation Library},
	volume = {30},
	year = {2024},
	doi = {https://doi.org/10.1093/mam/ozae044.017},
	author = {Julian L{\"u}ken and Claudia Fleischmann and Jan Sijbers and Jan De Beenhouwer}
}
@conference {2451,
	title = {AdAPTS: An Adaptive Atom Probe Tomography Simulation Library},
	year = {2024},
	abstract = {In atom probe tomography (APT), spatial reconstruction enables volumetric insight into a specimen{\textquoteright}s nanostructure. To this day, a fast reconstruction method which utilizes the true potential of APT in terms of resolution does not exist. A model of its effective inverse, the field evaporation, which provides a physically accurate description of the ion trajectories, is a crucial component in reconstruction. The simulation of each individual evaporation, such as in TAPSim, while being able to model some cases accurately, has been time inefficient. Novel methods track surface meshes, achieving better time efficiency, although at the cost of spatial accuracy.

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

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

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

The modular finite element methods library MFEM is utilized to determine the electric field by solving Poisson{\textquoteright}s equation given an electric potential Dirichlet boundary condition at the base of the specimen. Using an Euler-forward scheme with variable time steps, the trajectory starting at each evaporation site is calculated. The trajectory integration is terminated when the trajectory intersects with the detector plane. To conclude, AdAPTS provides an efficient and accurate framework which combines specimen modeling with realistic field evaporation due to voltage pulses using state-of-the-art mesh generation and numerical simulation tools. A thorough quantitative comparison in terms of simulation time of the different simulators will be provided by the time of the conference.},
	url = {https://plan.events.mpg.de/event/261/contributions/1337/},
	author = {Julian L{\"u}ken and Claudia Fleischmann and Jan Sijbers and Jan De Beenhouwer}
}
@article {2343,
	title = {Automated landmark annotation for morphometric analysis of distal femur and proximal tibia},
	journal = {Journal of Imaging},
	volume = {10},
	year = {2024},
	doi = {10.3390/jimaging10040090 },
	author = {Jonas Grammens and Annemieke Van Haver and Imelda Lumban-Gaol and Femke Danckaers and Peter Verdonk and Jan Sijbers}
}
@mastersthesis {2330,
	title = {Automating the planning of transcatheter Mitral Valve Interventions},
	volume = {Engineering Science},
	year = {2024},
	type = {PhD thesis},
	author = {Lopes, Patricia}
}
@article {2323,
	title = {CAD-ASTRA: A versatile and efficient mesh projector for X-ray tomography with the ASTRA-toolbox},
	journal = {Optics Express},
	volume = {32},
	year = {2024},
	pages = {3425-3439},
	doi = {https://doi.org/10.1364/OE.498194},
	author = {Pavel Paramonov and Nicholas Francken and Jens Renders and Domenico Iuso and Tim Elberfeld and Jan De Beenhouwer and Jan Sijbers}
}
@inproceedings {2419,
	title = {Coherent detection terahertz imaging with a continuous wave source},
	booktitle = {SPIE},
	volume = {13141},
	year = {2024},
	pages = {63-68},
	abstract = {The unique properties of terahertz (THz) radiation, coupled with its non-ionizing nature, enable high-contrast imaging of soft materials with less stringent safety precautions than those required for x-ray systems. In this study, we introduce novel methods for THz imaging using a system that employs two photoconductive antennas (PCAs): one as a Continuous-Wave (CW) source and the other as a coherent detector. To enable precise multiband imaging with high frequency resolution, a fiber stretcher for rapid phase modulation was included. This system allows for the registration of two types of imaging contrasts: light attenuation and phase shift. We outline methods for extracting these contrasts and evaluate the system{\textquoteright}s capabilities through experiments involving various materials and frequencies. Furthermore, its application in computed tomography is demonstrated. To the best of our knowledge, this is the first application of a CW setup based on PCAs for non-destructive inspection and imaging of concealed objects.},
	doi = {https://doi.org/10.1117/12.3027653},
	url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/13141/131410E/Coherent-detection-terahertz-imaging-with-a-continuous-wave-source/10.1117/12.3027653.short},
	author = {Lars-Paul Lumbeeck and Pavel Paramonov and Jan Sijbers and Jan De Beenhouwer}
}
@inproceedings {2374,
	title = {Deep image prior for sparse-view reconstruction in static, rectangular multi-source x-ray CT systems for cargo scanning},
	booktitle = {SPIE Developments in X-Ray Tomography XV},
	volume = {13152},
	year = {2024},
	doi = {https://doi.org/10.1117/12.3027668},
	author = {Caroline Bossuyt and Arnold Jan den Dekker and Domenico Iuso and T. Le Hoang and Julie Escoda and Marius Costin and Jan Sijbers and Jan De Beenhouwer}
}
@mastersthesis {2386,
	title = {Development of spoiled gradient echo sequences for MRI relaxometry},
	volume = {PhD in Biomedical Sciences},
	year = {2024},
	type = {PhD thesis},
	author = {Marco Andrea Zampini}
}
@article {2360,
	title = {Direct reconstruction of wet foam from sparse-view, dynamic X-ray CT scans},
	journal = {Advanced Engineering Materials},
	volume = {2400957},
	year = {2024},
	pages = {1-10},
	doi = {https://doi.org/10.1002/adem.202400957},
	author = {Jens Renders and Lucia Mancini and Jan De Beenhouwer and Jan Sijbers}
}
@inproceedings {2376,
	title = {Dynamic flat field correction in edge illumination imaging},
	booktitle = {SPIE Developments in X-Ray Tomography XV},
	volume = {13152},
	year = {2024},
	doi = {https://doi.org/10.1117/12.3028319},
	author = {Didem G{\"o}kbel Kekliko{\u g}lu and Nicholas Francken and Ben Huyge and Ziya Merdan and Jan De Beenhouwer and Jan Sijbers}
}
@article {2329,
	title = {Edge illumination x-ray phase contrast simulations using the CAD-ASTRA toolbox},
	journal = {Optics Express},
	volume = {32},
	year = {2024},
	pages = {10005-10021},
	doi = {10.1364/OE.516138},
	url = {https://opg.optica.org/oe/abstract.cfm?doi=10.1364/OE.516138},
	author = {Nicholas Francken and Jonathan Sanctorum and Pavel Paramonov and Jan Sijbers and Jan De Beenhouwer}
}
@mastersthesis {2413,
	title = {Ensuring Flawlessness in Additive Manufacturing: Advances in X-ray Inspection Techniques for Efficient Defect Detection},
	volume = {PhD in Sciences},
	year = {2024},
	type = {PhD thesis},
	author = {Domenico Iuso}
}
@inproceedings {2375,
	title = {Fast and efficient tetrahedral volume mesh reconstruction with CAD-ASTRA},
	booktitle = {SPIE Developments in X-Ray Tomography XV},
	volume = {13152},
	year = {2024},
	address = {San Diego, USA},
	doi = {https://doi.org/10.1117/12.3027610},
	author = {Jannes Merckx and Arnold Jan den Dekker and Jan De Beenhouwer and Jan Sijbers}
}
@article {2359,
	title = {Foot shape assessment techniques for orthotic and footwear applications: a methodological literature review},
	journal = {Frontiers in Bioengineering and Biotechnology},
	volume = {12},
	year = {2024},
	month = {Sept / 2024},
	pages = {1-15},
	doi = {https://doi.org/10.3389/fbioe.2024.1416499},
	author = {Femke Danckaers and Kristina Stankovi{\'c} and Toon Huysmans and Brian G. Booth and Jan Sijbers}
}
@inproceedings {2350,
	title = {High-resolution tiled X-ray cone-beam CT using the ASTRA toolbox},
	booktitle = {13th Conference on Industrial Computed Tomography (iCT) 2024},
	year = {2024},
	month = {03/2024},
	address = {School of Engineering, Wels Campus, Austria},
	abstract = {Dealing with large and/or elongated objects presents a challenge in micro computed tomography ({\textmu}CT). In conventional CT, the necessity for the X-ray beam to fully cover the scanned object which has to remain well within the detector boundaries is essential to avoid region-of-interest artifacts in the reconstructed images. Consequently, the magnification of the object and thereby the spatial resolution are limited by the physical dimensions and degrees of freedom of the imaging system. A possible way to increase the magnification and thus the resolution of a {\textmu}CT image is to perform a tiled scan of the object. In this paper, we propose an iterative reconstruction approach that yields a high-resolution image from such a tiled CT scan. The approach utilizes the ASTRA toolbox, leveraging its key features: the vector-based definition of projection geometry and the linear tomography operators. Our simulations demonstrate that the proposed approach achieves higher resolution through increased magnification.},
	keywords = {cone beam, iterative reconstruction, micro computed tomography, X-ray tomography},
	isbn = {1435-4934},
	doi = {10.58286/29258 },
	author = {Pavel Paramonov and Joaquim G. Sanctorum and Domenico Iuso and Jan Sijbers and Jan De Beenhouwer}
}
@article {2347,
	title = {Imaging Algorithm and Optimal Measurement Protocol for 3D Impedance Tomography on 2D High Density Micro Electrode Array},
	journal = {IEEE Sensors},
	volume = {24},
	year = {2024},
	pages = {24452-24465},
	doi = {10.1109/JSEN.2024.3417289},
	author = {Jian Deng and Jens Renders and Babu Linkoon P Meenaketan and Saeedeh Ebrahimi Takalloo and Dries Braeken and Jan De Beenhouwer and Jan Sijbers}
}
@mastersthesis {2363,
	title = {Improved 4DCT reconstruction algorithms for the imaging of foam microstructure formation},
	volume = {PhD in Sciences},
	year = {2024},
	type = {PhD thesis},
	author = {Jens Renders}
}
@mastersthesis {2326,
	title = {Improved X-ray CT reconstruction techniques with non-linear imaging models},
	year = {2024},
	type = {PhD thesis},
	author = {Nathana{\"e}l Six}
}
@inproceedings {2468,
	title = {Inline edge illumination through sample mask misalignment: a proof of concept},
	booktitle = {Proceedings of the 6th International Conference on X-ray and Neutron Phase Imaging with Gratings, Shenzhen, China},
	year = {2024},
	abstract = {Edge illumination (EI) is an X-ray phase contrast imaging setup that allows
measuring two additional complimentary contrasts. The setup consists of two absorbing
masks with slit shaped apertures. Typically, one of the masks is stepped during scans,
complicating the application of EI in inline settings. Current solutions exist, but require
either limiting the number of retrieved contrasts or fabricating specialized masks. In this
work, an EI scanning method with purposely misaligned conventional EI masks is
proposed to enable inline EI. Good agreement is found between conventional and inline EI
flatfield parameters, and first inline scan results are presented.},
	keywords = {X-ray phase contrast imaging},
	url = {https://files.sciconf.cn/upload/file/20240415/20240415160406_47435.pdf},
	author = {Nicholas Francken and Jan De Beenhouwer and Jan Sijbers}
}
@article {2348,
	title = {Inline edge illumination X-ray phase contrast imaging through mask misalignment},
	journal = {Optics Express},
	volume = {32},
	year = {2024},
	pages = {32048-32061},
	doi = {10.1364/OE.525730},
	author = {Nicholas Francken and Jonathan Sanctorum and Joaquim Sanctorum and Pieter-Jan Vanthienen and Jan Sijbers and Jan De Beenhouwer}
}
@article {2321,
	title = {Joint multi-contrast CT for edge illumination X-ray phase contrast imaging using split Barzilai-Borwein steps},
	journal = {Optics Express},
	volume = {32},
	year = {2024},
	pages = {1135-1150},
	doi = {https://doi.org/10.1364/OE.502542},
	author = {Nathana{\"e}l Six and Jens Renders and Jan De Beenhouwer and Jan Sijbers}
}
@article {2381,
	title = {Longitudinal Magnetic Resonance Imaging Method},
	number = {WO2024100240A1},
	year = {2024},
	chapter = {WO2024100240A1},
	author = {Jan Sijbers and Arnold Jan den Dekker}
}
@article {2467,
	title = {Mask assembly for edge illumination phase contrast radiation imaging},
	number = {EP2025062978W},
	year = {2024},
	month = {11/2025},
	edition = {G01N23/041 },
	chapter = {WO2025237942A1},
	abstract = {In general, the technology disclosed herein relates object inspection utilizing radiation imaging, more specifically, edge illumination phase contrast X-ray imaging. The apparatus and methods described in the present disclosure can be applied for inspection of various objects in different industrial settings, such as manufacturing, food, agriculture, aerospace, automotive, electronics, healthcare, transport, logistics, security, and others. An aspect of the present invention relates to a mask assembly for radiation phase contrast imaging apparatus, comprising a first mask comprising a plurality of X-ray absorbing element and apertures arranged in a manner that gradually diverges from one portion to an opposite portion of the body, resulting in a variable pitch perpendicular to the optical axis direction; and a mask alignment mechanism configured to adjust a position and/or orientation of the first mask to align the variable pitch of the first mask for phase contrast imaging.},
	author = {Jan De Beenhouwer and Jan Sijbers and Pieter-Jan Vanthienen}
}
@article {2380,
	title = {Methods and systems for determining an anatomical reconstruction of a broken bone},
	year = {2024},
	edition = {WO2024068984A1},
	chapter = {PCT/EP2023/077151},
	author = {Jan Sijbers and Femke Danckaers and Jana Osstyn}
}
@article {2325,
	title = {Model-based super-resolution reconstruction for pseudo-continuous Arterial Spin Labeling},
	journal = {NeuroImage},
	volume = {286},
	year = {2024},
	month = {01/2024},
	pages = {120506},
	abstract = {Arterial spin labeling (ASL) is a promising, non-invasive perfusion magnetic resonance imaging technique for quantifying cerebral blood flow (CBF). Unfortunately, ASL suffers from an inherently low signal-to-noise ratio (SNR) and spatial resolution, undermining its potential. Increasing spatial resolution without significantly sacrificing SNR or scan time represents a critical challenge towards routine clinical use. In this work, we propose a model-based super-resolution reconstruction (SRR) method with joint motion estimation that breaks the traditional SNR/resolution/scan-time trade-off. From a set of differently oriented 2D multi-slice pseudo-continuous ASL images with a low through-plane resolution, 3D-isotropic, high resolution, quantitative CBF maps are estimated using a Bayesian approach. Experiments on both synthetic whole brain phantom data, and on in vivo brain data, show that the proposed SRR Bayesian estimation framework outperforms state-of-the-art ASL quantification.},
	keywords = {Arterial spin labeling, CBF mapping, Model-based reconstruction, Perfusion, Quantitative MRI, super-resolution},
	issn = {1053-8119},
	doi = {10.1016/j.neuroimage.2024.120506},
	author = {Quinten Beirinckx and Piet Bladt and Merlijn C E van der Plas and M.J.P van Osch and Ben Jeurissen and Arnold Jan den Dekker and Jan Sijbers}
}
@inproceedings {2378,
	title = {Multi-resolution gratings for edge illumination x-ray phase contrast imaging: a simulation study},
	booktitle = {Developments in X-Ray Tomography XV},
	volume = {13152},
	year = {2024},
	pages = {1315208},
	publisher = {International Society for Optics and Photonics},
	organization = {International Society for Optics and Photonics},
	keywords = {cone-beam, edge illumination, grating design, magnification, mask, monte-carlo, multi-resolution, Phase contrast},
	doi = {10.1117/12.3025968},
	url = {https://doi.org/10.1117/12.3025968},
	author = {Pieter-Jan Vanthienen and Nicholas Francken and Jan Sijbers and Jan De Beenhouwer}
}
@article {2358,
	title = {PACS: Projection-driven with Adaptive CADs X-ray Scatter compensation for additive manufacturing inspection},
	journal = {Precision Engineering},
	volume = {90},
	year = {2024},
	pages = {108-121},
	doi = {https://doi.org/10.1016/j.precisioneng.2024.08.006},
	author = {Domenico Iuso and Pavel Paramonov and Jan De Beenhouwer and Jan Sijbers}
}
@article {2364,
	title = {Perceptual super-resolution in multiple sclerosis MRI},
	journal = {Frontiers in Neuroscience},
	volume = {18},
	year = {2024},
	doi = {10.3389/fnins.2024.1473132},
	author = {Diana Giraldo and Hamza Khan and Gustavo Pineda and Liang, Zhihua and Alfonso Lozano and Bart Van Mijweersch and Henry C. Woodruff and Philippe Lambin and Romero, Eduardo and Liesbet M. Peeters and Jan Sijbers}
}
@article {2336,
	title = {Practical multi-mesh registration for few-view poly-chromatic X-ray inspection},
	journal = {Journal of Non-destructive Testing},
	volume = {43},
	year = {2024},
	chapter = {63},
	doi = {10.1007/s10921-024-01071-y},
	author = {Domenico Iuso and Pavel Paramonov and Jan De Beenhouwer and Jan Sijbers}
}
@article {2296,
	title = {A preparation pulse for fast steady state approach in Actual Flip angle Imaging},
	journal = {Medical Physics},
	volume = {51},
	year = {2024},
	pages = {306-318},
	doi = {https://doi.org/10.1002/mp.16624},
	author = {Marco Andrea Zampini and Jan Sijbers and Marleen Verhoye and Ruslan Garipov}
}
@conference {2337,
	title = {Reconstruction with rigid motion correction technique in CT imaging: A simulation and application study},
	year = {2024},
	address = {Vietnam Institute for Advanced Study in Mathematics (VIASM), Hanoi, Vietnam},
	author = {Anh-Tuan Nguyen and Jens Renders and Jan Sijbers and Jan De Beenhouwer}
}
@conference {2449,
	title = {Simulating Atom Probe Tomography Using MFEM},
	year = {2024},
	abstract = {In atom probe tomography (APT), spatial reconstruction enables volumetric insight into a specimen{\textquoteright}s nanostructure. To this day, a fast reconstruction method which utilizes the true potential of APT in terms of resolution does not exist. A model of its effective inverse, the field evaporation, which provides a physically accurate description of the ion trajectories, is a crucial component in reconstruction. The simulation of each individual evaporation while has been time inefficient. We introduce AdAPTS, an adaptive atom probe tomography simulation library based on MFEM. AdAPTS is capable of generating accurate detector hit maps of various specimens, efficiently representing and simulating the experimental domain from specimen to detector. Using AdAPTS, we are able to accurately simulate the field evaporation of various specimens, revealing realistic poles and zone lines. },
	url = {https://mfem.org/workshop24/$\#$agenda},
	author = {Julian L{\"u}ken and Claudia Fleischmann and Jan Sijbers and Jan De Beenhouwer}
}
@inproceedings {2466,
	title = {A simulation study of optimized-aperture gratings for edge illumination cone-beam phase contrast x-ray imaging},
	booktitle = {Proceedings of the 6th International Conference on X-ray and Neutron Phase Imaging with Gratings},
	year = {2024},
	month = {04/2024},
	address = {Shenzhen, China},
	abstract = {Edge illumination (EI) is an established x-ray phase-contrast imaging method that relies on gratings to obtain attenuation, phase and dark field contrast. Despite the successful transition from synchrotron to lab sources, the cone-beam geometry of lab systems limits the effectiveness of conventional gratings. The non-parallel incidence of X-rays on planar gratings with equally sized apertures introduces unwanted shadowing effects. In this paper, a grating design with optimized position-dependent apertures is introduced and compared to a conventional grating and a grating with fan-beam shaped gratings bars. Numerical simulations show that this optimized-aperture design must be considered when constructing an EI setup.},
	author = {Pieter-Jan Vanthienen and Jan Sijbers and Jan De Beenhouwer}
}
@inproceedings {2327,
	title = {Statistical shape model guided virtual reduction of displaced distal radius fractures},
	booktitle = {IEEE International Symposium on Biomedical Imaging},
	year = {2024},
	pages = {1-5},
	address = {Athens, Greece},
	doi = {10.1109/ISBI56570.2024.10635672},
	author = {Jana Osstyn and Femke Danckaers and A Verstreken and Annemieke Van Haver and Matthias Vanhees and Jan Sijbers}
}
@article {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}
}
@inproceedings {2373,
	title = {X-ray image reconstruction for continuous acquisition with accelerated rotation},
	booktitle = {SPIE Developments in X-Ray Tomography XV},
	volume = {13152},
	year = {2024},
	doi = {https://doi.org/10.1117/12.3027622},
	author = {Ben Huyge and Jens Renders and Jan De Beenhouwer and Jan Sijbers}
}
@article {2365,
	title = {X-ray image reconstruction for continuous acquisitions with a generalized motion model},
	journal = {Optics Express},
	volume = {32},
	year = {2024},
	pages = {39192-39207},
	doi = {https://doi.org/10.1364/OE.531999},
	author = {Ben Huyge and Jens Renders and Joaquim Sanctorum and Jan De Beenhouwer and Jan Sijbers}
}
@inproceedings {2295,
	title = {Adapting an XCT-scanner to enable edge illumination X-ray phase contrast imaging},
	booktitle = {e-Journal of Nondestructive Testing},
	volume = {28},
	year = {2023},
	issn = {1435-4934},
	doi = {doi.org/10.58286/27755},
	author = {Ben Huyge and Pieter-Jan Vanthienen and Nathana{\"e}l Six and Jan Sijbers and Jan De Beenhouwer}
}
@article {2256,
	title = {ADEPT: Accurate Diffusion EPI with multi-contrast shoTs},
	journal = {Magnetic Resonance in Medicine},
	volume = {89},
	year = {2023},
	pages = {396-410},
	doi = {10.1002/mrm.29398},
	author = {Banafshe Shafieizargar and Ben Jeurissen and Dirk H J Poot and Stefan Klein and Johan Van Audekerke and Verhoye, Marleen and Arnold Jan den Dekker and Jan Sijbers}
}
@inproceedings {2282,
	title = {Automated virtual reduction of displaced distal radius fractures},
	booktitle = {IEEE International Symposium on Biomedical Imaging},
	year = {2023},
	pages = {1-4},
	address = {Cartagena, Colombia},
	doi = {10.1109/ISBI53787.2023.10230399},
	author = {Jana Osstyn and Femke Danckaers and Annemieke Van Haver and Jose Oramas and Matthias Vanhees and Jan Sijbers}
}
@inproceedings {2352,
	title = {Comparison of diffusion kurtosis tensor imaging and multi-tissue CSD for the investigation of group differences in Alzheimer{\textquoteright}s disease},
	booktitle = {International Society of Magnetic Resonance in Medicine (ISMRM)},
	year = {2023},
	address = {Toronto, ON, Canada},
	url = {https://submissions.mirasmart.com/ISMRM2023/Itinerary/PresentationDetail.aspx?evdid=6363},
	author = {Diana Giraldo and Robert E. Smith and Hanne Struyfs and Ellis Niemantsverdriet and Ellen De Roeck and Maria Bjerke and Sebastiaan Engelborghs and Eduardo Romero and Jan Sijbers and Ben Jeurissen}
}
@inproceedings {2287,
	title = {A condensed history approach to x-ray dark field effects in edge illumination phase contrast simulations},
	booktitle = {45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society},
	year = {2023},
	author = {Nicholas Francken and Jonathan Sanctorum and Jens Renders and Pavel Paramonov and Jan Sijbers and Jan De Beenhouwer}
}
@inproceedings {2292,
	title = {The Deep Steerable Convolutional Framelet Network for Suppressing Directional Artifacts in X-ray Tomosynthesis},
	booktitle = {31st European Signal Processing Conference, EUSIPCO},
	year = {2023},
	author = {Luis Filipe Alves Pereira and Jan De Beenhouwer and Jan Sijbers}
}
@inproceedings {2293,
	title = {Defect detectability analysis via Probability of defect detection between traditional and deep learning methods in numerical simulations},
	booktitle = {e-Journal of Nondestructive Testing},
	volume = {28},
	year = {2023},
	issn = {1435-4934 },
	doi = {doi.org/10.58286/27716},
	author = {Miroslav Yosifov and Patrick Weinberger and Michael Reiter and Bernhard Fr{\"o}hler and Jan De Beenhouwer and Jan Sijbers and Johann Kastner and Christoph Heinzl}
}
@inproceedings {2283,
	title = {DELTA-MRI: Direct deformation Estimation from LongiTudinally Acquired k-space data},
	booktitle = {IEEE International Symposium on Biomedical Imaging},
	year = {2023},
	pages = {1-4},
	doi = {10.1109/ISBI53787.2023.10230697},
	author = {Jens Renders and Banafshe Shafieizargar and Marleen Verhoye and Jan De Beenhouwer and Arnold Jan den Dekker and Jan Sijbers}
}
@article {2305,
	title = {Efficient iterative reconstruction with beam shape compensation for THz computed tomography},
	journal = {Applied Optics},
	volume = {62},
	year = {2023},
	month = {April 14, 2023},
	pages = {F31-F40},
	chapter = {F31},
	abstract = {Terahertz (THz) computed tomography is an emerging nondestructive and non-ionizing imaging method. Most THz reconstruction methods rely on the Radon transform, originating from x-ray imaging, in which x rays propagate in straight lines. However, a THz beam has a finite width, and ignoring its shape results in blurred reconstructed images. Moreover, accounting for the THz beam model in a straightforward way in an iterative reconstruction method results in extreme demands in memory and in slow convergence. In this paper, we propose an efficient iterative reconstruction that incorporates the THz beam shape, while avoiding the above disadvantages. Both simulation and real experiments show that our approach results in improved resolution recovery in the reconstructed image. Furthermore, we propose a suitable preconditioner to improve the convergence speed of our reconstruction.},
	keywords = {Beam shaping, computed tomography, Image quality, image reconstruction, THz imaging},
	doi = {https://doi.org/10.1364/AO.482511},
	url = {https://opg.optica.org/viewmedia.cfm?r=1\&rwjcode=ao\&uri=ao-62-17-F31\&seq=0},
	author = {Lars-Paul Lumbeeck and Pavel Paramonov and Jan Sijbers and Jan De Beenhouwer}
}
@inproceedings {2299,
	title = {Enhancing industrial inspection with efficient edge illumination x-ray phase contrast simulations},
	booktitle = {IEEE EUROCON 2023 -20th International Conference on Smart Technologies, Torino, Italy},
	year = {2023},
	author = {Nicholas Francken and Pavel Paramonov and Jan Sijbers and Jan De Beenhouwer}
}
@conference {2300,
	title = {Exploring the Correlation between Disability Status and Brain Volumetric Measurements Using Real-World Retrospective Magnetic Resonance Images in People with Multiple Sclerosis},
	number = {1637},
	year = {2023},
	address = {October 11-13, Milan, Italy},
	abstract = {This study investigated whether retrospective real-world MR image databases can be used to obtain MR-based biomarkers of multiple sclerosis (MS) disability by examining correlations between Expanded Disability Status Scale (EDSS) scores and volumetric measurements from reconstructed T2-weighted fluid-attenuated inversion recovery (FLAIR) in people with MS (PwMS).},
	author = {Hamza Khan and Diana Giraldo and Quinten Beirinckx and Jan Sijbers and Philippe Lambin and Henry C. Woodruff and Liesbet M. Peeters}
}
@article {2264,
	title = {Fast and accurate pose estimation of additive manufactured objects from few X-ray projections},
	journal = {Expert Systems With Applications},
	volume = {213},
	year = {2023},
	pages = {1-10},
	doi = {https://doi.org/10.1016/j.eswa.2022.118866},
	author = {Alice Presenti and Liang, Zhihua and Luis Filipe Alves Pereira and Jan Sijbers and Jan De Beenhouwer}
}
@article {2298,
	title = {Fiber Orientation Estimation from X-ray Dark Field Images of Fiber Reinforced Polymers using Constrained Spherical Deconvolution},
	journal = {Polymers},
	volume = {15},
	year = {2023},
	pages = {2887},
	doi = {https://doi.org/10.3390/polym15132887},
	author = {Ben Huyge and Jonathan Sanctorum and Ben Jeurissen and Jan De Beenhouwer and Jan Sijbers}
}
@inproceedings {2446,
	title = {Fitting a 2D mesh to X-ray measurements},
	booktitle = { 			 17th International Meeting on Fully 3D Image Reconstruction in Radiology and Nuclear Medicine},
	year = {2023},
	address = {Stony Brook},
	author = {Merckx, Jannes and Jan Sijbers and Jan De Beenhouwer}
}
@article {2301,
	title = {Grating designs for cone beam edge illumination X-ray phase contrast imaging: a simulation study},
	journal = {Optics Express},
	volume = {31},
	year = {2023},
	pages = {28051-28064},
	abstract = {Edge illumination is an emerging X-ray phase contrast imaging technique providing attenuation, phase and dark field contrast. Despite the successful transition from synchrotron to lab sources, the cone beam geometry of lab systems limits the effectiveness of using conventional planar gratings. The non-parallel incidence of X-rays introduces shadowing effects, worsening with increasing cone angle. To overcome this limitation, several alternative grating designs can be considered. In this paper, the effectiveness of three alternative designs is compared to conventional gratings using numerical simulations. Improvements in flux and contrast are discussed, taking into account practical considerations concerning the implementation of the designs.},
	doi = {10.1364/OE.495789},
	url = {https://opg.optica.org/oe/fulltext.cfm?uri=oe-31-17-28051\&id=536127},
	author = {Pieter-Jan Vanthienen and Jonathan Sanctorum and Ben Huyge and Nathana{\"e}l Six and Jan Sijbers and Jan De Beenhouwer}
}
@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 {2274,
	title = {Optimal experimental design and estimation for q-space trajectory imaging},
	journal = {Human Brain Mapping},
	volume = {44},
	year = {2023},
	pages = {1793-1809},
	abstract = {Tensor-valued diffusion encoding facilitates data analysis by q-space trajectory imaging. By modeling the diffusion signal of heterogeneous tissues with a diffusion tensor distribution (DTD) and modulating the encoding tensor shape, this novel approach allows disentangling variations in diffusivity from microscopic anisotropy, orientation dispersion, and mixtures of multiple isotropic diffusivities. To facilitate the estimation of the DTD parameters, a parsimonious acquisition scheme coupled with an accurate and precise estimation of the DTD is needed. In this work, we create two precision-optimized acquisition schemes: one that maximizes the precision of the raw DTD parameters, and another that maximizes the precision of the scalar measures derived from the DTD. The improved precision of these schemes compared to a naive sampling scheme is demonstrated in both simulations and real data. Furthermore, we show that the weighted linear least squares (WLLS) estimator that uses the squared reciprocal of the noisy signal as weights can be biased, whereas the iteratively WLLS estimator with the squared reciprocal of the predicted signal as weights outperforms the conventional unweighted linear LS and nonlinear LS estimators in terms of accuracy and precision. Finally, we show that the use of appropriate constraints can considerably increase the precision of the estimator with only a limited decrease in accuracy.},
	doi = {10.1002/hbm.26175},
	author = {Jan Morez and Szczepankiewicz, Filip and Arnold Jan den Dekker and Floris Vanhevel and Jan Sijbers and Ben Jeurissen}
}
@mastersthesis {2290,
	title = {Parametric Fiber Analysis for Glass Fiber-reinforced Composite Tomographic Images},
	year = {2023},
	type = {PhD thesis},
	author = {Tim Elberfeld}
}
@article {2270,
	title = {Prolonged microgravity induces reversible and persistent changes on human cerebral connectivity},
	journal = {Communications Biology},
	volume = {6},
	year = {2023},
	doi = {https://doi.org/10.1038/s42003-022-04382-w},
	author = {Steven Jillings and Ekaterina V. Pechenkova and Elena Tomilovskaya and Ilya Rukavishnikov and Ben Jeurissen and Angelique Van Ombergen and Nosikova, Inna and Alena Rumshiskaya and Liudmila Litvinova and Annen, Jitka and Chloe De Laet and Catho Schoenmaekers and Jan Sijbers and Petrovichev, Victor and Stefan Sunaert and Paul M Parizel and Valentin Sinitsyn and zu Eulenburg, P and Steven S L Laureys and Athena Demertzi and Floris L Wuyts}
}
@article {2306,
	title = {A reconstruction method for atom probe tomography},
	number = {18017575},
	year = {2023},
	edition = {US20230307207A1},
	chapter = {US Patent App. 18/017,575, 2023},
	author = {Jan Sijbers and Jan De Beenhouwer and Yu-Ting Ling and Wilfried Vandervorst}
}
@inproceedings {2279,
	title = {Region-based motion-compensated iterative reconstruction technique for dynamic computed tomography},
	booktitle = {IEEE International Symposium on Biomedical Imaging (ISBI)},
	year = {2023},
	address = {Cartagena de Indias, Colombia},
	doi = {https://doi.org/10.1109/ISBI53787.2023.10230608},
	author = {Anh-Tuan Nguyen and Jens Renders and Jan Sijbers and Jan De Beenhouwer}
}
@inproceedings {2281,
	title = {Sparse-view Medical Tomosynthesis via Mixed Scale Dense Convolutional Framelet Networks},
	booktitle = {IEEE International Symposium on Biomedical Imaging (ISBI)},
	year = {2023},
	pages = {880-884},
	doi = { https://doi.org/10.23919/EUSIPCO58844.2023.10289781},
	author = {Luis Filipe Alves Pereira and Jan De Beenhouwer and Jan Sijbers}
}
@inproceedings {2288,
	title = {Super-resolution reconstruction of multi-slice T2-w FLAIR MRI improves Multiple Sclerosis lesion segmentation},
	booktitle = {45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society},
	year = {2023},
	address = {Sydney, Australia},
	abstract = {Due to acquisition time constraints, T2-w FLAIR MRI of Multiple Sclerosis (MS) patients is often acquired with multi-slice 2D protocols with a low through-plane resolution rather than with high-resolution 3D protocols. Automated lesion segmentation on such low-resolution (LR) images, however, performs poorly and leads to inaccurate lesion volume estimates. Super-resolution reconstruction (SRR) methods can then be used to obtain a high-resolution (HR) image from multiple LR images to serve as input for lesion segmentation. In this work, we evaluate the effect on MS lesion segmentation of three SRR approaches: one based on interpolation, a state-of-the-art self-supervised CNN-based strategy, and a recently proposed model-based SRR method. These SRR strategies were applied to LR acquisitions simulated from 3D T2-w FLAIR MRI of MS patients. Each SRR method was evaluated in terms of image reconstruction quality and posterior lesion segmentation performance. When compared to segmentation on LR images, the three considered SRR strategies demonstrate improved lesion segmentation. Furthermore, in some scenarios, SRR achieves a similar segmentation performance compared to segmentation of HR images.},
	author = {Diana Giraldo and Quinten Beirinckx and Arnold Jan den Dekker and Ben Jeurissen and Jan Sijbers}
}
@mastersthesis {2277,
	title = {Surface and image-based registration methods with statistical modeling for biomedical applications},
	volume = {PhD},
	year = {2023},
	type = {PhD thesis},
	author = {Jeroen Van Houtte}
}
@article {2289,
	title = {Systematic review of reconstruction techniques for accelerated quantitative MRI},
	journal = {Magnetic Resonance in Medicine},
	volume = {90},
	year = {2023},
	pages = {1172-1208},
	doi = {https://doi.org/10.1002/mrm.29721},
	author = {Banafshe Shafieizargar and Riwaj Byanju and Jan Sijbers and Stefan Klein and Arnold Jan den Dekker and Dirk H J Poot}
}
@article {2255,
	title = {Tabu-DART: A dynamic update strategy for efficient discrete algebraic reconstruction},
	journal = {The Visual Computer},
	volume = {39},
	year = {2023},
	pages = {4671{\textendash}4683},
	doi = {10.1007/s00371-022-02616-w},
	author = {Daniel Frenkel and Nathana{\"e}l Six and Jan De Beenhouwer and Jan Sijbers}
}
@inproceedings {2294,
	title = {Toward denoising of 3D CT scans with few data},
	booktitle = {e-Journal of Nondestructive Testing},
	volume = {28},
	year = {2023},
	issn = {1435-4934},
	doi = {doi.org/10.58286/27741},
	author = {Liang, Zhihua and Anneke Van Heteren and Jan Sijbers and Jan De Beenhouwer}
}
@article {2286,
	title = {Use of support vector machines approach via ComBat harmonized diffusion tensor imaging for the diagnosis and prognosis of mild traumatic brain injury: a CENTER-TBI study},
	journal = {Journal of Neurotrauma},
	volume = {40},
	year = {2023},
	pages = {1317-1338},
	doi = {https://doi.org/10.1089/neu.2022.0365},
	author = {Maira Siqueira Pinto and Stefan Winzeck and Evgenios N. Kornaropoulos and Sophie Richter and Roberto Paolella and Marta M. Correia and Ben Glocker and Guy Williams and Anne Vik and Jussi Posti and Asta Kristine H{\r a}berg and Jonas Stenberg and Pieter-Jan Guns and Arnold Jan den Dekker and David K. Menon and Jan Sijbers and Pieter Van Dyck and Virginia F. J. Newcombe}
}
@article {2219,
	title = {Using particle systems for mitral valve segmentation from 3D transoesophageal echocardiography (3D TOE) - a proof of concept},
	journal = {Computer Methods in Biomechanics and Biomedical Engineering: Imaging \& Visualization},
	volume = {11},
	year = {2023},
	pages = {112-120},
	doi = {10.1080/21681163.2022.2058416},
	url = {https://doi.org/10.1080/21681163.2022.2058416},
	author = {Patricia Lopes and Paul Van Herck and Eefje Verhoelst and Roel Wirix-Speetjens and Jan Sijbers and Johan Bosmans and Jos Vander Sloten}
}
@article {2204,
	title = {3D total variation denoising in X-CT imaging applied to pore extraction in additively manufactured parts},
	journal = {Measurement Science and Technology},
	volume = {33},
	year = {2022},
	pages = {1-12},
	doi = {10.1088/1361-6501/ac459a},
	author = {Rob Heylen and Aditi Thanki and Dries Verhees and Domenico Iuso and Jan De Beenhouwer and Jan Sijbers and Ann Witvrouw and Han Haitjema and Abdellatif Bey-Temsamani}
}
@mastersthesis {2280,
	title = {3D X-ray radiography-based inspection of manufactured objects},
	volume = {PhD},
	year = {2022},
	type = {PhD thesis},
	author = {Alice Presenti}
}
@inproceedings {2239,
	title = {An accelerated motion-compensated iterative reconstruction technique for dynamic computed tomography},
	booktitle = {Proc. SPIE 12242, Developments in X-Ray Tomography XIV, 122421F},
	year = {2022},
	address = {San Diego, CA, United States},
	doi = {https://doi.org/10.1117/12.2631570},
	author = {Anh-Tuan Nguyen and Jens Renders and Jeroen Soete and Martine Wevers and Jan Sijbers and Jan De Beenhouwer}
}
@conference {2268,
	title = {Adaptive triangular mesh for phase contrast imaging},
	year = {2022},
	url = {https://ictms2022.sciencesconf.org/385325/document},
	author = {Jannes Merckx and Bart van Lith and Jan Sijbers and Jan De Beenhouwer}
}
@mastersthesis {2278,
	title = {Advances in biplanar X-ray imaging: calibration and 2D/3D registration},
	volume = {PhD},
	year = {2022},
	type = {PhD thesis},
	author = {Van Nguyen}
}
@inproceedings {2262,
	title = {Alternative grating designs for cone-beam edge illumination X-ray phase contrast imaging},
	booktitle = { Proc. SPIE 12242, Developments in X-Ray Tomography XIV},
	volume = {12242},
	year = {2022},
	month = {10/2022},
	pages = {122420Z},
	publisher = {SPIE},
	organization = {SPIE},
	address = {San Diego, USA},
	doi = {10.1117/12.2632301},
	author = {Pieter-Jan Vanthienen and Jonathan Sanctorum and Ben Huyge and Nathana{\"e}l Six and Jan Sijbers and Jan De Beenhouwer}
}
@inproceedings {2192,
	title = {Analytic derivatives of scaling motion-compensated projection operators for dynamic computed tomography},
	booktitle = {e-Journal of Nondestructive Testing},
	volume = { 27 (3)},
	year = {2022},
	pages = {1-5},
	doi = {https://doi.org/10.58286/26588},
	url = {https://www.ndt.net/search/docs.php3?id=26588},
	author = {Anh-Tuan Nguyen and Jens Renders and Jeroen Soete and Martine Wevers and Jan Sijbers and Jan De Beenhouwer}
}
@inproceedings {2237,
	title = {Augmenting a conventional X-ray scanner with edge illumination based phase contrast imaging: how to design the gratings?},
	booktitle = {Proc. SPIE 12242, Developments in X-Ray Tomography XIV},
	year = {2022},
	month = {10/2022},
	pages = {1224218},
	publisher = {SPIE},
	organization = {SPIE},
	address = {San Diego, USA},
	doi = {10.1117/12.2633455},
	author = {Jonathan Sanctorum and Nathana{\"e}l Six and Jan Sijbers and Jan De Beenhouwer}
}
@article {2273,
	title = {Automated Mitral Valve Assessment for Transcatheter Mitral Valve Replacement (TMVR) Planning},
	journal = {Frontiers in Bioengineering and Biotechnology},
	volume = {16},
	year = {2022},
	pages = {1-13},
	doi = {https://doi.org/10.3389/fbioe.2022.1033713},
	author = {Lopes, Patricia and Paul Van Herck and J. Ooms and N. Van Mieghem and Wirix-Speetjens, Roel and Jan Sijbers and Vander Sloten, Jos and Bosmans, Johan}
}
@article {2236,
	title = {Automatic anomaly detection from X-ray images based on autoencoder},
	journal = {Nondestructive Testing and Evaluation},
	volume = {37},
	year = {2022},
	doi = {10.1080/10589759.2022.2074415},
	author = {Alice Presenti and Zhihua Liang and Luis Filipe Alves Pereira and Jan Sijbers and Jan De Beenhouwer}
}
@article {2257,
	title = {Automatic landmark detection and mapping for 2D/3D registration with BoneNet},
	journal = {Frontiers Veterinary Science},
	year = {2022},
	doi = {https://doi.org/10.3389/fvets.2022.923449},
	author = {Van Nguyen and Luis Filipe Alves Pereira and Liang, Zhihua and Falk Mielke and Jeroen Van Houtte and Jan Sijbers and Jan De Beenhouwer}
}
@article {2212,
	title = {A Bottom-Up Volume Reconstruction Method for Atom Probe Tomography},
	journal = {Microscopy and Microanalysis},
	volume = {28},
	year = {2022},
	pages = {1-14},
	doi = {10.1017/S1431927621012836},
	author = {Yu-Ting Ling and Siegfried Cools and Janusz Bogdanowicz and Claudia Fleischmann and Jan De Beenhouwer and Jan Sijbers and Wilfried Vandervorst}
}
@article {2216,
	title = {Brain Connectometry Changes in Space Travelers After Long-Duration Spaceflight},
	journal = {Front. Neural Circuits},
	volume = {16},
	year = {2022},
	doi = {https://doi.org/10.3389/fncir.2022.815838},
	author = {Andrei Doroshin and Steven Jillings and Ben Jeurissen and Elena Tomilovskaya and Ekaterina V. Pechenkova and Nosikova, Inna and Alena Rumshiskaya and Liudmila Litvinova and Ilya Rukavishnikov and Chloe De Laet and Catho Schoenmaekers and Jan Sijbers and Steven S L Laureys and Petrovichev, Victor and Angelique Van Ombergen and Jitka Annen and Stefan Sunaert and Paul M Parizel and Valentin Sinitsyn and zu Eulenburg, Peter and Karol Osipowicz and Floris L Wuyts}
}
@inproceedings {2194,
	title = {CNN-based pose estimation from a single X-ray projection for 3D inspection of manufactured objects},
	booktitle = {11th Conference on Industrial Computed Tomography},
	year = {2022},
	author = {Alice Presenti and Zhihua Liang and Luis Filipe Alves Pereira and Jan Sijbers and Jan De Beenhouwer}
}
@mastersthesis {2218,
	title = {Computational anatomy strategies for characterization of brain patterns associated with Alzheimer{\textquoteright}s disease},
	volume = {PhD in Science},
	year = {2022},
	type = {PhD thesis},
	author = {Diana Giraldo}
}
@article {2276,
	title = {cuPARE: Parametric Reconstruction of Curved Fibres from Glass fibre-reinforced Composites},
	journal = {Nondestructive Testing and Evaluation},
	year = {2022},
	doi = {10.1080/10589759.2022.2155647},
	author = {Tim Elberfeld and Bernhard Fr{\"o}hler and Christoph Heinzl and Jan Sijbers and Jan De Beenhouwer}
}
@article {2213,
	title = {Deep learning-based 2D/3D registration of an atlas to biplanar X-ray images},
	journal = {International Journal of Computer Assisted Radiology and Surgery},
	volume = {309},
	year = {2022},
	pages = {1333{\textendash}1342},
	doi = {10.1007/s11548-022-02586-3},
	author = {Jeroen Van Houtte and Emmanuel Audenaert and Guoyan Zheng and Jan Sijbers}
}
@article {2193,
	title = {Discrete Terahertz tomography: a simulation study},
	journal = {e-Journal of Nondestructive Testing},
	volume = {27},
	year = {2022},
	issn = {1435-4934},
	doi = {doi.org/10.58286/26608},
	author = {Jana Christopher and Lars-Paul Lumbeeck and Pavel Paramonov and Jan De Beenhouwer and Jan Sijbers}
}
@conference {2269,
	title = {Edge Illumination Phase Contrast Simulations Using the OptiX GPU Ray Tracing Engine},
	year = {2022},
	author = {Nicholas Francken and Pavel Paramonov and Jan Sijbers and Jan De Beenhouwer}
}
@article {2215,
	title = {The effect of prolonged Spaceflight on Cerebrospinal Fluid and Perivascular Spaces of Astronauts and Cosmonauts},
	journal = {PNAS},
	volume = {119},
	year = {2022},
	chapter = {e2120439119},
	doi = {https://doi.org/10.1073/pnas.2120439119},
	author = {Giuseppe Barisano and Farshid Sepehrband and Heather R. Collins and Steven Jillings and Ben Jeurissen and Andrew J. Taylor and Catho Schoenmaekers and Chloe De Laet and Ilya Rukavishnikov and Inna Nosikova and Alena Rumshiskaya and Jitka Annen and Jan Sijbers and Steven S L Laureys and Angelique Van Ombergen and Petrovichev, Victor and Valentin Sinitsyn and Ekaterina V. Pechenkova and Alexey Grishin and Peter zu Eulenburg and Meng Law and Stefan Sunaert and Paul M. Parizel and Elena Tomilovskaya and Donna Roberts and Floris L Wuyts}
}
@inproceedings {2240,
	title = {Efficient X-ray projection of triangular meshes based on ray tracing and rasterization},
	booktitle = {SPIE Optical Engineering: Developments in X-Ray Tomography XIV },
	volume = {12242},
	year = {2022},
	pages = {122420W },
	doi = {https://doi.org/10.1117/12.2633448},
	author = {Pavel Paramonov and Jens Renders and Tim Elberfeld and Jan De Beenhouwer and Jan Sijbers}
}
@inproceedings {2243,
	title = {Evaluation of deeply supervised neural networks for 3D pore segmentation in additive manufacturing},
	booktitle = {SPIE Optical Engineering: Developments in X-Ray Tomography XIV },
	volume = {12242},
	year = {2022},
	pages = {122421K},
	doi = {https://doi.org/10.1117/12.2633318},
	author = {Domenico Iuso and Soumick Chatterjee and Rob Heylen and Sven Cornelissen and Jan De Beenhouwer and Jan Sijbers}
}
@inproceedings {2272,
	title = {Fiber orientation estimation by constrained spherical deconvolution of the anisotropic edge illumination x-ray dark field signal},
	booktitle = {SPIE: Developments in X-Ray Tomography XIV},
	volume = {12242},
	year = {2022},
	pages = {122420V },
	doi = {ttps://doi.org/10.1117/12.2633482},
	author = {Ben Huyge and Ben Jeurissen and Jan De Beenhouwer and Jan Sijbers}
}
@article {2235,
	title = {Improved diffusion parameter estimation by incorporating T2 relaxation properties into the DKI-FWE model},
	journal = {NeuroImage},
	volume = {256},
	year = {2022},
	pages = {119219},
	doi = {https://doi.org/10.1016/j.neuroimage.2022.119219},
	author = {Vincenzo Anania and Quinten Collier and Jelle Veraart and Annemieke Eline Buikema and Floris Vanhevel and Thibo Billiet and Ben Jeurissen and Arnold Jan den Dekker and Jan Sijbers}
}
@article {2234,
	title = {Inline nondestructive internal disorder detection in pear fruit using explainable deep anomaly detection on X-ray images},
	journal = {Computers and Electronics in Agriculture},
	volume = {197},
	year = {2022},
	pages = {1-14},
	doi = {https://doi.org/10.1016/j.compag.2022.106962},
	author = {Tim Van De Looverbosch and Jiaqi, He and Astrid Tempelaere and Klaas Kelchtermans and Pieter Verboven and Tinne Tuytelaars and Jan Sijbers and Bart Nicolai}
}
@article {2265,
	title = {Investigating tissue-specific abnormalities in Alzheimer{\textquoteright}s disease with multi-shell diffusion MRI},
	journal = {Journal of Alzheimer{\textquoteright}s Disease},
	volume = {90},
	year = {2022},
	pages = {1771-1791},
	doi = {10.3233/JAD-220551},
	author = {Diana Giraldo and Robert Elton Smith and Hanna Struyfs and Ellis Niemantsverdriet and Ellen De Roeck and Maria Bjerke and Sebastiaan Engelborghs and Romero, Eduardo and Jan Sijbers and Ben Jeurissen}
}
@inproceedings {2242,
	title = {Joint reconstruction of attenuation, refraction and dark field X-ray phase contrasts using split Barzilai-Borwein steps},
	booktitle = {SPIE Optical Engineering: Developments in X-Ray Tomography XIV },
	volume = {12242},
	year = {2022},
	pages = {122420O},
	doi = {https://doi.org/10.1117/12.2633587},
	author = {Nathana{\"e}l Six and Jens Renders and Jan De Beenhouwer and Jan Sijbers}
}
@article {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}
}
@conference {2233,
	title = {Optimal acquisition settings for simultaneous diffusion kurtosis, free water fraction and T2 estimation},
	year = {2022},
	author = {Vincenzo Anania and Ben Jeurissen and Jan Morez and Annemieke Eline Buikema and Thibo Billiet and Jan Sijbers and Arnold Jan den Dekker}
}
@inproceedings {2241,
	title = {Optimization of a multi-source rectangular X-ray CT geometry for inline inspection},
	booktitle = {SPIE Optical Engineering: Developments in X-Ray Tomography XIV },
	volume = {12242},
	year = {2022},
	pages = {1224219 },
	doi = {https://doi.org/10.1117/12.2633523},
	author = {Caroline Bossuyt and Jan De Beenhouwer and Jan Sijbers}
}
@article {2246,
	title = {Probability of Detection applied to X-ray inspection using numerical simulations},
	journal = {Nondestructive Testing and Evaluation},
	volume = {37},
	year = {2022},
	pages = {536-551},
	doi = {10.1080/10589759.2022.2071892},
	author = {Miroslav Yosifov and M. Reiter and S. Heupl and C. Gusenbauer and Bernhard Fr{\"o}hler and R. Fernandez- Gutierrez and Jan De Beenhouwer and Jan Sijbers and Johann Kastner and Christoph Heinzl}
}
@mastersthesis {2271,
	title = {Quantitative assessment of 3D foot shape using statistical shape analysis},
	volume = {PhD in Sciences},
	year = {2022},
	month = {10/2022},
	type = {PhD thesis},
	author = {Kristina Stankovi{\'c}}
}
@conference {2217,
	title = {Sparse view rectangular X-ray CT for cargo inspection using the ASTRA toolbox},
	year = {2022},
	author = {Caroline Bossuyt and Jan De Beenhouwer and Jan Sijbers}
}
@article {2249,
	title = {The sqstm1tmΔUBA zebrafish model, a proof-of-concept in vivo model for Paget{\textquoteright}s disease of bone?},
	journal = {Bone Reports},
	volume = {16},
	year = {2022},
	pages = {75-76},
	doi = {10.1016/j.bonr.2022.101483},
	author = {Yentl Huybrechts and Rapha{\"e}l De Ridder and Bjorn De Samber and Eveline Boudin and Francesca Tonelli and Dries Knapen and Dorien Schepers and Jan De Beenhouwer and Jan Sijbers and Antonella Forlino and Paul Coucke and P. Eckhard Witten and Ronald Kwon and Andy Willaert and Gretl Hendrickx and Wim Van Hul}
}
@article {2275,
	title = {To shift or to rotate? Comparison of acquisition strategies for multi-slice super-resolution magnetic resonance imaging},
	journal = {Frontiers in Neuroscience},
	year = {2022},
	pages = {1-18},
	doi = {https://doi.org/10.3389/fnins.2022.1044510},
	author = {Michele Nicastro and Ben Jeurissen and Quinten Beirinckx and C{\'e}line Smekens and Dirk H J Poot and Jan Sijbers and Arnold Jan den Dekker}
}
@article {2263,
	title = {Virtual grating approach for Monte Carlo simulations of edge illumination-based x-ray phase contrast imaging},
	journal = {Optics Express},
	volume = {31},
	year = {2022},
	pages = {38695-38708},
	doi = {https://doi.org/10.1364/OE.472145},
	author = {Jonathan Sanctorum and Jan Sijbers and Jan De Beenhouwer}
}
@inproceedings {2176,
	title = {2D/3D registration with a statistical deformation model prior using deep learning},
	booktitle = {the IEEE International Conference on Biomedical and Health Informatics (BHI{\textquoteright}21) },
	year = {2021},
	pages = {1-4},
	doi = {10.1109/BHI50953.2021.9508540},
	author = {Jeroen Van Houtte and Xiaoru Gao and Jan Sijbers and Guoyan Zheng}
}
@conference {2170,
	title = {3D atomic resolution tomography from iDPC-STEM images using multiple atom model prior},
	year = {2021},
	author = {Shiva Hosseinnejad and E. G. T. Bosch and H. Kohr and I. Lazi{\'c} and V. Zharinov and E. Franken and Jan Sijbers and Jan De Beenhouwer}
}
@inproceedings {2186,
	title = {3D THz Tomography Incorporating the Beam Shape},
	booktitle = {2021 OSA Imaging and Applied Optics Congress},
	year = {2021},
	doi = {10.1364/AIS.2021.JTu5A.36},
	author = {Lars-Paul Lumbeeck and Pavel Paramonov and Jan Sijbers and Jan De Beenhouwer}
}
@conference {2184,
	title = {Accelerated multi-shot diffusion weighted imaging with joint estimation of diffusion and phase parameters},
	volume = {34},
	year = {2021},
	pages = {S57-S58},
	doi = {10.1007/s10334-021-00947-8},
	author = {Banafshe Shafieizargar and Ben Jeurissen and Dirk H J Poot and Johan Van Audekerke and Marleen Verhoye and Arnold Jan den Dekker and Jan Sijbers}
}
@article {2079,
	title = {Accelerating in vivo fast spin echo high angular resolution diffusion imaging with an isotropic resolution in mice through compressed sensing},
	journal = {Magnetic Resonance in Medicine},
	volume = {85},
	year = {2021},
	pages = {1397-1413},
	doi = {https://doi.org/10.1002/mrm.28520},
	author = {Maarten Naeyaert and Jan Aelterman and Vladimir Golkov and Daniel Cremers and Aleksandra Pizurica and Jan Sijbers and Marleen Verhoye}
}
@article {2144,
	title = {Adjoint image warping using multivariate splines with application to 4D-CT},
	journal = {Medical Physics},
	volume = {48},
	year = {2021},
	pages = {6362-6374},
	doi = {10.1002/mp.14765},
	author = {Jens Renders and Jan Sijbers and Jan De Beenhouwer}
}
@inproceedings {2098,
	title = {Analysis of flat fields in edge illumination phase contrast imaging},
	booktitle = {2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)},
	year = {2021},
	pages = {1310-1313},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Nice, France},
	doi = {10.1109/ISBI48211.2021.9433849},
	url = {https://ieeexplore.ieee.org/document/9433849},
	author = {Ben Huyge and Jonathan Sanctorum and Nathana{\"e}l Six and Jan De Beenhouwer and Jan Sijbers}
}
@inproceedings {2172,
	title = {CAD-based scatter compensation for polychromatic reconstruction of additive manufactured parts},
	booktitle = {IEEE ICIP},
	year = {2021},
	pages = {2948-2952},
	doi = {10.1109/ICIP42928.2021.9506536},
	author = {Domenico Iuso and Ehsan Nazemi and Nathana{\"e}l Six and Bjorn De Samber and Jan De Beenhouwer and Jan Sijbers}
}
@inproceedings {2201,
	title = {CNN-based Pose Estimation of Manufactured Objects During Inline X-ray Inspection},
	booktitle = {2021 IEEE 6th International Forum on Research and Technology for Society and Industry (RTSI)},
	year = {2021},
	abstract = {X-ray Computed Tomography (CT) is a nondestructive technique widely used for inspection of manufactured objects generated from a reference computer-aided-design (CAD) representation. In a conventional CT inspection framework, a volumetric reconstruction is computed from a large number of X-ray projections of the object. Afterwards, a surface is extracted, aligned and compared to the CAD model. For an accurate comparison, a high-resolution reconstruction is needed, requiring hundreds of projections, making this procedure not suitable for real-time inspection. In contrast to CT-based inspection, radiograph-based inspection only requires a few radiographs that then can be compared with simulated projections from the reference CAD model. For an effective comparison, however, an accurate 3D pose estimation of the object and consequent alignment between the measured object and the reference model are crucial. In this paper, we present an inline projection-based 3D pose estimation framework using convolutional neural networks (CNNs). Through realistic simulation experiments, we show that, with only two projections, estimation of the pose of the object is possible at the resolution of the acquisition system.},
	author = {Alice Presenti and Zhihua Liang and Luis Filipe Alves Pereira and Jan Sijbers and Jan De Beenhouwer}
}
@inproceedings {2159,
	title = {Comparison of MR acquisition strategies for super-resolution reconstruction using the Bayesian Mean Squared Error},
	booktitle = { International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine},
	year = {2021},
	abstract = {In multi-slice super-resolution reconstruction (MS-SRR), a high resolution image, referred to as SRR image, is estimated from a series of multi-slice images with a low through-plane resolution. This work proposes a framework based on the Bayesian mean squared error of the Maximum A Posteriori estimator of a SRR image to compare the accuracy and precision of two commonly adopted MR acquisition strategies in MS-SRR. The first strategy consists in acquiring a set of multi-slice
images, where each image is shifted in the through-plane direction by a different, sub-pixel distance. The latter consists in acquiring a set of multi-slice images, where each image is rotated around the frequency or phase-encoding axis by a different rotation angle. Results show that MS-SRR based on rotated multi-slice images outperforms MS-SRR based on shifted multi-slice images in terms of accuracy, precision and mean squared error of the reconstructed image.},
	author = {Michele Nicastro and Ben Jeurissen and Quinten Beirinckx and C{\'e}line Smekens and Dirk H J Poot and Jan Sijbers and Arnold Jan den Dekker}
}
@inproceedings {2099,
	title = {Dark field sensitivity in single mask edge illumination lung imaging},
	booktitle = {2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)},
	year = {2021},
	pages = {775-778},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Nice, France},
	doi = {10.1109/ISBI48211.2021.9434024},
	url = {https://ieeexplore.ieee.org/document/9434024},
	author = {Jonathan Sanctorum and Jan Sijbers and Jan De Beenhouwer}
}
@conference {2180,
	title = {Decoding Multiple Sclerosis EDSS disability scores from MRI using Deep Learning},
	volume = {34},
	year = {2021},
	pages = {S57-S58},
	doi = {10.1007/s10334-021-00947-8},
	author = {Roberto Paolella and Ezequiel de la Rosa and Diana M. Sima and Dominique Dive and Francoise Durand-Dubief and Dominique Sappey-Marinier and Ben Jeurissen and Jan Sijbers and Thibo Billiet}
}
@inproceedings {2158,
	title = {A Deep Convolutional Framelet Network based on Tight Steerable Wavelet: application to sparse-view medical tomosynthesis},
	booktitle = { International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine},
	year = {2021},
	author = {Luis Filipe Alves Pereira and Vincent Van Nieuwenhove and Jan De Beenhouwer and Jan Sijbers}
}
@article {2049,
	title = {Diffusion Tensor Imaging of the Anterior Cruciate Ligament Following Primary Repair with Internal Bracing: a Longitudinal Study},
	journal = {Journal of Orthopaedic Research },
	volume = {39},
	year = {2021},
	pages = {1318{\textendash}1330},
	doi = {10.1002/jor.24684},
	author = {Pieter Van Dyck and Martijn Froeling and Christiaan H.W. Heusdens and Jan Sijbers and Annemie Ribbens and Thibo Billiet}
}
@article {2171,
	title = {Dynamic few-view X-ray imaging for inspection of CAD-based objects},
	journal = {Expert Systems with Applications},
	volume = {180},
	year = {2021},
	pages = {115012},
	keywords = {CAD, Dynamic acquisition, Few-view inspection, Pose estimation, Visibility angles},
	issn = {0957-4174},
	doi = {https://doi.org/10.1016/j.eswa.2021.115012},
	url = {https://www.sciencedirect.com/science/article/pii/S095741742100453X},
	author = {Alice Presenti and Jan Sijbers and Jan De Beenhouwer}
}
@conference {2183,
	title = {EPIFANI for ultrafast B1-corrected T1 and PD mapping},
	volume = {34},
	year = {2021},
	pages = {S59-S60},
	doi = {10.1007/s10334-021-00947-8},
	author = {Marco Andrea Zampini and Jan Sijbers and Marleen Verhoye and Ruslan Garipov}
}
@article {2146,
	title = {EquiSim: An open-source articulatable statistical model of the equine distal limb},
	journal = {Frontiers in Veterinary Science },
	volume = {8},
	year = {2021},
	doi = {10.3389/fvets.2021.623318},
	author = {Jeroen Van Houtte and Filip Vandenberghe and Guoyan Zheng and Toon Huysmans and Jan Sijbers}
}
@article {2188,
	title = {Extended imaging volume in cone-beam x-ray tomography using the weighted simultaneous iterative reconstruction technique},
	journal = {Physics in Medicine and Biology},
	volume = {66},
	year = {2021},
	chapter = {165008},
	doi = {10.1088/1361-6560/ac16bc},
	author = {Joaquim Sanctorum and Sam Van Wassenbergh and Van Nguyen and Jan De Beenhouwer and Jan Sijbers and Joris J. J. Dirckx}
}
@conference {2147,
	title = {Faster and better HARDI using FSE and holistic reconstruction},
	year = {2021},
	author = {Maarten Naeyaert and Vladimir Golkov and Daniel Cremers and Jan Sijbers and Marleen Verhoye}
}
@article {2092,
	title = {FleXCT: a Flexible X-ray CT scanner with 10 degrees of freedom},
	journal = {Optics Express},
	volume = {29},
	year = {2021},
	pages = {3438-3457},
	doi = {https://doi.org/10.1364/OE.409982},
	author = {Bjorn De Samber and Jens Renders and Tim Elberfeld and Yves Maris and Jonathan Sanctorum and Nathana{\"e}l Six and Liang, Zhihua and Jan De Beenhouwer and Jan Sijbers}
}
@article {2142,
	title = {Fracture patterns in midshaft clavicle fractures},
	journal = {Acta Orthop Belg },
	volume = {87},
	year = {2021},
	pages = {501-507},
	doi = {https://doi.org/10.52628/87.3.16},
	author = {Van Tongel, Alexander and Lieven De Wilde and Yasunori Shimamura and Jan Sijbers and Toon Huysmans}
}
@article {2203,
	title = {Gauss-Newton-Krylov for Reconstruction of Polychromatic X-ray CT Images},
	journal = {IEEE Transactions on Computational Imaging},
	volume = {7},
	year = {2021},
	pages = {1304-1313},
	doi = {10.1109/TCI.2021.3133226},
	author = {Nathana{\"e}l Six and Jens Renders and Jan Sijbers and Jan De Beenhouwer}
}
@article {2179,
	title = {On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: chronicles of the MEMENTO challenge},
	journal = {NeuroImage},
	volume = {240},
	year = {2021},
	doi = {https://doi.org/10.1016/j.neuroimage.2021.118367},
	author = {Alberto De Luca and Andrada Ianus and Alexander Leemans and Marco Palombo and Noam Shemesh and Hui Zhang and Daniel C Alexander and Markus Nilsson and Martijn Froeling and Geert-Jan Biessels and Mauro Zucchelli and Matteo Frigo and Enes Albay and Sara Sedlar and Abib Alimi and Samuel Deslauriers-Gauthier and Rachid Deriche and Rutger Fick and Maryam Afzali and Tomasz Pieciak and Fabian Bogusz and Santiago Aja-Fernandez and Evren Ozarslan and Derek K. Jones and Haoze Chen and Mingwu Jin and Zhijie Zhang and Fenxiang Wang and Vishwesh Nath and Prasanna Parvathaneni and Jan Morez and Jan Sijbers and Ben Jeurissen and Shreyas and Fadnavis and Stefan Endres and Ariel Rokem and Eleftherios Garyfallidis and Irina Sanchez and Vesna Prchkovska and Paulo Rodrigues and Bennet A. Landman and Kurt G Schilling}
}
@article {2148,
	title = {Geometry Calibration of a Modular Stereo Cone-Beam X-ray CT System},
	journal = {Journal of Imaging},
	volume = {7},
	year = {2021},
	pages = {1-12},
	doi = {https://doi.org/10.3390/jimaging7030054},
	author = {Van Nguyen and Joaquim Sanctorum and Sam Van Wassenbergh and Joris J. J. Dirckx and Jan Sijbers and Jan De Beenhouwer}
}
@inproceedings {2200,
	title = {Graphical User Interface for Joint Space Width Assessment by Optical Marker Tracking},
	booktitle = {4th International Conference on Bio-engineering for Smart Technologies},
	year = {2021},
	author = {Jeroen Van Houtte and Jan Sijbers and Guoyan Zheng}
}
@conference {2205,
	title = {High-resolution T2* mapping of the knee based on UTE Spiral VIBE MRI},
	volume = {34},
	year = {2021},
	pages = {S53-S54},
	author = {C{\'e}line Smekens and Quinten Beirinckx and Floris Vanhevel and Pieter Van Dyck and Arnold Jan den Dekker and Jan Sijbers and Thomas Janssens and Ben Jeurissen}
}
@article {2096,
	title = {Joint Deblurring and Denoising of THz Time-Domain Images},
	journal = {IEEE Access},
	volume = {9},
	year = {2021},
	pages = {162-176},
	doi = {10.1109/ACCESS.2020.3045605},
	author = {Marina Ljubenovi{\'c} and Lina Zhuang and Jan De Beenhouwer and Jan Sijbers}
}
@inbook {2197,
	title = {Just enough physics},
	booktitle = {Computed Tomography: Algorithms, Insight, and Just Enough Theory },
	volume = {4},
	year = {2021},
	publisher = {SIAM},
	organization = {SIAM},
	isbn = {978-1-611976-66-3},
	issn = {978-1-611976-66-3},
	doi = {https://doi.org/10.1137/1.9781611976670},
	author = {J S Jorgensen and Jan Sijbers}
}
@inproceedings {2198,
	title = {Mesh-based reconstruction of dynamic foam images using X-ray CT},
	booktitle = {International Conference on 3D Vision (3DV2021)},
	year = {2021},
	pages = {1312-1320},
	doi = {10.1109/3DV53792.2021.00138},
	author = {Jens Renders and Jan De Beenhouwer and Jan Sijbers}
}
@article {2163,
	title = {Monte-Carlo-Based Estimation of the X-ray Energy Spectrum for CT Artifact Reduction},
	journal = {Applied Sciences},
	volume = {11},
	year = {2021},
	chapter = {3145},
	doi = {https://doi.org/10.3390/app11073145},
	author = {Ehsan Nazemi and Nathana{\"e}l Six and Domenico Iuso and Bjorn De Samber and Jan Sijbers and Jan De Beenhouwer}
}
@conference {2178,
	title = {Motion compensating X-ray micro-CT of diamonds in a processing stage},
	year = {2021},
	month = {September},
	author = {Jens Renders and Anh-Tuan Nguyen and Jan De Beenhouwer and Jan Sijbers}
}
@inproceedings {2189,
	title = {Multi-contrast multi-shot EPI for accelerated diffusion MRI},
	booktitle = {43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society},
	year = {2021},
	pages = {3869-3872},
	doi = {10.1109/EMBC46164.2021.9630069},
	author = {Banafshe Shafieizargar and Ben Jeurissen and Dirk H J Poot and Arnold Jan den Dekker and Jan Sijbers}
}
@article {2164,
	title = {Non-destructive internal disorder detection of Conference pears by semantic segmentation of X-ray CT scans using deep learning},
	journal = {Expert Systems with Applications},
	volume = {176},
	year = {2021},
	pages = {1-12},
	doi = {https://doi.org/10.1016/j.eswa.2021.114925},
	author = {Tim Van De Looverbosch and Ellen Raeymaekers and Pieter Verboven and Jan Sijbers and Bart Nicolai}
}
@conference {2182,
	title = {Optimal experimental design for the T2-weighted diffusion kurtosis imaging free water elimination model},
	volume = {34},
	year = {2021},
	pages = {S54-S55},
	doi = {https://doi.org/10.1007/s10334-021-00947-8},
	author = {Vincenzo Anania and Ben Jeurissen and Jan Morez and Annemieke Eline Buikema and Thibo Billiet and Jan Sijbers and Arnold Jan den Dekker}
}
@conference {2174,
	title = {Outcome prediction in Mild Traumatic Brain Injury patients using conventional and diffusion MRI via Support Vector Machine: A CENTER-TBI study},
	year = {2021},
	author = {Maira Siqueira Pinto and Stefan Winzeck and Marta M. Correia and Evgenios N. Kornaropoulos and David K. Menon and Ben Glocker and Arnold Jan den Dekker and Jan Sijbers and Pieter-Jan Guns and Pieter Van Dyck and Virginia F. J. Newcombe}
}
@conference {2185,
	title = {Outcome prediction of mild traumatic brain injury using support vector machine based on longitudinal MRdiffusion imaging from CENTER-TBI},
	volume = {34},
	year = {2021},
	pages = {S54},
	doi = {10.1007/s10334-021-00947-8},
	author = {Maira Siqueira Pinto and Stefan Winzeck and S. Richter and Marta M. Correia and Evgenios N. Kornaropoulos and David K. Menon and Ben Glocker and Pieter-Jan Guns and Arnold Jan den Dekker and Jan Sijbers and Virginia F. J. Newcombe and Pieter Van Dyck}
}
@article {2095,
	title = {Outlier detection for foot complaint diagnosis: modeling confounding factors using metric learning},
	journal = {IEEE Intelligent Systems},
	volume = {36},
	year = {2021},
	pages = {41-49},
	doi = {10.1109/MIS.2020.3046431},
	author = {Brian G. Booth and No{\"e}l L.W. Keijsers and Jan Sijbers}
}
@article {2071,
	title = {Projection-angle-dependent distortion correction in high-speed image-intensifier-based x-ray computed tomography},
	journal = {Measurement Science and Technology},
	volume = {32},
	year = {2021},
	pages = {1-11},
	doi = {10.1088/1361-6501/abb33e},
	author = {Joaquim Sanctorum and Sam Van Wassenbergh and Van Nguyen and Jan De Beenhouwer and Jan Sijbers and Joris J. J. Dirckx}
}
@article {2196,
	title = {Quantification of cognitive impairment to characterize heterogeneity of patients at risk of developing Alzheimer{\textquoteright}s disease dementia},
	journal = {Alzheimer{\textquoteright}s \& Dementia: Diagnosis, Assessment \& Disease Monitoring},
	volume = {13},
	year = {2021},
	pages = {e12237},
	doi = {https://doi.org/10.1002/dad2.12237},
	author = {Diana Giraldo and Jan Sijbers and Romero, Eduardo}
}
@conference {2173,
	title = {RAMSES: Relaxation Alternate Mapping of Spoiled Echo Signals sequence for simultaneous accurate T1 and T2* mapping},
	year = {2021},
	month = {May 2021},
	author = {Marco Andrea Zampini and Jan Sijbers and Marleen Verhoye and Ruslan Garipov}
}
@article {2190,
	title = {Recurrent Inference Machines as inverse problem solvers for MR relaxometry},
	journal = {Medical Image Analysis},
	volume = {74},
	year = {2021},
	pages = {1-11},
	doi = {https://doi.org/10.1016/j.media.2021.102220},
	author = {Emanoel Ribeiro Sabidussi and Stefan Klein and Matthan Caan and Shabab Bazrafkan and Arnold Jan den Dekker and Jan Sijbers and Wiro J Niessen and Dirk H J Poot}
}
@inproceedings {2169,
	title = {Recurrent Inference Machines as Inverse Problem Solvers for MR Relaxometry},
	booktitle = { MIDL 2021 - Medical Imaging with Deep Learning},
	year = {2021},
	author = {Emanoel Ribeiro Sabidussi and Matthan Caan and Shabab Bazrafkan and Arnold Jan den Dekker and Jan Sijbers and Wiro J Niessen and Dirk H J Poot}
}
@conference {2181,
	title = {Rotated or shifted sets of multi-slice MR images for super-resolution reconstruction? A Bayesian answer},
	volume = {34},
	year = {2021},
	pages = {S56-S57},
	doi = {10.1007/s10334-021-00947-8},
	author = {Michele Nicastro and Ben Jeurissen and Quinten Beirinckx and C{\'e}line Smekens and Dirk H J Poot and Jan Sijbers and Arnold Jan den Dekker}
}
@inproceedings {2100,
	title = {Statistical shape and pose model of the forearm for custom splint design},
	booktitle = {IEEE International Symposium on Biomedical Imaging (ISBI)},
	year = {2021},
	pages = {1669-1672},
	doi = {10.1109/ISBI48211.2021.9434067},
	author = {Femke Danckaers and Jeroen Van Houtte and Brian G Booth and Frederik Verstreken and Jan Sijbers}
}
@mastersthesis {2145,
	title = {Strategies for efficient acquisition and reconstruction of structural and quantitative MRI},
	volume = {PhD in Biomedical Sciences},
	year = {2021},
	type = {PhD thesis},
	author = {Maarten Naeyaert}
}
@inproceedings {2187,
	title = {A study of terahertz beam simulation with ray tracing for computed tomography},
	booktitle = {2021 OSA Imaging and Applied Optics Congress},
	year = {2021},
	doi = {10.1364/AIS.2021.JTu5A.37},
	author = {Pavel Paramonov and Lars-Paul Lumbeeck and Jan Sijbers and Jan De Beenhouwer}
}
@conference {2160,
	title = {Super-resolution T2* mapping of the knee using UTE Spiral VIBE MRI},
	year = {2021},
	pages = {3920},
	abstract = {T2* mapping using ultrashort echo time (UTE) MRI allows for quantitative evaluation of collagen-rich knee structures with short mean transverse relaxation times. However, acquisitions with low through-plane resolution are commonly used to obtain T2* maps within reasonable scan times, affecting the accuracy of the estimations because of partial volume effects. In this work, model-based super-resolution reconstructions based on UTE Spiral VIBE MRI were performed to obtain high-resolution T2* maps of knee structures within a reasonable scan time. The obtained T2* maps are comparable to maps generated with direct 3D UTE Spiral VIBE acquisitions while requiring approximately 25\% less scan time.},
	author = {C{\'e}line Smekens and Quinten Beirinckx and Floris Vanhevel and Pieter Van Dyck and Arnold Jan den Dekker and Jan Sijbers and Thomas Janssens and Ben Jeurissen}
}
@inproceedings {2157,
	title = {Tabu-DART: a dynamic update strategy for the Discrete Algebraic Reconstruction Technique based on Tabu-search},
	booktitle = { International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine},
	year = {2021},
	author = {Daniel Frenkel and Jan De Beenhouwer and Jan Sijbers}
}
@article {2087,
	title = {To Recurse or not to Recurse A Low Dose CT Study},
	journal = {Progress in Artificial Intelligence},
	volume = {10},
	year = {2021},
	pages = {65{\textendash}81},
	doi = {https://doi.org/10.1007/s13748-020-00224-0},
	author = {Shabab Bazrafkan and Vincent Van Nieuwenhove and Joris Soons and Jan De Beenhouwer and Jan Sijbers}
}
@mastersthesis {2069,
	title = {Accurate and precise perfusion parameter estimation in pseudo-continuous arterial spin labeling MRI},
	volume = {PhD in Sciences/Physics},
	year = {2020},
	type = {PhD thesis},
	author = {Piet Bladt}
}
@inproceedings {2053,
	title = {Accurate terahertz simulation with ray tracing incorporating beam shape and refraction},
	booktitle = {IEEE ICIP },
	year = {2020},
	pages = {3035-3039},
	doi = {10.1109/ICIP40778.2020.9190937},
	author = {Pavel Paramonov and Lars-Paul Lumbeeck and Jan De Beenhouwer and Jan Sijbers}
}
@inproceedings {1979,
	title = {An adaptive probability map for the Discrete Algebraic Reconstruction Technique},
	booktitle = {10th Conference on Industrial Computed Tomography (ICT 2020)},
	year = {2020},
	pages = {1-6},
	author = {Daniel Frenkel and Jan De Beenhouwer and Jan Sijbers}
}
@inproceedings {2028,
	title = {Adjoint pairs of image warping operators for motion modeling in 4D-CT},
	booktitle = {6th International Conference on Image Formation in X-Ray Computed Tomography},
	year = {2020},
	author = {Jens Renders and Jan Sijbers and Jan De Beenhouwer}
}
@article {2057,
	title = {Analysis and Comparison of Algorithms for the Tomographic Reconstruction of Curved Fibres},
	journal = {Nondestructive Testing and Evaluation},
	volume = {35},
	year = {2020},
	pages = {328-341},
	doi = {https://doi.org/10.1080/10589759.2020.1774583},
	author = {Bernhard Fr{\"o}hler and Tim Elberfeld and Torsten M{\"o}ller and Hans-Christian Hege and Jan De Beenhouwer and Jan Sijbers and Johann Kastner and Christoph Heinzl}
}
@inproceedings {2027,
	title = {BeadNet: a network for automated spherical marker detection in radiographs for geometry calibration},
	booktitle = {6th International Conference on Image Formation in X-Ray Computed Tomography},
	year = {2020},
	pages = {518-521},
	author = {Van Nguyen and Jan De Beenhouwer and Shabab Bazrafkan and A-T. Hoang and Sam Van Wassenbergh and Jan Sijbers}
}
@inproceedings {2034,
	title = {CNN-based Deblurring of Terahertz Images},
	booktitle = {Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP)},
	volume = {4},
	year = {2020},
	pages = {323-330},
	doi = {10.5220/0008973103230330},
	author = {Marina Ljubenovi{\'c} and Shabab Bazrafkan and Jan De Beenhouwer and Jan Sijbers}
}
@inproceedings {1951,
	title = {A Comparative Study Between Three Measurement Methods to Predict 3D Body Dimensions Using Shape Modelling},
	booktitle = {Advances in Additive Manufacturing, Modeling Systems and 3D Prototyping},
	volume = {975},
	year = {2020},
	pages = {464{\textendash}470},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {In times of online shopping, it is a challenge to select the right size of the desired clothing without fitting it before ordering. Therefore, this study describes three techniques to predict 3D upper body dimensions. The first method used basic personal parameters (gender, age, weight and length), the second technique used also the shoulder width and the last method used a 3D Styku scan to add extra input parameters. The accuracy of the three prediction methods was compared against hand measurements for 17 upper body dimensions of 37 subjects. The Intraclass Correlation Coefficient increases with 11.2\% for the Styku method compared to the other methods. For chest, hip and waist measurements, the basic method is reliable to predict 3D body dimensions and indicate the right size from an existing collection. For more accurate upper body dimensions as needed for producing custom made clothing, a 3D Styku scan can supply the desired input.},
	isbn = {978-3-030-20216-3},
	doi = {10.1007/978-3-030-20216-3_43},
	author = {Peeters, Thomas and Vleugels, Jochen and Stijn Verwulgen and Femke Danckaers and Toon Huysmans and Jan Sijbers and De Bruyne, Guido},
	editor = {Di Nicolantonio, Massimo and Rossi, Emilio and Alexander, Thomas}
}
@article {2080,
	title = {Constrained spherical deconvolution of non-spherically sampled diffusion MRI data},
	journal = {Human Brain Mapping},
	volume = {42},
	year = {2020},
	pages = {521{\textendash}538},
	doi = {10.1002/hbm.25241},
	author = {Jan Morez and Jan Sijbers and Floris Vanhevel and Ben Jeurissen}
}
@article {1956,
	title = {The costs and benefits of estimating T1 of tissue alongside cerebral blood flow and arterial transit time in pseudo-continuous arterial spin labeling},
	journal = {NMR in Biomedicine},
	volume = {33},
	year = {2020},
	pages = {1-17},
	doi = {https://doi.org/10.1002/nbm.4182},
	author = {Piet Bladt and Arnold Jan den Dekker and Clement, Patricia and Eric Achten and Jan Sijbers}
}
@inproceedings {1977,
	title = {Deep learning-based 2D-3D sample pose estimation for X-ray 3DCT},
	booktitle = {10th Conference on Industrial Computed Tomography (ICT 2020)},
	year = {2020},
	author = {Alice Presenti and Shabab Bazrafkan and Jan Sijbers and Jan De Beenhouwer}
}
@conference {Jillings2020-nb,
	title = {Diffusion MRI reveals macro- and microstructural changes in cosmonauts{\textquoteright} brains after long-duration spaceflight},
	year = {2020},
	pages = {4531},
	author = {Steven Jillings and Angelique Van Ombergen and Tomilovskaya, E and Rumshiskaya, A and Litvinova, L and Nosikova, I and Pechenkova, E and Rukavishnikov, I and Kozlovskaya, I and Stefan Sunaert and Paul M Parizel and Valentin Sinitsyn and Petrovichev, V and Laureys, S and zu Eulenburg, P and Jan Sijbers and Floris L Wuyts and Ben Jeurissen}
}
@article {2064,
	title = {Diffusion tensor imaging of the anterior cruciate ligament graft following reconstruction: a longitudinal study},
	journal = {European Radiology},
	volume = {34},
	year = {2020},
	pages = {6673{\textendash}6684},
	doi = {10.1007/s00330-020-07051-w},
	author = {Pieter Van Dyck and Thibo Billiet and Damien Desbuquoit and Peter Verdonk and Christiaan H. Heusdens and Ella Roelant and Jan Sijbers and Martijn Froeling}
}
@article {2011,
	title = {The effect of nasal shape on the thermal conditioning of inhaled air: Using clinical tomographic data to build a large-scale statistical shape model},
	journal = {Computers in Biology and Medicine},
	volume = {117},
	year = {2020},
	pages = {1-13},
	doi = {https://doi.org/10.1016/j.compbiomed.2020.103600},
	author = {Keustermans, William and Toon Huysmans and Bert Schmelzer and Jan Sijbers and Joris J. J. Dirckx}
}
@inproceedings {2072,
	title = {Extreme Sparse X-ray Computed Laminography Via Convolutional Neural Networks},
	booktitle = {ICTAI 2020},
	year = {2020},
	author = {Luis Filipe Alves Pereira and Jan De Beenhouwer and Johann Kastner and Jan Sijbers}
}
@article {2037,
	title = {Harmonisation of Brain Diffusion MRI: Concepts and Methods},
	journal = {Frontiers in Neuroscience },
	volume = {14},
	year = {2020},
	month = {03/2020},
	pages = {1-17},
	doi = {https://doi.org/10.3389/fnins.2020.00396},
	author = {Maira Siqueira Pinto and Roberto Paolella and Thibo Billiet and Pieter Van Dyck and Pieter-Jan Guns and Ben Jeurissen and Annemie Ribbens and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {2021,
	title = {Improved voxel-wise quantification of diffusion and kurtosis metrics in the presence of noise and intensity outliers},
	year = {2020},
	url = {https://www.ismrm-benelux.org/wp-content/uploads/2020/01/Proceedings2020.pdf},
	author = {Vincenzo Anania and Thibo Billiet and Ben Jeurissen and Annemie Ribbens and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {2024,
	title = {Joint estimation of phase and diffusion tensor parameters from multi-shot k-q-space data: a proof of concept},
	volume = {28},
	year = {2020},
	abstract = {To address the issue of phase induced artifacts in multi-shot diffusion weighted imaging, we propose a model-based framework which enables the joint estimation of diffusion and phase parameters
directly from the multi-shot k-q-space. In a simulation study, we show that using this framework, diffusion parameters can be estimated more accurately and precisely than with the conventional
method (image reconstruction followed by voxel-wise model fitting) that ignores phase differences.},
	keywords = {Diffusion MRI, MRI, Phase estimation},
	author = {Banafshe Shafieizargar and Ben Jeurissen and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {2025,
	title = {Joint estimation of phase and diffusion tensor parameters from multi-shot k-q-space data: a proof of concept},
	volume = {12},
	year = {2020},
	abstract = {To address the issue of phase induced artifacts in multi-shot diffusion weighted imaging, we propose a model-based framework which enables the joint estimation of diffusion and phase parameters
directly from the multi-shot k-q-space. In a simulation study, we show that using this framework, diffusion parameters can be estimated more accurately and precisely than with the conventional
method (image reconstruction followed by voxel-wise model fitting) that ignores phase differences.},
	keywords = {Diffusion MRI, MRI, Phase estimation},
	author = {Banafshe Shafieizargar and Ben Jeurissen and Arnold Jan den Dekker and Jan Sijbers}
}
@article {1971,
	title = {Joint Maximum Likelihood estimation of motion and T1 parameters from magnetic resonance images in a super-resolution framework: a simulation study},
	journal = {Fundamenta Informaticae},
	volume = {172},
	year = {2020},
	pages = {105{\textendash}128},
	abstract = {Magnetic resonance imaging (MRI) based T1 mapping allows spatially resolved quantification of the tissue-dependent spin-lattice relaxation time constant T1, which is a potential biomarker of various neurodegenerative diseases, including Multiple Sclerosis, Alzheimer disease, and Parkinson{\textquoteright}s disease. In conventional T1 MR relaxometry, a quantitative T1 map is obtained from a series of T1-weighted MR images. Acquiring such a series, however, is time consuming. This has sparked the development of more efficient T1 mapping methods, one of which is a super-resolution reconstruction (SRR) framework in which a set of low resolution (LR) T1-weighted images is acquired and from which a high resolution (HR) T1 map is directly estimated. In this paper, the SRR T1 mapping framework is augmented with motion estimation. That is, motion between the acquisition of the LR T1-weighted images is modeled and the motion parameters are estimated simultaneously with the T1 parameters. Based on Monte Carlo simulation experiments, we show that such an integrated motion/relaxometry estimation approach yields more accurate T1 maps compared to a previously reported SRR based T1 mapping approach.},
	doi = {10.3233/FI-2020-1896},
	author = {Quinten Beirinckx and Gabriel Ramos-Llord{\'e}n and Ben Jeurissen and Dirk H J Poot and Paul M Parizel and Marleen Verhoye and Jan Sijbers and Arnold Jan den Dekker}
}
@article {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 {2074,
	title = {Macro- and microstructural changes in cosmonauts{\textquoteright} brains after long-duration spaceflight},
	journal = {Science Advances},
	volume = {6},
	year = {2020},
	month = {Apr-09-2020},
	pages = {eaaz9488},
	abstract = {Long-duration spaceflight causes widespread physiological changes, although its effect on brain structure remains poorly understood. In this work, we acquired diffusion magnetic resonance imaging to investigate alterations of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) compositions in each voxel, before, shortly after, and 7 months after long-duration spaceflight. We found increased WM in the cerebellum after spaceflight, providing the first clear evidence of sensorimotor neuroplasticity. At the region of interest level, this increase persisted 7 months after return to Earth. We also observe a widespread redistribution of CSF, with concomitant changes in the voxel fractions of adjacent GM. We show that these GM changes are the result of morphological changes rather than net tissue loss, which remained unclear from previous studies. Our study provides evidence of spaceflight-induced neuroplasticity to adapt motor strategies in space and evidence of fluid shift{\textendash}induced mechanical changes in the brain.},
	doi = {10.1126/sciadv.aaz9488},
	url = {https://advances.sciencemag.org/content/6/36/eaaz9488},
	author = {Steven Jillings and Angelique Van Ombergen and Tomilovskaya, Elena and Rumshiskaya, Alena and Litvinova, Liudmila and Nosikova, Inna and Pechenkova, Ekaterina and Rukavishnikov, Ilya and Kozlovskaya, Inessa B. and Manko, Olga and Danilichev, Sergey and Stefan Sunaert and Paul M Parizel and Valentin Sinitsyn and Petrovichev, Victor and Laureys, Steven and zu Eulenburg, Peter and Jan Sijbers and Floris L Wuyts and Ben Jeurissen}
}
@inproceedings {2133,
	title = {Newton-Krylov Methods For Polychromatic X-Ray CT},
	booktitle = {2020 IEEE International Conference on Image Processing (ICIP)},
	year = {2020},
	pages = {3045-3049},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Abu Dhabi},
	keywords = {Attenuation, computed tomography, image reconstruction, Jacobian matrices, Linear programming, Mathematical model, Structural beams},
	isbn = {978-1-7281-6396-3},
	doi = {10.1109/ICIP40778.2020.9190717},
	url = {https://ieeexplore.ieee.org/abstract/document/9190717},
	author = {Nathana{\"e}l Six and Jens Renders and Jan Sijbers and Jan De Beenhouwer}
}
@article {2018,
	title = {Nondestructive internal quality inspection of pear fruit by X-ray CT using machine learning},
	journal = {Food Control},
	volume = {113},
	year = {2020},
	pages = {1-13},
	doi = {https://doi.org/10.1016/j.foodcont.2020.107170},
	author = {Tim Van De Looverbosch and Hafizur Rahman Bhuiyan and Pieter Verboven and Manuel Dierick and Van Loo, D. and Jan De Beenhouwer and Jan Sijbers and Bart Nicolai}
}
@conference {2019,
	title = {Optimal design of a T1 super-resolution reconstruction experiment: a simulation study},
	year = {2020},
	author = {Michele Nicastro and Quinten Beirinckx and Piet Bladt and Ben Jeurissen and Stefan Klein and Jan Sijbers and Dirk H J Poot and Arnold Jan den Dekker}
}
@conference {Morez2020-al,
	title = {Optimal experimental design for multi-tissue spherical deconvolution of diffusion MRI},
	year = {2020},
	pages = {4321},
	author = {Jan Morez and Jan Sijbers and Ben Jeurissen}
}
@article {2017,
	title = {PAPPI: Personalized analysis of plantar pressure images using statistical modelling and parametric mapping},
	journal = {PlosOne},
	volume = {15},
	year = {2020},
	pages = {e0229685},
	doi = {https://doi.org/10.1371/journal.pone.0229685},
	author = {Brian G Booth and Eva Hoefnagels and Toon Huysmans and Jan Sijbers and No{\"e}l L.W. Keijsers}
}
@inproceedings {2052,
	title = {Projection angle adapted distortion correction in high-speed image-intensifier based tomography},
	booktitle = {6th International Conference on Image Formation in X-Ray Computed Tomography},
	year = {2020},
	author = {Joaquim Sanctorum and Van Nguyen and Jan Sijbers and Sam Van Wassenbergh and Joris J. J. Dirckx}
}
@inproceedings {2054,
	title = {The Radon Transform for Terahertz Computed Tomography Incorporating the Beam Shape},
	booktitle = {IEEE ICIP},
	year = {2020},
	pages = {3040-3044},
	doi = {10.1109/ICIP40778.2020.9191175},
	author = {Lars-Paul Lumbeeck and Pavel Paramonov and Jan Sijbers and Jan De Beenhouwer}
}
@inproceedings {2029,
	title = {Ring Artifact Reduction in Sinogram Space Using Deep Learning},
	booktitle = {6th International Conference on Image Formation in X-Ray Computed Tomography},
	year = {2020},
	author = {Maxime Nauwynck and Shabab Bazrafkan and Anneke Van Heteren and Jan De Beenhouwer and Jan Sijbers}
}
@inproceedings {2068,
	title = {Ringing Artefact Removal From Sparse View Tomosynthesis using Deep Neural Networks},
	booktitle = {The 6th International Conference on Image Formation in X-Ray Computed Tomography},
	year = {2020},
	pages = {380-383},
	author = {Shabab Bazrafkan and Vincent Van Nieuwenhove and Joris Soons and Jan De Beenhouwer and Jan Sijbers}
}
@conference {Smekens2020-mo,
	title = {Short T2* quantification of knee structures based on accelerated UTE Spiral VIBE MRI with SPIRiT reconstruction},
	year = {2020},
	pages = {2687},
	author = {C{\'e}line Smekens and Floris Vanhevel and Ben Jeurissen and Pieter Van Dyck and Jan Sijbers and Janssens, T}
}
@conference {2026,
	title = {Short T2* quantification of knee structures based on accelerated UTE Spiral VIBE MRI with SPIRiT reconstruction},
	year = {2020},
	author = {C{\'e}line Smekens and Floris Vanhevel and Ben Jeurissen and Pieter Van Dyck and Jan Sijbers and Thomas Janssens}
}
@article {2070,
	title = {Small medial femoral condyle morphotype is associated with medial compartment degeneration and distinct morphological characteristics: a comparative pilot study},
	journal = {Knee Surgery, Sports Traumatology, Arthroscopy},
	year = {2020},
	doi = {https://doi.org/10.1007/s00167-020-06218-8},
	author = {Jonas Grammens and Annemieke Van Haver and Femke Danckaers and Brian G Booth and Jan Sijbers and Peter Verdonk}
}
@article {2030,
	title = {Subject-specific identification of three dimensional foot shape deviations using statistical shape analysis},
	journal = {Expert Systems With Applications},
	volume = {151},
	year = {2020},
	pages = {1-11},
	doi = {https://doi.org/10.1016/j.eswa.2020.113372},
	author = {Kristina Stankovi{\'c} and Toon Huysmans and Femke Danckaers and Jan Sijbers and Brian G Booth}
}
@article {2031,
	title = {Super-Resolution Magnetic Resonance Imaging of the Knee Using 2-Dimensional Turbo Spin Echo Imaging},
	journal = {Investigative Radiology},
	volume = {55},
	year = {2020},
	pages = {481-493},
	doi = {10.1097/RLI.0000000000000676},
	author = {Pieter Van Dyck and C{\'e}line Smekens and Floris Vanhevel and Eline De Smet and Ella Roelant and Jan Sijbers and Ben Jeurissen}
}
@conference {2139,
	title = {Super-resolution reconstruction of single-PLD pseudo-continuous ASL images},
	year = {2020},
	pages = {3293},
	abstract = {Super-resolution reconstruction (SRR) allows for 3D high-resolution image reconstruction from a set of low-resolution multi-slice images with different orientations. Arterial spin labeling (ASL) is an interesting albeit complicated candidate for SRR, as it relies on subtraction. SRR-ASL can be performed on low-SNR subtracted or on low-contrast unsubtracted ASL data. Different ASL-SRR implementations were applied to single-PLD PCASL data and validated against traditional ASL-scans. Combining motion correction, super-resolution post-processing and pairwise subtraction of label-control pairs in a single framework yielded comparable CBF maps as with traditional HR-ASL. Furthermore, in certain slices, SRR-ASL appears to reconstruct the anatomical structure with higher fidelity.},
	author = {Piet Bladt and Quinten Beirinckx and Merlijn C E van der Plas and Sophie Schmid and Wouter M Teeuwisse and Ben Jeurissen and Arnold Jan den Dekker and Jan Sijbers and M.J.P van Osch}
}
@conference {Bladt2020-mp,
	title = {Super-resolution reconstruction of single-PLD pseudo-continuous ASL images},
	year = {2020},
	pages = {3293},
	author = {Bladt, P and Beirinckx, Q and Van der Plas, M and Schmid, S and Teeuwisse, W and Ben Jeurissen and Arnold Jan den Dekker and Jan Sijbers and van Osch, M}
}
@conference {2015,
	title = {Super-resolution strategies for single-PLD pseudo-continuous ASL},
	year = {2020},
	address = {Arnhem, The Netherlands},
	abstract = {Super-resolution reconstruction (SRR) allows for 3D high-resolution image reconstruction from a set of low-resolution multi-slice images with different orientations. Arterial spin labeling (ASL) is an interesting albeit complicated candidate for SRR, as it relies on subtraction. SRR-ASL can be performed on low-SNR subtracted or on low-contrast unsubtracted ASL data. Different ASL-SRR implementations were applied to single-PLD PCASL data and validated against traditional ASL-scans. Combining motion correction, super-resolution post-processing and pairwise subtraction of label-control pairs in a single framework yielded comparable CBF maps as with traditional HR-ASL. Furthermore, in certain slices, SRR-ASL appears to reconstruct the anatomical structure with higher fidelity.},
	author = {Quinten Beirinckx and Piet Bladt and Merlijn C E van der Plas and Sophie Schmid and Wouter M Teeuwisse and Ben Jeurissen and Arnold Jan den Dekker and Jan Sijbers and M.J.P van Osch}
}
@article {2051,
	title = {Supporting measurements or more averages? How to quantify cerebral blood flow most reliably in 5 minutes by arterial spin labeling},
	journal = {Magnetic Resonance in Medicine},
	volume = {84},
	year = {2020},
	pages = {2523-2536},
	doi = {10.1002/mrm.28314},
	author = {Piet Bladt and M.J.P van Osch and Clement, Patricia and Eric Achten and Jan Sijbers and Arnold Jan den Dekker}
}
@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}
}
@inbook {1960,
	title = {Adaptable digital human models from 3D body scans},
	booktitle = {DHM and Posturography},
	volume = {VI},
	year = {2019},
	pages = {459-470},
	publisher = {Academic Press},
	organization = {Academic Press},
	chapter = {33},
	doi = {https://doi.org/10.1016/B978-0-12-816713-7.00033-7},
	author = {Femke Danckaers and Toon Huysmans and Jan Sijbers}
}
@conference {1924,
	title = {Advancing Analysis Techniques for Plantar Pressure Videos via Open-Access Contributions},
	year = {2019},
	author = {Brian G Booth and No{\"e}l L.W. Keijsers and Toon Huysmans and Jan Sijbers}
}
@article {1964,
	title = {Alterations of Functional Brain Connectivity After Long-Duration Spaceflight as Revealed by fMRI.},
	journal = {Front Physiol},
	volume = {10},
	year = {2019},
	pages = {1-23},
	abstract = {<p>The present study reports alterations of task-based functional brain connectivity in a group of 11 cosmonauts after a long-duration spaceflight, compared to a healthy control group not involved in the space program. To elicit the postural and locomotor sensorimotor mechanisms that are usually most significantly impaired when space travelers return to Earth, a plantar stimulation paradigm was used in a block design fMRI study. The motor control system activated by the plantar stimulation involved the pre-central and post-central gyri, SMA, SII/operculum, and, to a lesser degree, the insular cortex and cerebellum. While no post-flight alterations were observed in terms of activation, the network-based statistics approach revealed task-specific functional connectivity modifications within a broader set of regions involving the activation sites along with other parts of the sensorimotor neural network and the visual, proprioceptive, and vestibular systems. The most notable findings included a post-flight increase in the stimulation-specific connectivity of the right posterior supramarginal gyrus with the rest of the brain; a strengthening of connections between the left and right insulae; decreased connectivity of the vestibular nuclei, right inferior parietal cortex (BA40) and cerebellum with areas associated with motor, visual, vestibular, and proprioception functions; and decreased coupling of the cerebellum with the visual cortex and the right inferior parietal cortex. The severity of space motion sickness symptoms was found to correlate with a post- to pre-flight difference in connectivity between the right supramarginal gyrus and the left anterior insula. Due to the complex nature and rapid dynamics of adaptation to gravity alterations, the post-flight findings might be attributed to both the long-term microgravity exposure and to the readaptation to Earth{\textquoteright}s gravity that took place between the landing and post-flight MRI session. Nevertheless, the results have implications for the multisensory reweighting and gravitational motor system theories, generating hypotheses to be tested in future research.</p>},
	issn = {1664-042X},
	doi = {10.3389/fphys.2019.00761},
	author = {Pechenkova, Ekaterina and Nosikova, Inna and Rumshiskaya, Alena and Litvinova, Liudmila and Rukavishnikov, Ilya and Mershina, Elena and Valentin Sinitsyn and Angelique Van Ombergen and Ben Jeurissen and Steven Jillings and Laureys, Steven and Jan Sijbers and Grishin, Alexey and Chernikova, Ludmila and Naumov, Ivan and Kornilova, Ludmila and Floris L Wuyts and Tomilovskaya, Elena and Kozlovskaya, Inessa}
}
@article {1955,
	title = {Aortic root sizing for transcatheter aortic valve implantation using a shape model parameterisation},
	journal = {Medical \& Biological Engineering \& Computing},
	volume = {57},
	year = {2019},
	month = {Jul},
	pages = {2081{\textendash}2092},
	abstract = {During a transcatheter aortic valve implantation, an axisymmetric implant is placed in an irregularly shaped aortic root. Implanting an incorrect size can cause complications such as leakage of blood alongside or through the implant. The aim of this study was to construct a method that determines the optimal size of the implant based on the three-dimensional shape of the aortic root. Based on the pre-interventional computed tomography scan of 89 patients, a statistical shape model of their aortic root was constructed. The weights associated with the principal components and the volume of calcification in the aortic valve were used as parameters in a classification algorithm. The classification algorithm was trained using the patients with no or mild leakage after their intervention. Subsequently, the algorithms were applied to the patients with moderate to severe leakage. Cross validation showed that a random forest classifier assigned the same size in 65 {\textpm} 7\% of the training cases, while 57 {\textpm} 8\% of the patients with moderate to severe leakage were assigned a different size. This initial study showed that this semi-automatic method has the potential to correctly assign an implant size. Further research is required to assess whether the different size implants would improve the outcome of those patients.},
	issn = {1741-0444},
	doi = {10.1007/s11517-019-01996-x},
	author = {Bosmans, Bart and Toon Huysmans and Lopes, Patricia and Verhoelst, Eva and Dezutter, Tim and de Jaegere, Peter and Jan Sijbers and Vander Sloten, Jos and Bosmans, Johan}
}
@inproceedings {1854,
	title = {An Articulating Statistical Shape Model of the Human Hand},
	booktitle = {Advances in Human Factors in Simulation and Modeling (AHFE 2018)},
	volume = {780},
	year = {2019},
	pages = {433{\textendash}445},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {This paper presents a registration framework for the construction of a statistical shape model of the human hand in a standard pose. It brings a skeletonized reference model of an individual human hand into correspondence with optical 3D surface scans of hands by sequentially applying articulation-based registration and elastic surface registration. Registered surfaces are then fed into a statistical shape modelling algorithm based on principal component analysis. The model-building technique has been evaluated on a dataset of optical scans from 100 healthy individuals, acquired with a 3dMD scanning system. It is shown that our registration framework provides accurate geometric and anatomical alignment, and that the shape basis of the resulting statistical model provides a compact representation of the specific population. The model also provides insight into the anatomical variation of the lower arm and hand, which is useful information for the design of well-fitting products.},
	isbn = {978-3-319-94223-0},
	doi = {10.1007/978-3-319-94223-0_41},
	author = {Jeroen Van Houtte and Kristina Stankovi{\'c} and Brian G Booth and Femke Danckaers and Bertrand, V{\'e}ronique and Frederik Verstreken and Jan Sijbers and Toon Huysmans},
	editor = {Cassenti, Daniel N.}
}
@article {1947,
	title = {Assessment of Anterior Cruciate Ligament Graft Maturity With Conventional Magnetic Resonance Imaging},
	journal = {The Orthopaedic Journal of Sports Medicine},
	volume = {7},
	year = {2019},
	pages = {1-9},
	doi = {https://doi.org/10.1177/2325967119849012},
	author = {Pieter Van Dyck and Katja Zazulia and C{\'e}line Smekens and Christiaan H.W. Heusdens and Thomas Janssens and Jan Sijbers}
}
@article {1927,
	title = {An assessment of the information lost when applying data reduction techniques to dynamic plantar pressure measurements},
	journal = {Journal of Biomechanics },
	volume = {87},
	year = {2019},
	pages = {161-166},
	doi = {https://doi.org/10.1016/j.jbiomech.2019.02.008},
	author = {Brian G Booth and No{\"e}l L.W. Keijsers and Jan Sijbers and Toon Huysmans}
}
@inproceedings {1852,
	title = {Automatic Generation of Statistical Shape Models in Motion},
	booktitle = {Advances in Human Factors in Simulation and Modeling (AHFE 2018)},
	volume = {780},
	year = {2019},
	pages = {170{\textendash}178},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {Statistical body shape modeling (SBSM) is a well-known technique to map out the variability of body shapes and is commonly used in 3D anthropometric analyses. In this paper, a new approach to integrate movement acquired by a motion capture system with a body shape is proposed. This was done by selecting landmarks on a body shape model, and predicting a body shape based on features. Then, a virtual skeleton was generated relative to those landmarks. This skeleton was parented to a body shape, allowing to modify its pose and to add pre-recorded motion to different body shapes in a realistic way.},
	isbn = {978-3-319-94223-0},
	doi = {10.1007/978-3-319-94223-0_16},
	author = {Femke Danckaers and Scataglini, Sofia and Haelterman, Robby and Van Tiggelen, Damien and Toon Huysmans and Jan Sijbers},
	editor = {Cassenti, Daniel N.}
}
@conference {1993,
	title = {Beyond the consensus: is sacrificing part of the PCASL scan time for measurement of labeling efficiency and T1 of blood beneficial?},
	year = {2019},
	author = {Piet Bladt and M.J.P van Osch and Eric Achten and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {1994,
	title = {Beyond the consensus: should measurement of T1 of blood and labeling efficiency be included and should a single- or multi-PLD protocol be used in a five-minute protocol for PCASL?},
	year = {2019},
	author = {Piet Bladt and M.J.P van Osch and Eric Achten and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {1992,
	title = {Beyond the consensus: what to include when 5 minutes are available for perfusion imaging by PCASL?},
	year = {2019},
	author = {Piet Bladt and M.J.P van Osch and Eric Achten and Arnold Jan den Dekker and Jan Sijbers}
}
@article {1941,
	title = {Brain ventricular volume changes induced by long-duration spaceflight},
	journal = {Proceedings of the National Academy of Sciences},
	volume = {116},
	year = {2019},
	pages = {10531-10536},
	abstract = {Long-duration spaceflight induces detrimental changes in human physiology due to microgravity. One example is a cephalic fluid shift. Here, we prospectively investigated the quantitative changes in cerebrospinal fluid (CSF) volume of the brain ventricular regions in space crew by means of a region of interest, observer-independent analysis on structural brain MRI scans. MRI scans were collected before the mission, shortly after and 7 mo after return to Earth. We found a significant increase in lateral and third ventricles at postflight and a trend to normalization at follow-up, but still significantly increased ventricular volumes. The observed spatiotemporal pattern of CSF compartment enlargement and recovery points to a reduced CSF resorption in microgravity as the underlying cause.Long-duration spaceflight induces detrimental changes in human physiology. Its residual effects and mechanisms remain unclear. We prospectively investigated the changes in cerebrospinal fluid (CSF) volume of the brain ventricular regions in space crew by means of a region of interest analysis on structural brain scans. Cosmonaut MRI data were investigated preflight (n = 11), postflight (n = 11), and at long-term follow-up 7 mo after landing (n = 7). Post hoc analyses revealed a significant difference between preflight and postflight values for all supratentorial ventricular structures, i.e., lateral ventricle (mean \% change {\textpm} SE = 13.3 {\textpm} 1.9), third ventricle (mean \% change {\textpm} SE = 10.4 {\textpm} 1.1), and the total ventricular volume (mean \% change {\textpm} SE = 11.6 {\textpm} 1.5) (all P \&lt; 0.0001), with higher volumes at postflight. At follow-up, these structures did not quite reach baseline levels, with still residual increases in volume for the lateral ventricle (mean \% change {\textpm} SE = 7.7 {\textpm} 1.6; P = 0.0009), the third ventricle (mean \% change {\textpm} SE = 4.7 {\textpm} 1.3; P = 0.0063), and the total ventricular volume (mean \% change {\textpm} SE = 6.4 {\textpm} 1.3; P = 0.0008). This spatiotemporal pattern of CSF compartment enlargement and recovery points to a reduced CSF resorption in microgravity as the underlying cause. Our results warrant more detailed and longer longitudinal follow-up. The clinical impact of our findings on the long-term cosmonauts{\textquoteright} health and their relation to ocular changes reported in space travelers requires further prospective studies.},
	issn = {0027-8424},
	doi = {10.1073/pnas.1820354116},
	url = {https://www.pnas.org/content/early/2019/05/01/1820354116},
	author = {Angelique Van Ombergen and Steven Jillings and Ben Jeurissen and Tomilovskaya, Elena and Alena Rumshiskaya and Liudmila Litvinova and Nosikova, Inna and Ekaterina V. Pechenkova and Ilya Rukavishnikov and Manko, Olga and Danylichev, Sergey and R{\"u}hl, R. Maxine and Inessa B. Kozlovskaya and Stefan Sunaert and Paul M Parizel and Valentin Sinitsyn and Steven S L Laureys and Jan Sijbers and zu Eulenburg, Peter and Floris L Wuyts}
}
@inproceedings {2090,
	title = {CAD-based defect inspection with optimal view angle selection based on polychromatic X-ray projection images},
	booktitle = {9th Conference on Industrial Computed Tomography},
	year = {2019},
	pages = {1-5},
	address = {Padova, Italy},
	author = {Alice Presenti and Jan Sijbers and Arnold Jan den Dekker and Jan De Beenhouwer}
}
@article {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}
}
@conference {Morez2019-uz,
	title = {A comparison of response function tensor models for multi-tissue spherical deconvolution},
	volume = {32 (Suppl. 1)},
	year = {2019},
	pages = {S138{\textendash}S139},
	author = {Jan Morez and Jan Sijbers and Ben Jeurissen}
}
@inproceedings {1966,
	title = {A Deep Learning Approach to Horse Bone Segmentation from Digitally Reconstructed Radiographs},
	booktitle = {International Conference on Image Processing Theory, Tools, and Applications},
	year = {2019},
	doi = {10.1109/IPTA.2019.8936082},
	author = {Jeroen Van Houtte and Shabab Bazrafkan and Filip Vandenberghe and Guoyan Zheng and Jan Sijbers}
}
@conference {1984,
	title = {A deep learning approach to T1 mapping in quantitative MRI},
	volume = {32 (Suppl. 1)},
	number = {S09.05},
	year = {2019},
	publisher = {Magn Reson Mater Phy},
	abstract = {Quantitative MRI aims to measure biophysical tissue parameters through the analysis of the MR signal. Conventional parameter estimation methods, which often rely on a voxel-wise mapping, ignores spatial redundancies. In this work, a deep learning method for T1 mapping is proposed to overcome this limitation.},
	doi = {10.1007/s10334-019-00754-2},
	author = {Ribeiro Sabidussi, Emanoel and Michele Nicastro and Shabab Bazrafkan and Quinten Beirinckx and Ben Jeurissen and Jan Sijbers and Arnold Jan den Dekker and Stefan Klein and Dirk H J Poot}
}
@conference {1965,
	title = {Deep learning based missing wedge artefact removal for electron tomography},
	year = {2019},
	pages = {660-661},
	author = {Juho Rimpelainen and Shabab Bazrafkan and Jan Sijbers and Jan De Beenhouwer}
}
@inbook {1961,
	title = {Design smart clothing using digital human models},
	booktitle = {DHM and Posturography},
	year = {2019},
	pages = {683-698},
	publisher = {Academic Press},
	organization = {Academic Press},
	doi = {https://doi.org/10.1016/B978-0-12-816713-7.00053-2},
	author = {Scataglini, Sofia and Femke Danckaers and Toon Huysmans and Jan Sijbers and Giuseppe Andreoni}
}
@mastersthesis {1930,
	title = {The Development of 3D Statistical Shape Models for Diverse Applications},
	volume = {PhD in Sciences},
	year = {2019},
	type = {PhD thesis},
	author = {Femke Danckaers}
}
@conference {1981,
	title = {Diffusion time dependence in the mid-time regime: a simulation study using PGSE.},
	number = {3572},
	year = {2019},
	address = {Montreal, Canada},
	abstract = {The purpose of this work is to study the effect of varying the diffusion time on the estimation of the parameters of the two-compartment diffusion tensor model in the mid-time regime. Simulation results show that the precision of the diffusion time-dependent compartmental parameter estimates increases when a variable echo time acquisition scheme is used. At low SNR, however, including diffusion time-dependence may lead to a high bias and variance compared to the more conventional non diffusion time-dependent model.},
	author = {Annelinde E. Buikema and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {Jillings2019-al,
	title = {Diffusion-weighted imaging reveals structural brain changes in cosmonauts after long-duration spaceflight},
	volume = {32 (Suppl. 1)},
	year = {2019},
	pages = {Magn Reson Mater Phy. 2019; 32(Suppl. 1):S101},
	author = {Steven Jillings and Angelique Van Ombergen and Tomilovskaya, E and Laureys, S and zu Eulenburg, P and Stefan Sunaert and Jan Sijbers and Floris L Wuyts and Ben Jeurissen}
}
@inproceedings {1945,
	title = {Dynamic angle selection for few-view X-ray inspection of CAD based objects},
	booktitle = {Proc. SPIE, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully3D)},
	volume = {11072},
	year = {2019},
	doi = {https://doi.org/10.1117/12.2534894},
	author = {Alice Presenti and Jan Sijbers and Jan De Beenhouwer}
}
@conference {1921,
	title = {Effect of diffusion time dependence on parameter estimation in the clinical time frame: a simulation study using PGSE},
	year = {2019},
	address = {Leiden, the Netherlands},
	abstract = {Fieremans et al. modelled the diffusion time dependence of diffusion longitudinal and transverse to white matter tracts, which can be interpreted as an effect of structural disorder at the mesoscopic scale. To our knowledge, this diffusion time-dependent (DT-dependent) model has not yet been studied in the mid-time regime (Δ=20-180ms) with varying echo times, which would be valuable for clinical practice. In this simulation study, we use the pulsed-gradient spin-echo (PGSE) sequence5 and study the effects of varying the diffusion time on the parameter estimation in a mid-time regime.},
	author = {Annelinde E. Buikema and Arnold Jan den Dekker and Jan Sijbers}
}
@inproceedings {1883,
	title = {Fast detection of cracks in ultrasonically welded parts by inline X-ray inspection},
	booktitle = {9th Conference on Industrial Computed Tomography},
	year = {2019},
	address = {Padova, Italy},
	url = {https://www.ndt.net/article/ctc2019/papers/iCT2019_Full_paper_48.pdf},
	author = {Eline Janssens and Jan Sijbers and Manuel Dierick and Jan De Beenhouwer}
}
@inproceedings {1946,
	title = {Fiber assignment by continuous tracking for parametric fiber reinforced polymer reconstruction},
	booktitle = {15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully3D)},
	volume = {11072},
	year = {2019},
	doi = {http://dx.doi.org/10.1117/12.2534836},
	author = {Tim Elberfeld and Jan De Beenhouwer and Jan Sijbers}
}
@conference {Jeurissen2019-sb,
	title = {Improved precision and accuracy in q-space trajectory imaging by model-based super-resolution reconstruction},
	year = {2019},
	pages = {556},
	author = {Ben Jeurissen and Westin, Carl-Fredrik and Jan Sijbers and Szczepankiewicz, Filip}
}
@inproceedings {1918,
	title = {An Interactive Visual Comparison Tool for 3D Volume Datasets represented by Nonlinearly Scaled 1D Line Plots through Space-filling Curves},
	booktitle = {9th Conference on Industrial Computed Tomography},
	year = {2019},
	address = {Padova, Italy},
	author = {Johannes Weissenb{\"o}ck and Bernhard Fr{\"o}hler and Eduard Gr{\"o}ller and Jonathan Sanctorum and Jan De Beenhouwer and Jan Sijbers and Santhosh Ayalur Karunakaran and Helmuth Hoeller and Johann Kastner and Christoph Heinzl}
}
@inproceedings {1885,
	title = {A low-cost and easy-to-use phantom for cone-beam geometry calibration of a tomographic X-ray system},
	booktitle = {9th Conference on Industrial Computed Tomography},
	year = {2019},
	address = {Padova, Italy},
	url = {https://www.ndt.net/article/ctc2019/papers/iCT2019_Full_paper_54.pdf},
	author = {Van Nguyen and Jan De Beenhouwer and Joaquim Sanctorum and Sam Van Wassenbergh and Peter Aerts and Joris J. J. Dirckx and Jan Sijbers}
}
@article {1914,
	title = {Matlab{\textregistered} toolbox for semi-automatic segmentation of the human nasal cavity based on active shape modeling},
	journal = {Computers in Biology and Medicine},
	volume = {105},
	year = {2019},
	pages = {27-38},
	doi = {10.1016/j.compbiomed.2018.12.008},
	author = {Keustermans, William and Toon Huysmans and Bert Schmelzer and Jan Sijbers and Joris J. J. Dirckx}
}
@article {1944,
	title = {Methods for characterization and optimisation of measuring performance of stereoscopic x-ray systems with image intensifiers},
	journal = {Measurement Science and Technology},
	volume = {30},
	year = {2019},
	doi = {https://doi.org/10.1088/1361-6501/ab23e7},
	author = {Joaquim Sanctorum and Dominique Adriaens and Joris J. J. Dirckx and Jan Sijbers and Chris Van Ginneken and Peter Aerts and Sam Van Wassenbergh}
}
@conference {1952,
	title = {Mixed-Scale Dense Convolutional Neural Network based Improvement of Glass Fiber-reinforced Composite CT Images},
	year = {2019},
	month = {07/2019},
	abstract = {For the study of glass fiber-reinforced polymers (GFRP), {\textmu}CT is the method of choice. Obtaining
GFRP parameters from a {\textmu}CT scan is difficult, due to the presence of noise and artifacts. We propose a method
to improve GFRP image quality using a recently introduced deep neural network. We describe the network{\textquoteright}s
setup and the data generation and show how the trained network improves the reconstruction.},
	author = {Tim Elberfeld and Shabab Bazrafkan and Jan De Beenhouwer and Jan Sijbers}
}
@inproceedings {1873,
	title = {Moving Statistical Body Shape Models Using Blender},
	booktitle = {Proceedings of the 20th Congress of the International Ergonomics Association (IEA 2018)},
	year = {2019},
	pages = {28{\textendash}38},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {In this paper, we present a new framework to integrate movement acquired by a motion capture system to a statistical body shape model using Blender. This provides a visualization of a digital human model based upon anthropometry and biomechanics of the subject. A moving statistical body shape model helps to visualize physical tasks with inter-individual variability in body shapes as well as anthropometric dimensions. This parametric modeling approach is useful for reliable prediction and simulation of the body shape movement of a specific population with a few given predictors such as stature, body mass index and age.},
	isbn = {978-3-319-96077-7},
	doi = {10.1007/978-3-319-96077-7_4},
	author = {Scataglini, Sofia and Femke Danckaers and Haelterman, Robby and Toon Huysmans and Jan Sijbers},
	editor = {Bagnara, Sebastiano and Tartaglia, Riccardo and Albolino, Sara and Alexander, Thomas and Fujita, Yushi}
}
@inproceedings {1853,
	title = {Multi-patch B-Spline Statistical Shape Models for CAD-Compatible Digital Human Modeling},
	booktitle = {Advances in Human Factors in Simulation and Modeling},
	year = {2019},
	pages = {179{\textendash}189},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {Parametric 3D human body models are valuable tools for ergonomic product design and statistical shape modelling (SSM) is a powerful technique to build realistic body models from a database of 3D scans. Like the underlying 3D scans, body models built from SSMs are typically represented with triangle meshes. Unfortunately, triangle meshes are not well supported by CAD software where spline geometry dominates. Therefore, we propose a methodology to convert databases of pre-corresponded triangle meshes into multi-patch B-spline SSMs. An evaluation on four 3D scan databases shows that our method is able to generate accurate and water-tight models while preserving inter-subject correspondences by construction. In addition, we demonstrate that such SSMs can be used to generate design manikins which can be readily used in SolidWorks for designing well conforming product parts.},
	isbn = {978-3-319-94223-0},
	doi = {10.1007/978-3-319-94223-0_17},
	author = {Toon Huysmans and Femke Danckaers and Jochen Vleugels and Daniel Lacko and Guido De Bruyne and Stijn Verwulgen and Jan Sijbers}
}
@article {1916,
	title = {Normalized Averaged Range (nAR), a Robust Quantification Method for MPIO-content},
	journal = {Journal of Magnetic Resonance},
	volume = {300},
	year = {2019},
	pages = {18-27},
	doi = {10.1016/j.jmr.2018.12.019},
	author = {Maarten Naeyaert and Dimitri Roose and Zhenhua Mai and Aneta J Keliris and Jan Sijbers and Annemie Van Der Linden and Marleen Verhoye}
}
@conference {1982,
	title = {Optimal design of a blended diffusion/relaxometry experiment},
	year = {2019},
	address = {Rotterdam, the Netherlands},
	author = {Annelinde E. Buikema and Arnold Jan den Dekker and Jan Sijbers}
}
@article {1973,
	title = {poly-DART: A discrete algebraic reconstruction technique for polychromatic X-ray CT},
	journal = {Optics Express},
	volume = {27},
	year = {2019},
	pages = {33427-33435},
	doi = {https://doi.org/10.1364/OE.27.033670},
	author = {Nathana{\"e}l Six and Jan De Beenhouwer and Jan Sijbers}
}
@article {1893,
	title = {Posture normalization of 3D body scans},
	journal = {Ergonomics},
	volume = {62},
	year = {2019},
	pages = {834-848},
	doi = {10.1080/00140139.2019.1581262},
	author = {Femke Danckaers and Toon Huysmans and Ann Hallemans and Guido De Bruyne and Steven Truijen and Jan Sijbers}
}
@article {1909,
	title = {Reproducibility and intercorrelation of graph theoretical measures in structural brain connectivity networks},
	journal = {Medical Image Analysis},
	volume = {52},
	year = {2019},
	pages = {56-67},
	doi = {https://doi.org/10.1016/j.media.2018.10.009},
	author = {Timo Roine and Ben Jeurissen and Daniele Perrone and Jan Aelterman and Wilfried Philips and Jan Sijbers and Alexander Leemans}
}
@conference {2020,
	title = {Robust outlier detection for diffusion kurtosis MRI based on IRLLS},
	volume = {32},
	number = {1},
	year = {2019},
	url = {https://link.springer.com/article/10.1007/s10334-019-00756-0},
	author = {Vincenzo Anania and Thibo Billiet and Ben Jeurissen and Jan Sijbers and Arnold Jan den Dekker}
}
@inproceedings {1884,
	title = {Simulated grating-based x-ray phase contrast images of CFRP-like objects},
	booktitle = {9th Conference on Industrial Computed Tomography},
	year = {2019},
	pages = {1-8},
	address = {Padova, Italy},
	url = {https://www.ndt.net/article/ctc2019/papers/iCT2019_Full_paper_45.pdf},
	author = {Jonathan Sanctorum and Jan De Beenhouwer and Johannes Weissenb{\"o}ck and Christoph Heinzl and Johann Kastner and Jan Sijbers}
}
@conference {1983,
	title = {Super-resolution T1 mapping with integrated motion compensation in a joint maximum likelihood framework},
	volume = {32 (Suppl. 1)},
	number = {S14.05},
	year = {2019},
	publisher = {Magn Reson Mater Phy},
	abstract = {To date, 3D high resolution (HR) quantitative T1 mapping is not feasible in clinical practice due to prohibitively long acquisition times. Recent work has shown that super-resolution reconstruction (SRR), in which a 3D HR T1 map is directly estimated from a set of low through-plane resolution (LR) multi-slice (ms) T1-weighted (T1w) images with different slice orientations, can improve the trade-off between SNR, spatial resolution, and acquisition time. In that work, however, inter-image motion compensation for SRR is performed in a preprocessing step in which the transformation parameters of each LR image are updated after image registration. As a result, potential registration errors might propagate in the T1 estimation as no feedback mechanism is in place. Moreover, due to missing subvoxel accuracy no HR information is readily available during preprocessing. In the current work, we explore the potential of an improved SRR T1 mapping method that aims at more accurate T1 maps by combining T1 and motion estimation in a joint Maximum Likelihood estimation (jMLE) framework.
},
	doi = {10.1007/s10334-019-00754-2},
	author = {Quinten Beirinckx and Ben Jeurissen and Marleen Verhoye and Arnold Jan den Dekker and Jan Sijbers}
}
@inproceedings {1917,
	title = {Tools for the Analysis of Datasets from X-Ray Computed Tomography based on Talbot-Lau Grating Interferometry},
	booktitle = {9th Conference on Industrial Computed Tomography},
	year = {2019},
	address = {Padova, Italy},
	url = {https://www.ndt.net/article/ctc2019/papers/iCT2019_Full_paper_52.pdf},
	author = {Bernhard Fr{\"o}hler and Lucas da Cunha Melo and Johannes Weissenb{\"o}ck and Johann Kastner and Torsten M{\"o}ller and Hans-Christian Hege and Eduard Gr{\"o}ller and Jonathan Sanctorum and Jan De Beenhouwer and Jan Sijbers and Christoph Heinzl}
}
@conference {1980,
	title = {A Trajectory Based Bottom-Up Volume Reconstruction Method for Atom Probe Tomography},
	year = {2019},
	author = {Yu-Ting Ling and Siegfried Cools and Jan De Beenhouwer and Jan Sijbers and Wilfried Vandervorst}
}
@inproceedings {1874,
	title = {Using 3D Statistical Shape Models for Designing Smart Clothing},
	booktitle = {Proceedings of the 20th Congress of the International Ergonomics Association (IEA 2018)},
	year = {2019},
	pages = {18{\textendash}27},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {In this paper we present an innovative approach to design smart clothing using statistical body shape modeling (SBSM) from the CAESAR{\texttrademark} dataset. A combination of different digital technologies and applications are used to create a common co-design workflow for garment design. User and apparel product design and developers can get personalized prediction of cloth sizing, fitting and aesthetics.},
	isbn = {978-3-319-96077-7},
	doi = {10.1007/978-3-319-96077-7_3},
	author = {Scataglini, Sofia and Femke Danckaers and Haelterman, Robby and Toon Huysmans and Jan Sijbers and Andreoni, Giuseppe},
	editor = {Bagnara, Sebastiano and Tartaglia, Riccardo and Albolino, Sara and Alexander, Thomas and Fujita, Yushi}
}
@article {1923,
	title = {A Visual Tool for the Analysis of Algorithms for Tomographic Fiber Reconstruction in Materials Science},
	journal = {Computer Graphics Forum},
	volume = {38},
	year = {2019},
	pages = {273-283},
	doi = {10.1111/cgf.13688},
	author = {Bernhard Fr{\"o}hler and Tim Elberfeld and Torsten M{\"o}ller and Johannes Weissenb{\"o}ck and Jan De Beenhouwer and Jan Sijbers and Hans-Christian Hege and Johann Kastner and Christoph Heinzl}
}
@conference {2022,
	title = {Voxelwise harmonisation of FA on a cohort of 605 healthy subjects using ComBat: an exploratory study},
	year = {2019},
	author = {Maira Siqueira Pinto and Roberto Paolella and Thibo Billiet and Pieter Van Dyck and Pieter-Jan Guns and Ben Jeurissen and Annemie Ribbens and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {1995,
	title = {Absolute CBF quantification in multi-time point ASL: the T1 issue},
	year = {2018},
	author = {Piet Bladt and Arnold Jan den Dekker and Clement, Patricia and Eric Achten and Jan Sijbers}
}
@conference {1827,
	title = {Accurate and precise MRI relaxometry: the often disregarded but critical role of statistical parameter estimation},
	year = {2018},
	pages = {5664},
	address = {Paris, France},
	author = {Gabriel Ramos-Llord{\'e}n and Quinten Beirinckx and Arnold Jan den Dekker and Jan Sijbers}
}
@article {1857,
	title = {Advanced X-ray tomography: experiment, modeling, and algorithms},
	journal = {Measurement Science and Technology},
	volume = {29},
	year = {2018},
	pages = {080101},
	doi = {https://doi.org/10.1088/1361-6501/aacd25},
	author = {Kees Joost Batenburg and Francesco De Carlo and Lucia Mancini and Jan Sijbers}
}
@mastersthesis {1867,
	title = {Advances in X-ray reconstruction algorithms for limited data problems in conventional and non-conventional projection geometries},
	volume = {PhD in Sciences / Physics},
	year = {2018},
	type = {PhD thesis},
	author = {Eline Janssens}
}
@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}
}
@conference {1949,
	title = {Changes in intrinsic functional brain connectivity after first-time exposure to parabolic flight},
	year = {2018},
	doi = {10.3389/conf.fphys.2018.26.00017},
	author = {Angelique Van Ombergen and Floris L Wuyts and Ben Jeurissen and Jan Sijbers and Floris Vanhevel and Steven Jillings and Paul M Parizel and Stefan Sunaert and Paul H Van de Heyning and Vincent Dousset and Steven S L Laureys and Athena Demertzi}
}
@inproceedings {1770,
	title = {A Combined Statistical Shape Model of the Scalp and Skull of the Human Head},
	booktitle = { 8th International Conference on Applied Human Factors and Ergonomics (AHFE 2017)},
	volume = {591},
	year = {2018},
	pages = {538-548},
	publisher = {Springer},
	organization = {Springer},
	address = {Los Angeles, California, USA},
	doi = {10.1007/978-3-319-60591-3_49},
	author = {Femke Danckaers and Daniel Lacko and Stijn Verwulgen and Guido De Bruyne and Toon Huysmans and Jan Sijbers}
}
@inproceedings {1831,
	title = {Comparative Visualization of Orientation Tensors in Fiber-Reinforced Polymers},
	booktitle = {8th Conference on Industrial Computed Tomography, Wels, Austria },
	year = {2018},
	author = {Johannes Weissenb{\"o}ck and M Arikan and D Salaberger and Johann Kastner and Jan De Beenhouwer and Jan Sijbers and S Rauchenzauner and T Raab-Wernig and E Gr{\"o}ller and Christoph Heinzl}
}
@article {1817,
	title = {Diffusion kurtosis imaging allows the early detection and longitudinal follow-up of amyloid β-induced pathology.},
	journal = {Alzheimer{\textquoteright}s Research \& Therapy },
	volume = {10},
	year = {2018},
	pages = {1-16},
	doi = {10.1186/s13195-017-0329-8},
	author = {Jelle Praet and Nikolay Manyakov and Leacky Muchene and Zhenhua Mai and Vasilis Terzopoulos and Steve De Backer and An Torremans and Pieter-Jan Guns and Tom Van De Casteele and Astrid Bottelbergs and Bianca Van Broeck and Jan Sijbers and Dirk Smeets and Ziv Shkedy and Luc Bijnens and Darrel Pemberton and Mark Schmidt and Annemie Van Der Linden and Marleen Verhoye}
}
@article {1820,
	title = {Diffusion kurtosis imaging with free water elimination: a Bayesian estimation approach},
	journal = {Magnetic Resonance in Medicine},
	volume = {80},
	year = {2018},
	pages = {802-813},
	doi = {10.1002/mrm.27075},
	author = {Quinten Collier and Jelle Veraart and Ben Jeurissen and Floris Vanhevel and Pim Pullens and Paul M Parizel and Arnold Jan den Dekker and Jan Sijbers}
}
@article {1731,
	title = {Discrete tomography in an in vivo small animal bone study},
	journal = {Journal of Bone and Mineral Metabolism},
	volume = {36},
	year = {2018},
	pages = {40{\textendash}53},
	doi = {10.1007/s00774-017-0815-x},
	author = {Elke Van de Casteele and Egon Perilli and Wim Van Aarle and Karen Reynolds and Jan Sijbers}
}
@conference {1828,
	title = {An educational presentation on accurate and precise MRI relaxometry: the often disregarded but critical role of statistical parameter estimation},
	year = {2018},
	address = {Antwerp, Belgium},
	abstract = {MRI relaxometry holds the promise of providing biomarkers for monitoring, staging and follow up of diseases. Imperative to meet minimum standards for objective, reproducible, and reliable biomarkers is the need for accurate, precise, quantitative parameters maps, such as T1~or T2. While unrealistic physical modelling is often argued as the main cause of lack of accuracy, little effort has been made on discussing the impact that inadequate parameter estimation methods have on the accuracy and precision of MRI relaxometry techniques. This educational poster attempts to introduce young MR students/researchers into the basics of modern statistical parameter estimation theory, and its application for accurate and precise relaxometry. 
},
	author = {Gabriel Ramos-Llord{\'e}n and Quinten Beirinckx and Arnold Jan den Dekker and Jan Sijbers}
}
@inproceedings {1830,
	title = {An efficient CAD projector for X-ray projection based 3D inspection with the ASTRA Toolbox},
	booktitle = {8th Conference on Industrial Computed Tomography, Wels, Austria},
	year = {2018},
	author = {{\'A}rp{\'a}d Marinovszki and Jan De Beenhouwer and Jan Sijbers}
}
@article {1872,
	title = {Enhanced contrast in X-ray microtomographic images of the membranous labyrinth using different X-ray sources and scanning modes},
	journal = {Journal of Anatomy},
	volume = {233},
	year = {2018},
	pages = {770--782},
	doi = {doi: 10.1111/joa.12885},
	author = {Jana Goyens and Menelia Vasilopoulou-Kampitsi and Raf Claes and Jan Sijbers and Lucia Mancini}
}
@inproceedings {1769,
	title = {Full Body Statistical Shape Modeling with Posture Normalization},
	booktitle = {The 8th International Conference on Applied Human Factors and Ergonomics (AHFE 2017)},
	volume = {591},
	year = {2018},
	pages = {437-448},
	publisher = {Springer},
	organization = {Springer},
	address = {Los Angeles, California, USA},
	doi = {10.1007/978-3-319-60591-3_39},
	author = {Femke Danckaers and Toon Huysmans and Ann Hallemans and Guido De Bruyne and Steven Truijen and Jan Sijbers}
}
@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}
}
@mastersthesis {1832,
	title = {Improved MRI Relaxometry through Statistical Signal Processing},
	volume = { Doctor of Science},
	year = {2018},
	month = {02/2018},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	abstract = {Magnetic Resonance Imaging (MRI) relaxometry is a quantitative MRI modality that deals with the estimation of the spin-lattice, T1, and the spin-spin, T2, relaxation times. Both relaxation times are fundamental parameters that describe the spin dynamics within a tissue during the relaxation process of the Nuclear Magnetic Resonance (NMR) phenomenon. During the last decades, spatial T1 and T2 maps have been analyzed to study and monitor the states of a multitude of human diseases. Those studies have shown that MRI relaxometry holds the promise of generating robust, objective image-based biomarkers for central nervous system pathologies, cardiovascular diseases and beyond. Unfortunately, quantitative biomarkers derived from MRI relaxometry are not yet sufficiently specific, sensitive, and robust to be routinely used in clinical practice. On top of that, high-resolution relaxation maps demand a clinically unfeasible long scanning time.

This PhD thesis tries to reduce the obstacles that preclude MRI relaxometry from being a fast, accurate, and precise quantitative MRI modality for clinical use by improving the way MR relaxometry data are acquired, processed and analyzed. In particular, by adopting a statistical signal processing approach, three contributions that address common problems of the field are given. Firstly, we present a unified relaxometry-based processing framework where the T1 map estimation and motion correction are accounted for in a synergistic manner, being both the T1 map and the motion parameters simultaneously estimated with a Maximum-Likelihood estimator. It is demonstrated that substantially more accurate T1 maps are obtained with our proposed integrated approach in comparison to the yet typical but suboptimal two-step approach: T1 model fitting after image registration.  Secondly, we developed a fast, robust, T1 estimator for Variable Flip Angle (VFA) T1 mapping that can provide statistically optimal T1 estimates with unprecedentedly short computation time, thereby enabling optimal, real-time, VFA T1 mapping.  Finally, we were able to reduce the long overall scanning time of MRI relaxometry studies using our novel k-space reconstruction technique that permits the reconstruction of individual MR images with less number of samples than it is commonly required.},
	author = {Gabriel Ramos-Llord{\'e}n}
}
@article {1791,
	title = {IntensityPatches and RegionPatches for Image Recognition},
	journal = {Applied Soft Computing},
	volume = {62},
	year = {2018},
	pages = {176-186},
	doi = {https://doi.org/10.1016/j.asoc.2017.09.046},
	url = {http://www.sciencedirect.com/science/article/pii/S1568494617305859},
	author = {Tiago B. A. de Carvalho and Maria A. Sibaldo and Ing Jyh Tsang and George D C Cavalcanti and Jan Sijbers and Ing Ren Tsang}
}
@conference {1860,
	title = {Investigating the relationship between shape and flow in the human nose using a statistical shape model},
	year = {2018},
	url = {http://www.eccm-ecfd2018.org/admin/files/fileabstract/a781.pdf},
	author = {Keustermans, William and Toon Huysmans and Femke Danckaers and Andrzej Zarowski and Bert Schmelzer and Jan Sijbers and Joris J. J. Dirckx}
}
@inbook {1835,
	title = {Iterative reconstruction methods in X-ray CT},
	booktitle = {Handbook of X-ray Imaging: Physics and Technology},
	year = {2018},
	pages = {693-712},
	publisher = {CRC Press},
	organization = {CRC Press},
	chapter = {34},
	author = {Van Eyndhoven, Geert and Jan Sijbers}
}
@inproceedings {1869,
	title = {Joint reconstruction and flat-field estimation using support estimation},
	booktitle = {IEEE Nuclear Science Symposium and Medical Imaging Conference},
	year = {2018},
	address = {Sydney, Australia},
	doi = {10.1109/NSSMIC.2018.8824406},
	author = {Nathana{\"e}l Six and Jan De Beenhouwer and Vincent Van Nieuwenhove and Wim Vanroose and Jan Sijbers}
}
@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 {1842,
	title = {NOVIFAST: A fast algorithm for accurate and precise VFA MRI T1 mapping},
	journal = {IEEE Transactions on Medical Imaging},
	volume = {37},
	year = {2018},
	pages = {2414 - 2427},
	doi = {10.1109/TMI.2018.2833288},
	author = {Gabriel Ramos-Llord{\'e}n and Gonzalo Vegas-S{\'a}nchez-Ferrero and Marcus Bj{\"o}rk and Floris Vanhevel and Paul M Parizel and Raul San Jos{\'e} Est{\'e}par and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {1847,
	title = {Parametric Reconstruction of Advanced Glass Fiber-reinforced Polymer Composites from X-ray Images},
	year = {2018},
	address = {Wels, Austria},
	abstract = {A novel approach to the reconstruction of glass fiber-reinforced polymers (GFRP) from X-ray micro-computed tomography
(μCT) data is presented. The traditional fiber analysis workflow requires complete sample reconstruction, pre-processing and
segmentation, followed by the analysis of fiber distribution, orientation, and other features of interest. Each step in the chain
introduces errors that propagate through the pipeline and impair the accuracy of the estimation of those features. In the
approach presented in this paper, we combine iterative reconstruction techniques and a priori knowledge about the sample, to
reconstruct the volume and estimate the orientation of the fibers simultaneously. Fibers are modeled using rigid cylinders in
space whose orientation and position is then iteratively refined. The output of the algorithm is a non voxel-based dataset of the
fibers{\textquoteright} parametric representation, allowing to directly assess fiber features and distribution characteristics and to simulate the resulting material properties.},
	keywords = {GFRP, Materials Science, Modeling of Microstructures, Parametric Reconstruction, Tomography, {\textmu}CT},
	author = {Tim Elberfeld and Jan De Beenhouwer and Arnold Jan den Dekker and Christoph Heinzl and Jan Sijbers}
}
@article {1859,
	title = {Parametric Reconstruction of Glass Fiber-reinforced Polymer Composites from X-ray Projection Data - A Simulation Study},
	journal = {Journal of Nondestructive Evaluation},
	volume = {37},
	year = {2018},
	month = {Jul},
	pages = {1573-4862},
	abstract = {We present a new approach to estimate geometry parameters of glass fibers in glass fiber-reinforced polymers from simulated X-ray micro-computed tomography scans. Traditionally, these parameters are estimated using a multi-step procedure including image reconstruction, pre-processing, segmentation and analysis of features of interest. Each step in this chain introduces errors that propagate through the pipeline and impair the accuracy of the estimated parameters. In the approach presented in this paper, we reconstruct volumes from a low number of projection angles using an iterative reconstruction technique and then estimate position, direction and length of the contained fibers incorporating a priori knowledge about their shape, modeled as a geometric representation, which is then optimized. Using simulation experiments, we show that our method can estimate those representations even in presence of noisy data and only very few projection angles available.},
	keywords = {GFRP, Glass fiber reinforced polymer, Materials Science, Modeling of micro-structures, Parametric model, Tomography, {\textmu}CT},
	doi = {10.1007/s10921-018-0514-0},
	url = {https://doi.org/10.1007/s10921-018-0514-0},
	author = {Tim Elberfeld and Jan De Beenhouwer and Arnold Jan den Dekker and Christoph Heinzl and Jan Sijbers}
}
@inproceedings {1833,
	title = {pDART: Discrete algebraic reconstruction using a polychromatic forward model},
	booktitle = {The Fifth International Conference on Image Formation  in X-Ray Computed Tomography},
	year = {2018},
	address = {Salt Lake City, Utah, USA},
	author = {Nathana{\"e}l Six and Jan De Beenhouwer and Jan Sijbers}
}
@inproceedings {1882,
	title = {Performance and memory use trade-off in CPU and GPU implementations of a deformation operator for 4D-CT},
	booktitle = {8th Conference on Industrial Computed Tomography, Wels, Austria},
	year = {2018},
	author = {Stijn Manhaeve and Vincent Van Nieuwenhove and Jan Sijbers}
}
@article {1935,
	title = {Principal component analysis as a tool for determining optimal tibial baseplate geometry in modern TKA design},
	journal = {Acta Orthop Belg},
	volume = {84},
	year = {2018},
	pages = {452-460},
	author = {Sare Huijs and Toon Huysmans and A De Jong and Nele Arnout and Jan Sijbers and Johan Bellemans}
}
@mastersthesis {1911,
	title = {Robust estimation of diffusion tensor and diffusion kurtosis imaging parameters},
	volume = {PhD in Sciences/Physics},
	year = {2018},
	month = {Oct/2018},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	author = {Quinten Collier}
}
@conference {1920,
	title = {Simultaneous T2/diffusion estimation: do we need a diffusion time dependent diffusion model?},
	year = {2018},
	address = {Antwerp, Belgium},
	abstract = {Joint estimation of the transverse relaxation (T2) and the diffusion tensor allows for probing white matter integrity in a more efficient way compared to estimating these parameters from sequential relaxometry and diffusion imaging protocols. For simultaneous estimation of diffusion and relaxation parameters, short echo times are needed and, as a consequence, short diffusion times. Therefore we need a flexible diffusion model that accounts for diffusion time dependence. Currently used diffusion models do not account for this dependence. An acknowledged model describing the properties of diffusion in coherently oriented fiber populations is the diffusion tensor. Alternative models are proposed, which allow to resolve multiple fiber directions, such as multiple tensor fitting and estimation of the fiber orientation distribution function using high angular resolution data acquisition. When the diffusion time is kept short enough, the diffusion behavior reflects that of free diffusion, meaning that the diffusion tensor is isotropic. With increasing diffusion time, diffusion is hindered by extracellular and intracellular components and the diffusion tensor evolves towards an ellipsoid. To the authors{\textquoteright} knowledge there is no model available to correct for diffusion time dependence in clinically short range diffusion times, allowing joint estimation of relaxation and diffusion properties. This work provides an overview of the state-of-the-art and challenges of diffusion time dependent diffusion modeling.},
	author = {Annelinde E. Buikema and Arnold Jan den Dekker and Jan Sijbers}
}
@article {1837,
	title = {STAPP: SpatioTemporal Analysis of Plantar Pressure Measurements using Statistical Parametric Mapping},
	journal = {Gait and Posture},
	volume = {3},
	year = {2018},
	pages = {268-275},
	abstract = {Background: Pedobarography produces large sets of plantar pressure samples that are routinely subsampled (e.g. using regions of interest) or aggregated (e.g. center of pressure trajectories, peak pressure images) in order to simplify statistical analysis and provide intuitive clinical measures.

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

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

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

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

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

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

Conclusion
A detailed analysis of the 3D foot shape, allowed by geometric morphometrics, provides insights in foot variations in three dimensions that can not be obtained from 2D footprints. These insights could be applied in various scientific disciplines, including orthotics and shoe design.},
	issn = {1757-1146},
	doi = {10.1186/s13047-018-0251-8},
	url = {https://doi.org/10.1186/s13047-018-0251-8},
	author = {Kristina Stankovi{\'c} and Brian G Booth and Femke Danckaers and Fien Burg and Vermaelen, Philippe and Saartje Duerinck and Jan Sijbers and Toon Huysmans}
}
@article {1792,
	title = {TomoBank: A Tomographic Data Repository for Computational X-ray Science},
	journal = {Measurement Science and Technology},
	volume = {29},
	year = {2018},
	pages = {1-10},
	doi = {https://doi.org/10.1088/1361-6501/aa9c19},
	author = {Francesco De Carlo and Doga Gursoy and Daniel Ching and Kees Joost Batenburg and Ludwig, Wolfgang and Lucia Mancini and Federica Marone Welford and Rajmund Mokso and Daan Pelt and Jan Sijbers and Mark Rivers}
}
@article {1824,
	title = {White matter microstructural organization of interhemispheric pathways predicts different stages of bimanual coordination learning in young and older adults},
	journal = {European Journal of Neuroscience },
	volume = {47},
	year = {2018},
	pages = {446{\textendash}459},
	doi = {10.1111/ejn.13841},
	author = {Hamed Zivari Adab and Sima Chalavi and Iseult Beets and Jolien Gooijers and Inge Leunissen and Boris Cheval and Quinten Collier and Jan Sijbers and Ben Jeurissen and S. P. Swinnen and Matthieu Boisgontier}
}
@inproceedings {1858,
	title = {X-ray Phase-contrast Simulations of Fibrous Phantoms using GATE},
	booktitle = {2018 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)},
	year = {2018},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Sydney, Australia},
	doi = {10.1109/NSSMIC.2018.8824641},
	author = {Jonathan Sanctorum and Jan De Beenhouwer and Jan Sijbers}
}
@article {1752,
	title = {Altered functional brain connectivity in patients with visually induced dizziness},
	journal = {NeuroImage: Clinical},
	volume = {14},
	year = {2017},
	pages = {538{\textendash}545},
	doi = {http://dx.doi.org/10.1016/j.nicl.2017.02.020},
	author = {Angelique Van Ombergen and Lizette Heine and Steven Jillings and Edward Roberts and Ben Jeurissen and V. Van Rompaey and Viviana Mucci and Stefanie Vanhecke and Jan Sijbers and Floris Vanhevel and Stefan Sunaert and Mohamed Ali Bahri and Paul M Parizel and Paul H Van de Heyning and Steven S L Laureys and Floris L Wuyts}
}
@mastersthesis {1758,
	title = {The application of 3D anthropometry for the development of headgear: a case study on the design of ergonometric brain-computer interfaces},
	year = {2017},
	type = {PhD thesis},
	author = {Daniel Lacko}
}
@article {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}
}
@inproceedings {1754,
	title = {Comparison of methods for online inspection of apple internal quality},
	booktitle = {7th Conference on Industrial Computed Tomography},
	year = {2017},
	address = {Leuven, Belgium},
	url = {http://www.ndt.net/events/iCT2017/app/content/Extended_Abstract/35_VanDael.pdf},
	author = {Mattias Van Dael and Pieter Verboven and Luc Van Hoorebeke and Jan Sijbers and Bart Nicolai}
}
@article {1727,
	title = {Data-Driven Affine Deformation Estimation and Correction in Cone Beam Computed Tomography},
	journal = {IEEE Transactions on Image Processing},
	volume = {26},
	year = {2017},
	pages = {1441-1451},
	doi = {10.1109/TIP.2017.2651370},
	author = {Vincent Van Nieuwenhove and Jan De Beenhouwer and Thomas De Schryver and Luc Van Hoorebeke and Jan Sijbers}
}
@article {1744,
	title = {Diffusion Tensor Imaging of the Anterior Cruciate Ligament Graft},
	journal = {Journal of Magnetic Resonance Imaging},
	volume = {46},
	year = {2017},
	pages = {1423{\textendash}1432},
	doi = {10.1002/jmri.25666},
	author = {Pieter Van Dyck and Martijn Froeling and Eline De Smet and Pim Pullens and Micha{\"e}l Torfs and Peter Verdonk and Jan Sijbers and Paul M Parizel and Ben Jeurissen}
}
@conference {Van_Dyck2017-jo,
	title = {Diffusion Tensor Imaging of the Anterior Cruciate Ligament Graft},
	year = {2017},
	pages = {377},
	author = {Pieter Van Dyck and De Smet, Eline and Froeling, Martijn and Peter Verdonk and Torfs, Micha{\"e}l and Pim Pullens and Jan Sijbers and Paul M Parizel and Ben Jeurissen}
}
@conference {1785,
	title = {Dual axis Dark Field Contrast Tomography for visualisation of scattering directions in a CFRP sample},
	year = {2017},
	pages = {79-80},
	address = {Z{\"u}rich, Switzerland},
	abstract = {In Carbon Fiber Reinforced Polymer (CFRP), the carbon fibers embedded into the polymer matrix cause scattering when inspected with X-rays. This scattering is captured within the dark field image when the sample is scanned with a grating based interferometer. The main disadvantage is, however, that only directional scattering information can be achieved due to the orientation of the gratings. By scanning the sample twice, with a 90 degrees rotation of the sample in between, information from two different scattering directions can be combined into one 3D reconstruction volume. In this abstract, such an approach is shown to improve the representation of scattering inside the sample.},
	keywords = {Dark field tomography, Dual axis, Phase contrast, Tomography, X-rays},
	url = {https://indico.psi.ch/getFile.py/access?resId=1\&materialId=2\&confId=5055},
	author = {Eline Janssens and Jan De Beenhouwer and Jonathan Sanctorum and Sascha Senck and Christoph Heinzl and Jan Sijbers}
}
@article {1756,
	title = {The effect of spaceflight and microgravity on the human brain},
	journal = {Journal of Neurology},
	volume = {246},
	year = {2017},
	pages = {18-22},
	doi = {10.1007/s00415-017-8427-x},
	author = {Angelique Van Ombergen and Athena Demertzi and Elena Tomilovskaya and Ben Jeurissen and Jan Sijbers and Inessa B. Kozlovskaya and Paul M Parizel and Paul H Van de Heyning and Stefan Sunaert and Steven S L Laureys and Floris L Wuyts}
}
@article {1676,
	title = {Ergonomic design of an EEG headset using 3D anthropometry},
	journal = {Applied Ergonomics},
	volume = {58},
	year = {2017},
	month = {2017},
	pages = {128{\textendash}136},
	doi = {doi:10.1016/j.apergo.2016.06.002},
	author = {Daniel Lacko and Jochen Vleugels and Erik Fransen and Toon Huysmans and Guido De Bruyne and Marc M. Van Hulle and Jan Sijbers and Stijn Verwulgen}
}
@article {1766,
	title = { Exploring sex differences in the adult zebra finch brain: In vivo diffusion tensor imaging and ex vivo super-resolution track density imaging},
	journal = {NeuroImage},
	volume = {146},
	year = {2017},
	pages = {789-803},
	doi = {10.1016/j.neuroimage.2016.09.067},
	author = {Hamaide, Julie and G. De Groof and Gwendolyn Van Steenkiste and Ben Jeurissen and Johan Van Audekerke and Maarten Naeyaert and Van Ruijssevelt, Lisbeth and Cornil, Charlotte and Jan Sijbers and Marleen Verhoye and Annemie Van Der Linden}
}
@inproceedings {1753,
	title = {Fast Reconstruction of CFRP X-ray Images based on a Neural Network Filtered Backprojection Approach},
	booktitle = {7th Conference on Industrial Computed Tomography},
	year = {2017},
	address = {Leuven, Belgium},
	url = {http://www.ndt.net/events/iCT2017/app/content/Extended_Abstract/63_Janssens_Rev4.pdf},
	author = {Eline Janssens and Sascha Senck and Christoph Heinzl and Johann Kastner and Jan De Beenhouwer and Jan Sijbers}
}
@conference {Giraldo2017-nq,
	title = {Fixel-Based Analysis of Alzheimer{\textquoteright}s Disease Using Multi-Tissue Constrained Spherical Deconvolution of Multi-Shell Diffusion MRI},
	year = {2017},
	pages = {400},
	author = {Diana Giraldo and Struyfs, Hanne and Raffelt, David and Paul M Parizel and Sebastiaan Engelborghs and Romero, Eduardo and Jan Sijbers and Ben Jeurissen}
}
@conference {1768,
	title = {Fracture Patterns In Midshaft Clavicle Fracture},
	year = {2017},
	author = {Van Tongel, Alexander and Lieven De Wilde and Ken De Smet and Thomas Decock and Edward Van Herzele and Robin Van Den Broecke and Yasunori Shimamura and Jan Sijbers and Toon Huysmans}
}
@mastersthesis {1777,
	title = {Improved reliability of fiber orientation estimation and graph theoretical analysis of structural brain networks with diffusion MRI},
	volume = {Doctor of Science},
	year = {2017},
	school = {University of Antwerp},
	type = {PhD thesis},
	author = {Timo Roine}
}
@article {1761,
	title = {Inline Discrete Tomography system: application to agricultural product inspection},
	journal = {Computers and Electronics in Agriculture},
	volume = {138},
	year = {2017},
	pages = {117{\textendash}126},
	doi = {https://doi.org/10.1016/j.compag.2017.04.010},
	author = {Luis Filipe Alves Pereira and Eline Janssens and George D C Cavalcanti and Ing Ren Tsang and Mattias Van Dael and Pieter Verboven and Bart Nicolai and Jan Sijbers}
}
@article {1762,
	title = {Intrinsic connectivity reduces in vestibular-related regions after first-time exposure to short-term gravitational alterations},
	journal = {Scientific Reports},
	volume = {7},
	year = {2017},
	doi = {10.1038/s41598-017-03170-5},
	author = {Angelique Van Ombergen and Floris L Wuyts and Ben Jeurissen and Jan Sijbers and Floris Vanhevel and Steven Jillings and Paul M Parizel and Stefan Sunaert and Paul H Van de Heyning and Vincent Dousset and Steven S L Laureys and Athena Demertzi}
}
@mastersthesis {1738,
	title = {Iterative reconstruction for mobile chest tomosynthesis},
	volume = {PhD in Sciences/Physics},
	year = {2017},
	type = {PhD thesis},
	author = {Jeroen Cant}
}
@conference {1996,
	title = {Maximizing precision in PCASL MRI using an optimized sampling strategy},
	year = {2017},
	author = {Piet Bladt and Arnold Jan den Dekker and Clement, Patricia and Eric Achten and Jan Sijbers}
}
@article {2047,
	title = {Mobile imaging of an object using penetrating radiation},
	number = {WO/2017/178334},
	year = {2017},
	chapter = {PCT/EP2017/058270},
	author = {Jan Sijbers and Jan De Beenhouwer}
}
@mastersthesis {1821,
	title = {Model-based reconstruction algorithms for dynamic X-ray CT},
	volume = {PhD in Sciences/Physics},
	year = {2017},
	type = {PhD thesis},
	author = {Vincent Van Nieuwenhove}
}
@article {1773,
	title = {MoVIT: A tomographic reconstruction framework for 4D-CT},
	journal = {Optics Express},
	volume = {25},
	year = {2017},
	pages = {19236-19250},
	abstract = {4D computed tomography (4D-CT) aims to visualise the temporal dynamics of a 3D sample with a sufficiently high temporal and spatial resolution.  Successive time frames are typically obtained by sequential scanning, followed by independent reconstruction of each 3D dataset. Such an approach requires a large number of projections for each scan to obtain images with sufficient quality (in terms of artefacts and SNR). Hence, there is a clear trade-off between the rotation speed of the gantry (i.e. time resolution) and the quality of the reconstructed images.
In this paper, the MotionVector-based Iterative Technique (MoVIT) is introduced which reconstructs a particular time frame by including the projections of neighbouring time frames as well. It is shown that such a  strategy improves the trade-off between the rotation speed and the SNR.
The framework is tested on both numerical simulations and on 4D X-ray CT datasets of polyurethane foam under compression.
Results show that reconstructions obtained with MoVIT have a significantly higher SNR compared to the SNR of conventional 4D reconstructions.},
	doi = {https://doi.org/10.1364/OE.25.019236},
	author = {Vincent Van Nieuwenhove and Jan De Beenhouwer and Jelle Vlassenbroeck and Mark Brennan and Jan Sijbers}
}
@article {1759,
	title = {Multisensor X-ray inspection of internal defects in horticultural products},
	journal = {Postharvest Biology and Technology},
	volume = {128},
	year = {2017},
	month = {June},
	pages = {33{\textendash}43},
	doi = {https://doi.org/10.1016/j.postharvbio.2017.02.002},
	author = {Mattias Van Dael and Pieter Verboven and Jelle Dhaene and Luc Van Hoorebeke and Jan Sijbers and Bart Nicolai}
}
@article {1775,
	title = {A nonlocal maximum likelihood estimation method for enhancing magnetic resonance phase maps},
	journal = {Signal Image and Video Processing},
	volume = {11},
	year = {2017},
	pages = { 913-920},
	author = {P V Sudeep and Palanisamy, P and Chandrasekharan Kesavadas and Jan Sijbers and Arnold Jan den Dekker and Jeny Rajan}
}
@inproceedings {1755,
	title = {A Novel adaptive PCA Based Denoising Technique for Ultra-High-Rate Computed Tomography},
	booktitle = {7th Conference on Industrial Computed Tomography (iCT 2017)},
	year = {2017},
	month = {02/2017},
	publisher = {ndt.net},
	organization = {ndt.net},
	address = {Leuven (Belgium)},
	abstract = {Increasing the X-ray tomography acquisition rate is of high importance especially in medical applications as well as dynamic processes investigation. Thanks to the high brilliance of synchrotron radiation, it is possible to reduce the exposure time and do a tomographic scan with a sub-second temporal resolution which allows following dynamic processes in 4D (3D space + time).
On the other hand, increasing the acquisition rate leads to more background noise which strictly limits the advantages of high rate scan. We apply a new fast denoising technique using universal properties of eigen-spectrum of random covariance matrices. Our proposed technique is established based on the principal component analysis (PCA) of redundant data which shows that most of the signal-related variance is contained in a few components, whereas the noise is spread over all components. Extensive numerical evaluations of the proposed technique on a real dataset were acquired at the TOMCAT beamline with its ultra-fast endstation, show significant improvement in the quality of reconstructed images and elimination of noise.},
	keywords = {denoising, eigenvalue, principal component analysis (PCA), ultra-fast scan},
	author = {Karim Zarei Zefreh and Federica Marone Welford and Jan Sijbers},
	editor = {Mehrdad Taki}
}
@mastersthesis {1783,
	title = {Online quality control of fruit and vegetables using X-ray imaging},
	volume = {Doctor of Bioscience Engineering},
	year = {2017},
	type = {PhD thesis},
	author = {Mattias Van Dael}
}
@conference {1997,
	title = {Optimal sampling strategy for pseudo-continuous arterial spin labeling MRI},
	year = {2017},
	author = {Piet Bladt and Arnold Jan den Dekker and Clement, Patricia and Eric Achten and Jan Sijbers}
}
@mastersthesis {1926,
	title = {Optimal statistical experiment design for detecting and locating light atoms using quantitative high resolution (scanning) transmission electron microscopy},
	volume = {PhD in Sciences/Physics},
	year = {2017},
	type = {PhD thesis},
	author = {Julie Gonnissen}
}
@article {1722,
	title = {Partial Discreteness: a Novel Prior for Magnetic Resonance Image Reconstruction},
	journal = {IEEE Transactions on Medical Imaging},
	volume = {36},
	year = {2017},
	pages = {1041 - 1053},
	doi = {10.1109/TMI.2016.2645122},
	author = {Gabriel Ramos-Llord{\'e}n and Arnold Jan den Dekker and Jan Sijbers}
}
@article {1774,
	title = {Product sizing with 3D anthropometry and k-medoids clustering},
	journal = {Computer-Aided Design},
	volume = {91},
	year = {2017},
	pages = {60-74},
	doi = {https://doi.org/10.1016/j.cad.2017.06.004},
	author = {Daniel Lacko and Toon Huysmans and Jochen Vleugels and Guido De Bruyne and Marc M. Van Hulle and Jan Sijbers and Stijn Verwulgen}
}
@inproceedings {1743,
	title = {Registration Based SIRT: A reconstruction algorithm for 4D CT},
	booktitle = {7th Conference on Industrial Computed Tomography},
	year = {2017},
	address = {Leuven, Belgium},
	abstract = {The goal of 4D computed tomography (4D CT) is to study the temporal deformation of a 3D sample with a sufficiently high temporal and spatial resolution. Conventionally, the sample is sequentially scanned, resulting in datasets of successive time frames. Each of these datasets is then independently reconstructed. This framework results in a trade-off between the temporal resolution and the signal-to-noise ratio (SNR) of the reconstructed images. The proposed registration based simultaneous iterative reconstruction technique (RBSIRT) allows shortening the acquisition time per time frame, leading to improved temporal resolution at comparable SNR. To this end, the algorithm estimates the deformation field between different time frames, which allows incorporating projections of other time frames into the reconstruction of a particular time frame. The technique was validated on numeric simulations and on a real dynamic experiment of a polyurethane foam sample. The reconstructions obtained with RBSIRT have a significantly higher SNR compared to the SNR of conventional 4D reconstructions.},
	url = {http://www.ndt.net/events/iCT2017/app/content/Paper/42_VanNieuwenhove.pdf},
	author = {Vincent Van Nieuwenhove and Jan De Beenhouwer and Jelle Vlassenbroeck and Maarten Moesen and Mark Brennan and Jan Sijbers}
}
@article {1723,
	title = {A safe, cheap and easy-to-use isotropic diffusion phantom for clinical and multicenter studies},
	journal = {Medical Physics},
	volume = {44},
	year = {2017},
	pages = {1063{\textendash}1070},
	doi = {10.1002/mp.12101},
	author = {Pim Pullens and Piet Bladt and Jan Sijbers and Andrew I.R. Maas and Paul M Parizel}
}
@conference {1764,
	title = {Solving the Free Water Elimination Estimation Problem by Incorporating T2 Relaxation Properties},
	year = {2017},
	author = {Quinten Collier and Jelle Veraart and Arnold Jan den Dekker and Floris Vanhevel and Paul M Parizel and Jan Sijbers}
}
@conference {1794,
	title = {Spherical Deconvolution of Non-Spherically Sampled Diffusion MRI Data},
	year = {2017},
	author = {Jan Morez and Jan Sijbers and Ben Jeurissen}
}
@conference {Morez2017-he,
	title = {Spherical Deconvolution of Non-Spherically Sampled Diffusion MRI Data},
	year = {2017},
	pages = {66},
	author = {Jan Morez and Jan Sijbers and Ben Jeurissen}
}
@conference {1818,
	title = {Statistically optimal separation of multi-component MR signals with a Majorize-Minimize approach: application to MWF estimation},
	year = {2017},
	author = {Gabriel Ramos-Llord{\'e}n and Arnold Jan den Dekker and Piet Bladt and A. Cuyt and Jan Sijbers}
}
@conference {1826,
	title = {Super-resolution multi-PLD PCASL: a simulation study},
	volume = {30 (Suppl. 1)},
	number = {S396},
	year = {2017},
	publisher = {Magn Reson Mater Phy},
	abstract = {Cerebral blood flow (CBF) can be estimated non-invasively with arterial spin labeling (ASL). Multi-post-labeling-delay (PLD) pseudo-continuous ASL (PCASL) allows for accurate CBF estimation by sampling the dynamic perfusion signal at different PLDs and fitting a model to the perfusion data. Unfortunately, ASL difference images have a low SNR. Therefore, CBF estimation in multi-PLD PCASL is imprecise, unless a large number of images is acquired (long scan time) or spatial resolution is lowered significantly. It has been shown that model-based super-resolution reconstruction (SRR) techniques can improve the trade-off between SNR, spatial resolution and acquisition time. The results presented in this work show the promising potential of SRR ASL to outperform conventional ASL readout schemes in terms of achievable precision of HR perfusion measurements in a given acquisition time.},
	doi = {10.1007/s10334-017-0634-z},
	author = {Piet Bladt and Quinten Beirinckx and Gwendolyn Van Steenkiste and Ben Jeurissen and Eric Achten and Arnold Jan den Dekker and Jan Sijbers}
}
@article {1668,
	title = {Super-resolution T1 estimation: quantitative high resolution T1 mapping from a set of low resolution T1 weighted images with different slice orientations},
	journal = {Magnetic Resonance in Medicine},
	volume = {77},
	year = {2017},
	pages = {1818{\textendash}1830},
	doi = {10.1002/mrm.26262},
	author = {Gwendolyn Van Steenkiste and Dirk H J Poot and Ben Jeurissen and Arnold Jan den Dekker and Floris Vanhevel and Paul M Parizel and Jan Sijbers}
}
@article {1706,
	title = {A unified Maximum Likelihood framework for simultaneous motion and T1 estimation in quantitative MR T1 mapping},
	journal = {IEEE Transactions on Medical Imaging},
	volume = {36},
	year = {2017},
	pages = {433 - 446},
	doi = {10.1109/TMI.2016.2611653},
	author = {Gabriel Ramos-Llord{\'e}n and Arnold Jan den Dekker and Gwendolyn Van Steenkiste and Ben Jeurissen and Floris Vanhevel and Johan Van Audekerke and Marleen Verhoye and Jan Sijbers}
}
@conference {1784,
	title = {A workflow to reconstruct grating-based X-ray phase contrast CT images: application to CFRP samples},
	year = {2017},
	pages = {139-140},
	address = {Z{\"u}rich, Switzerland},
	abstract = {Carbon fiber reinforced polymer (CFRP) is an extremely strong and lightweight plastic of which the strength depends on the distribution of its fibers. Fiber bundles can be visualized by means of phase contrast X-ray computed tomography (PCCT) based on grating-based interferometry (GBI). However, many steps are involved in the reconstruction of GBI-PCCT images. In this abstract, a workflow for the reconstruction of 3D CFRP phase contrast images based on GBI projection data is presented.},
	keywords = {Carbon fiber reinforced polymer, Image processing, Phase contrast, Tomography, X-rays},
	url = {https://indico.psi.ch/getFile.py/access?resId=1\&materialId=2\&confId=5055},
	author = {Jonathan Sanctorum and Eline Janssens and Arnold Jan den Dekker and Sascha Senck and Christoph Heinzl and Jan De Beenhouwer and Jan Sijbers}
}
@article {1644,
	title = {3D morphometric analysis of the human incudomallear complex using clinical cone-beam CT},
	journal = {Hearing research},
	volume = {340},
	year = {2016},
	pages = {79-88},
	abstract = {Human middle ears show large morphological variations. This could affect our perception of hearing and explain large variation in experimentally obtained transfer functions. Most morphological studies focus on capturing variation by using landmarks on cadaveric temporal bones. We present statistical shape analysis based on clinical cone beam CT (CBCT) scans of 100 patients. This allowed us to include surface information on the incudomallear (IM) complex (joint, ligaments and tendon not included) of 123 healthy ears with a scanning resolution of 150~μm and without a priori assumptions. Statistical shape modeling yields an average geometry for the IM complex and the variations present in the population with a high precision. Mean values, variation and correlations among anatomical features (length of manubrium, combined length of malleus head and neck, lengths of incus long and short process, enclosing angles, ossicular lever ratio, incudomallear angle, and principal moments of inertia) are reported and compared to results from the literature. Most variation is found in overall size and the angle between incus and malleus. The compact representation provided by statistical shape modeling is demonstrated and its benefits for surface modeling are discussed.},
	issn = {1878-5891},
	doi = {10.1016/j.heares.2016.01.014},
	author = {Joris Soons and Femke Danckaers and Keustermans, William and Toon Huysmans and Jan Sijbers and Casselman, Jan W and Joris J. J. Dirckx}
}
@article {1778,
	title = {Automated quality control and selection },
	number = {PCT/EP2016/055718},
	year = {2016},
	chapter = {WO2016146703 A1},
	author = {Mattias Van Dael and Pieter Verboven and Bart Nicolai and Jelle Dhaene and Luc Van Hoorebeke and Jan Sijbers}
}
@inproceedings {1691,
	title = {Automatic geometric calibration of chest tomosynthesis using data consistency conditions},
	booktitle = {The 4th International Conference on Image Formation in X-Ray Computed Tomography},
	year = {2016},
	pages = {161-164},
	address = {Salt Lake City, Utah, USA},
	author = {Jeroen Cant and Gert Behiels and Jan Sijbers}
}
@conference {1643,
	title = {Building 3D Statistical Shape Models of Vegetables and Fruit},
	year = {2016},
	abstract = {Introduction
Statistical shape modeling is a promising approach to map out the variability of a population. By adapting the parameters of the shape model, a new, realistic surface can be obtained. In this work, the framework for surface registration and building a statistical shape model of fruit is described.

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

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

Applications
Possible applications of a statistical shape model of fruit are predicting the final size of the fruit, search for correlations between stages of growth, estimate the volume from a single view and evaluation of the effect of the fruit shape on airflow characteristics to obtain cooling uniformity.},
	author = {Femke Danckaers and Toon Huysmans and Jan Sijbers}
}
@conference {1694,
	title = {Building a statistical shape model of the interior and exterior of the bell pepper},
	volume = {28-30 November},
	year = {2016},
	address = {Vienna, Austria},
	author = {Femke Danckaers and Toon Huysmans and Seppe Rogge and Mattias Van Dael and Pieter Verboven and Bart Nicolai and Jan Sijbers}
}
@article {1683,
	title = {Chronic exposure to haloperidol and olanzapine leads to common and divergent shape changes in the rat hippocampus in the absence of grey-matter volume loss},
	journal = {Psychological Medicine},
	volume = {46},
	year = {2016},
	pages = {3081-3093},
	doi = {10.1017/S0033291716001768},
	author = {William R Crum and Femke Danckaers and Toon Huysmans and Marie-Caroline Cotel and Sridhar Natesan and Michel M. Modo and Jan Sijbers and Steven C.R. Williams and Shitij Kapur and Anthony C. Vernon}
}
@conference {1728,
	title = {Combining 3D vision and X-ray radiography for internal quality inspection of foods},
	year = {2016},
	month = {28-30 November},
	address = {Austrian Economic Chamber, Vienna, Austria},
	author = {Mattias Van Dael and Femke Danckaers and Thomas De Schryver and Toon Huysmans and Pieter Verboven and Luc Van Hoorebeke and Jan Sijbers and Bart Nicolai}
}
@article {1716,
	title = {Computerized Tomographic Image Exposure and Reconstruction Method},
	number = {PCT/EP2015/059956},
	year = {2016},
	abstract = {A method to improve reconstruction quality for continuous projections. The use of continuous projection
might be advantageous to use cheaper panels and simpler setups for tomosynthesis and CT reconstruction.},
	author = {Jeroen Cant and Jan Sijbers}
}
@inproceedings {1669,
	title = {Continuous Digital Laminography},
	booktitle = {6th Conference on Industrial Computed Tomography},
	year = {2016},
	address = {Wels, Austria},
	url = {http://www.ndt.net/article/ctc2016/papers/ICT2016_paper_id42.pdf},
	author = {Jeroen Cant and Gert Behiels and Jan Sijbers}
}
@article {1584,
	title = {Cortical reorganization in an astronaut{\textquoteright}s brain after long-duration spaceflight},
	journal = {Brain Structure and Function},
	volume = {221},
	year = {2016},
	pages = {2873{\textendash}2876},
	doi = {10.1007/s00429-015-1054-3},
	author = {Athena Demertzi and Angelique Van Ombergen and Elena Tomilovskaya and Ben Jeurissen and Ekaterina V. Pechenkova and Carol Di Perri and Liudmila Litvinova and Enrico Amico and Alena Rumshiskaya and Ilya Rukavishnikov and Jan Sijbers and Valentin Sinitsyn and Inessa B. Kozlovskaya and Stefan Sunaert and Paul M Parizel and Paul H Van de Heyning and Steven S L Laureys and Floris L Wuyts}
}
@article {1651,
	title = {D-BRAIN: Anatomically Accurate Simulated Diffusion MRI Brain Data},
	journal = {PlosOne},
	volume = {11},
	number = {3},
	year = {2016},
	pages = {1-23},
	doi = {10.1371/journal.pone.0149778},
	author = {Daniele Perrone and Ben Jeurissen and Jan Aelterman and Timo Roine and Jan Sijbers and Aleksandra Pizurica and Alexander Leemans and Wilfried Philips}
}
@article {1763,
	title = {Denoising of diffusion MRI using random matrix theory},
	journal = {NeuroImage},
	volume = {142},
	year = {2016},
	pages = {384-396},
	doi = {10.1016/j.neuroimage.2016.08.016},
	author = {Jelle Veraart and Dmitry S. Novikov and Christiaens Daan and Ades-Aron, Benjamin and Jan Sijbers and Els Fieremans}
}
@article {1688,
	title = {Detecting and locating light atoms from high-resolution STEM images: the quest for a single optimal design Ultramicroscopy},
	journal = {Ultramicroscopy},
	volume = {170},
	year = {2016},
	pages = {128-138},
	doi = {http://dx.doi.org/10.1016/j.ultramic.2016.07.014},
	author = {Julie Gonnissen and Annick De Backer and Arnold Jan den Dekker and Jan Sijbers and Sandra Van Aert}
}
@inbook {1673,
	title = {Diffusion Kurtosis Imaging},
	booktitle = { Diffusion Tensor Imaging: a practical handbook},
	year = {2016},
	pages = {407-418},
	publisher = {Springer-Verlag},
	organization = {Springer-Verlag},
	chapter = {Diffusion Kurtosis Imaging},
	address = {New York},
	issn = {ISBN 978-1-4939-3118-7},
	doi = {10.1007/978-1-4939-3118-7},
	author = {Jelle Veraart and Jan Sijbers}
}
@article {1620,
	title = {Diffusion kurtosis imaging probes cortical alterations and white matter pathology following cuprizone-induced demyelination and spontaneous remyelination},
	journal = {NeuroImage},
	volume = {125},
	year = {2016},
	pages = {363{\textendash}377},
	doi = {10.1016/j.neuroimage.2015.10.052},
	author = {Caroline Guglielmetti and Jelle Veraart and Ella Roelant and Zhenhua Mai and Jasmijn Daans and Johan Van Audekerke and Maarten Naeyaert and Greetje Vanhoutte and Rafael  Delgado Y Palacios and Jelle Praet and Els Fieremans and Peter Ponsaerts and Jan Sijbers and Annemie Van Der Linden and Marleen Verhoye}
}
@inproceedings {1652,
	title = {Discrete tomographic reconstruction from deliberately motion blurred X-ray projections},
	booktitle = {6th Conference on Industrial Computed Tomography},
	year = {2016},
	pages = {1-6},
	address = {Wels, Austria},
	author = {Wim Van Aarle and Jeroen Cant and Jan De Beenhouwer and Jan Sijbers}
}
@article {1715,
	title = {A distributed ASTRA Toolbox},
	journal = {Advanced Structural and Chemical Imaging},
	volume = {2},
	year = {2016},
	doi = {10.1186/s40679-016-0032-z},
	author = {Willem Jan Palenstijn and Jeroen B{\'e}dorf and Jan Sijbers and Kees Joost Batenburg}
}
@inproceedings {1619,
	title = {Dynamic flat field correction in X-ray computed tomography},
	booktitle = {Optimess conference},
	year = {2016},
	edition = {Antwerp},
	author = {Vincent Van Nieuwenhove and Jan De Beenhouwer and Jan Sijbers}
}
@inproceedings {1627,
	title = {A dynamic region estimation method for cerebral perfusion CT},
	booktitle = {6th International Conference on Optical Measurement Techniques for Structures and Systems (OPTIMESS)},
	year = {2016},
	pages = {331-342},
	abstract = {In cerebral perfusion computed tomography (PCT), multiple scans of the brain are acquired after an intravenous contrast bolus injection. Therefore, radiation dose is a major issue. 
Recently, methods have been proposed that can reconstruct high quality dynamic (i.e., 4D) images, while keeping the radiation dose limited. These methods typically require an accurate estimate of the dynamic region inside the brain volume, i.e., the region containing tissue/vessels. Conventionally, the dynamic region is indicated manually.
In this work, a method for low-dose cerebral PCT is presented in which the dynamic region is estimated in an automatic way.
Simulation results on two PCT phantoms show that the dynamic region can be accurately estimated, even in a very low-dose regime, which is an important step towards more powerful reconstruction methods for low-dose cerebral PCT.
},
	author = {Van Eyndhoven, Geert and Jan De Beenhouwer and Jan Sijbers}
}
@article {1600,
	title = {Easy implementation of advanced tomography algorithms using the ASTRA toolbox with Spot operators},
	journal = {Numerical Algorithms},
	volume = {71},
	number = {3},
	year = {2016},
	pages = {673-697},
	doi = {10.1007/s11075-015-0016-4},
	author = {Folkert Bleichrodt and Tristan van Leeuwen and Willem Jan Palenstijn and Wim Van Aarle and Jan Sijbers and Kees Joost Batenburg}
}
@article {1701,
	title = {Fast and Flexible X-ray Tomography Using the ASTRA Toolbox},
	journal = {Optics Express},
	volume = {24},
	number = {22},
	year = {2016},
	pages = {25129-25147},
	doi = {10.1364/OE.24.025129},
	author = {Wim Van Aarle and Willem Jan Palenstijn and Jeroen Cant and Eline Janssens and Folkert Bleichrodt and Andrei Dabravolski and Jan De Beenhouwer and Kees Joost Batenburg and Jan Sijbers}
}
@article {1665,
	title = {Fast inline inspection by neural network based filtered backprojection: Application to apple inspection},
	journal = {Case Studies in Nondestructive Testing and Evaluation},
	volume = {6},
	year = {2016},
	pages = {14{\textendash}20},
	doi = {10.1016/j.csndt.2016.03.003},
	author = {Eline Janssens and Luis Filipe Alves Pereira and Jan De Beenhouwer and Ing Ren Tsang and Mattias Van Dael and Pieter Verboven and Bart Nicolai and Jan Sijbers}
}
@inproceedings {1663,
	title = {Fast X-ray Computed Tomography via Image Completion},
	booktitle = {6th Conference on Industrial Computed Tomography(iCT)},
	year = {2016},
	pages = {1-5},
	address = {Wels, Austria},
	author = {Luis Filipe Alves Pereira and Eline Janssens and Mattias Van Dael and Pieter Verboven and Bart Nicolai and George D C Cavalcanti and Ing Jyh Tsang and Jan Sijbers}
}
@inproceedings {1695,
	title = {Foot Abnormality Mapping using Statistical Shape Modelling},
	booktitle = {7th International Conference and Exhibition on 3D Body Scanning Technologies (3DBST)},
	year = {2016},
	pages = {70-79},
	edition = {Lugano, Switzerland, 30 November - 1 December},
	doi = {doi:10.15221/16.070},
	author = {Kristina Stankovi{\'c} and Femke Danckaers and Brian G Booth and Fien Burg and Saartje Duerinck and Jan Sijbers and Toon Huysmans}
}
@conference {Van_Steenkiste2016-fy,
	title = {High Resolution Diffusion Tensor Reconstruction from Simultaneous Multi-Slice Acquisitions in a Clinically Feasible Scan Time},
	year = {2016},
	pages = {2},
	author = {Gwendolyn Van Steenkiste and Ben Jeurissen and Baete, Steven and Arnold Jan den Dekker and Dirk H J Poot and Boada, Fernando and Jan Sijbers}
}
@conference {1642,
	title = {In vivo high resolution diffusion tensor imaging in a clinically acceptable scan time by combining super resolution reconstruction with simultaneous multi-slice acquisition},
	volume = {8},
	number = {P-036},
	year = {2016},
	author = {Gwendolyn Van Steenkiste and Ben Jeurissen and Steven Baete and Arnold Jan den Dekker and Dirk H J Poot and Fernando Boada and Jan Sijbers}
}
@article {1708,
	title = {In-line NDT with X-Ray CT combining sample rotation and translation},
	journal = {NDT \& E International},
	volume = {89},
	year = {2016},
	pages = {89{\textendash}98},
	doi = {10.1016/j.ndteint.2016.09.001},
	author = {Thomas De Schryver and Jelle Dhaene and Manuel Dierick and Boone, M.N. and Eline Janssens and Jan Sijbers and Mattias Van Dael and Pieter Verboven and Bart Nicolai and Luc Van Hoorebeke}
}
@article {1664,
	title = {Integration of TomoPy and the ASTRA toolbox for advanced processing and reconstruction of tomographic synchrotron data},
	journal = {Journal of Synchrotron Radiation},
	volume = {23},
	year = {2016},
	pages = {842-849},
	doi = {10.1107/S1600577516005658},
	author = {Daan Pelt and Doga Gursoy and Willem Jan Palenstijn and Jan Sijbers and Francesco De Carlo and Kees Joost Batenburg}
}
@conference {1741,
	title = {Investigating lattice strain in Au nanodecahedrons},
	year = {2016},
	doi = {10.1002/9783527808465.EMC2016.5519},
	author = {Bart Goris and Jan De Beenhouwer and Annick De Backer and Daniele Zanaga and Kees Joost Batenburg and Ana S{\'a}nchez-Iglesias and Luis M Liz-Marzán and Sandra Van Aert and Jan Sijbers and Van Tendeloo, Gustaaf and Sara Bals}
}
@inproceedings {1667,
	title = {Investigation on Effect of scintillator thickness on Afterglow in Indirect X-ray Detectors},
	booktitle = {6th Conference on Industrial Computed Tomography, Wels, Austria (iCT 2016)},
	year = {2016},
	abstract = {Solid-state scintillation detectors are widely used in modern multi-slice CT systems as well as synchrotron micro-tomography beamlines. Amongst other parameters, the performance of these detectors depends on the thickness of the scintillator. Thicker scintillators result in higher emission intensities, yet the resolution deteriorates as the thickness increases. To achieve a higher scan speed, thicker scintillators are more common. The thickness of scintillators however may influence the afterglow.  In this paper, we investigate the effect of scintillator thickness on the afterglow, using scintillating screens of two different materials (LAG:Ce and Gadox) and different thicknesses.  
Experimental results show that, apart from the scintillator material and excitation condition, the thickness of scintillator has a decisive role on the scintillator decay and particularly on the afterglow.
},
	keywords = {afterglow, flat-panel, image lag, scintillator thickness, synchrotron, x-ray detector},
	url = {http://www.ndt.net/article/ctc2016/papers/ICT2016_paper_id74.pdf},
	author = {Karim Zarei Zefreh and Jan De Beenhouwer and Federica Marone Welford and Jan Sijbers}
}
@inproceedings {1719,
	title = {Investigation on the effect of exposure time on scintillator afterglow for ultra-fast tomography acquisition},
	booktitle = {18th International Workshop on Radiation Imaging Detectors},
	volume = {11},
	year = {2016},
	pages = {C12014},
	publisher = {IOP Publishing},
	organization = {IOP Publishing},
	address = {Barcelona, Spain},
	doi = {10.1088/1748-0221/11/12/C12014},
	author = {Karim Zarei Zefreh and Federica Marone Welford and Jan Sijbers}
}
@article {1748,
	title = {Investigation on the effect of exposure time on scintillator afterglow for ultra-fast tomography acquisition},
	journal = {Journal of Instrumentation},
	volume = {11},
	year = {2016},
	month = {12/2016},
	abstract = {Thanks to the ultra-fast endstation of the TOMCAT beamline, it is possible to do a tomographic scan with a sub-second temporal resolution which allows following dynamic processes in 4D (3D space + time). This ultra- high-rate tomography acquisition, exploiting the distinctive peculiarities of synchrotron radiation, provides nondestructive investigation of many dynamic processes which were not possible in the past. For example a continuous tensile test has been conducted recently in-situ for the first time with a frequency of 20 tomograms per second (20 Hz acquisition frequency). In the ultra-fast endstation a scintillator is used to convert X-ray to visible photons that can be detected by the camera. However, this conversion is not ideal and the scintillator response decays exponentially with afterglow. Afterglow can cause resolution degradation and artifacts (such as ring and band) especially with high rotation speed. On the other hand, to achieve a higher scan speed, thicker scintillators are more common because they result in higher emission intensities that can compensate the short exposure time in fast scans. However, the resolution deteriorates as the scintillator{\textquoteright}s thickness increases and thicker scintillators show higher afterglow. Performing many ultra-fast scans at the TOMCAT beamline with different acquisition rate, we demonstrate how the exposure time effects on the projection data and reconstructed images. Using two different thicknesses of LAG scintillator we also investigate the afterglow artifacts for different acquisition rate and exposure time.},
	keywords = {4DCT, computed tomography, exposure time, scintillator},
	doi = {10.1088/1748-0221/11/12/C12014},
	url = {http://iopscience.iop.org/article/10.1088/1748-0221/11/12/C12014/meta;jsessionid=DE490396BDCED3C5746F4059065713A1.c3.iopscience.cld.iop.org},
	author = {Karim Zarei Zefreh},
	editor = {Federica Marone Welford}
}
@article {1703,
	title = {Local Attenuation Curve Optimization (LACO) framework for high quality perfusion maps in low-dose cerebral perfusion CT},
	journal = {Medical Physics},
	volume = {43},
	year = {2016},
	pages = {6429-6438},
	doi = {10.1118/1.4967263},
	author = {Vincent Van Nieuwenhove and Van Eyndhoven, Geert and Kees Joost Batenburg and Nico Buls and Jaf Vandemeulebroucke and Jan De Beenhouwer and Jan Sijbers}
}
@article {1781,
	title = {Method and system for correcting geometric misalignment during image reconstruction in chest tomosynthesis},
	number = {EP16173767.1},
	year = {2016},
	abstract = {A method to find panel misalignment. Necessary when exact panel position is unknown.},
	issn = {EP16173767.1},
	author = {Jeroen Cant and Jan Sijbers}
}
@conference {Roine2016-je,
	title = {Methodological Considerations on Graph Theoretical Analysis of Structural Brain Networks},
	year = {2016},
	pages = {3437},
	author = {Roine, Timo and Ben Jeurissen and Perrone, Daniele and Jan Aelterman and Philips, Wilfried and Jan Sijbers and Leemans, Alexander}
}
@inproceedings {1653,
	title = {Multi-voxel algorithm for quantitative bi-exponential MRI T1 estimation},
	booktitle = {SPIE Medical Imaging},
	volume = {9784},
	year = {2016},
	pages = {978402},
	address = {San Diego, California, United States of America},
	abstract = {In this work, we propose a joint multi-voxel bi-exponential estimator (JMBE) for quantitative bi-exponential T1 estimation in magnetic resonance imaging, to account for partial volume effects and to yield more accurate results compared to single-voxel bi-exponential estimators (SBEs). Using a numerical brain phantom with voxels containing two tissues, the minimal signal-to-noise ratio (SNR) needed to estimate both T1 values with sufficient accuracy was investigated. Compared to the SBE, and for clinically achievable single-voxel SNRs, the JMBE yields accurate T1 estimates if four or more neighboring voxels are used in the joint estimation framework, in which case it is also efficient.},
	doi = {http://dx.doi.org/10.1117/12.2216831},
	author = {Piet Bladt and Gwendolyn Van Steenkiste and Gabriel Ramos-Llord{\'e}n and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {1647,
	title = {NOVIFAST: A fast non-linear least squares method for accurate and precise estimation of T1 from SPGR signals},
	year = {2016},
	author = {Gabriel Ramos-Llord{\'e}n and Arnold Jan den Dekker and Marcus Bj{\"o}rk and Marleen Verhoye and Jan Sijbers}
}
@article {1730,
	title = {Orthosis},
	number = { WO/2016/181282},
	year = {2016},
	url = {https://www.google.com/patents/WO2016181282A1?cl=en},
	author = {Toon Huysmans and Jan Sijbers and Frederik Verstreken}
}
@inproceedings {1710,
	title = {Pixel clustering for face recognition},
	booktitle = {5th Brazilian Conference on Intelligent Systems},
	year = {2016},
	pages = {121-126},
	address = {Brazil},
	doi = {https://doi.org/10.1109/BRACIS.2016.032},
	author = {Tiago B. A. de Carvalho and Maria A. A. Sibaldo and Ing Ren Tsang and George D C Cavalcanti and Ing Jyh Tsang and Jan Sijbers}
}
@inproceedings {1573,
	title = {Projection-based polygon estimation in X-ray computed tomography},
	booktitle = {6th International Conference on Optical Measurement Techniques for Structures and Systems (OPTIMESS)},
	year = {2016},
	pages = {41-50},
	author = {Andrei Dabravolski and Jan De Beenhouwer and Jan Sijbers}
}
@article {1674,
	title = {Quantitative 3D analysis of huge nanoparticle assemblies.},
	journal = {Nanoscale},
	volume = {8},
	year = {2016},
	month = {2016 Jan 7},
	pages = {292-9},
	abstract = {Nanoparticle assemblies can be investigated in 3 dimensions using electron tomography. However, it is not straightforward to obtain quantitative information such as the number of particles or their relative position. This becomes particularly difficult when the number of particles increases. We propose a novel approach in which prior information on the shape of the individual particles is exploited. It improves the quality of the reconstruction of these complex assemblies significantly. Moreover, this quantitative Sparse Sphere Reconstruction approach yields directly the number of particles and their position as an output of the reconstruction technique, enabling a detailed 3D analysis of assemblies with as many as 10,000 particles. The approach can also be used to reconstruct objects based on a very limited number of projections, which opens up possibilities to investigate beam sensitive assemblies where previous reconstructions with the available electron tomography techniques failed.},
	issn = {2040-3372},
	doi = {10.1039/c5nr06962a},
	author = {Daniele Zanaga and Folkert Bleichrodt and Thomas Altantzis and Winckelmans, Naomi and Willem Jan Palenstijn and Jan Sijbers and de Nijs, Bart and Marijn A van Huis and Ana S{\'a}nchez-Iglesias and Luis M Liz-Marzán and van Blaaderen, Alfons and Kees Joost Batenburg and Sara Bals and Van Tendeloo, Gustaaf}
}
@conference {1672,
	title = {Robust DKI parameter estimation in case of CSF partial volume effects},
	year = {2016},
	abstract = {Diffusion kurtosis imaging (DKI) suffers from partial volume effects caused by cerebrospinal fluid (CSF). We propose a DKI+CSF model combined with a framework to robustly estimate the DKI parameters. Since the estimation problem is ill-conditioned, a Bayesian estimation approach with a shrinkage prior is incorporated. Both simulation and real data experiments suggest that the use of this prior leads to a more accurate, precise and robust estimation of the DKI+CSF model parameters. Finally, we show that not correcting for the CSF compartment can lead to severe biases in the parameter estimations.},
	author = {Quinten Collier and Arnold Jan den Dekker and Ben Jeurissen and Jan Sijbers}
}
@conference {Collier2016-tu,
	title = {Robust DKI Parameter Estimation in Case of CSF Partial Volume Effects},
	year = {2016},
	pages = {1044},
	author = {Collier, Quinten and Arnold Jan den Dekker and Ben Jeurissen and Jan Sijbers}
}
@conference {1671,
	title = {A robust framework for combined estimation of DKI and CSF partial volume fraction parameters},
	year = {2016},
	abstract = {Diffusion kurtosis imaging (DKI) suffers from partial volume effects caused by cerebrospinal fluid (CSF). We propose a DKI+CSF model combined with a framework to robustly estimate the DKI parameters. Since the estimation problem is ill-conditioned, a Bayesian estimation approach with a shrinkage prior is incorporated. Both simulation and real data experiments suggest that the use of this prior leads to a more accurate, precise and robust estimation of the DKI+CSF model parameters. Finally, we show that not correcting for the CSF compartment can lead to severe biases in the parameter estimations.},
	author = {Quinten Collier and Arnold Jan den Dekker and Ben Jeurissen and Jan Sijbers}
}
@article {1612,
	title = {A segmentation and classification algorithm for online detection of internal disorders in citrus using X-ray radiographs},
	journal = {Postharvest Biology and Technology},
	volume = {112},
	year = {2016},
	pages = {205-214},
	doi = {10.1016/j.postharvbio.2015.09.020},
	author = {Mattias Van Dael and S Lebotsa and E Herremans and Pieter Verboven and Jan Sijbers and U. L. Opara and U. L. Cronje and Bart Nicolai}
}
@article {1704,
	title = {StatSTEM: An efficient approach for accurate and precise model-based quantification of atomic resolution electron microscopy images},
	journal = {Ultramicroscopy},
	volume = {171},
	year = {2016},
	pages = {104{\textendash}116},
	doi = {http://dx.doi.org/10.1016/j.ultramic.2016.08.018},
	author = {Annick De Backer and K.H.W. van den Bos and Wouter Van den Broek and Jan Sijbers and Sandra Van Aert}
}
@mastersthesis {1713,
	title = {Super-resolution estimation of quantitative MRI parameters},
	volume = {Doctor of Science},
	year = {2016},
	school = {University of Antwerp},
	type = {PhD thesis},
	author = {Gwendolyn Van Steenkiste}
}
@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}
}
@conference {1765,
	title = {Understanding microstructural deformation of apple tissue from 4D micro-CT imaging},
	year = {2016},
	doi = {10.17660/ActaHortic.2018.1197.2},
	author = {Wang, Z and Seppe Rogge and Mattias Van Dael and Vincent Van Nieuwenhove and Pieter Verboven and Jan Sijbers and Bart Nicolai}
}
@article {1648,
	title = {Unsupervised Retinal Vessel Segmentation Using Combined Filters},
	journal = {Plos One},
	volume = {11},
	number = {2},
	year = {2016},
	pages = {1-21},
	doi = {10.1371/journal.pone.0149943},
	author = {Wendeson S. Oliveira and Joyce Vitor Teixeira and Ing Ren Tsang and George D C Cavalcanti and Jan Sijbers}
}
@conference {1589,
	title = {A 4D CT reconstruction algorithm for fast liquid flow imaging},
	year = {2015},
	abstract = {  The study of fluid flow through solid matter by computed tomography (CT) imaging has a broad range of applications, ranging from oil extraction to scientific research on fluid dynamics. Current techniques are often limited by a low temporal/spatial resolution. In this talk, a new iterative CT reconstruction algorithm for improved temporal/spatial resolution in the imaging of fluid flowing through solid matter is introduced. The proposed algorithm exploits prior knowledge in two ways. Firstly, the time-varying object is assumed to consist of stationary (the solid matter) and dynamic regions (the fluid flow). Secondly, the attenuation of a particular voxel in the dynamic region is modeled by a piecewise constant function over time (i.e., the voxel consists of fluid or air). Experiments on simulation data and on a real neutron tomography dataset demonstrate that the proposed approach can significantly increase the temporal resolution in comparison to conventional algorithms.},
	author = {Van Eyndhoven, Geert and Kees Joost Batenburg and Kazantsev, Daniil and Vincent Van Nieuwenhove and Lee, Peter D. and Katherine J Dobson and Jan Sijbers}
}
@inproceedings {1592,
	title = {Affine deformation correction in cone beam Computed Tomography},
	booktitle = {Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine},
	year = {2015},
	pages = {182-185},
	address = {Newport, Rhode Island, USA},
	abstract = {In Computed Tomography (CT), motion and deformation during the acquisition produce streaks and blurring, known as motion artefacts. In contrast to other deformation correction techniques, this work introduces an efficient algorithm to correct for global affine deformations directly on the cone beam projections. During an experiment, the exact deformation parameters are unknown. These parameters are estimated in the projection domain by minimizing a plane based raw data redundancy criterion. Simulations and experimental data show a reduction of motion artefacts in the reconstructed images.},
	url = {http://fully3d.org/proceedings.html},
	author = {Vincent Van Nieuwenhove and Jan De Beenhouwer and Thomas De Schryver and Luc Van Hoorebeke and Jan Sijbers}
}
@conference {1599,
	title = {Assessing inter-subject variability of white matter response functions used for constrained spherical deconvolution},
	volume = {23},
	year = {2015},
	pages = {2834},
	author = {Ben Jeurissen and Jan Sijbers and Jacques-Donald Tournier}
}
@article {1580,
	title = {The ASTRA Toolbox: a platform for advanced algorithm development in electron tomography},
	journal = {Ultramicroscopy},
	volume = {157},
	year = {2015},
	pages = {35{\textendash}47},
	doi = {10.1016/j.ultramic.2015.05.002},
	author = {Wim Van Aarle and Willem Jan Palenstijn and Jan De Beenhouwer and Thomas Altantzis and Sara Bals and Kees Joost Batenburg and Jan Sijbers}
}
@article {1582,
	title = {Building a Statistical Shape Model of the Apple from Corresponded Surfaces},
	journal = {Chemical Engineering Transactions},
	volume = {44},
	year = {2015},
	pages = {49-54},
	address = {Milano, Italy},
	abstract = {In this paper, a method for building a 3D statistical shape model of the apple is described. The framework consists of two parts. First, a reference surface is registered to each apple surface, derived from 3D CT scans of apples, of the population to obtain meaningful correspondences between the shapes. In the second part, the corresponded surfaces are used to build a statistical shape model from the population of apples. This model maps out the variability within the population and by adapting the shape model parameters, new, realistic surfaces can be obtained. By parameterizing the surface, an apple can be described with a compact set of basis functions, which has applications in surface fitting description, recognition, or meshing, e.g. for storage simulation. The constructed apple shape model is tested on performance and has proven to be a good representation of the population and can be used in many applications.},
	doi = { DOI:10.3303/CET1544009},
	author = {Femke Danckaers and Toon Huysmans and Mattias Van Dael and Pieter Verboven and Bart Nicolai and Jan Sijbers}
}
@conference {1598,
	title = {Combination of super-resolution reconstruction diffusion tensor imaging and track density imaging reveals song control system connectivity in zebra finches},
	volume = {23},
	year = {2015},
	pages = {2861},
	author = {Gwendolyn Van Steenkiste and Hamaide, Julie and Ben Jeurissen and Dirk H J Poot and Johan Van Audekerke and Jan Sijbers and Marleen Verhoye}
}
@conference {Van_Steenkiste2015-va,
	title = {Combination of super-resolution reconstruction diffusion tensor imaging and track density imaging reveals song control system connectivity in zebra finches},
	year = {2015},
	pages = {2861},
	author = {Gwendolyn Van Steenkiste and Hamaide, Julie and Ben Jeurissen and Dirk H J Poot and Johan Van Audekerke and Jan Sijbers and Verhoye, Marleen}
}
@conference {1624,
	title = {CSF partial volume modeling in diffusion kurtosis imaging: a comparative parameter estimation study},
	year = {2015},
	month = {11/2015},
	doi = {10.3389/conf.fninf.2015.19.00039},
	url = {http://www.frontiersin.org/myfrontiers/events/abstractdetails.aspx?abs_doi=10.3389/conf.fninf.2015.19.00039},
	author = {Quinten Collier and Jelle Veraart and Arnold Jan den Dekker and Ben Jeurissen and Jan Sijbers}
}
@article {1604,
	title = {Diffusion Kurtosis Imaging: a possible MRI biomarker for AD diagnosis?},
	journal = {Journal of Alzheimer{\textquoteright}s Disease},
	volume = {48},
	number = {4},
	year = {2015},
	pages = {937-948},
	doi = {10.3233/JAD-150253},
	author = {Hanna Struyfs and Wim Van Hecke and Jelle Veraart and Jan Sijbers and Sylvie Slaets and Maya De Belder and Laura Wuyts and Benjamin Peters and Kristel Sleegers and Caroline Robberecht and Van Broeckhoven, Christine and Frank De Belder and Paul M Parizel and Sebastiaan Engelborghs}
}
@conference {1594,
	title = {Diffusion kurtosis imaging probes cortical alterations and white matter pathology following cuprizone-induced demyelination and spontaneous remyelination},
	volume = {23},
	number = {4332},
	year = {2015},
	author = {Caroline Guglielmetti and Jelle Veraart and Ella Roelant and Zhenhua Mai and Jasmijn Daans and Johan Van Audekerke and Jelle Praet and Peter Ponsaerts and Jan Sijbers and Annemie Van Der Linden and Marleen Verhoye}
}
@article {1615,
	title = {Dynamic intensity normalization using eigen flat fields in X-ray imaging},
	journal = {Optics Express},
	volume = {23},
	year = {2015},
	pages = {27975-27989},
	abstract = {In X-ray imaging, it is common practice to normalize the acquired projection data with averaged flat fields taken prior to the scan. Unfortunately, due to source instabilities, vibrating beamline components such as the monochromator, time varying detector properties, or other confounding factors, flat fields are often far from stationary, resulting in significant systematic errors in intensity normalization. In this work, a simple and efficient method is proposed to account for dynamically varying flat fields. Through principal component analysis of a set of flat fields, eigen flat fields are computed. A linear combination of the most important eigen flat fields is then used to individually normalize each X-ray projection. Experiments show that the proposed dynamic flat field correction leads to a substantial reduction of systematic errors in projection intensity normalization compared to conventional flat field correction. },
	doi = {10.1364/OE.23.027975},
	author = {Vincent Van Nieuwenhove and Jan De Beenhouwer and Francesco De Carlo and Lucia Mancini and Federica Marone and Jan Sijbers}
}
@inproceedings {1621,
	title = {Evaluation of 3D Body Shape Predictions Based on Features},
	booktitle = {6th International Conference on 3D Body Scanning Technologies},
	year = {2015},
	pages = {258-265},
	address = {Lugano, Switserland},
	abstract = {The human body comes in many sizes and shapes. For design purposes, it is useful to be able to quickly simulate a virtual mannequin of a customer. A statistical shape model can be used for this purpose, because it describes the main variations of body shape inside the model{\textquoteright}s population. From this model, the specific features of each person in the population are known. Therefore, a mapping between the shape model parameters and specific features can be calculated, which allows adjusting the body shape, in an intuitive way. In this work, we have investigated how accurate a body shape can be predicted based on a set of features and which features are most suitable for this purpose. Height, weight, and hip circumference appeared to be the most suitable features to accurately predict the body shape.},
	keywords = {body features, shape prediction},
	doi = {10.15221/15.258},
	author = {Femke Danckaers and Toon Huysmans and Daniel Lacko and Jan Sijbers}
}
@article {1517,
	title = {Evaluation of an anthropometric shape model of the human scalp},
	journal = {Applied Ergonomics},
	volume = {48},
	year = {2015},
	pages = {70-85},
	doi = {10.1016/j.apergo.2014.11.008},
	author = {Daniel Lacko and Toon Huysmans and Paul M Parizel and Guido De Bruyne and Stijn Verwulgen and Marc M. Van Hulle and Jan Sijbers}
}
@conference {1618,
	title = {A fast 4D CT reconstruction algorithm for affine deforming objects},
	year = {2015},
	edition = {Helsinki},
	author = {Vincent Van Nieuwenhove and Jan De Beenhouwer and Jan Sijbers}
}
@article {1593,
	title = {Fast Fourier-based phase unwrapping on the graphics processing unit in real-time imaging applications},
	journal = {Journal of Imaging},
	volume = {1},
	year = {2015},
	pages = {31-44},
	doi = {doi:10.3390/jimaging1010031 },
	author = {Sam Van der Jeught and Jan Sijbers and Joris J. J. Dirckx}
}
@article {1591,
	title = {Fast Neural Network Based X-Ray Tomography of Fruit on a Conveyor Belt},
	journal = {Chemical Engineering Transactions},
	volume = {44},
	year = {2015},
	pages = {181-186},
	address = {Milano, Italy},
	doi = {10.3303/CET1544031},
	author = {Eline Janssens and Daan Pelt and Jan De Beenhouwer and Mattias Van Dael and Pieter Verboven and Bart Nicolai and Jan Sijbers}
}
@inproceedings {1542,
	title = {Filtered backprojection using algebraic filters; Application to biomedical micro-CT data},
	booktitle = {International Symposium on Biomedical Imaging (ISBI)},
	year = {2015},
	pages = {1596-1599},
	author = {Linda Plantagie and Wim Van Aarle and Kees Joost Batenburg and Jan Sijbers}
}
@mastersthesis {2078,
	title = {Full-field X-ray orientation imaging using convex optimization and a discrete representation of six-dimensional position - orientation space},
	year = {2015},
	type = {PhD thesis},
	author = {Nicola Roberto Vigano}
}
@conference {1597,
	title = {Gibbs ringing removal in diffusion MRI using second order total variation minimization},
	volume = {23},
	year = {2015},
	pages = {2809},
	author = {Jelle Veraart and Florian Knoll and Jan Sijbers and Els Fieremans and Dmitry S. Novikov}
}
@conference {1633,
	title = {High resolution diffusion tensor imaging in a clinically feasible scan time},
	year = {2015},
	doi = {10.3389/conf.fninf.2015.19.00016},
	author = {Gwendolyn Van Steenkiste and Ben Jeurissen and Steven Baete and Arnold Jan den Dekker and Dirk H J Poot and Fernando Boada and Jan Sijbers}
}
@inproceedings {1548,
	title = {High resolution T1 estimation from multiple low resolution magnetic resonance images},
	booktitle = {IEEE International Symposium on Biomedical Imaging (ISBI): From nano to macro},
	volume = {12},
	year = {2015},
	pages = {1036-1039},
	doi = {10.1109/ISBI.2015.7164048},
	author = {Gwendolyn Van Steenkiste and Dirk H J Poot and Ben Jeurissen and Arnold Jan den Dekker and Jan Sijbers}
}
@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}
}
@conference {1588,
	title = {An iterative prior knowledge based reconstruction algorithm for increased temporal/spatial resolution in the CT imaging of fluid flow through solid matter},
	year = {2015},
	abstract = {The study of fluid flow through solid matter by computed tomography (CT) imaging has a broad range of applications, e.g., in oil production (the manner in which water displaces oil in rocks determines the fraction of oil that can be recovered) and in scientific research on fluid dynamics (validation of fluid flow models) [1-4]. In this work, a novel iterative computed tomography reconstruction algorithm for improved temporal/spatial resolution in the imaging of fluid flowing through solid matter is introduced. The proposed algorithm exploits prior knowledge in a twofold manner. Firstly, a dynamic reconstruction is generated assuming the existence of stationary regions (the solid matter) and dynamic regions (the fluid flow) throughout the reconstruction domain. This assumption is enforced by sharing iterative updates in the stationary regions over different time frames. Secondly, the fact that a particular voxel in the dynamic region can typically be described by a piecewise constant (PWC) function over time (i.e., the voxel consists of fluid or air) is exploited by estimating a PWC function in a robust manner in all voxels belonging to the dynamic region at intermediate iterations. The proposed reconstruction algorithm was validated on simulation data and on a real neutron tomography dataset (PSI ICON experiment, courtesy of Manchester X-ray Imaging Facility). These experiments demonstrated that the new iterative technique is able to significantly increase the temporal resolution in comparison to more conventional algorithms such as the Simultaneous Iterative Reconstruction Technique (SIRT) or Filtered Backprojection (FBP) [5-6].

[1]	V. Cnudde and M. Boone: High-resolution x-ray computed tomography in geosciences: A review of the current technology and applications. Earth-Science Reviews, vol. 123, pp. 1 {\textendash} 17, 2013
[2]	S. Akin and A. Kovscek: Computed tomography in petroleum engineering research. Geological Society, London, Special Publications, vol. 215, no. 1, pp. 23{\textendash}38, 2003
[3]	O. P. Wennberg, L. Rennan, and R. Basquet: Computed tomography scan imaging of natural open fractures in a porous rock; geometry and fluid flow. Geophysical Prospecting, vol. 57, no. 2, pp. 239{\textendash}249, 2009
[4]	M. Kumar, T. J. Senden, A. P. Sheppard, J. P. Middleton, and M. A. Knackstedt: Visualizing and quantifying the residual phase distribution in core material. Petrophysics, vol. 51, no. 5, p. 323, 2010
[5]	J. Gregor and T. Benson: Computational analysis and improvement of SIRT. IEEE Trans. Med. Imag., vol. 27, no. 7, pp. 918{\textendash}24, 2008
[6]	L. Feldkamp, L. Davis, and J. Kress: Practical cone-beam algorithm. J. Opt. Soc. Am. A, vol. 1, no. 6, pp. 612{\textendash}619, 1984
},
	author = {Van Eyndhoven, Geert and Kees Joost Batenburg and Kazantsev, Daniil and Jan Sijbers}
}
@article {1492,
	title = {Iterative Reweighted Linear Least Squares for Accurate, Fast, and Robust Estimation of Diffusion Magnetic Resonance Parameters},
	journal = {Magnetic Resonance in Medicine},
	volume = {73},
	year = {2015},
	pages = {2174{\textendash}2184},
	abstract = {Purpose: Diffusion-weighted magnetic resonance imaging suffers from physiological noise, such as artifacts caused by motion or system instabilities. Therefore, there is a need for robust diffusion parameter estimation techniques. In the past, several techniques have been proposed, including RESTORE and iRESTORE. However, these techniques are based on nonlinear estimators and are consequently computationally intensive.
Method: In this work, we present a new, robust, iteratively reweighted linear least squares (IRLLS) estimator. IRLLS performs a voxel-wise identification of outliers in diffusion-weighted magnetic resonance images, where it exploits the natural skewness of the data distribution to become more sensitive to both signal hyperintensities and signal dropouts. 
Results: Both simulations and real data experiments were conducted to compare IRLLS with other state-of-the-art techniques. While IRLLS showed no significant loss in accuracy or precision, it proved to be substantially faster than both RESTORE and iRESTORE. In addition, IRLLS proved to be even more robust when considering the overestimation of the noise level or when the signal-to-noise ratio is low. 
Conclusion: The substantially shortened calculation time in combination with the increased robustness and accuracy, make IRLLS a practical and reliable alternative to current state-of-theart
techniques for the robust estimation of diffusion-weighted magnetic resonance parameters.},
	keywords = {diffusion tensor imaging, MRI, outlier detection, robust, weighted linear least squares},
	doi = {10.1002/mrm.25351},
	author = {Quinten Collier and Jelle Veraart and Ben Jeurissen and Arnold Jan den Dekker and Jan Sijbers}
}
@article {1611,
	title = {Measuring Lattice Strain in Three Dimensions through Electron Microscopy},
	journal = {Nano Letters},
	volume = {15},
	year = {2015},
	pages = {6996{\textendash}7001},
	doi = {10.1021/acs.nanolett.5b03008},
	author = {Bart Goris and Jan De Beenhouwer and Annick De Backer and Daniele Zanaga and Kees Joost Batenburg and Ana S{\'a}nchez-Iglesias and Luis M Liz-Marzán and Sandra Van Aert and Sara Bals and Jan Sijbers and Van Tendeloo, Gustaaf}
}
@mastersthesis {1631,
	title = {Model-based iterative reconstruction algorithms for computed tomography},
	volume = {PhD in Sciences},
	year = {2015},
	month = {12/2015},
	type = {PhD thesis},
	author = {Van Eyndhoven, Geert}
}
@article {1563,
	title = {Modeling blurring effects due to continuous gantry rotation: application to region of interest tomography},
	journal = {Medical Physics},
	volume = {42},
	year = {2015},
	pages = {2709-2717},
	doi = { http://dx.doi.org/10.1118/1.4914422},
	author = {Jeroen Cant and Willem Jan Palenstijn and Gert Behiels and Jan Sijbers}
}
@article {1540,
	title = {A multi-level preconditioned Krylov method for the efficient solution of algebraic tomographic reconstruction problems},
	journal = {Journal of Computational and Applied Mathematics},
	volume = {238},
	year = {2015},
	pages = {1-16},
	abstract = {Classical iterative methods for tomographic reconstruction include the class of Algebraic Reconstruction Techniques (ART). Convergence of these stationary linear iterative methods is however notably slow. In this paper we propose the use of Krylov solvers for tomographic linear inversion problems. These advanced iterative methods feature fast convergence at the expense of a higher computational cost per iteration, causing them to be generally uncompetitive without the inclusion of a suitable preconditioner. Combining elements from standard multigrid (MG) solvers and the theory of wavelets, a novel wavelet-based multi-level (WMG) preconditioner is introduced, which is shown to significantly speed-up Krylov convergence. The performance of the WMG-preconditioned Krylov method is analyzed through a spectral analysis, and the approach is compared to existing methods like the classical Simultaneous Iterative Reconstruction Technique (SIRT) and unpreconditioned Krylov methods on a 2D tomographic benchmark problem. Numerical experiments are promising, showing the method to be competitive with the classical Algebraic Reconstruction Techniques in terms of convergence speed and overall performance (CPU time) as well as precision of the reconstruction. },
	doi = {doi:10.1016/j.cam.2014.12.044},
	author = {Siegfried Cools and Pieter Ghysels and Wim Van Aarle and Jan Sijbers and Wim Vanroose}
}
@inproceedings {1576,
	title = {Neural Network Based X-Ray Tomography for Fast Inspection of Apples on a Conveyor Belt},
	booktitle = {IEEE International Conference on Image Processing},
	year = {2015},
	month = {Sept 21-27},
	pages = {917-921},
	doi = {10.1109/ICIP.2015.7350933},
	author = {Eline Janssens and Jan De Beenhouwer and Mattias Van Dael and Pieter Verboven and Bart Nicolai and Jan Sijbers}
}
@article {1587,
	title = {Online Tomato Inspection Using X-Ray Radiographies and 3- Dimensional Shape Models},
	journal = {Chemical Engineering Transactions},
	volume = {44},
	year = {2015},
	pages = {37-42},
	isbn = {978-88-95608-35-8},
	doi = {10.3303/CET1544007},
	author = {Mattias Van Dael and Seppe Rogge and Pieter Verboven and Wouter Saeys and Jan Sijbers and Bart Nicolai}
}
@mastersthesis {1782,
	title = {Optical techniques for real-time morphology measurement of the tympanic membrane},
	year = {2015},
	type = {PhD thesis},
	author = {Sam Van der Jeught}
}
@conference {1561,
	title = {Partial discreteness: a new type of prior knowledge for MRI reconstruction},
	volume = {23},
	year = {2015},
	pages = {3417},
	author = {Gabriel Ramos-Llord{\'e}n and Segers, Hilde and Willem Jan Palenstijn and Arnold Jan den Dekker and Jan Sijbers}
}
@inproceedings {7351081,
	title = {Partially discrete magnetic resonance tomography},
	booktitle = {2015 IEEE International Conference on Image Processing (ICIP)},
	year = {2015},
	month = {Sept},
	pages = {1653-1657},
	keywords = {Bayes methods, Bayesian segmentation, Bayesian segmentation regularization, biomedical MRI, Breast, breast implant MR images, computerised tomography, Discrete tomography, image reconstruction, image representation, image segmentation, Implants, medical image processing, MR angiography images, MR image reconstruction, MRI, partially discrete magnetic resonance tomography, reconstruction, Tomography, TV},
	doi = {10.1109/ICIP.2015.7351081},
	author = {Gabriel Ramos-Llord{\'e}n and Segers, Hilde and Willem Jan Palenstijn and Arnold Jan den Dekker and Jan Sijbers}
}
@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}
}
@inproceedings {1590,
	title = {Region based 4D tomographic image reconstruction: application to cardiac X-ray CT},
	booktitle = {IEEE International Conference on Image Processing},
	year = {2015},
	pages = {113 - 117},
	abstract = {X-ray computed tomography (CT) is a powerful tool for noninvasive cardiac imaging. However, radiation dose is a major issue. In this paper, we propose an iterative reconstruction method that reduces the radiation dose without compromising image quality. This is achieved by exploiting prior knowledge in two ways: the reconstructed object is assumed to consist of both stationary and dynamic regions over time and the dynamic region is assumed to have sparse structures after a proper sparsifying space-time transform. Experiments on simulation data and a real micro CT cardiac mouse dataset show that, with comparable image quality, the radiation dose can be substantially reduced compared to conventional acquisition/reconstruction protocols.},
	doi = {10.1109/ICIP.2015.7350770},
	author = {Van Eyndhoven, Geert and Kees Joost Batenburg and Jan Sijbers}
}
@conference {1537,
	title = {Simultaneous group-wise rigid registration and T1 ML estimation for T1 mapping},
	year = {2015},
	author = {Gabriel Ramos-Llord{\'e}n and Arnold Jan den Dekker and Gwendolyn Van Steenkiste and Johan Van Audekerke and Marleen Verhoye and Jan Sijbers}
}
@conference {1537,
	title = {Simultaneous group-wise rigid registration and T1 ML estimation for T1 mapping},
	volume = {23},
	year = {2015},
	pages = {447},
	author = {Gabriel Ramos-Llord{\'e}n and Arnold Jan den Dekker and Gwendolyn Van Steenkiste and Johan Van Audekerke and Marleen Verhoye and Jan Sijbers}
}
@inproceedings {7351386,
	title = {Simultaneous motion correction and T1 estimation in quantitative T1 mapping: An ML restoration approach},
	booktitle = {2015 IEEE International Conference on Image Processing (ICIP)},
	year = {2015},
	month = {Sept},
	pages = {3160-3164},
	keywords = {alignment, Approximation methods, biomedical MRI, image restoration, interpolation effect, Magnetic Resonance Imaging, maximum likelihood approach, maximum likelihood estimation, medical image processing, ML approach, ML restoration approach, motion estimation, motion model parameters, quantitative T1 mapping, registration, relaxometry, Rician channels, Signal to noise ratio, simultaneous motion correction, Standards, T1 estimation, T1 mapping, T1-weighted images, tissue spin-lattice relaxation time, voxel-wise estimation},
	doi = {10.1109/ICIP.2015.7351386},
	author = {Gabriel Ramos-Llord{\'e}n and Arnold Jan den Dekker and Gwendolyn Van Steenkiste and Johan Van Audekerke and Marleen Verhoye and Jan Sijbers}
}
@conference {1605,
	title = {Statistical shape modeling of the incudomalleolar complex using micro-CT and clinical cone-beam CT},
	year = {2015},
	month = {July},
	address = {Aalborg, Denmark},
	abstract = {Introduction: A large variation in material properties and geometry exists in the human temporal bone. These variations can affect hearing and middle ear sound transmission. Middle ear computer (finite element, FE) models are successfully used to predict sound transmission and its dependence on material properties. The model geometry, however, is mostly based on data of a single sample. Here we use statistical shape models (SSM) to characterize the natural anatomical variations present in the incudomalleolar (IM) complex of humans. SSM can later be used in FE models to study the effect of geometry on sound transmission, or parameters can be fitted to clinical CT data to obtain a patient-specific computer model.

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

Results and conclusions: We will obtain an average geometrical model for malleus, incus and IM complex and characterize the deviations present in the patient population. We will do this by reporting natural variation of size and thickness of malleus head, neck and manubrium, the long and short process of the incus and relative angles in the IM complex.},
	author = {Joris Soons and Femke Danckaers and Toon Huysmans and Jan Sijbers and Jan W. Casselamn and Joris J. J. Dirckx}
}
@article {1535,
	title = {Subcortical volumetric changes across the adult lifespan: subregional thalamic atrophy accounts for age-related sensorimotor performance declines},
	journal = {Cortex},
	volume = {65},
	year = {2015},
	pages = {128-138},
	doi = { 10.1016/j.cortex.2015.01.003},
	author = {L. Serbruyns and Inge Leunissen and Toon Huysmans and K. Cuypers and R. L. Meesen and P. van Ruitenbeek and Jan Sijbers and S. P. Swinnen}
}
@conference {1520,
	title = {Super-resolution structural connectivity and anatomy of the zebra finch brain },
	year = {2015},
	author = {Gwendolyn Van Steenkiste and Hamaide, Julie and Ben Jeurissen and Dirk H J Poot and Johan Van Audekerke and Jan Sijbers and Marleen Verhoye}
}
@conference {1596,
	title = {Super-resolution T1 mapping: a simulation study},
	volume = {23},
	year = {2015},
	pages = {1679},
	author = {Gwendolyn Van Steenkiste and Dirk H J Poot and Ben Jeurissen and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {Collier2015-pa,
	title = {Theoretical study of the free water elimination model},
	volume = {15},
	year = {2015},
	pages = {2757},
	author = {Collier, Quinten and Veraart, Jelle and Ben Jeurissen and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {1544,
	title = {Theoretical study of the free water elimination model},
	year = {2015},
	pages = {78},
	abstract = {Partial volume effects caused by cerebrospinal fluid are an important issue in diffusion MRI. In this work, we study the free water elimination model by analyzing the Cram{\'e}r-Rao lower bound (CRLB) of its parameters. We show that through optimizing the acquisition protocol by minimizing the trace of the CRLB, a significant gain in the precision of the parameter estimation can be achieved. Moreover, further analysis indicates that regularization and/or constraints are necessary for parameter estimation from voxels with large CSF fractions and/or low FA values. These theoretical findings are confirmed by both simulation and real data experiments.},
	author = {Quinten Collier and Jelle Veraart and Ben Jeurissen and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {1545,
	title = {Theoretical study of the free water elimination model},
	volume = {23},
	year = {2015},
	pages = {2757},
	abstract = {Partial volume effects caused by cerebrospinal fluid are an important issue in diffusion MRI. In this work, we study the free water elimination model by analyzing the Cram{\'e}r-Rao lower bound (CRLB) of its parameters. We show that through optimizing the acquisition protocol by minimizing the trace of the CRLB, a significant gain in the precision of the parameter estimation can be achieved. Moreover, further analysis indicates that regularization and/or constraints are necessary for parameter estimation from voxels with large CSF fractions and/or low FA values. These theoretical findings are confirmed by both simulation and real data experiments.},
	author = {Quinten Collier and Jelle Veraart and Ben Jeurissen and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {1595,
	title = {Tissue-type segmentation using non-negative matrix factorization of multi-shell diffusion-weighted MRI images},
	volume = {23},
	year = {2015},
	pages = {349},
	author = {Ben Jeurissen and Jacques-Donald Tournier and Jan Sijbers}
}
@article {1998,
	title = {Tomography for in-line product inspection},
	number = {EP3106863A1},
	year = {2015},
	chapter = {EP3106863A1},
	author = {Thomas De Schryver and Manuel Dierick and Luc Van Hoorebeke and Pieter Verboven and Mattias Van Dael and Jan Sijbers}
}
@mastersthesis {1632,
	title = {Towards In Loco X-ray Computed Tomography},
	year = {2015},
	month = {12/2015},
	type = {PhD thesis},
	author = {Andrei Dabravolski}
}
@inproceedings {1499,
	title = {3D imaging of semiconductor components by discrete laminography},
	booktitle = {Stress induced phenomena and reliability in 3D microelectronics: AIP Conference Proceedings},
	volume = {1601},
	year = {2014},
	pages = {168-179},
	doi = { http://dx.doi.org/10.1063/1.4881350},
	author = {Kees Joost Batenburg and Willem Jan Palenstijn and Jan Sijbers}
}
@conference {1607,
	title = {Accurate reconstruction of porous samples in CT},
	year = {2014},
	month = {2014},
	abstract = {Quantification of a sample{\textquoteright}s porosity from computed tomography (CT) images is important in many industrial applications. However, in non-standard acquisition set-ups (e.g., limited data or angular range), the reconstruction and subsequent segmentation of pore-space is challenging. In this paper, a novel algorithm exploiting prior knowledge about the porous structure of the scanned is proposed, improving the quality of both reconstruction and segmentation.},
	author = {Van Eyndhoven, Geert and Kees Joost Batenburg and Jan Sijbers}
}
@article {1424,
	title = {Adaptive zooming in X-ray computed tomography},
	journal = {Journal of X-Ray Science and Technology},
	volume = {22},
	number = {1},
	year = {2014},
	pages = {77-89},
	keywords = {Acquisition geometry, adaptive zooming, computed tomography, prior information},
	doi = {10.3233/XST-130410},
	author = {Andrei Dabravolski and Kees Joost Batenburg and Jan Sijbers}
}
@article {1471,
	title = {Aligning Projection Images from Binary Volumes},
	journal = {Fundamenta Informaticae},
	volume = {135},
	year = {2014},
	pages = {1-22},
	doi = {10.3233/FI-2014-1090},
	author = {Folkert Bleichrodt and Jan De Beenhouwer and Jan Sijbers and Kees Joost Batenburg}
}
@article {1421,
	title = {The anatomy of the clavicle: A Three-dimensional Cadaveric Study},
	journal = {Clinical anatomy},
	volume = {27},
	year = {2014},
	month = {2013 Oct 21},
	pages = {712{\textendash}723},
	abstract = {The clavicle has a complex osteologic structure that makes morphological analysis extremely difficult. A three-dimensional study was conducted to examine the anatomical variations and characteristics of the bone. Sixty-eight human cadaver clavicles were dissected, CAT-scanned, and reconstructed. An automated parameterization and correspondence shape analysis system was developed. A new length, designated as centerline (CL) length, was defined and measured. This length represents the true length of the clavicle. The endpoint length was measured as the distance between two endpoints. The width and curvature were measured in the axial (AX) and frontal (FR) plane and defined along the CL. Next gender and side characteristics and variations were examined. The mean CL length was 159.0 {\textpm} 11.0 mm. The mean endpoint length was 149.4 {\textpm} 10.3 mm, which was statistically significantly shorter than the CL. The male clavicle was significantly longer (166.8 {\textpm} 7.3 mm vs. 151.0 {\textpm} 8.2 mm), wider (14.6 {\textpm} 1.5 mm vs. 12.7 {\textpm} 1.3 mm lateral FR plane, 25.9 {\textpm} 4.1 mm vs. 23.5 {\textpm} 3.0 mm lateral AX plane and 24.7 {\textpm} 2.8 mm vs. 22.8 {\textpm} 2.8 mm medial AX plane), and more curved (10.8 {\textpm} 2.8 mm vs. 8.6 {\textpm} 2.3 mm medial and 10.5 {\textpm} 3.3 mm vs. 9.1 {\textpm} 2.5 mm lateral) than the female one. Left clavicles were significant longer (159.8 {\textpm} 10.9 mm vs. 158.0 {\textpm} 11.2 mm) than right clavicles. A novel three-dimensional system was developed, used and tested in order to explore the anatomical variations and characteristics of the human clavicle. This information, together with the automated system, can be applied to future clavicle populations and to the design of fixation plates for clavicle fractures. Clin. Anat., 2013. {\textcopyright} 2013 Wiley Periodicals, Inc.},
	issn = {1098-2353},
	doi = {10.1002/ca.22288},
	author = {Amit Bernat and Toon Huysmans and Francis Van Glabbeek and Jan Sijbers and Gielen, Jan and Van Tongel, Alexander}
}
@conference {1487,
	title = {An Anthropometric Shape Model for the Design of Sports Helmets},
	year = {2014},
	author = {Daniel Lacko and Toon Huysmans and Annelies Claeskens and Stijn Verwulgen and Peter Aerts and Jan Sijbers and Guido De Bruyne}
}
@inproceedings {1501,
	title = {Anthropometrics 2.0: Enrichment of Classical Anthropometry through Multidisciplinary Collaboration},
	booktitle = {6th International Conference on Engineering \& Product Design Education.},
	year = {2014},
	author = {Stijn Verwulgen and Daniel Lacko and Guido De Bruyne and Femke Danckaers and Naomi Christis and Jan Sijbers and Toon Huysmans}
}
@article {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}
}
@conference {1474,
	title = {Brain Tissue Types Resolved Using Spherical Deconvolution of Multi-Shell Diffusion MRI Data},
	year = {2014},
	month = {May},
	pages = {973},
	address = {Milan, Italy},
	author = {Ben Jeurissen and Jacques-Donald Tournier and Thijs Dhollander and Alan Connelly and Jan Sijbers}
}
@article {1504,
	title = {Combined Estimation of Affine Movement and Reconstruction in  Tomography},
	year = {2014},
	publisher = {3D Materials Science Conference},
	author = {Vincent Van Nieuwenhove and Van Eyndhoven, Geert and Jan De Beenhouwer and Jan Sijbers}
}
@conference {1503,
	title = {Compensation of affine deformations in fan and cone beam projections},
	year = {2014},
	pages = {187-189},
	author = {Vincent Van Nieuwenhove and Van Eyndhoven, Geert and Jan De Beenhouwer and Jan Sijbers}
}
@inproceedings {1500,
	title = {Conveyor belt X-ray CT using Domain Constrained Discrete Tomography},
	booktitle = {Sibgrapi conference on Graphics, Patterns and Images},
	year = {2014},
	pages = {290 - 297},
	doi = {10.1109/SIBGRAPI.2014.21},
	author = {Luis Filipe Alves Pereira and Andrei Dabravolski and Ing Ren Tsang and George D C Cavalcanti and Jan Sijbers}
}
@inproceedings {1455,
	title = {Correspondence Preserving Elastic Surface Registration with Shape Model Prior},
	booktitle = {International Conference of Pattern Recognition},
	volume = {22},
	year = {2014},
	pages = {2143-2148},
	abstract = {In this paper, we describe a framework for surface
registration. The framework consists of a combination of rigid
registration, elasticity modulated registration and the use of a
shape model prior. The main goal in this paper is to mini-
mize the geometric surface registration error while maintain-
ing correspondences. Experiments show improved geometric fit,
correspondence, and timing compared to the current state of
the art. Possible applications of the framework are construction
of correspondences for shape models, reconstruction of missing
parts, and artifact reduction.},
	doi = {10.1109/ICPR.2014.373},
	author = {Femke Danckaers and Toon Huysmans and Daniel Lacko and A. Ledda and Stijn Verwulgen and Van Dongen, Stefan and Jan Sijbers}
}
@article {1476,
	title = {Data distributions in magnetic resonance images: a review},
	journal = {Physica Medica},
	volume = {30},
	year = {2014},
	pages = {725{\textendash}741},
	doi = {http://dx.doi.org/10.1016/j.ejmp.2014.05.002},
	author = {Arnold Jan den Dekker and Jan Sijbers}
}
@article {1418,
	title = {Dynamic Angle Selection in X-ray Computed Tomography},
	journal = {Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms},
	volume = {324},
	year = {2014},
	pages = {17-24},
	keywords = {Angle selection, computed tomography, dynamic imaging, information gain},
	doi = {10.1016/j.nimb.2013.08.077},
	author = {Andrei Dabravolski and Kees Joost Batenburg and Jan Sijbers}
}
@article {1488,
	title = {Evaluation of prominence of straight plates and precontoured clavicle plates using automated plate-to-bone fitting},
	journal = {Acta Orthopaedica Belgica},
	volume = {80},
	year = {2014},
	pages = {301-308},
	author = {Van Tongel, Alexander and Toon Huysmans and Amit Bernat and Iwein Piepers and Jan Sijbers and Francis Van Glabbeek and Lieven De Wilde}
}
@article {1678,
	title = {Filter for Tomographic Reconstructions},
	number = {WO2013011031A1},
	year = {2014},
	chapter = {WO2013011031A1},
	author = {Kees Joost Batenburg and Jan Sijbers and Linda Plantagie}
}
@conference {roine2014ismrm,
	title = {Improving fiber orientation estimation in constrained spherical deconvolution under non-white matter partial volume effects},
	year = {2014},
	month = {05/2014},
	pages = {4492},
	author = {Timo Roine and Ben Jeurissen and Wilfried Philips and Alexander Leemans and Jan Sijbers}
}
@inproceedings {1485,
	title = {Influence of Correspondence Method on Statistical Model Based Shape Prediction},
	booktitle = {IEEE International Symposium on Biomedical Imaging (ISBI)},
	year = {2014},
	author = {Christophe Van Dijck and Wirix-Speetjens, Roel and Toon Huysmans and Femke Danckaers and Jan Sijbers and Vander Sloten, Jos}
}
@conference {1486,
	title = {Influence of Correspondence Method on Statistical Model Based Shape Prediction},
	year = {2014},
	author = {Christophe Van Dijck and Wirix-Speetjens, Roel and Toon Huysmans and Femke Danckaers and Jan Sijbers and Vander Sloten, Jos}
}
@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}
}
@conference {1443,
	title = {Isotropic non-white matter partial volume effects in constrained spherical deconvolution},
	year = {2014},
	author = {Timo Roine and Ben Jeurissen and Wilfried Philips and Alexander Leemans and Jan Sijbers}
}
@article {1481,
	title = {Iterative bilateral filter for Rician noise reduction in MR images},
	journal = {Signal, Image and Video Processing },
	year = {2014},
	doi = {10.1007/s11760-013-0611-6},
	author = {R. Riji and Jeny Rajan and Jan Sijbers and Madhu S. Nair}
}
@booklet {1538,
	title = {Joint motion correction and estimation for T1 mapping: proof of concept},
	howpublished = {Medical Imaging Summer School 2014, Favignana, Italy},
	year = {2014},
	author = {Gabriel Ramos-Llord{\'e}n and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {1449,
	title = {A local enhancement based tomographic reconstruction technique for radiation exposure reduction in cerebral perfusion computed tomography},
	year = {2014},
	doi = {10.1594/ecr2014/C-0282},
	url = {http://posterng.netkey.at/esr/online_viewing/index.php?module=view_postercoverpage\&task=viewcoverpage\&start=0\&ls=authorlist\&pi=121333\&perid=248156\&num=1\&count=1\&cid=515},
	author = {Van Eyndhoven, Geert and Kees Joost Batenburg and Jan Sijbers}
}
@article {1473,
	title = {A memory efficient method for fully three-dimensional object reconstruction with HAADF STEM Ultramicroscopy},
	journal = {Ultramicroscopy},
	volume = {141},
	year = {2014},
	pages = {22{\textendash}31},
	doi = {http://dx.doi.org/10.1016/j.ultramic.2014.03.008},
	author = {Wouter Van den Broek and A. Rosenauer and Jan Sijbers and Dirk Van Dyck and Sandra Van Aert}
}
@article {1502,
	title = {A Multiresolution Approach to Discrete Tomography Using DART},
	journal = {PLoS ONE},
	volume = {9},
	year = {2014},
	doi = {10.1371/journal.pone.0106090},
	url = {http://www.plosone.org/article/info\%3Adoi\%2F10.1371\%2Fjournal.pone.0106090},
	author = {Andrei Dabravolski and Kees Joost Batenburg and Jan Sijbers}
}
@conference {1457,
	title = {A multiresolution approach to the Discrete Algebraic Reconstruction Technique (DART)},
	year = {2014},
	month = {07/2014},
	keywords = {computed tomography, DART, Discrete tomography, prior information},
	author = {Andrei Dabravolski and Kees Joost Batenburg and Jan Sijbers}
}
@article {1494,
	title = {Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data},
	journal = {NeuroImage},
	volume = {103},
	year = {2014},
	pages = {411{\textendash}426},
	abstract = {Constrained spherical deconvolution (CSD) has become one of the most widely used methods to extract white matter (WM) fibre orientation information from diffusion-weighted MRI (DW-MRI) data, overcoming the crossing fibre limitations inherent in the diffusion tensor model. It is routinely used to obtain high quality fibre orientation distribution function (fODF) estimates and fibre tractograms and is increasingly used to obtain apparent fibre density (AFD) measures. Unfortunately, CSD typically only supports data acquired on a single shell in q-space. With multi-shell data becoming more and more prevalent, there is a growing need for CSD to fully support such data. Furthermore, CSD can only provide high quality fODF estimates in voxels containing WM only. In voxels containing other tissue types such as grey matter (GM) and cerebrospinal fluid (CSF), the WM response function may no longer be appropriate and spherical deconvolution produces unreliable, noisy fODF estimates.

The aim of this study is to incorporate support for multi-shell data into the CSD approach as well as to exploit the unique b-value dependencies of the different tissue types to estimate a multi-tissue ODF. The resulting approach is dubbed multi-shell, multi-tissue CSD (MSMT-CSD) and is compared to the state-of-the-art single-shell, single-tissue CSD (SSST-CSD) approach. Using both simulations and real data, we show that MSMT-CSD can produce reliable WM/GM/CSF volume fraction maps, directly from the DW data, whereas SSST-CSD has a tendency to overestimate the WM volume in voxels containing GM and/or CSF. In addition, compared to SSST-CSD, MSMT-CSD can substantially increase the
precision of the fODF fibre orientations and reduce the presence of spurious fODF peaks in voxels containing GM and/or CSF. Both effects translate into more reliable AFD measures and tractography results with MSMT-CSD compared to SSST-CSD.},
	doi = {10.1016/j.neuroimage.2014.07.061},
	author = {Ben Jeurissen and Jacques-Donald Tournier and Thijs Dhollander and Alan Connelly and Jan Sijbers}
}
@article {1472,
	title = {Neutron radiography and tomography applied to fuel degradation during ramp tests and loss of coolant accident tests in a research reactor},
	journal = {Progress in Nuclear Energy},
	volume = {72},
	year = {2014},
	pages = {55-62},
	doi = {http://dx.doi.org/10.1016/j.pnucene.2013.11.001},
	author = {Hakon Kristian Jenssen and B.C. Oberlander and Jan De Beenhouwer and Jan Sijbers and M. Verwerft}
}
@article {1441,
	title = {A new non local maximum likelihood estimation method for Rician noise reduction in Magnetic Resonance images using the Kolmogorov-Smirnov test},
	journal = {Signal Processing},
	volume = {103},
	year = {2014},
	pages = {16-23},
	doi = {http://dx.doi.org/10.1016/j.sigpro.2013.12.018},
	author = {Jeny Rajan and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {1559,
	title = {A Novel Method for Realistic DWI Data Generation},
	volume = {22},
	year = {2014},
	pages = {4427},
	address = {Milan, Italy},
	author = {Daniele Perrone and Jan Aelterman and Ben Jeurissen and Aleksandra Pizurica and Wilfried Philips and Jan Sijbers}
}
@article {1497,
	title = {Optimal experimental design for the detection of light atoms from high-resolution scanning transmission electron microscopy images},
	journal = {Applied Physics Letters},
	volume = {105},
	year = {2014},
	doi = {10.1063/1.4892884},
	author = {Julie Gonnissen and Annick De Backer and Arnold Jan den Dekker and G T Martinez and A. Rosenauer and Jan Sijbers and Sandra Van Aert}
}
@conference {1617,
	title = {Post-processing of diffusion-weighted MR data lowers the accuracy of the weighted linear least squares estimator },
	volume = {22},
	year = {2014},
	pages = {2573},
	author = {Jelle Veraart and Jan Sijbers}
}
@article {1480,
	title = {The reconstructed residual error: A novel segmentation evaluation measure for reconstructed images in tomography},
	journal = {Computer Vision and Image Understanding},
	volume = {126},
	year = {2014},
	pages = {28-37},
	doi = {10.1016/j.cviu.2014.05.007},
	author = {Roelandts, Tom and Kees Joost Batenburg and Arnold Jan den Dekker and Jan Sijbers}
}
@article {1436,
	title = {Region-based iterative reconstruction of structurally changing objects in CT},
	journal = {IEEE Transactions on Image Processing},
	volume = {23},
	year = {2014},
	pages = {909-919},
	chapter = {909-919},
	doi = {10.1109/TIP.2013.2297024},
	author = {Van Eyndhoven, Geert and Kees Joost Batenburg and Jan Sijbers}
}
@conference {1454,
	title = {Reliable Pore-size Measurements Based on a Procedure Specifically Designed for Electron Tomography Measurements of Nanoporous Samples},
	year = {2014},
	author = {Van Eyndhoven, Geert and Kees Joost Batenburg and Cynthia Van Oers and Mert Kurttepeli and Sara Bals and Pegie Cool and Jan Sijbers}
}
@article {1437,
	title = {Super-resolution for computed tomography based on discrete tomography},
	journal = {IEEE Transactions on Image Processing},
	volume = {23},
	year = {2014},
	pages = {1181 - 1193},
	doi = {10.1109/TIP.2013.2297025},
	author = {Wim Van Aarle and Kees Joost Batenburg and Gert Van Gompel and Elke Van de Casteele and Jan Sijbers}
}
@conference {1482,
	title = {Super-resolution reconstruction of diffusion parameters from multi-oriented diffusion weighted images},
	year = {2014},
	month = {january},
	address = {Maastricht, The Netherlands},
	author = {Gwendolyn Van Steenkiste and Ben Jeurissen and Dirk H J Poot and Paul M Parizel and Jan Sijbers}
}
@conference {1483,
	title = {Super-resolution reconstruction of diffusion parameters from multi-oriented diffusion weighted images},
	year = {2014},
	month = {May},
	address = {Milan, Italy},
	author = {Gwendolyn Van Steenkiste and Ben Jeurissen and Paul M Parizel and Dirk H J Poot and Jan Sijbers}
}
@inproceedings {1495,
	title = {Tomographic image reconstruction from continuous projections},
	booktitle = {The third international conference on image formation in X-ray computed tomography},
	year = {2014},
	month = {06/2014},
	pages = {295-298},
	address = {Salt Lake City, USA},
	author = {Jeroen Cant and Willem Jan Palenstijn and Gert Behiels and Jan Sijbers}
}
@inproceedings {1478,
	title = {Type-2 fuzzy GMMs for robust text-independent speaker verification in noisy environments},
	booktitle = {International Conference of Pattern Recognition},
	year = {2014},
	author = {Hector N. B. Pinheiro and Ing Ren Tsang and George D C Cavalcanti and Ing Jyh Tsang and Jan Sijbers}
}
@inproceedings {1386,
	title = {Adaptive Zooming in X-ray Computed Tomography},
	booktitle = {1st International Conference on Tomography of Materials and Structures (ICTMS)},
	year = {2013},
	pages = {5-8},
	address = {Ghent, Belgium},
	author = {Andrei Dabravolski and Kees Joost Batenburg and Jan Sijbers}
}
@inproceedings {1399,
	title = {An Algebraic Reconstruction Technique for the Study of Local Structural Changes during CT},
	booktitle = {1st International Conference on Tomography of Materials and Structures (ICTMS)},
	year = {2013},
	pages = {137-141},
	abstract = {If an object undergoes structural changes during a CT-scan, artefacts are introduced in the reconstruction. Current methods that compensate for these changes assume a continuous deformation model, which is insufficient to capture the complexity of structural changes. In this paper, an iterative method is proposed that incorporates prior knowledge about the changing region in the reconstruction algorithm, which results in significantly more accurate reconstructions.},
	author = {Van Eyndhoven, Geert and Kees Joost Batenburg and Jan Sijbers}
}
@inproceedings {1650,
	title = {An Alignment Method for Fan Beam Tomography},
	booktitle = {International Conference on Tomography of Materials and Structures (ICTMS)},
	year = {2013},
	pages = {103-107},
	author = {Folkert Bleichrodt and Jan De Beenhouwer and Jan Sijbers and Kees Joost Batenburg}
}
@article {1402,
	title = {Altered diffusion tensor imaging measurements in aged transgenic Huntington disease rats.},
	journal = {Brain structure \& function},
	volume = {218},
	year = {2013},
	month = {2013 May},
	pages = {767-78},
	abstract = {Rodent models of Huntington disease (HD) are valuable tools for investigating HD pathophysiology and evaluating new therapeutic approaches. Non-invasive characterization of HD-related phenotype changes is important for monitoring progression of pathological processes and possible effects of interventions. The first transgenic rat model for HD exhibits progressive late-onset affective, cognitive, and motor impairments, as well as neuropathological features reflecting observations from HD patients. In this report, we contribute to the anatomical phenotyping of this model by comparing high-resolution ex vivo DTI measurements obtained in aged transgenic HD rats and wild-type controls. By region of interest analysis supplemented by voxel-based statistics, we find little evidence of atrophy in basal ganglia regions, but demonstrate altered DTI measurements in the dorsal and ventral striatum, globus pallidus, entopeduncular nucleus, substantia nigra, and hippocampus. These changes are largely compatible with DTI findings in preclinical and clinical HD patients. We confirm earlier reports that HD rats express a moderate neuropathological phenotype, and provide evidence of altered DTI measures in specific HD-related brain regions, in the absence of pronounced morphometric changes.},
	issn = {1863-2661},
	doi = {10.1007/s00429-012-0427-0},
	author = {Antonsen, Bj{\o}rnar T and Yi Jiang and Jelle Veraart and Qu, Hong and Nguyen, Huu Phuc and Jan Sijbers and Von H{\"o}rsten, Stephan and Allan G Johnson and Trygve B Leergaard}
}
@article {1406,
	title = {Alveolar Nerve Unfolding Technique for Synoptic Analysis: Visualization and Quantification of Inferior Alveolar Nerve Tracings in Three-dimensional Cone-Beam Computed Tomography.},
	journal = {The Journal of craniofacial surgery},
	volume = {24},
	year = {2013},
	month = {2013 Jul},
	pages = {e374-7},
	abstract = {The aim of the technique presented here is to visualize the anatomical context of the inferior alveolar nerve (IAN) canal. For 2 cases, cone-beam computed tomography images of the mandible were obtained from patient files together with the manual preoperative IAN canal tracings. For both cases, similar to simulated panoramic images, a two-dimensional image is extracted from a three-dimensional cone-beam computed tomography image. Unlike panoramic images, the unfolding does not follow the general curvature of the mandible but follows the nerve tracing closely and places the traced nerve track on a horizontal central line. Because of the centering of the nerve tracing together with the nerve canal and its surroundings in a two-dimensional representation, the technique (ANUTSA [Alveolar Nerve Unfolding Technique for Synoptic Analysis]) allowed the first case to evidence the adjacency of root tips along the IAN, whereas in the second case the degree of penetration of the IAN by an implant is revealed. The global aspect of the representation through unfolding allowed for the detection of the anomalies and the IAN-penetrating lesion along the IAN canal at a glance.},
	issn = {1536-3732},
	doi = {10.1097/SCS.0b013e3182903013},
	author = {Jacquet, Wolfgang and Nyssen, Edgard and Sun, Yi and De Munter, Stephanie and Jan Sijbers and Politis, Constantinus}
}
@conference {1433,
	title = {Analysis and modeling of isotropic partial volume effects in diffusion MRI},
	volume = {7},
	year = {2013},
	month = {2013},
	doi = {10.3389/conf.fninf.2013.10.00027},
	author = {Timo Roine and Ben Jeurissen and Alexander Leemans and Wilfried Philips and Jan Sijbers}
}
@inproceedings {1403,
	title = {The ASTRA Tomography Toolbox},
	booktitle = {13th International Conference on Computational and Mathematical Methods in Science and Engineering, CMMSE 2013},
	volume = {4},
	year = {2013},
	pages = {1139-1145},
	isbn = {978-84-616-2723-3},
	author = {Willem Jan Palenstijn and Kees Joost Batenburg and Jan Sijbers}
}
@article {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 {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 {1377,
	title = {Diffusion kurtosis imaging to detect amyloidosis in an APP/PS1 mouse model for Alzheimer{\textquoteright}s disease},
	journal = {Magnetic Resonance in Medicine},
	volume = {69},
	year = {2013},
	pages = {1115{\textendash}1121},
	doi = {10.1002/mrm.24680},
	author = {Greetje Vanhoutte and S. Pereson and Rafael  Delgado Y Palacios and Pieter-Jan Guns and B. Asselbergh and Jelle Veraart and Jan Sijbers and Marleen Verhoye and Van Broeckhoven, Christine and Annemie Van Der Linden}
}
@conference {1405,
	title = {Discrete Tomography in MRI: a proof of concept},
	volume = {21},
	year = {2013},
	pages = {2662},
	author = {Segers, Hilde and Willem Jan Palenstijn and Kees Joost Batenburg and Jan Sijbers}
}
@article {1404,
	title = {Discrete Tomography in MRI: a Simulation Study},
	journal = {Fundamenta Informaticae},
	volume = {125},
	year = {2013},
	pages = {223-237},
	doi = {10.3233/FI-2013-861},
	author = {Segers, Hilde and Willem Jan Palenstijn and Kees Joost Batenburg and Jan Sijbers}
}
@article {1361,
	title = {Does the use of hormonal contraceptives cause microstructural changes in cerebral white matter? Preliminary results of a DTI and tractography study.},
	journal = {European radiology},
	volume = {23},
	year = {2013},
	month = {2012 Jul 20},
	pages = {57-64},
	abstract = {OBJECTIVE: To evaluate the effect of monophasic combined oral contraceptive pill (COCP) and menstrual cycle phase in healthy young women on white matter (WM) organization using diffusion tensor imaging (DTI). METHODS: Thirty young women were included in the study; 15 women used COCP and 15 women had a natural cycle. All subjects underwent DTI magnetic resonance imaging during the follicular and luteal phase of their cycle, or in different COCP cycle phases. DTI parameters were obtained in different WM structures by performing diffusion tensor fibre tractography. Fractional anisotropy and mean diffusivity were calculated for different WM structures. Hormonal plasma concentrations were measured in peripheral venous blood samples and correlated with the DTI findings. RESULTS: We found a significant difference in mean diffusivity in the fornix between the COCP and the natural cycle group. Mean diffusivity values in the fornix were negatively correlated with luteinizing hormone and estradiol blood concentrations. CONCLUSION: An important part in the limbic system, the fornix, regulates emotional processes. Differences in diffusion parameters in the fornix may contribute to behavioural alternations related to COCP use. This finding also suggests that the use of oral contraceptives needs to be taken into account when designing DTI group studies. KEY POINTS: {\textbullet} Diffusion tensor MRI offers new insights into brain white matter microstructure. {\textbullet} The effects of oral hormonal contraception were examined in young women. {\textbullet} Diffusion tensor images and hormone blood concentrations were evaluated. {\textbullet} Women using hormonal contraception demonstrated higher mean diffusivity in the fornix. {\textbullet} These changes may contribute to behavioural alternations related to contraception use.},
	issn = {1432-1084},
	doi = {10.1007/s00330-012-2572-5},
	author = {De Bondt, Timo and Wim Van Hecke and Jelle Veraart and Alexander Leemans and Jan Sijbers and Stefan Sunaert and Jacquemyn, Yves and Paul M Parizel}
}
@article {1370,
	title = {Dynamic angle selection in binary tomography},
	journal = {Computer Vision and Image Understanding},
	volume = {117},
	year = {2013},
	pages = {306{\textendash}318},
	doi = {http://dx.doi.org/10.1016/j.cviu.2012.07.005},
	author = {Kees Joost Batenburg and Willem Jan Palenstijn and Peter Balazs and Jan Sijbers}
}
@inproceedings {1387,
	title = {Dynamic Angle Selection in X-ray Computed Tomography},
	booktitle = {1st International Conference on Tomography of Materials and Structures (ICTMS)},
	year = {2013},
	pages = {27-30},
	address = {Ghent, Belgium},
	author = {Andrei Dabravolski and Kees Joost Batenburg and Jan Sijbers}
}
@article {1677,
	title = {Dynamic tomography angle selection},
	number = {PCT/EP2012/071421},
	year = {2013},
	chapter = {WO2013064472 A1},
	issn = {US2014307934A1},
	author = {Kees Joost Batenburg and Jan Sijbers}
}
@article {1395,
	title = {Estimation of unknown structure parameters from high-resolution (S)TEM images: what are the limits?},
	journal = {Ultramicroscopy},
	volume = {134},
	year = {2013},
	pages = {34-43},
	doi = {http://dx.doi.org/10.1016/j.ultramic.2013.05.017},
	url = {http://www.sciencedirect.com/science/article/pii/S0304399113001368},
	author = {Arnold Jan den Dekker and Julie Gonnissen and Annick De Backer and Jan Sijbers and Sandra Van Aert}
}
@inproceedings {1423,
	title = {Fast Tomographic Reconstruction from Highly Limited Data Using Artificial Neural Networks},
	booktitle = {1st International Conference on Tomography of Materials and Structures (ICTMS)},
	year = {2013},
	pages = {109-112},
	author = {Daan Pelt and Jan Sijbers and Kees Joost Batenburg}
}
@conference {1389,
	title = {How to make sure you are using the correct gradient orientations in your diffusion MRI study?},
	year = {2013},
	month = {April},
	pages = {2047},
	address = {Salt Lake City, USA},
	author = {Ben Jeurissen and Alexander Leemans and Jan Sijbers}
}
@inproceedings {1401,
	title = {Inline 3D X-ray Inspection of Food using Discrete Tomography},
	booktitle = {InsideFood Symposium},
	year = {2013},
	address = {Leuven, Belgium},
	author = {Luis Filipe Alves Pereira and Roelandts, Tom and Jan Sijbers}
}
@article {1348,
	title = {Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging.},
	journal = {Human Brain Mapping},
	volume = {34},
	number = {11},
	year = {2013},
	pages = {2747-66},
	abstract = {It has long been recognized that the diffusion tensor model is inappropriate to characterize complex fiber architecture, causing tensor-derived measures such as the primary eigenvector and fractional anisotropy to be unreliable or misleading in these regions. There is however still debate about the impact of this problem in practice. A recent study using a Bayesian automatic relevance detection (ARD) multicompartment model suggested that a third of white matter (WM) voxels contain crossing fibers, a value that, whilst already significant, is likely to be an underestimate. The aim of this study is to provide more robust estimates of the proportion of affected voxels, the number of fiber orientations within each WM voxel, and the impact on tensor-derived analyses, using large, high-quality diffusion-weighted data sets, with reconstruction parameters optimized specifically for this task. Two reconstruction algorithms were used: constrained spherical deconvolution (CSD), and the ARD method used in the previous study. We estimate the proportion of WM voxels containing crossing fibers to be \~{}90\% (using CSD) and 63\% (using ARD). Both these values are much higher than previously reported, strongly suggesting that the diffusion tensor model is inadequate in the vast majority of WM regions. This has serious implications for downstream processing applications that depend on this model, particularly tractography, and the interpretation of anisotropy and radial/axial diffusivity measures.},
	issn = {1097-0193},
	doi = {10.1002/hbm.22099},
	author = {Ben Jeurissen and Alexander Leemans and Jacques-Donald Tournier and Derek K. Jones and Jan Sijbers}
}
@conference {1426,
	title = {Iterative reweighted linear least squares for the accurate, fast, and robust estimation of diffusion magnetic resonance parameters},
	year = {2013},
	author = {Quinten Collier and Jelle Veraart and Jan Sijbers}
}
@article {1368,
	title = {Limbic and callosal white matter changes in euthymic bipolar I disorder: an advanced diffusion MRI tractography study},
	journal = {Biologicial Psychiatry},
	volume = {73},
	year = {2013},
	pages = {194-201},
	doi = {http://dx.doi.org/10.1016/j.biopsych.2012.09.023},
	author = {Louise Emsell and Alexander Leemans and Camilla Langan and Wim Van Hecke and Gareth J. Barker and Peter McCarthy and Ben Jeurissen and Jan Sijbers and Stefan Sunaert and Dara M. Cannon and Colm McDonald}
}
@mastersthesis {1428,
	title = {Local Prior Knowledge in Tomography},
	volume = {PhD in Sciences: Physics},
	year = {2013},
	month = {11/2013},
	type = {PhD thesis},
	author = {Roelandts, Tom}
}
@inproceedings {1400,
	title = {Localizing DART using the Reconstructed Residual Error},
	booktitle = {1st International Conference on Tomography of Materials and Structures (ICTMS)},
	volume = {Book of Abstracts: Talks},
	year = {2013},
	pages = {113-116},
	address = {Ghent, Belgium},
	author = {Roelandts, Tom and Kees Joost Batenburg and Jan Sijbers}
}
@inproceedings {1442,
	title = {Memory access optimization for iterative tomography on many-core architectures},
	booktitle = {The 12th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine},
	year = {2013},
	pages = {364-367},
	author = {Wim Van Aarle and Pieter Ghysels and Jan Sijbers and Wim Vanroose}
}
@conference {1475,
	title = {Misalignment correction for T1 maps using a maximum likelihood estimator approach},
	year = {2013},
	doi = {10.3389},
	author = {Gabriel Ramos-Llord{\'e}n and Jan Sijbers}
}
@inproceedings {Sabino:2013:MCT:2571272.2572228,
	title = {Motion Compensation Techniques in Permutation-Based Video Encryption},
	booktitle = {Proceedings of the 2013 IEEE International Conference on Systems, Man, and Cybernetics},
	series = {SMC {\textquoteright}13},
	year = {2013},
	pages = {1578{\textendash}1581},
	publisher = {IEEE Computer Society},
	organization = {IEEE Computer Society},
	address = {Washington, DC, USA},
	keywords = {motion compensation, multimedia data security, spatial correlation, video coding, Video encryption},
	isbn = {978-1-4799-0652-9},
	doi = {10.1109/SMC.2013.272},
	url = {http://dx.doi.org/10.1109/SMC.2013.272},
	author = {Sabino, Caio C. and Andrade, Lais S. and Ing Ren Tsang and George D C Cavalcanti and Ing Jyh Tsang and Jan Sijbers}
}
@inproceedings {1439,
	title = {A New Nonlocal Maximum Likelihood Estimation Method for Denoising Magnetic Resonance Images},
	booktitle = {5th International Conference, PReMI 2013, Kolkata, India, December 10-14, 2013. Proceedings},
	volume = {8251},
	year = {2013},
	month = {2013},
	edition = {Lecture Notes in Computer Science},
	doi = {10.1007/978-3-642-45062-4_62},
	author = {Jeny Rajan and Arnold Jan den Dekker and Juntu, Jaber and Jan Sijbers}
}
@mastersthesis {1427,
	title = {Optimal estimation of diffusion MRI parameters},
	volume = {PhD in Sciences: Physics},
	year = {2013},
	month = {10/2013},
	type = {PhD thesis},
	author = {Jelle Veraart}
}
@inproceedings {1422,
	title = {Practical Error Bounds for Binary Tomography},
	booktitle = {1st International Conference on Tomography of Materials and Structures},
	year = {2013},
	pages = {97-100},
	author = {Wagner Fortes and Jan Sijbers and Kees Joost Batenburg}
}
@article {1470,
	title = {Regional gray matter volume differences and sex-hormone correlations as a function of menstrual cycle phase and hormonal contraceptives use.},
	journal = {Brain research},
	volume = {1530},
	year = {2013},
	month = {2013 Sep 12},
	pages = {22-31},
	abstract = {During the menstrual cycle, hormone-driven functional and morphological changes occur in the female brain. The influence of hormonal contraceptives on these changes has received only little attention in the medical literature. The purpose of our study is to measure regional gray matter volume changes as a function of the cycle phase and use of hormonal contraceptives, in relation to blood concentrations of sex hormones. We performed a prospective study in 30 healthy young women; 15 women had a natural menstrual cycle and 15 were using monophasic combined hormonal contraceptives. MRI examinations were acquired at 2 specific time-points in the cycle (follicular and luteal phase). MRI studies included a T1-weighted, isotropic, high-resolution 3-D gradient echo acquisition, for the purpose of performing voxel based morphometry. Peripheral venous blood samples were obtained to determine concentrations of luteinizing hormone (LH), follicle stimulating hormone (FSH), estradiol, and progesterone. We found a highly significant negative correlation of regional gray matter volume in the anterior cingulate cortex with estradiol concentrations. To the best of our knowledge, this result has not been described before, and was only present in the natural cycle group, not in women using hormonal contraceptives. The anterior cingulate cortex is involved in emotion processing and there is literature describing behavioral alternations with changing hormone levels. Our findings provide a structural, morphological basis to support these data. Therefore, we advise neuroscientists to take into account the menstrual cycle phase and use of hormonal contraceptives, in order to avoid obtaining heterogeneous data sets, leading to a significant loss of accuracy and precision.},
	issn = {1872-6240},
	doi = {10.1016/j.brainres.2013.07.034},
	author = {De Bondt, Timo and Jacquemyn, Y and Wim Van Hecke and Jan Sijbers and Stefan Sunaert and Paul M Parizel}
}
@conference {1398,
	title = {Region-Based SIRT algorithm for the reconstrcution of phase bins in dynamic micro-CT},
	year = {2013},
	author = {Van Eyndhoven, Geert and Kees Joost Batenburg and Jan Sijbers}
}
@article {1431,
	title = {Statistical shape modeling and population analysis of the aortic root of TAVI patients},
	journal = {Journal of Medical Devices},
	volume = {7},
	year = {2013},
	month = {12/2013},
	publisher = {ASME},
	edition = {040925 },
	abstract = {The new transcatheter technique to implant synthetic aortic valves offers a treatment to patients previously considered untreatable. However the majority of patients suffer from leakage alongside the implant. Using a statistical shape model of the anatomy a correlation was discovered between leakage and the shape of the sinuses of valsalva.},
	doi = {10.1115/1.4025904},
	url = {http://medicaldevices.asmedigitalcollection.asme.org/article.aspx?articleid=1767192},
	author = {Bosmans, Bart and Toon Huysmans and Wirix-Speetjens, Roel and Verschueren, Peter and Jan Sijbers and Bosmans, Johan and Vander Sloten, Jos}
}
@article {1396,
	title = {Subchronic memantine induced concurrent functional disconnectivity and altered ultra-structural tissue integrity in the rodent brain: revealed by multimodal MRI.},
	journal = {Psychopharmacology},
	volume = {227},
	year = {2013},
	month = {2013 Jun},
	pages = {479-91},
	abstract = {BACKGROUND: An effective NMDA antagonist imaging model may find key utility in advancing schizophrenia drug discovery research. We investigated effects of subchronic treatment with the NMDA antagonist memantine by using behavioural observation and multimodal MRI.

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

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

CONCLUSION: Our findings substantiate the hypothesis that repeated NMDA receptor blockade with nonspecific, noncompetitive NMDA antagonists may lead to functional and ultra-structural alterations, particularly in the hippocampus and cingulate cortex. These changes may underlie the behavioural effects. Furthermore, the present findings underscore the utility and the translational potential of multimodal MR imaging and acute/subchronic memantine model in the search for novel disease-modifying treatments for schizophrenia.},
	issn = {1432-2072},
	doi = {10.1007/s00213-013-2966-3},
	author = {Sekar, S and Jonckers, E and Marleen Verhoye and Willems, R and Jelle Veraart and Johan Van Audekerke and Couto, J and Giugliano, M and Wuyts, K and Dedeurwaerdere, S and Jan Sijbers and Mackie, C and Ver Donck, L and Steckler, T and Annemie Van Der Linden}
}
@conference {1384,
	title = {Super resolution reconstruction from  differently oriented diffusion tensor data sets},
	year = {2013},
	month = {January},
	address = {Rotterdam, The Netherlands},
	author = {Gwendolyn Van Steenkiste and Ben Jeurissen and Jan Sijbers and Dirk H J Poot}
}
@conference {1385,
	title = {Super resolution reconstruction from differently oriented diffusion tensor data sets},
	year = {2013},
	month = {April},
	pages = {3186},
	address = {Salt Lake City, USA},
	author = {Gwendolyn Van Steenkiste and Ben Jeurissen and Jan Sijbers and Dirk H J Poot}
}
@conference {1425,
	title = {Super resolution reconstruction of diffusion tensor parameters from multi-oriented diffusion weighted images},
	year = {2013},
	author = {Gwendolyn Van Steenkiste and Ben Jeurissen and Dirk H J Poot and Jan Sijbers}
}
@article {1328,
	title = {Super-Resolution for Multislice Diffusion Tensor Imaging},
	journal = {Magnetic Resonance in Medicine},
	volume = {69},
	year = {2013},
	pages = {103{\textendash}113},
	abstract = {Diffusion weighted (DW) magnetic resonance images are often recorded with single shot multislice
imaging sequences, because of their short scanning times and robustness to motion. To minimize
noise and acquisition time, images are generally acquired with either anisotropic or isotropic
low resolution voxels, which impedes subsequent posterior image processing and visualization. In
this paper, we propose a super-resolution method for diffusion weighted imaging that combines
anisotropic multislice images to enhance the spatial resolution of diffusion tensor (DT) data. Each
DW image is reconstructed from a set of arbitrarily oriented images with a low through-plane
resolution. The quality of the reconstructed DW images was evaluated by DT metrics and tractography.
Experiments with simulated data, a hardware DTI phantom, as well as in vivo human
brain data were conducted. Our results show a significant increase in spatial resolution of the DT
data while preserving high signal to noise ratio.},
	doi = {10.1002/mrm.24233},
	author = {Dirk H J Poot and Ben Jeurissen and Yannick Bastiaensen and Jelle Veraart and Wim Van Hecke and Paul M Parizel and Jan Sijbers}
}
@article {1419,
	title = {Super-Resolution of License Plate Images using Algebraic Reconstruction Technique},
	journal = {Journal of Image and Graphics},
	volume = {1},
	year = {2013},
	month = {2013},
	pages = {94 - 98},
	publisher = {Engineering and Technology Publishing (ETP)},
	address = {USA},
	abstract = {In this paper, an iterative super-resolution reconstruction method is introduced for license plate recognition. A high-resolution image of the license plate is reconstructed by fusing the information derived from a set of subpixel shifted low-resolution images. The reconstruction problem is formulated as a system of linear equations that is solved by using the simultaneous algebraic reconstruction technique (SIRT). Simulation experiments show that SIRT can reconstruct a HR image with superior quality compared to conventional super-resolution reconstruction methods.},
	keywords = {algebraic reconstruction technique, license plate, reconstruction, SIRT, super-resolution},
	issn = {23013699},
	doi = {10.12720/joig.1.2.94-98},
	url = {http://www.joig.org/index.php?m=content\&c=index\&a=show\&catid=31\&id=41},
	author = {Karim Zarei Zefreh and Wim Van Aarle and Kees Joost Batenburg and Jan Sijbers}
}
@article {1397,
	title = {Weighted linear least squares estimation of diffusion MRI parameters: Strengths, limitations, and pitfalls.},
	journal = {NeuroImage},
	volume = {81},
	year = {2013},
	month = {2013 May 16},
	pages = {335-346},
	abstract = {PURPOSE: Linear least squares estimators are widely used in diffusion MRI for the estimation of diffusion parameters. Although adding proper weights is necessary to increase the precision of these linear estimators, there is no consensus on how to practically define them. In this study, the impact of the commonly used weighting strategies on the accuracy and precision of linear diffusion parameter estimators is evaluated and compared with the nonlinear least squares estimation approach. METHODS: Simulation and real data experiments were done to study the performance of the weighted linear least squares estimators with weights defined by (a) the squares of the respective noisy diffusion-weighted signals; and (b) the squares of the predicted signals, which are reconstructed from a previous estimate of the diffusion model parameters. RESULTS: The negative effect of weighting strategy (a) on the accuracy of the estimator was surprisingly high. Multi-step weighting strategies yield better performance and, in some cases, even outperformed the nonlinear least squares estimator. CONCLUSION: If proper weighting strategies are applied, the weighted linear least squares approach shows high performance characteristics in terms of accuracy/precision and may even be preferred over nonlinear estimation methods.},
	issn = {1095-9572},
	doi = {10.1016/j.neuroimage.2013.05.028},
	author = {Jelle Veraart and Jan Sijbers and Stefan Sunaert and Alexander Leemans and Ben Jeurissen}
}
@conference {1558,
	title = {{\textquotedblleft}The weighted linear least squares for estimating diffusion (kurtosis) tensors: Revisited},
	year = {2013},
	address = {Podstrana, Croatia},
	author = {Jelle Veraart and Jan Sijbers and Stefan Sunaert and Alexander Leemans and Ben Jeurissen}
}
@article {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}
}
@inproceedings {1332,
	title = {An adaptive non local maximum likelihood estimation method for denoising magnetic resonance images},
	booktitle = { IEEE International Symposium on Biomedical Imaging (ISBI)},
	year = {2012},
	author = {Jeny Rajan and Johan Van Audekerke and Annemie Van Der Linden and Marleen Verhoye and Jan Sijbers}
}
@mastersthesis {1339,
	title = {Advances in the quality and resolution improvement of Magnetic Resonance images},
	volume = {PhD in Sciences: Physics},
	year = {2012},
	month = {03/2012},
	type = {PhD thesis},
	author = {Zhenhua Mai}
}
@conference {1341,
	title = {Assessing the implications of complex fiber configurations for DTI metrics in real data sets},
	year = {2012},
	month = {May},
	pages = {3584},
	address = {Melbourne, Australia},
	author = {Ben Jeurissen and Alexander Leemans and Jacques-Donald Tournier and Derek K. Jones and Jan Sijbers}
}
@article {1350,
	title = {Automatic parameter estimation for the Discrete Algebraic Reconstruction Technique (DART)},
	journal = {IEEE Transactions on Image Processing},
	volume = {21},
	year = {2012},
	pages = {4608-4621},
	doi = {10.1109/TIP.2012.2206042},
	author = {Wim Van Aarle and Kees Joost Batenburg and Jan Sijbers}
}
@inproceedings {1367,
	title = {Combined Motion Estimation and Reconstruction in Tomography},
	booktitle = {12th European Conference on Computer Vision},
	volume = {7583},
	year = {2012},
	pages = {12-21},
	publisher = {Lecture Notes on Computer Science},
	organization = {Lecture Notes on Computer Science},
	address = {Firenze, Italy},
	abstract = {If objects or patients move during a CT scan, reconstructions suffer from severe motion artifacts. Time dependent computed tomography (4DCT) tries to minimize these artifacts by estimating motion and/or reconstruction simultaneously. Most current methods assume a known deformation or a reconstruction without artifacts at a certain time point. This work explores the possibilities of estimating the motion model and reconstruction simultaneously. It does so by modifying the simultaneous iterative reconstruction technique (SIRT) to incorporate motion (trans-SIRT) and uses this method in an optimization routine that computes motion and reconstruction at the same time. Results show that the optimization routine is able to estimate motion accurately, assuming only the type of parametrization for the motion model. Our approach can potentially be extended to more complex motion models.},
	doi = {10.1007/978-3-642-33863-2_2},
	author = {Van Eyndhoven, Geert and Jan Sijbers and Kees Joost Batenburg}
}
@article {CampI.L.N.mverhoyejveraartwvheckeE.S.jsijbersA.avdlinde2011,
	title = {A complementary DTI-histological study in a model of Huntingtons disease},
	journal = {Neurobiology of Aging},
	volume = {33},
	number = {5},
	year = {2012},
	pages = {945-959},
	doi = {http://dx.doi.org/10.1016/j.neurobiolaging.2010.07.001},
	author = {N. Van Camp and Ines Blockx and L. Camon and N. de Vera and Marleen Verhoye and Jelle Veraart and Wim Van Hecke and E. Martinez and Guadelupe S. and Jan Sijbers and A. Planas and Annemie Van Der Linden}
}
@conference {1325,
	title = {Correction of Gibbs ringing in diffusion MRI data using total variation regularization},
	year = {2012},
	month = {January},
	pages = {99},
	keywords = {MRI distortion artefacts},
	author = {Daniele Perrone and Jan Aelterman and Maryna Kudzinava and Jan Sijbers and Aleksandra Pizurica and Wilfried Philips and Alexander Leemans}
}
@article {1304,
	title = {Diffusion kurtosis imaging in the grading of gliomas},
	journal = {Radiology},
	volume = {2},
	number = {263},
	year = {2012},
	pages = {492-501},
	doi = {10.1148/radiol.12110927},
	author = {Van Cauter, Sofie and Jelle Veraart and Jan Sijbers and Ron R Peeters and U. Himmelreich and S. Van Gool and Van Calenbergh, F. and De Vleeschouwer, S. and Wim Van Hecke and Stefan Sunaert}
}
@conference {1366,
	title = {Discrete algebraic reconstruction in MRI: a simulation study},
	volume = {25},
	year = {2012},
	pages = {480-481},
	keywords = {compressed sensing, DART, iterative reconstruction, prior knowledge},
	doi = {10.1007/s10334-012-0324-9},
	author = {Quinten Collier and Segers, Hilde and Jan Sijbers}
}
@mastersthesis {1378,
	title = {Estimation and removal of noise from single and multiple coil  Magnetic Resonance images},
	year = {2012},
	month = {11/2012},
	type = {PhD Thesis},
	author = {Jeny Rajan}
}
@article {1515,
	title = {Extraction of airways from CT (EXACT{\textquoteright}09).},
	journal = {IEEE Transactions on Medical Imaging},
	volume = {31},
	year = {2012},
	month = {2012 Nov},
	pages = {2093-107},
	abstract = {This paper describes a framework for establishing a reference airway tree segmentation, which was used to quantitatively evaluate fifteen different airway tree extraction algorithms in a standardized manner. Because of the sheer difficulty involved in manually constructing a complete reference standard from scratch, we propose to construct the reference using results from all algorithms that are to be evaluated. We start by subdividing each segmented airway tree into its individual branch segments. Each branch segment is then visually scored by trained observers to determine whether or not it is a correctly segmented part of the airway tree. Finally, the reference airway trees are constructed by taking the union of all correctly extracted branch segments. Fifteen airway tree extraction algorithms from different research groups are evaluated on a diverse set of twenty chest computed tomography (CT) scans of subjects ranging from healthy volunteers to patients with severe pathologies, scanned at different sites, with different CT scanner brands, models, and scanning protocols. Three performance measures covering different aspects of segmentation quality were computed for all participating algorithms. Results from the evaluation showed that no single algorithm could extract more than an average of 74\% of the total length of all branches in the reference standard, indicating substantial differences between the algorithms. A fusion scheme that obtained superior results is presented, demonstrating that there is complementary information provided by the different algorithms and there is still room for further improvements in airway segmentation algorithms.},
	keywords = {Algorithms, Analysis of Variance, Databases, Factual, Humans, Lung, Radiographic Image Enhancement, Tomography, X-Ray Computed, Trachea},
	issn = {1558-254X},
	doi = {10.1109/TMI.2012.2209674},
	author = {Lo, Pechin and van Ginneken, Bram and Reinhardt, Joseph M and Yavarna, Tarunashree and de Jong, Pim A and Irving, Benjamin and Fetita, Catalin and Ortner, Margarete and R{\^o}mulo Pinho and Jan Sijbers and Feuerstein, Marco and Fabija{\'n}ska, Anna and Bauer, Christian and Beichel, Reinhard and Mendoza, Carlos S and Wiemker, Rafael and Lee, Jaesung and Reeves, Anthony P and Born, Silvia and Weinheimer, Oliver and van Rikxoort, Eva M and Tschirren, Juerg and Mori, Ken and Odry, Benjamin and Naidich, David P and Hartmann, Ieneke and Eric A. Hoffman and Prokop, Mathias and Pedersen, Jesper H and de Bruijne, Marleen}
}
@article {1313,
	title = {Force Feedback to Assist Active Contour Modelling for Tracheal Stenosis Segmentation},
	journal = {Advances in Human-Computer Interaction},
	volume = {2012},
	year = {2012},
	abstract = {Manual segmentation of structures for diagnosis and treatment of various diseases is a very time-consuming procedure. Therefore, some level of automation during the segmentation is desired, as it often significantly reduces the segmentation time. A typical solution is to allow manual interaction to steer the segmentation process, which is known as semiautomatic segmentation. In 2D, such interaction is usually achieved with click-and-drag operations, but in 3D a more sophisticated interface is called for. In this paper, we propose a semi-automatic Active Contour Modelling for the delineation of medical structures in 3D, tomographic images. Interaction is implemented with the employment of a 3D haptic device, which is used to steer the contour deformation towards the correct boundaries. In this way, valuable haptic feedback is provided about the 3D surface and its deformation. Experiments on simulated and real tracheal CT data showed that the proposed technique is an intuitive and effective segmentation mechanism.},
	doi = {doi:10.1155/2012/632498},
	url = {http://www.hindawi.com/journals/ahci/2012/632498/},
	author = {Lode Vanacken and R{\^o}mulo Pinho and Jan Sijbers and Karen Coninx}
}
@conference {1714,
	title = {A framework for markerless alignment with full 3D flexibility},
	year = {2012},
	author = {Jan De Beenhouwer and Willem Jan Palenstijn and Folkert Bleichrodt and Kees Joost Batenburg and Jan Sijbers}
}
@conference {1340,
	title = {HARDI-based methods for fiber orientation estimation},
	year = {2012},
	note = {Received the Summa Cum Laude Merit Award for the 20th Annual ISMRM meeting.},
	month = {May},
	pages = {3585},
	address = {Melbourne, Australia},
	author = {Ben Jeurissen and Alexander Leemans and Jacques-Donald Tournier and Jan Sijbers}
}
@article {1349,
	title = {Identification and characterization of Huntington related pathology: an in vivo DKI imaging study},
	journal = {NeuroImage},
	volume = {63},
	year = {2012},
	month = {09/2012},
	pages = {653-662},
	doi = {http://dx.doi.org/10.1016/j.neuroimage.2012.06.032},
	author = {Ines Blockx and Marleen Verhoye and Johan Van Audekerke and Irene  Bergwerf and Jack X Kane and Rafael  Delgado Y Palacios and Jelle Veraart and Ben Jeurissen and Kerstin Raber and Von H{\"o}rsten, Stephan and Peter Ponsaerts and Jan Sijbers and Trygve B Leergaard and Annemie Van Der Linden}
}
@mastersthesis {1337,
	title = {Improved analysis of brain connectivity using high angular resolution diffusion MRI},
	volume = {PhD in Sciences},
	year = {2012},
	month = {03/2012},
	pages = {208},
	school = {University of Antwerp},
	type = {PhD thesis},
	keywords = {complex WM architecture, constrained spherical deconvolution, crossing fibers, CSD, Diffusion MRI, diffusion tensor imaging, DTI, HARDI, high angular resolution diffusion imaging, Magnetic Resonance Imaging, MRI, probabilistic tractography, Spherical deconvolution, Tractography, white matter, WM},
	author = {Ben Jeurissen}
}
@article {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 {1438,
	title = {Nonlocal maximum likelihood estimation method for denoising multiple-coil magnetic resonance images.},
	journal = {Magnetic resonance imaging},
	volume = {30},
	year = {2012},
	month = {2012 Dec},
	pages = {1512-8},
	abstract = {Effective denoising is vital for proper analysis and accurate quantitative measurements from magnetic resonance (MR) images. Even though many methods were proposed to denoise MR images, only few deal with the estimation of true signal from MR images acquired with phased-array coils. If the magnitude data from phased array coils are reconstructed as the root sum of squares, in the absence of noise correlations and subsampling, the data is assumed to follow a non central-χ distribution. However, when the k-space is subsampled to increase the acquisition speed (as in GRAPPA like methods), noise becomes spatially varying. In this note, we propose a method to denoise multiple-coil acquired MR images. Both the non central-χ distribution and the spatially varying nature of the noise is taken into account in the proposed method. Experiments were conducted on both simulated and real data sets to validate and to demonstrate the effectiveness of the proposed method.},
	keywords = {Algorithms, Animals, Brain, Brain Mapping, Computer Simulation, Fourier Analysis, Image Processing, Computer-Assisted, Likelihood Functions, Magnetic Resonance Imaging, Mice, Models, Statistical, Normal Distribution, Signal-To-Noise Ratio, Stochastic Processes},
	issn = {1873-5894},
	doi = {10.1016/j.mri.2012.04.021},
	author = {Jeny Rajan and Jelle Veraart and Johan Van Audekerke and Marleen Verhoye and Jan Sijbers}
}
@article {1344,
	title = {Nonlocal maximum likelihood estimation method for denoising multiple-coil magnetic resonance images},
	journal = {Magnetic Resonance Imaging},
	volume = {30},
	year = {2012},
	pages = {1512-1518},
	doi = {10.1016/j.mri.2012.04.021},
	author = {Jeny Rajan and Jelle Veraart and Johan Van Audekerke and Marleen Verhoye and Jan Sijbers}
}
@conference {1388,
	title = {Optimization of Compressed Sensing/RARE Combining Acquisition Schemes},
	volume = {25},
	year = {2012},
	pages = {513-514},
	doi = {10.1007/s10334-012-0324-9},
	author = {Maarten Naeyaert and Jan Aelterman and Johan Van Audekerke and Kasper Claes and Annemie Van Der Linden and Jan Sijbers and Marleen Verhoye}
}
@inproceedings {1299,
	title = {Quantitative evaluation of ASiR image quality: an adaptive statistical iterative reconstruction technique},
	booktitle = {SPIE Medical Imaging},
	volume = {8313},
	year = {2012},
	month = {02/2012},
	abstract = {Adaptive statistical iterative reconstruction (ASiR) is a new algorithm used in medical X-ray imaging. It combines the standard FBP and the strength of iterative reconstruction by including a noise model in the reconstruction scheme. In this paper the effect of ASiR on the contrast to noise ratio (CNR) is studied using a low contrast phantom (Catphan). The experiments were done at different voltages and currents. The results show reduced noise and increased contrast for the ASiR reconstructions compared to FBP. For the same CNR the images from ASiR are obtained using 60\% less current, leading to a 60\% dose reduction.},
	doi = {doi:10.1117/12.911283},
	author = {Elke Van de Casteele and Paul M Parizel and Jan Sijbers}
}
@conference {1430,
	title = {Segmentation of MR Images by k-space distance minimization: a proof of concept},
	volume = {25},
	year = {2012},
	pages = {63-64},
	author = {Segers, Hilde and Willem Jan Palenstijn and Kees Joost Batenburg and Jan Sijbers}
}
@inproceedings {1360,
	title = {Spatial Variations in Reconstruction Methods for CT},
	booktitle = {The Second International Conference on Image Formation in X-Ray Computed Tomography (CT Meeting)},
	year = {2012},
	month = {07/2012},
	pages = {170-173},
	author = {Linda Plantagie and Willem Jan Palenstijn and Jan Sijbers and Kees Joost Batenburg}
}
@conference {1365,
	title = {Statistical shape modelling in support of user-centred BCI headset design},
	year = {2012},
	author = {Daniel Lacko and Toon Huysmans and Stijn Verwulgen and Paul M Parizel and Marc M. Van Hulle and Jan Sijbers}
}
@mastersthesis {1327,
	title = {Tomographic segmentation and discrete tomography for quantitative analysis of transmission tomography data},
	year = {2012},
	pages = {190},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp, Belgium},
	author = {Wim Van Aarle}
}
@inproceedings {1358,
	title = {Visualizing the Segmentation Error of a Tomogram using the Residual Projection Error},
	booktitle = {The Second International Conference on Image Formation in X-Ray Computed Tomography (CT Meeting)},
	year = {2012},
	pages = {293-296},
	address = {Salt Lake City, UT, USA},
	author = {Roelandts, Tom and Kees Joost Batenburg and Jan Sijbers}
}
@article {rpinhoTournoyjsijbers2011,
	title = {Assessment and Stenting of Tracheal Stenosis using Deformable Shape Models},
	journal = {Medical Image Analysis},
	volume = {15},
	number = {2},
	year = {2011},
	pages = {250-266},
	abstract = {This work presents a decision support system for the assessment of tracheal stenosis. In the proposed method, a statistical shape model of healthy tracheas is registered to a 3D CT image of a patient with tracheal stenosis. The registration yields an estimation of the shape of the patient{\textquoteright}s trachea as if stenosis was not present. From this point, the extent and the severity of the stenosis is assessed and stent parameters are obtained automatically. The method was extensively evaluated on simulation as well on real data and the results showed that it is accurate and fast enough to be used in the clinical setting.},
	issn = {1361-8423},
	doi = {http://dx.doi.org/doi:10.1016/j.media.2010.12.001},
	author = {R{\^o}mulo Pinho and Kurt G. Tournoy and Jan Sijbers}
}
@article {zmaiR.jbatenbumverhoyepscheundjsijbers2011,
	title = {Bias field reduction by localized Lloyd-Max quantization},
	journal = {Magnetic Resonance Imaging},
	volume = {29},
	year = {2011},
	pages = {536-545},
	doi = {http://dx.doi.org/10.1016/j.mri.2010.10.015},
	author = {Zhenhua Mai and Rudolf Hanel and Kees Joost Batenburg and Marleen Verhoye and Paul Scheunders and Jan Sijbers}
}
@inproceedings {1298,
	title = {Bone segmentation using discrete tomography},
	booktitle = {Micro-CT User Meeting},
	year = {2011},
	pages = {178-183},
	isbn = {9789081678100},
	issn = {2033-8031},
	author = {Elke Van de Casteele and Kees Joost Batenburg and Salmon, Phil and Jan Sijbers}
}
@inbook {1263,
	title = {Classification of Soft Tissue Tumors by Machine Learning Algorithms},
	booktitle = {Soft Tissue Tumors},
	year = {2011},
	publisher = {InTech},
	organization = {InTech},
	issn = {978-953-307-862-5},
	author = {Juntu, Jaber and Arthur M. De Schepper and Pieter Van Dyck and Dirk Van Dyck and Jan L Gielen and Paul M Parizel and Jan Sijbers}
}
@article {jveraartwvheckejsijbers2011,
	title = {Constrained Maximum Likelihood Estimation of the Diffusion Kurtosis Tensor Using a Rician Noise Model},
	journal = {Magnetic Resonance in Medicine},
	volume = {66},
	number = {3},
	year = {2011},
	pages = {678-686},
	doi = {http://dx.doi.org/doi:10.1002/mrm.22835},
	author = {Jelle Veraart and Wim Van Hecke and Jan Sijbers}
}
@conference {jveraartwvheckedpootjsijbers2011,
	title = {Constrained maximum likelihood estimator for more accurate diffusion kurtosis tensor estimates},
	year = {2011},
	month = {January},
	address = {Hoeven, The Netherlands},
	author = {Jelle Veraart and Wim Van Hecke and Dirk H J Poot and Jan Sijbers}
}
@conference {bjeurissM.aleemansjsijbers2011,
	title = {Correction of DWI gradient orientations using registration techniques},
	year = {2011},
	month = {January},
	author = {Ben Jeurissen and Maarten Naeyaert and Alexander Leemans and Jan Sijbers}
}
@article {jbatenbujsijbers2011,
	title = {DART: A practical reconstruction algorithm for discrete tomography},
	journal = {IEEE Transactions on Image Processing},
	volume = {20},
	number = {9},
	year = {2011},
	pages = {2542-2553},
	doi = {10.1109/TIP.2011.2131661},
	author = {Kees Joost Batenburg and Jan Sijbers}
}
@conference {1245,
	title = {Denoising magnitude MRI using an adaptive NLML method},
	year = {2011},
	pages = {383},
	address = {Leipzig, Germay},
	author = {Jeny Rajan and Johan Van Audekerke and Marleen Verhoye and Annemie Van Der Linden and Jan Sijbers}
}
@conference {Kudzinava_2011_ESMRMB,
	title = {Denoising of DKI images: effect on feasibility and accuracy of kurtosis parameters},
	year = {2011},
	month = {October},
	pages = {236},
	address = {Leipzig, Germany},
	abstract = {Diffusion Kurtosis Imaging (DKI) is a new MRI technique, which quantifies non-Gaussianity of water diffusion. DKI requires high b-values, resulting in a low SNR of acquired diffusion weighted images (DWIs). Generally, kurtosis tensors are estimated from these DWIs using Weighted Least Squares (WLS) or Maximum Likelihood (ML) methods. In this work, we apply the Restricted Local Maximum Likelihood (RLML) denoising algorithm to remove noise from DWIs prior to WLS estimation. Then, we compare the RLML+WLS approach to the WLS and ML methods in terms of feasibility, that is the percentage of estimated tensors that satisfy certain DKI model constraints; and in terms of accuracy of calculated values of Mean Kurtosis.},
	keywords = {denoising, diffusion kurtosis imaging, maximum likelihood estimation},
	author = {Maryna Kudzinava and Jeny Rajan and Jan Sijbers}
}
@conference {1306,
	title = {Denoising SENSE reconstructed MR images},
	year = {2011},
	author = {Jeny Rajan and Jan Sijbers}
}
@conference {Kudzinava_2011_WMSG,
	title = {Diffusion and kurtosis parameters in white matter of premature newborns},
	year = {2011},
	month = {August},
	pages = {21},
	address = {Reykjavik, Iceland},
	abstract = {Diffusion Tensor Imaging (DTI) is often used to study brain maturation processes and white matter (WM) injuries in premature newborns. However, DTI assumes that water diffusion in biological tissues is free. In reality, there is always structural hindrance in WM, which only increases with age due to myelination. Diffusional Kurtosis Imaging (DKI) relaxes the assumption of free diffusion and allows estimation of conventional diffusion parameters plus new kurtosis measures, which are related to tissue complexity. In this work, we present and discuss the first statistics of DKI parameters in WM of two prematurely born infants.},
	keywords = {anisotropy, diffusion, kurtosis, premature newborns, white matter},
	author = {Maryna Kudzinava and A. Plaisier and J. Dudink and Jan Sijbers}
}
@conference {R.jveraartG.H.mverhoyeP.jsijbersavdlinde2011,
	title = {Diffusion kurtosis abnormalities in pre-symptomatic (alpha)-synycleunopathy mouse model},
	year = {2011},
	month = {January},
	address = {Hoeven, The Netherlands},
	author = {Rafael  Delgado Y Palacios and Jelle Veraart and Greetje Vanhoutte and H. Schell and Marleen Verhoye and P. Kahle and Jan Sijbers and Annemie Van Der Linden}
}
@conference {1307,
	title = {Distortion correction of DKI data: affine approach},
	year = {2011},
	month = {December},
	abstract = {In this work, the performance of FMRIB{\textquoteright}s Linear Registration Tool (FLIRT) is evaluated with respect to motion and distortion correction of diffusion kurtosis imaging (DKI) data. FLIRT is tested on two DKI datasets heavily distorted by motion and eddy currents artefacts. Six different criteria are used for evaluation. Our results show that distortion correction using FLIRT substantially improves the original data. However, being an affine registration tool, FLIRT cannot cope with the amount of non-linear distortion present in DKI datasets. Hence, we conclude that, for correction of DKI data, non-linear registration techniques are more suitable.},
	keywords = {medical imaging},
	author = {Maryna Kudzinava and Alexander Leemans and Wilfried Philips and Daniele Perrone and Jan Aelterman and J. Dudink and Jan Sijbers}
}
@article {wvheckealeemansC.jveraartjsijbersS.2011,
	title = {The effect of template selection on diffusion tensor voxel based analysis results},
	journal = {NeuroImage},
	volume = {55},
	number = {2},
	year = {2011},
	pages = {566-573},
	author = {Wim Van Hecke and Alexander Leemans and Caroline A Sage and Jelle Veraart and Jan Sijbers and Stefan Sunaert}
}
@inproceedings {1429,
	title = {Efficient parameter estimation for discrete tomography using adaptive modeling},
	booktitle = {11th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine},
	year = {2011},
	month = {07/2011},
	author = {Wim Van Aarle and K. Crombecq and I. Couckuyt and Kees Joost Batenburg and Jan Sijbers}
}
@conference {1331,
	title = {An extended NLML method for denoising non-central chi distributed data - application to parallel MRI},
	year = {2011},
	pages = {41},
	author = {Jeny Rajan and Johan Van Audekerke and Jelle Veraart and Marleen Verhoye and Jan Sijbers}
}
@inproceedings {1942,
	title = {Feasibility and advantages of diffusion weighted imaging atlas construction in Q-space},
	booktitle = {MICCAI 2011: Medical Image Computing and Computer-Assisted Intervention},
	year = {2011},
	pages = {166-173},
	author = {Thijs Dhollander and Jelle Veraart and Wim Van Hecke and F. Maes and Stefan Sunaert and Jan Sijbers and Paul Suetens}
}
@conference {1308,
	title = {Gibbs artifact suppression for DT-MRI data},
	year = {2011},
	month = {December},
	keywords = {medical imaging},
	author = {Daniele Perrone and Jan Aelterman and Maryna Kudzinava and Jan Sijbers and Aleksandra Pizurica and Wilfried Philips and Alexander Leemans}
}
@article {gvgompelSlambrouckDefrisejbatenbuMeyjsijbersNuyts2011,
	title = {Iterative correction of beam hardening artifacts in CT},
	journal = {Medical Physics},
	volume = {38},
	number = {1},
	year = {2011},
	month = {July},
	pages = {36-49},
	doi = {http://dx.doi.org/10.1118/1.3577758},
	author = {Gert Van Gompel and Van Slambrouck, Katrien and M. Defrise and Kees Joost Batenburg and Johan De Mey and Jan Sijbers and Johan Nuyts}
}
@article {R.AdriaanKimmverhoyedpootJoukeJ.HjsijbersOavdlinde2011,
	title = {Magnetic resonance imaging and spectroscopy reveal differential hippocampal changes in anhedonic and resilient subtypes of the chronic mild stress rat model},
	journal = {Biological psychiatry},
	volume = {70},
	number = {5},
	year = {2011},
	pages = {449-457},
	author = {Rafael  Delgado Y Palacios and Campo Adriaan and Henningsen Kim and Marleen Verhoye and Dirk H J Poot and Dijkstra Jouke and Johan Van Audekerke and H Benveniste and Jan Sijbers and O. Wiborg and Annemie Van Der Linden}
}
@article {jrajanbjeurissmverhoyeJ.jsijbers2011,
	title = {Maximum likelihood estimation based denoising of magnetic resonance images using restricted local neighborhoods},
	journal = {Physics in Medicine and Biology},
	volume = {56},
	number = {16},
	year = {2011},
	pages = {5221-5234},
	doi = {doi:10.1088/0031-9155/56/16/009},
	author = {Jeny Rajan and Ben Jeurissen and Marleen Verhoye and Johan Van Audekerke and Jan Sijbers}
}
@inproceedings {jrajanmverhoyejsijbers2011,
	title = {A maximum likelihood estimation method for denoising magnitude MRI using restricted local neighborhood},
	booktitle = {SPIE Medical Imaging},
	volume = {7962},
	year = {2011},
	publisher = {SPIE},
	organization = {SPIE},
	author = {Jeny Rajan and Marleen Verhoye and Jan Sijbers},
	editor = {B.M. Dawant and D.R. Haynor}
}
@conference {S.jveraartjsijbersU.R.S.wvhecke2011,
	title = {Mean Kurtosis: a new potential biomarker for brain tumor grading},
	year = {2011},
	month = {January},
	address = {Hoeven, The Netherlands},
	author = {Van Cauter, Sofie and Jelle Veraart and Jan Sijbers and U. Himmelreich and Ron R Peeters and S. Van Gool and Wim Van Hecke}
}
@conference {1303,
	title = {Mediolateral shape and curvature analysis of the clavicle},
	year = {2011},
	month = {June},
	author = {Van Tongel, Alexander and Toon Huysmans and Amit Bernat and Jan Sijbers and Francis Van Glabbeek}
}
@article {jveraartdpootwvheckeBlockxavdlindemverhoyejsijbers2011,
	title = {More accurate estimation of diffusion tensor parameters using diffusion kurtosis imaging},
	journal = {Magnetic Resonance in Medicine},
	volume = {65},
	number = {1},
	year = {2011},
	month = {January},
	pages = {138-145},
	doi = {10.1002/mrm.22603},
	author = {Jelle Veraart and Dirk H J Poot and Wim Van Hecke and Ines Blockx and Annemie Van Der Linden and Marleen Verhoye and Jan Sijbers}
}
@article {wvaarlejbatenbujsijbers2011,
	title = {Optimal threshold selection for segmentation of dense homogeneous objects in tomographic reconstructions},
	journal = {IEEE Transactions on Medical Imaging},
	volume = {30},
	number = {4},
	year = {2011},
	pages = {980-989},
	doi = {http://dx.doi.org/doi:10.1109/TMI.2010.2104328},
	author = {Wim Van Aarle and Kees Joost Batenburg and Jan Sijbers}
}
@inproceedings {Kudzinava_2011_ISBI,
	title = {Optimized Workflow for Diffusion Kurtosis Imaging of Newborns},
	booktitle = {ISBI, 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro},
	year = {2011},
	month = {April},
	pages = {922-926},
	address = {Chicago, USA},
	abstract = {Diffusional kurtosis imaging (DKI) is a recently proposed extension of the conventional DTI model. It has been shown to offer more sensitive characterization of neural tissues than DTI. So far, DKI has only been applied to adult human and small animal studies, but not yet to human newborns. In this work, we present an optimized workflow for the acquisition and processing of DKI images of newborns. First, optimal set of diffusion weighting gradients for DKI studies of newborn subjects is proposed. Optimized gradients allow to estimate DKI parameters with the highest precision. Next, preprocessing and segmentation of the DKI data is considered, including motion correction, eddy currents suppression, intensity modulation and gradients reorientation. Finally, statistics of estimated diffusion and kurtosis parameters for different neonatal brain tissues are calculated.},
	keywords = {diffusion and kurtosis parameters, DKI, motion  correction, newborns, optimal gradients, precision},
	doi = {10.1109/ISBI.2011.5872554},
	url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=5872554},
	author = {Maryna Kudzinava and Dirk H J Poot and A. Plaisier and Jan Sijbers}
}
@inproceedings {1194,
	title = {PDART: A Partially Discrete Algorithm for the Reconstruction of Dense Particles},
	booktitle = {11th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully 3D)},
	year = {2011},
	pages = {448-451},
	address = {Potsdam, Germany},
	author = {Roelandts, Tom and Kees Joost Batenburg and Jan Sijbers}
}
@article {1249,
	title = {Performance improvements for iterative electron tomography reconstruction using graphics processing units (GPUs)},
	journal = {Journal of structural biology},
	volume = {176},
	year = {2011},
	month = {2011 Nov},
	pages = {250-253},
	abstract = {Iterative reconstruction algorithms are becoming increasingly important in electron tomography of biological samples. These algorithms, however, impose major computational demands. Parallelization must be employed to maintain acceptable running times. Graphics Processing Units (GPUs) have been demonstrated to be highly cost-effective for carrying out these computations with a high degree of parallelism. In a recent paper by Xu et al. (2010), a GPU implementation strategy was presented that obtains a speedup of an order of magnitude over a previously proposed GPU-based electron tomography implementation. In this technical note, we demonstrate that by making alternative design decisions in the GPU implementation, an additional speedup can be obtained, again of an order of magnitude. By carefully considering memory access locality when dividing the workload among blocks of threads, the GPU{\textquoteright}s cache is used more efficiently, making more effective use of the available memory bandwidth.},
	issn = {1095-8657},
	doi = {10.1016/j.jsb.2011.07.017},
	author = {Willem Jan Palenstijn and Kees Joost Batenburg and Jan Sijbers}
}
@article {jveraartB.T.wvheckeI.bjeurissY.avdlindeA.mverhoyejsijbers2011,
	title = {Population-averaged diffusion tensor imaging atlas of the Sprague Dawley rat brain},
	journal = {NeuroImage},
	volume = {58},
	year = {2011},
	pages = {975-983},
	doi = {10.1016/j.neuroimage.2011.06.063},
	author = {Jelle Veraart and Trygve B Leergaard and Antonsen, Bj{\o}rnar T and Wim Van Hecke and Ines Blockx and Ben Jeurissen and Yi Jiang and Annemie Van Der Linden and Allan G Johnson and Marleen Verhoye and Jan Sijbers}
}
@article {1258,
	title = {Probabilistic fiber tracking using the residual bootstrap with constrained spherical deconvolution},
	journal = {Human Brain Mapping},
	volume = {32},
	year = {2011},
	month = {March, 2011},
	pages = {461 - 479},
	keywords = {Diffusion MRI, Spherical deconvolution, Tractography},
	doi = {10.1002/hbm.21032},
	author = {Ben Jeurissen and Alexander Leemans and Derek K. Jones and Jacques-Donald Tournier and Jan Sijbers}
}
@conference {bjeurissM.aleemansjsijbers2011,
	title = {Registration based correction of DWI gradient orientations},
	year = {2011},
	month = {May},
	address = {Montreal, Canada},
	author = {Ben Jeurissen and Maarten Naeyaert and Alexander Leemans and Jan Sijbers}
}
@conference {1256,
	title = {Robust edge directed interpolation of diffusion weighted MR images},
	year = {2011},
	pages = {382},
	author = {Zhenhua Mai and Jeny Rajan and Marleen Verhoye and Jan Sijbers}
}
@conference {zmaijrajanmverhoyejsijbers2011,
	title = {Robust Edge-directed Interpolation: Application to Diffusion MR Images},
	year = {2011},
	month = {May},
	author = {Zhenhua Mai and Jeny Rajan and Marleen Verhoye and Jan Sijbers}
}
@article {1261,
	title = {Robust edge-directed interpolation of magnetic resonance images},
	journal = {Physics in medicine and biology},
	volume = {56},
	year = {2011},
	pages = {7287-7303},
	author = {Zhenhua Mai and Jeny Rajan and Marleen Verhoye and Jan Sijbers}
}
@article {jbatenbuwvaarlejsijbers2011,
	title = {A Semi-Automatic Algorithm for Grey Level Estimation in Tomography},
	journal = {Pattern Recognition Letters},
	volume = {32},
	number = {9},
	year = {2011},
	month = {July},
	pages = {1395-1405},
	author = {Kees Joost Batenburg and Wim Van Aarle and Jan Sijbers}
}
@conference {1204,
	title = {Ultra-High Resolution Electron Tomography for Materials Science: A Roadmap},
	year = {2011},
	address = {Nashville, TN, United States},
	doi = {http://dx.doi.org/10.1017/S143192761100554X},
	author = {Kees Joost Batenburg and Sara Bals and Sandra Van Aert and Roelandts, Tom and Jan Sijbers}
}
@inproceedings {wvaarlegvgompeljbatenbuevdcastejsijbers2010,
	title = {A 3-dimensional discrete tomography approach for superresolution micro-CT images: application to foams},
	booktitle = {The first international conference on image formation in X-ray computed tomography},
	year = {2010},
	month = {June},
	pages = {45-48},
	author = {Wim Van Aarle and Gert Van Gompel and Kees Joost Batenburg and Elke Van de Casteele and Jan Sijbers},
	editor = {Fr{\'e}d{\'e}ric Noo}
}
@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}
}
@inbook {rpinhoTournoyjsijbers2010,
	title = {Computer-Aided Assessment and Stenting of Tracheal Stenosis},
	booktitle = {Computer Aided Diagnosis of Lung Imaging},
	year = {2010},
	author = {R{\^o}mulo Pinho and Kurt G. Tournoy and Jan Sijbers},
	editor = {Ayman El-Baz}
}
@conference {jveraartAntonsenBlockxwvheckeJiangJohnsonavdlindeLeergaardmverhoyejsijbers2010,
	title = {Construction of a population based diffusion tensor image atlas of the Sprague Dawley rat brain},
	year = {2010},
	month = {May},
	address = {Stockholm, Sweden},
	author = {Jelle Veraart and Antonsen, Bj{\o}rnar T and Ines Blockx and Wim Van Hecke and Yi Jiang and Allan G Johnson and Annemie Van Der Linden and Trygve B Leergaard and Marleen Verhoye and Jan Sijbers}
}
@article {wvheckeNagelsaleemansVandervlietjsijbersParizel2010,
	title = {Correlation of cognitive dysfunction and diffusion tensor MRI measures in patients with mild and moderate multiple sclerosis},
	journal = {Journal of magnetic resonance imaging},
	volume = {31},
	number = {6},
	year = {2010},
	month = {May},
	pages = {1492-1498},
	author = {Wim Van Hecke and Guy Nagels and Alexander Leemans and Everhard Vandervliet and Jan Sijbers and Paul M Parizel}
}
@conference {bjeurissaleemansdkjonesjdtournierjsijbers2010,
	title = {Counting the number of fiber orientations in diffusion MRI voxels using constrained spherical deconvolution},
	year = {2010},
	month = {January},
	address = {Utrecht, The Netherlands},
	author = {Ben Jeurissen and Alexander Leemans and Derek K. Jones and Jacques-Donald Tournier and Jan Sijbers}
}
@article {jbatenbujsijbersH.F.E.2010,
	title = {DART: A Robust Algorithm for Fast Reconstruction of 3D Grain Maps},
	journal = {Journal of Applied Crystallography},
	volume = {43},
	year = {2010},
	pages = {1464-1473},
	doi = {https://doi.org/10.1107/S0021889810034114},
	author = {Kees Joost Batenburg and Jan Sijbers and H.F. Poulsen and E. Knudsen}
}
@mastersthesis {1284,
	title = {A Decision Support System for the Assessment and Stenting of Tracheal Stenosis},
	year = {2010},
	month = {18/11/2010},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	abstract = {This thesis sets forth a decision support system that proposes a method for automatic assessment of tracheal stenosis and prediction of stent length and diameter from chest CT scans. The main idea behind the proposed method is to estimate the shape of the trachea of a patient as if stenosis were not present. This shape can be used the by the physician for surgery planning and is the basis for the automatic assessment of the stenosis and the prediction of patient-specific stents.
},
	author = {R{\^o}mulo Pinho}
}
@inproceedings {rpinhoTournoyGosselinjsijbers2010,
	title = {A Decision Support System for the Treatment of Tracheal Stenosis},
	booktitle = {Proc. of Workshop on Discrete Geometry and Mathematical Morphology (WADGMM)},
	year = {2010},
	month = {August},
	pages = {72-76},
	publisher = {IAPR},
	organization = {IAPR},
	address = {Istanbul},
	author = {R{\^o}mulo Pinho and Kurt G. Tournoy and Robert Gosselin and Jan Sijbers}
}
@article {zmaimverhoyeavdlindejsijbers2010,
	title = {Diffusion tensor image up-sampling: a registration-based approach},
	journal = {Magnetic resonance Imaging},
	volume = {28},
	number = {10},
	year = {2010},
	month = {December},
	pages = {1497-1506},
	doi = {10.1016/j.mri.2010.06.018},
	author = {Zhenhua Mai and Marleen Verhoye and Annemie Van Der Linden and Jan Sijbers}
}
@inproceedings {zmaimverhoyejsijbers2010,
	title = {Diffusion Tensor Images Edge-Directed Interpolation},
	booktitle = {International Symposium on Biomedical Imaging 2010},
	year = {2010},
	month = {April},
	pages = {732-735},
	author = {Zhenhua Mai and Marleen Verhoye and Jan Sijbers}
}
@inproceedings {gvgompeljbatenbuevdcastewvaarlejsijbers2010,
	title = {A discrete tomography approach for superresolution micro-CT images},
	booktitle = {IEEE International Symposium on Biomedical Imaging},
	year = {2010},
	month = {April},
	pages = {816-819},
	address = {Rotterdam},
	author = {Gert Van Gompel and Kees Joost Batenburg and Elke Van de Casteele and Wim Van Aarle and Jan Sijbers}
}
@conference {bjeurissaleemansdkjonesjdtournierjsijbers2010,
	title = {Estimating the number of fiber orientations in diffusion MRI voxels: a constrained spherical deconvolution study},
	year = {2010},
	month = {May},
	address = {Stockholm, Sweden},
	author = {Ben Jeurissen and Alexander Leemans and Derek K. Jones and Jacques-Donald Tournier and Jan Sijbers}
}
@inproceedings {1319,
	title = {General and Efficient Super-Resolution method for Multi-Slice MRI},
	booktitle = {Medical Image Computing and Computer Assisted Intervention},
	volume = {13},
	year = {2010},
	pages = {615-622},
	doi = {10.1007/978-3-642-15705-9_75},
	author = {Dirk H J Poot and V. Van Meir and Jan Sijbers}
}
@article {dpootwpintjenmverhoyeavdlindejsijbers2010,
	title = {Improved B0 field map estimation for high field EPI},
	journal = {Magnetic Resonance Imaging},
	volume = {28},
	number = {3},
	year = {2010},
	month = {April},
	pages = {441-450},
	author = {Dirk H J Poot and W. Pintjens and Marleen Verhoye and Annemie Van Der Linden and Jan Sijbers}
}
@inproceedings {1322,
	title = {Iterative correction of beam hardening artifacts in CT},
	booktitle = {The First International Conference on Image Formation in X-Ray Computed Tomography},
	year = {2010},
	month = {06/2010},
	author = {Van Slambrouck, Katrien and Gert Van Gompel and M. Defrise and Kees Joost Batenburg and Jan Sijbers and Johan Nuyts}
}
@article {jjuntujsijberssdbackerjrajandvandyck2010,
	title = {A Machine Learning Study of Several Classifiers Trained with Texture Analysis Features to Differentiate Benign from Malignant Soft Tissue Tumors in T1-MRI Images},
	journal = {Journal of Magnetic Resonance Imaging},
	volume = {31},
	year = {2010},
	pages = {680{\textendash}689},
	doi = {10.1002/jmri.22095},
	author = {Juntu, Jaber and Jan Sijbers and Steve De Backer and Jeny Rajan and Dirk Van Dyck}
}
@article {1196,
	title = {Method for Mapping Tubular Surfaces to a Cylinder},
	number = {PCT/EP2010/057882},
	year = {2010},
	month = {01/2015},
	edition = {G06T 17/20 (2006.01), A61B 1/00 (2006.01)},
	abstract = {The present invention relates to the field of cylindrical surface parameterization such as colon flattening. Methods are provided for parameterizing tubular surfaces onto a cylinder, wherein the length of said cylinder is modified so that parameterization distortion is reduced.},
	url = {https://www.google.com/patents/US8933932},
	author = {Toon Huysmans and Jan Sijbers}
}
@conference {jveraartwvheckedpootBlockxavdlindemverhoyejsijbers2010,
	title = {A more accurate and b-value independent estimation of diffusion parameters using Diffusion Kurtosis Imaging,},
	year = {2010},
	month = {May},
	address = {Stockholm, Sweden},
	author = {Jelle Veraart and Wim Van Hecke and Dirk H J Poot and Ines Blockx and Annemie Van Der Linden and Marleen Verhoye and Jan Sijbers}
}
@conference {jveraartwvheckedpootBlockxavdlindemverhoyejsijbers2010,
	title = {A more accurate and b-value independent estimation of diffusion parameters using Diffusion Kurtosis Imaging},
	year = {2010},
	month = {January},
	pages = {10},
	address = {Utrecht, the Netherlands},
	author = {Jelle Veraart and Wim Van Hecke and Dirk H J Poot and Ines Blockx and Annemie Van Der Linden and Marleen Verhoye and Jan Sijbers}
}
@article {1479,
	title = {Morphologic and functional changes in the unilateral 6-hydroxydopamine lesion rat model for Parkinson{\textquoteright}s disease discerned with microSPECT and quantitative MRI.},
	journal = {Magnetic Resonance Materials in Physics, Biology and Medicine},
	volume = {23},
	year = {2010},
	month = {04/2010},
	pages = {65-75},
	abstract = {OBJECT: In the present study, we aimed to evaluate the impact of neurodegeneration of the nigrostriatal tract in a rodent model of Parkinson{\textquoteright}s disease on the different MR contrasts (T(2), T(1), CBF and CBV) measured in the striatum.

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

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

CONCLUSIONS: These PD-related asymmetry ratios might be the result of counteracting changes in both intact and affected striatum and allowed us to diagnose PD lesions. As lateralization is known to occur also in PD patients and might be expected in transgenic PD models as well, we propose that MR-derived asymmetry ratios in the striatum might be a useful tool for in vivo phenotyping of animal models of PD.},
	keywords = {Animals, Corpus Striatum, Disease Models, Animal, Female, Magnetic Resonance Imaging, Oxidopamine, Parkinsonian Disorders, Positron-Emission Tomography, Rats, Rats, Wistar, Reproducibility of Results, Sensitivity and Specificity},
	issn = {1352-8661},
	doi = {10.1007/s10334-010-0198-7},
	author = {N. Van Camp and Vreys, Ruth and Koen Van Laere and E. Lauwers and Dirk Beque and Marleen Verhoye and Casteels, Cindy and Alfons Verbruggen and Zeger Debyser and Luc Mortelmans and Jan Sijbers and Johan Nuyts and Veerle Baekelandt and Annemie Van Der Linden}
}
@article {1189,
	title = {Noise measurement from magnitude MRI using local estimates of variance and skewness.},
	journal = {Physics in medicine and biology},
	volume = {55},
	year = {2010},
	month = {2010 Aug 21},
	pages = {N441-9},
	abstract = {In this note, we address the estimation of the noise level in magnitude magnetic resonance (MR) images in the absence of background data. Most of the methods proposed earlier exploit the Rayleigh distributed background region in MR images to estimate the noise level. These methods, however, cannot be used for images where no background information is available. In this note, we propose two different approaches for noise level estimation in the absence of the image background. The first method is based on the local estimation of the noise variance using maximum likelihood estimation and the second method is based on the local estimation of the skewness of the magnitude data distribution. Experimental results on synthetic and real MR image datasets show that the proposed estimators accurately estimate the noise level in a magnitude MR image, even without background data.},
	keywords = {Algorithms, Artifacts, Brain, Data Interpretation, Statistical, Fourier Analysis, Humans, Image Processing, Computer-Assisted, Likelihood Functions, Magnetic Resonance Imaging, Models, Statistical, Myocardium, Normal Distribution, Reproducibility of Results},
	issn = {1361-6560},
	doi = {10.1088/0031-9155/55/16/N02},
	author = {Jeny Rajan and Dirk H J Poot and Juntu, Jaber and Jan Sijbers}
}
@inproceedings {jveraartwvheckeBlockxavdlindemverhoyejsijbers2010,
	title = {Non-Rigid coregistration of diffusion kurtosis data},
	booktitle = {Biomedical Imaging: From Nano to Macro, 2010 IEEE International Symposium on},
	year = {2010},
	month = {April},
	pages = {392-395},
	address = {Rotterdam, the Netherlands},
	author = {Jelle Veraart and Wim Van Hecke and Ines Blockx and Annemie Van Der Linden and Marleen Verhoye and Jan Sijbers}
}
@article {dpootajdendekAchtenmverhoyejsijbers2010,
	title = {Optimal experimental design for Diffusion Kurtosis Imaging},
	journal = {IEEE Transactions on Medical Imaging},
	volume = {29},
	number = {3},
	year = {2010},
	pages = {819-829},
	doi = {http://dx.doi.org/10.1109/TMI.2009.2037915},
	author = {Dirk H J Poot and Arnold Jan den Dekker and Eric Achten and Marleen Verhoye and Jan Sijbers}
}
@conference {1203,
	title = {Partially Discrete Tomography for the Reconstruction of Dense Particles},
	year = {2010},
	pages = {I7.19},
	address = {Rio de Janeiro, Brazil},
	author = {Roelandts, Tom and Kees Joost Batenburg and E. Biermans and Sara Bals and Jan Sijbers}
}
@inproceedings {1568,
	title = {Projection and backprojection in tomography: design choices and considerations},
	booktitle = {Workshop on Applications of Discrete Geometry and Mathematical Morphology},
	year = {2010},
	pages = {106-110},
	author = {Kees Joost Batenburg and Willem Jan Palenstijn and Jan Sijbers}
}
@inproceedings {DBLP:conf/iciar/RajanPJS10,
	title = {Segmentation Based Noise Variance Estimation from Background MRI Data},
	booktitle = {ICIAR },
	volume = {6111},
	year = {2010},
	month = {2010},
	pages = {62-70},
	publisher = {Springer LNCS},
	organization = {Springer LNCS},
	address = {Porto, Portugal},
	author = {Jeny Rajan and Dirk H J Poot and Juntu, Jaber and Jan Sijbers}
}
@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}
}
@mastersthesis {1290,
	title = {Advances in the reconstruction and statistical processing of Magnetic Resonance images},
	volume = {PhD in Sciences: Physics},
	year = {2009},
	type = {PhD Thesis},
	author = {Dirk H J Poot}
}
@inproceedings {rpinhoTournoyGosselinjsijbers2009,
	title = {Assessment of Tracheal Stenosis Using Active Shape Models of Healthy Tracheas: A Surface Registration Study},
	booktitle = {Proceedings of 2nd International Workshop on Pulmonary Image Analysis},
	year = {2009},
	month = {September},
	pages = {125-136},
	author = {R{\^o}mulo Pinho and Kurt G. Tournoy and Robert Gosselin and Jan Sijbers}
}
@article {wvheckejsijberssdbackerdpootParizelaleemans2009,
	title = {On the construction of a ground truth framework for evaluating voxel-based diffusion tensor MRI analysis methods},
	journal = {NeuroImage},
	volume = {46},
	number = {3},
	year = {2009},
	month = {July},
	pages = {692-707},
	doi = {10.1016/j.neuroimage.2008.07.006},
	author = {Wim Van Hecke and Jan Sijbers and Steve De Backer and Dirk H J Poot and Paul M Parizel and Alexander Leemans}
}
@inproceedings {jrajanbjeurissjsijbersK.2009,
	title = {Denoising Magnetic Resonance Images using Fourth Order Complex Diffusion},
	booktitle = {13th International Machine Vision and Image Processing Conference},
	year = {2009},
	pages = {123-127},
	address = {Dublin, Ireland},
	author = {Jeny Rajan and Ben Jeurissen and Jan Sijbers and Keizer Kannan}
}
@conference {BlockxmverhoyeGroofAudekerkeRaberdpootjsijbersHorstenavdlinde2009,
	title = {Diffusion Kurtosis Imaging (DKI) reveals an early phenotype (P30) in a transgenic rat model for Huntington{\textquoteright}s disease},
	volume = {2009},
	year = {2009},
	month = {April},
	pages = {359},
	author = {Ines Blockx and Marleen Verhoye and G. De Groof and Johan Van Audekerke and Kerstin Raber and Dirk H J Poot and Jan Sijbers and S. von Horsten and Annemie Van Der Linden}
}
@inproceedings {zmaimverhoyeavdlindejsijbers2009,
	title = {Diffusion Tensor Images Upsampling: a Registration-based Approach},
	booktitle = {13th International Machine Vision and Image Processing Conference},
	volume = {13},
	year = {2009},
	month = {September},
	pages = {36-40},
	address = {Dublin, Ireland},
	author = {Zhenhua Mai and Marleen Verhoye and Annemie Van Der Linden and Jan Sijbers}
}
@article {1376,
	title = {A diffusion tensor imaging group study of the spinal cord in multiple sclerosis patients with and without T2 spinal cord lesions.},
	journal = {Journal of magnetic resonance imaging : JMRI},
	volume = {30},
	year = {2009},
	month = {2009 Jul},
	pages = {25-34},
	abstract = {PURPOSE: To examine the T(2)-normal appearing spinal cord of patients with multiple sclerosis (MS) using diffusion tensor imaging.

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

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

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

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

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

Discussion This is the first 3D analysis performed on the clavicle. Females have a smaller clavicle with a shorter and less curved acromial curvature and a posteroinferior bending. The right clavicle is slightly shorter, thicker and more robust.},
	author = {Hilde Elisa Bortier and Amit Bernat and Toon Huysmans and Francis Van Glabbeek and Jan Sijbers and R{\^o}mulo Pinho and Gielen, Jan and Guy Hubens}
}
@mastersthesis {1172,
	title = {Parameterization and Correspondence for Improved Modeling, Analysis, and Visualization of Tubular Surfaces.},
	year = {2009},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	author = {Toon Huysmans}
}
@conference {dpootajdendekjsijbers2009,
	title = {Pearson Set of Distributions as Improved Signal Model for Diffusion Kurtosis Imaging},
	year = {2009},
	month = {April},
	pages = {1383},
	publisher = {ISMRM},
	author = {Dirk H J Poot and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {bjeurissaleemansjdtournierjsijbers2009,
	title = {Probabilistic Fiber Tracking using the Residual Bootstrap with Constrained Spherical Deconvolution MRI},
	year = {2009},
	month = {April},
	address = {Honolulu, USA},
	author = {Ben Jeurissen and Alexander Leemans and Jacques-Donald Tournier and Jan Sijbers}
}
@article {1374,
	title = {Quantitative diffusion tensor imaging in amyotrophic lateral sclerosis: revisited.},
	journal = {Human brain mapping},
	volume = {30},
	year = {2009},
	month = {2009 Nov},
	pages = {3657-75},
	abstract = {Voxel-based analyses (VBA) are increasingly being used to detect white matter abnormalities with diffusion tensor imaging (DTI) in different types of pathologies. However, the validity, specificity, and sensitivity of statistical inferences of group differences to a large extent depend on the quality of the spatial normalization of the DTI images. Using high-dimensional nonrigid coregistration techniques that are able to align both the spatial and orientational diffusion information and incorporate appropriate templates that contain this complete DT information may improve this quality. Alternatively, a hybrid technique such as tract-based spatial statistics (TBSS) may improve the reliability of the statistical results by generating voxel-wise statistics without the need for perfect image alignment and spatial smoothing. In this study, we have used (1) a coregistration algorithm that was optimized for coregistration of DTI data and (2) a population-based DTI atlas to reanalyze our previously published VBA, which compared the fractional anisotropy and mean diffusivity maps of patients with amyotrophic lateral sclerosis (ALS) with those of healthy controls. Additionally, we performed a complementary TBSS analysis to improve our understanding and interpretation of the VBA results. We demonstrate that, as the overall variance of the diffusion properties is lowered after normalizing the DTI data with such recently developed techniques (VBA using our own optimized high-dimensional nonrigid coregistration and TBSS), more reliable voxel-wise statistical results can be obtained than had previously been possible, with our VBA and TBSS yielding very similar results. This study provides support for the view of ALS as a multisystem disease, in which the entire frontotemporal lobe is implicated.},
	keywords = {Adult, Aged, Algorithms, Amyotrophic Lateral Sclerosis, anisotropy, Brain, Brain Mapping, Case-Control Studies, diffusion tensor imaging, Female, Humans, Image Processing, Computer-Assisted, Male, Middle Aged, Reproducibility of Results, Sensitivity and Specificity, Statistics as Topic},
	issn = {1097-0193},
	doi = {10.1002/hbm.20794},
	author = {Caroline A Sage and Wim Van Hecke and Ron R Peeters and Jan Sijbers and Robberecht, Wim and Paul M Parizel and Marchal, Guy and Alexander Leemans and Stefan Sunaert}
}
@inproceedings {rpinhoLuyckxjsijbers2009,
	title = {Robust Region Growing Based Intrathoracic Airway Tree Segmentation},
	booktitle = {Proceedings of 2nd International Workshop on Pulmonary Image Analysis},
	year = {2009},
	month = {September},
	pages = {261-271},
	author = {R{\^o}mulo Pinho and Sten Luyckx and Jan Sijbers}
}
@mastersthesis {1270,
	title = {Towards accurate image reconstruction from truncated X-ray CT projections},
	year = {2009},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	author = {Gert Van Gompel}
}
@inproceedings {svdmaarjbatenbujsijbers2008,
	title = {Algebraic tomographic reconstruction on the GPU},
	booktitle = {Li{\`e}ge Image Days 2008: Medical Imaging},
	year = {2008},
	month = {March},
	author = {S. van der Maar and Kees Joost Batenburg and Jan Sijbers}
}
@inproceedings {jbatenbujsijbers2008,
	title = {Automatic local thresholding of tomographic reconstructions based on the projection data},
	booktitle = {SPIE Medical Imaging},
	volume = {6913},
	year = {2008},
	month = {February},
	pages = {69132},
	address = {San Diego, CA, USA},
	author = {Kees Joost Batenburg and Jan Sijbers},
	editor = {Jiang Hsieh, Ehsan Samei}
}
@conference {bjeurissaleemansjdtournierjsijbers2008,
	title = {Bootstrap methods for estimating uncertainty in Constrained Spherical Deconvolution fiber orientations},
	year = {2008},
	month = {May},
	pages = {3324},
	address = {Toronto, Canada},
	author = {Ben Jeurissen and Alexander Leemans and Jacques-Donald Tournier and Jan Sijbers}
}
@conference {bjeurissaleemansjdtournierjsijbers2008,
	title = {Can residual bootstrap reliably estimate uncertainty in fiber orientation obtained by spherical deconvolution from diffusion-weighted MRI?},
	volume = {41},
	year = {2008},
	month = {June},
	address = {Melbourne, Australia},
	author = {Ben Jeurissen and Alexander Leemans and Jacques-Donald Tournier and Jan Sijbers}
}
@conference {1493,
	title = {Computed tomography on all scales using the ASTRA toolbox},
	year = {2008},
	author = {Wim Van Aarle and S. van der Maar and Kees Joost Batenburg and Jan Sijbers}
}
@inproceedings {wvheckealeemanssdbackerParizeljsijbers2008,
	title = {On the construction of a ground truth methodology to evaluate VBM analysis results of diffusion tensor images},
	booktitle = {European Society for Magnetic Resonance in Medicine and Biology},
	year = {2008},
	month = {October},
	address = {Valencia, Spain},
	author = {Wim Van Hecke and Alexander Leemans and Steve De Backer and Paul M Parizel and Jan Sijbers}
}
@article {wvheckejsijbersDagostinoMaessdbackerVandervlietParizelaleemans2008,
	title = {On the construction of an inter-subject diffusion tensor magnetic resonance atlas of the healthy human brain},
	journal = {NeuroImage},
	volume = {43},
	number = {1},
	year = {2008},
	pages = {69-80},
	doi = {0.1016/j.neuroimage.2008.07.006},
	author = {Wim Van Hecke and Jan Sijbers and E. Dagostino and F. Maes and Steve De Backer and Everhard Vandervliet and Paul M Parizel and Alexander Leemans}
}
@inproceedings {wvheckejsijbersGoethemParizel2008,
	title = {Diffusion tensor tractography reveals white matter alterations in the normal appearing spinal cord of Multiple Sclerosis patients},
	booktitle = {European Society for Magnetic Resonance in Medicine and Biology},
	year = {2008},
	month = {October},
	address = {Valencia, Spain},
	author = {Wim Van Hecke and Jan Sijbers and J. Van Goethem and Paul M Parizel}
}
@inproceedings {bjeurissaleemansjdtournierjsijbers2008,
	title = {Estimation of Uncertainty in Constrained Spherical Deconvolution Fiber Orientations},
	booktitle = {5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro},
	year = {2008},
	pages = {907-910},
	address = {Paris, France},
	author = {Ben Jeurissen and Alexander Leemans and Jacques-Donald Tournier and Jan Sijbers}
}
@inproceedings {wvheckealeemansAgostinosdbackerVandervlietParizeljsijbers2008,
	title = {The evaluation of a population based diffusion tensor image atlas using a ground truth method},
	booktitle = {Proceedings of SPIE Medical Imaging},
	volume = {6914},
	year = {2008},
	month = {February},
	address = {San Diego, USA},
	author = {Wim Van Hecke and Alexander Leemans and E. Dagostino and Steve De Backer and Everhard Vandervliet and Paul M Parizel and Jan Sijbers}
}
@inproceedings {dpootjsijbersajdendek2008,
	title = {An exploration of spatial similarities in temporal noise spectra in fMRI measurements},
	booktitle = {Proceedings of SPIE Medical Imaging 2008},
	volume = {6914},
	year = {2008},
	month = {February},
	pages = {69142},
	address = {San Diego, CA, USA},
	author = {Dirk H J Poot and Jan Sijbers and Arnold Jan den Dekker}
}
@inproceedings {Haneljbatenbujsijbers2008,
	title = {Fast bias field estimation by localized Lloyd-Max quantization},
	booktitle = {SPIE Medical Imaging},
	year = {2008},
	month = {February},
	address = {San Diego, CA, USA},
	author = {Rudolf Hanel and Kees Joost Batenburg and Jan Sijbers}
}
@conference {1382,
	title = {FASTRA: Fast Tomographic Reconstructions Using an Eight GPU Desktop Super Computer},
	year = {2008},
	address = {San Jose, California, USA},
	author = {S. van der Maar and Kees Joost Batenburg and Toon Huysmans and Jan Sijbers}
}
@conference {bjeurissaleemansefieremajsijbers2008,
	title = {Fiber Tractography on a Crossing Fiber Phantom using Constrained Spherical Deconvolution MRI},
	year = {2008},
	address = {Li{\`e}ge, Belgium},
	author = {Ben Jeurissen and Alexander Leemans and Els Fieremans and Jan Sijbers}
}
@conference {thuysmanBernatrpinhojsijbersGlabbeeckParizelBortier2008,
	title = {A Framework for Morphometric Analysis of Long Bones: Application to the Human Clavicle},
	year = {2008},
	month = {March},
	author = {Toon Huysmans and Amit Bernat and R{\^o}mulo Pinho and Jan Sijbers and Francis Van Glabbeek and Paul M Parizel and H. Bortier}
}
@article {BoumansVignalasmolderjsijbersmverhoyeAudekerkeavdlinde2008,
	title = {Functional magnetic resonance imaging in zebra finch discerns the neural substrate involved in segregation of conspecific song from background noise},
	journal = {Journal of Neurophysiology},
	volume = {99},
	year = {2008},
	month = {November},
	pages = {931-938},
	author = {T. Boumans and C. Vignal and Alain Smolders and Jan Sijbers and Marleen Verhoye and Johan Van Audekerke and Annemie Van Der Linden}
}
@conference {wvheckealeemanssdbackerVandervlietjsijberspmparize2008,
	title = {A Ground Truth Analysis of the Preservation of Diffusion Tensor Information in a Population Specific Atlas},
	year = {2008},
	month = {May},
	address = {Toronto, Canada},
	author = {Wim Van Hecke and Alexander Leemans and Steve De Backer and Everhard Vandervliet and Jan Sijbers and Paul M Parizel}
}
@conference {wpintjendpootmverhoyeavdlindejsijbers2008,
	title = {Improved EPI Correction: Upgrading An Ultrafast Imaging Technique},
	year = {2008},
	month = {March},
	author = {W. Pintjens and Dirk H J Poot and Marleen Verhoye and Annemie Van Der Linden and Jan Sijbers}
}
@conference {gvgompeljbatenbuDefrisejsijbers2008,
	title = {An iterative post-reconstruction method for beam hardening reduction without prior information},
	year = {2008},
	month = {March},
	author = {Gert Van Gompel and Kees Joost Batenburg and M. Defrise and Jan Sijbers}
}
@conference {thuysmanBernatGlabbeekjsijbersGielenBortier2008,
	title = {Left-Right and Gender Analysis of The Human Clavicle, A Problem in Developing an Anatomical Plate?},
	year = {2008},
	month = {September},
	pages = {101},
	address = {Brugge, Belgium},
	author = {Toon Huysmans and Amit Bernat and Francis Van Glabbeek and Jan Sijbers and Jan L Gielen and H. Bortier}
}
@inproceedings {BernatthuysmanGlabbeekjsijbersBortierGielen2008,
	title = {Morphometric Study of the Human Clavicle for the Development of an Anatomical Plate},
	booktitle = {54th Annual Meeting of the Orthopaedic Research Society},
	year = {2008},
	month = {March},
	address = {San Francisco, Ca, United States},
	author = {Amit Bernat and Toon Huysmans and Francis Van Glabbeek and Jan Sijbers and H. Bortier and Jan L Gielen}
}
@inproceedings {dpootjsijbersajdendek2008,
	title = {Optimizing the Diffusion Kurtosis imaging acquisition},
	booktitle = {European Society for Magnetic Resonance in Medicine and Biology},
	year = {2008},
	month = {October},
	address = {Valencia, Spain},
	author = {Dirk H J Poot and Jan Sijbers and Arnold Jan den Dekker}
}
@inproceedings {rpinhoBatenburgjsijbers2008,
	title = {Seeing Through the Window: Pre-fetching Strategies for Out-of-core Image Processing Algorithms},
	booktitle = {Proceedings of SPIE Medical Imaging},
	volume = {6919},
	year = {2008},
	month = {February},
	publisher = {SPIE},
	organization = {SPIE},
	address = {San Diego, CA, USA},
	doi = {doi:10.1117/12.769423},
	author = {R{\^o}mulo Pinho and Kees Joost Batenburg and Jan Sijbers}
}
@inproceedings {wpintjendpootmverhoyeavdlindejsijbers2008,
	title = {Susceptibility correction for improved tractography using high field DT-EPI},
	booktitle = {Proceedings of SPIE Medical Imaging},
	volume = {6914},
	year = {2008},
	month = {February},
	address = {San Diego, USA},
	author = {W. Pintjens and Dirk H J Poot and Marleen Verhoye and Annemie Van Der Linden and Jan Sijbers}
}
@inproceedings {wvaarlejbatenbujsijbers2008,
	title = {Threshold Selection for Segmentation of Dense Objects in Tomograms},
	booktitle = {International Symposium on Visual Computing},
	series = {Lecture Notes on Computer Science},
	volume = {5358},
	year = {2008},
	month = {December},
	pages = {700-709},
	publisher = {Springer-Verlag},
	organization = {Springer-Verlag},
	address = {Berlin Heidelberg},
	author = {Wim Van Aarle and Kees Joost Batenburg and Jan Sijbers},
	editor = {G. Bebis}
}
@inproceedings {rpinhothuysmanVosjsijbers2008,
	title = {Tracheal Stent Prediction Using Statistical Deformable Models of Tubular Shapes},
	booktitle = {Proceedings of SPIE Medical Imaging},
	year = {2008},
	month = {February},
	publisher = {SPIE},
	organization = {SPIE},
	address = {San Diego, CA, USA},
	doi = {http://dx.doi.org/10.1117/12.770237},
	author = {R{\^o}mulo Pinho and Toon Huysmans and W. Vos and Jan Sijbers}
}
@inproceedings {rpinhothuysmanVosjsijbers2008,
	title = {Tracheal Stent Prediction Using Statistical Deformable Models of Healthy Tracheas},
	booktitle = {Liege Image Days 2008: Medical Imaging},
	year = {2008},
	month = {March},
	author = {R{\^o}mulo Pinho and Toon Huysmans and W. Vos and Jan Sijbers}
}
@article {wvheckealeemansjsijbersVandervlietGoethemParizel2008,
	title = {A tracking based DTI segmentation method for the detection of diffusion-related changes of the cervical spinal cord with aging},
	journal = {Journal of Magnetic Resonance Imaging},
	volume = {27},
	number = {5},
	year = {2008},
	month = {March},
	pages = {978-991},
	doi = {10.1002/jmri.21338},
	author = {Wim Van Hecke and Alexander Leemans and Jan Sijbers and Everhard Vandervliet and J. Van Goethem and Paul M Parizel}
}
@article {jsijbersdpootajdendekwpintjen2007,
	title = {Automatic estimation of the noise variance from the histogram of a magnetic resonance image},
	journal = {Physics in Medicine and Biology},
	volume = {52},
	number = {5},
	year = {2007},
	month = {February},
	pages = {1335-1348},
	doi = {10.1088/0031-9155/52/5/009},
	author = {Jan Sijbers and Dirk H J Poot and Arnold Jan den Dekker and W. Pintjens}
}
@inproceedings {Batenburgjsijbers2007,
	title = {Automatic multiple threshold scheme for segmentation of tomograms},
	booktitle = {Proceedings of SPIE Medical Imaging: Physics of Medical Imaging},
	year = {2007},
	month = {February},
	address = {San Diego, CA, USA},
	author = {Kees Joost Batenburg and Jan Sijbers},
	editor = {Michael J. Flynn, Jiang Hsieh}
}
@inproceedings {Juntu:2007:CST:1710523.1710591,
	title = {Classification of soft tissue tumors in MRI images using kernel PCA and regularized least square classifier},
	booktitle = {Proceedings of the Fourth IASTED International Conference on Signal Processing, Pattern Recognition, and Applications},
	series = {SPPRA {\textquoteright}07},
	year = {2007},
	pages = {362{\textendash}367},
	publisher = {ACTA Press},
	organization = {ACTA Press},
	address = {Anaheim, CA, USA},
	keywords = {Fourier space, kernel PCA, MRI, RLSC, soft tissue tumors},
	isbn = {978-0-88986-647-8},
	url = {http://dl.acm.org/citation.cfm?id=1710523.1710591},
	author = {Juntu, Jaber and Jan Sijbers and Dirk Van Dyck}
}
@article {zmaithuysmanjsijbers2007,
	title = {Colon Visualization Using Cylindrical Parameterization},
	journal = {Lecture Notes in Computer Science},
	volume = {4678},
	year = {2007},
	pages = {607-615},
	author = {Zhenhua Mai and Toon Huysmans and Jan Sijbers}
}
@inproceedings {wvheckeDagostinoaleemansParizelMaesjsijbers2007,
	title = {On the construction of a healthy brain inter-subject diffusion tensor image and tractography atlas},
	booktitle = {32nd Congress of the Europea Society of NeuroRadiolog},
	year = {2007},
	pages = {146},
	author = {Wim Van Hecke and E. Dagostino and Alexander Leemans and Paul M Parizel and F. Maes and Jan Sijbers}
}
@inproceedings {jbatenbujsijbers2007,
	title = {DART: a fast heuristic algebraic reconstruction algorithm for discrete tomography},
	booktitle = {Proceedings of the IEEE International Conference on Image Processing (ICIP)},
	year = {2007},
	month = {September},
	address = {San Antonio, Texas, USA},
	doi = {10.1109/ICIP.2007.4379972},
	author = {Kees Joost Batenburg and Jan Sijbers}
}
@mastersthesis {1291,
	title = {Data-driven methods for the analysis of time-resolved mental chronometry fMRI data sets},
	year = {2007},
	type = {PhD thesis},
	author = {Alain Smolders}
}
@inproceedings {wvheckealeemansBrabanderLaridonClaeysCeulemansGoethemParizeljsijbers2007,
	title = {Diffusion Tensor Fiber Tracking Reveals Probst Bundles in Patients with Agenesis of the Corpus Callosum},
	booktitle = {32nd Congress of the Europea Society of NeuroRadiolog},
	year = {2007},
	pages = {135},
	author = {Wim Van Hecke and Alexander Leemans and N. De Brabander and A. Laridon and K. Claeys and B. Ceulemans and J. Van Goethem and Paul M Parizel and Jan Sijbers}
}
@inproceedings {wvheckealeemansjsijbersParizelGoethem2007,
	title = {Diffusion Tensor Tractoghraphy Reaveals Spinal Cord Alterations in Patients with Multiple Sclerosis},
	booktitle = {32nd Congress of the Europea Society of NeuroRadiolog},
	year = {2007},
	pages = {129},
	author = {Wim Van Hecke and Alexander Leemans and Jan Sijbers and Paul M Parizel and J. Van Goethem}
}
@article {asmolderMartinoStaerenpscheundjsijbersGoebelFormisano2007,
	title = {Dissecting cognitive stages with time-resolved fMRI data: a comparison of fuzzy clustering and independent component analysis},
	journal = {Magnetic Resonance Imaging},
	volume = {25},
	number = {6},
	year = {2007},
	month = {July},
	pages = {860-868},
	doi = {doi:10.1016/j.mri.2007.02.018},
	author = {Alain Smolders and F. De Martino and N. Staeren and Paul Scheunders and Jan Sijbers and R. Goebel and E. Formisano}
}
@inproceedings {PeersmanVanhoenackerBrysDyckStamVerstraeteBloemHeymanHerendaeljsijbersSchepper2007,
	title = {Imaging features of Ewing{\textquoteright}s sarcoma},
	booktitle = {International Skeletal Society 2007, 34th annual ISS Radiology Refresher Course},
	year = {2007},
	month = {October},
	pages = {22},
	address = {Budapest},
	author = {B. Peersman and F. M. Vanhoenacker and P. Brys and Pieter Van Dyck and M. Stam and K. L. Verstraete and J. L. Bloem and S. Heyman and B. Van Herendael and Jan Sijbers and A. M. De Schepper}
}
@article {ParizelRompaeyLoockwvheckeGoethemaleemansjsijbers2007,
	title = {Influence of User-Defined Parameters on Diffusion Tensor Tractography of the Corticospinal Tract},
	journal = {The Neuroradiology Journal},
	number = {20},
	year = {2007},
	pages = {139-147},
	author = {Paul M Parizel and V. Van Rompaey and R. Van Loock and Wim Van Hecke and J. Van Goethem and Alexander Leemans and Jan Sijbers}
}
@inproceedings {aleemanswvheckeLebelWalkerjsijbersBeaulieu2007,
	title = {A model based approach for voxelwise analysis of multi-subject diffusion tensor data},
	booktitle = {Joint Annual Meeting ISMRM-ESMRMB},
	year = {2007},
	address = {Berlin},
	author = {Alexander Leemans and Wim Van Hecke and C. Lebel and L. Walker and Jan Sijbers and C. Beaulieu}
}
@article {wvheckealeemansD{\textquoteright}AgostinosdbackerVandervlietParizeljsijbers2007,
	title = {Nonrigid Coregistration of Diffusion Tensor Images Using a Viscous Fluid Model and Mutual Information},
	journal = {IEEE Transactions on Medical Imaging},
	volume = {26},
	number = {11},
	year = {2007},
	pages = {1598-1612},
	author = {Wim Van Hecke and Alexander Leemans and E. Dagostino and Steve De Backer and Everhard Vandervliet and Paul M Parizel and Jan Sijbers}
}
@inproceedings {wvheckealeemansVandervlietParizeljsijbers2007,
	title = {An optimized tensor orientation strategy for non-rigid alignment of DT-MRI data},
	booktitle = {Joint Annual Meeting ISMRM-ESMRMB},
	year = {2007},
	address = {Berlin},
	author = {Wim Van Hecke and Alexander Leemans and Everhard Vandervliet and Paul M Parizel and Jan Sijbers}
}
@article {BalsjbatenbuVerbeeckjsijbersTendeloo2007,
	title = {Quantitative three-dimensional reconstruction of catalyst particles for bamboo-like carbon nanotubes},
	journal = {Nano Letters},
	volume = {7},
	number = {12},
	year = {2007},
	pages = {3669-3674},
	doi = {10.1021/nl071899m},
	author = {Sara Bals and Kees Joost Batenburg and Jo Verbeeck and Jan Sijbers and Van Tendeloo, Gustaaf}
}
@inproceedings {rpinhojsijbersthuysman2007,
	title = {Segmentation of The Human Trachea Using Deformable Statistical Models of Tubular Shapes},
	booktitle = {Proceedings of Advanced Concepts for Intelligent Vision Systems},
	series = {Lecture Notes in Computer Science},
	volume = {4678},
	year = {2007},
	month = {August},
	pages = {531-542},
	doi = {http://dx.doi.org/10.1007/978-3-540-74607-2_48},
	author = {R{\^o}mulo Pinho and Jan Sijbers and Toon Huysmans}
}
@inproceedings {CampthuysmanmverhoyeGaljartjsijbersavdlinde2007,
	title = {Statistical shape analysis on 3D MRI of the ventricular system of the Cyln2/Rsn double knock-out mice},
	booktitle = {Proceedings of the International Society for Magnetic Resonance in Medicine},
	year = {2007},
	pages = {1869},
	address = {Berlin, Germany},
	author = {N. Van Camp and Toon Huysmans and Marleen Verhoye and N. Galjart and Jan Sijbers and Annemie Van Der Linden}
}
@inproceedings {Meirthuysmanjsijbersavdlinde2007,
	title = {Statistical shape and position analysis on 3D structural MRI data of a motor region involved in vocal behavior of songbirds},
	booktitle = {Proceedings of the International Society for Magnetic Resonance in Medicine},
	year = {2007},
	pages = {431},
	address = {Berlin, Germany},
	author = {V. Van Meir and Toon Huysmans and Jan Sijbers and Annemie Van Der Linden}
}
@inproceedings {dpootjsijbersDekkerwpintjen2006,
	title = {Automatic estimation of the noise variance from the histogram of a magnetic resonance image},
	booktitle = {IEEE/EBMS Benelux Symposium proceedings},
	year = {2006},
	month = {December},
	pages = {135-138},
	publisher = {IEEE/EMBS},
	organization = {IEEE/EMBS},
	author = {Dirk H J Poot and Jan Sijbers and Arnold Jan den Dekker and W. Pintjens}
}
@inproceedings {Batenburgjsijbersevdcaste2006,
	title = {Automatic multiple threshold scheme for segmentation of tomograms},
	booktitle = {Proceedings of the Biomedical Engineering IEEE/EMBS Benelux Symposium},
	volume = {2},
	year = {2006},
	month = {December},
	pages = {143-146},
	address = {Brussels, Belgium},
	author = {Kees Joost Batenburg and Jan Sijbers and Elke Van de Casteele}
}
@inproceedings {VandervlietNagelsHeineckewvheckealeemansjsijbersParizel2006,
	title = {On the cause and mechanisms of the negative BOLD response in fMRI},
	booktitle = {23rd Annual Scientific Meeting of the European Society for Magnetic Resonance in Medicine and Biology},
	year = {2006},
	pages = {308},
	address = {Warsaw, Poland},
	author = {Everhard Vandervliet and Guy Nagels and A. Heinecke and Wim Van Hecke and Alexander Leemans and Jan Sijbers and Paul M Parizel}
}
@inproceedings {wpintjenjsijbersmverhoyeAudekerkeavdlinde2006,
	title = {A Combined Correction Scheme For EPI Distortions at 7 Tesla},
	booktitle = {Belgian Day on Biomedical Engineering - IEEE/EMBS Benelux Symposium},
	year = {2006},
	pages = {147-150},
	address = {Brussels, Belgium},
	author = {W. Pintjens and Jan Sijbers and Marleen Verhoye and Johan Van Audekerke and Annemie Van Der Linden}
}
@inproceedings {wvheckealeemansjsijbersParizelGoethem2006,
	title = {A comparison of diffusion tensor analysis methods for detecting age-related changes of the normal appearing spinal cord},
	booktitle = {23rd Annual Scientific Meeting of the European Society for Magnetic Resonance in Medicine and Biology},
	year = {2006},
	pages = {293-294},
	address = {Warsaw, Poland},
	author = {Wim Van Hecke and Alexander Leemans and Jan Sijbers and Paul M Parizel and J. Van Goethem}
}
@inproceedings {wvheckealeemansBrabanderLaridonCeulemansJParizel2006,
	title = {Diffusion tensor fiber tracking in patients with agenesis of the corpus callosum},
	booktitle = {23rd Annual Scientific Meeting of the European Society for Magnetic Resonance in Medicine and Biology},
	year = {2006},
	pages = {34-35},
	address = {Warsaw, Poland},
	author = {Wim Van Hecke and Alexander Leemans and N. De Brabander and A. Laridon and B. Ceulemans and Jan Sijbers and Paul M Parizel}
}
@inproceedings {Batenburgjsijbers2006,
	title = {Discrete tomography from micro-CT data: application to the mouse trabecular bone structure},
	booktitle = {Proceedings of SPIE Medical Imaging: Physics of Medical Imaging},
	volume = {6142},
	year = {2006},
	month = {February},
	pages = {1325-1335},
	address = {San Diego, CA, USA},
	doi = {https://doi.org/10.1117/12.652603},
	author = {Kees Joost Batenburg and Jan Sijbers},
	editor = {Michael J. Flynn, Jiang Hsieh}
}
@inproceedings {wvheckealeemansjsijbersParizelGoethem2006,
	title = {DTI of normal appearing spinal cord in elderly},
	booktitle = {44th Annual Meeting of the American Society of Neuroradiology},
	year = {2006},
	month = {May},
	pages = {380-381},
	address = {San Diego, USA},
	author = {Wim Van Hecke and Alexander Leemans and Jan Sijbers and Paul M Parizel and J. Van Goethem}
}
@inproceedings {rpinhojsijbersVos2006,
	title = {Efficient approaches to intrathoracic airway tree segmentations},
	booktitle = {Proceedings of the Biomedical Engineering IEEE/EMBS Benelux Symposium},
	volume = {2},
	year = {2006},
	month = {December},
	pages = {151-154},
	address = {Brussels, Belgium},
	author = {R{\^o}mulo Pinho and Jan Sijbers and W. Vos}
}
@inproceedings {dpootjsijbersajdendekBos2006,
	title = {Estimation of the noise variance from the background histogram mode of an MR image},
	booktitle = {Proceedings of the 25th Benelux Meeting on Systems and Control},
	series = {TuM06-5},
	year = {2006},
	month = {March},
	address = {Heeze, The Netherlands},
	author = {Dirk H J Poot and Jan Sijbers and Arnold Jan den Dekker and R. Bos}
}
@inproceedings {dpootjsijbersajdendekBos2006,
	title = {Estimation of the noise variance from the background histogram mode of an MR image},
	booktitle = {Proceedings of SPS-DARTS 2006 (The second annual IEEE BENELUX/DSP Valley Signal Processing Symposium)},
	year = {2006},
	month = {March},
	pages = {159-162},
	address = {Antwerp, Belgium},
	author = {Dirk H J Poot and Jan Sijbers and Arnold Jan den Dekker and R. Bos}
}
@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}
}
@mastersthesis {1273,
	title = {Modeling and Processing of Diffusion Tensor Magnetic Resonance Images for Improved Analysis of Brain Connectivity},
	year = {2006},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	author = {Alexander Leemans}
}
@mastersthesis {1274,
	title = {Modeling and Processing of Diffusion Tensor Magnetic Resonance Images for Improved Analysis of Brain Connectivity},
	volume = {PhD in Sciences: Physics},
	year = {2006},
	author = {Alexander Leemans}
}
@inproceedings {wvheckealeemanssdbackerVandervlietParizelAgostinojsijbers2006,
	title = {Multi-channel coregistration of diffusion tensor images based on a viscous fluid model},
	booktitle = {Belgian Day on Biomedical Engineering - IEEE/EMBS Benelux Symposium},
	year = {2006},
	pages = {139-142},
	address = {Brussels, Belgium},
	author = {Wim Van Hecke and Alexander Leemans and Steve De Backer and Everhard Vandervliet and Paul M Parizel and E. Dagostino and Jan Sijbers}
}
@inproceedings {BertelsthuysmanjsijbersVerdonkParizel2006,
	title = {Multi-scale registration of spherical parametrizations of the human cortex},
	booktitle = {23rd Annual Scientific Meeting of the European Society for Magnetic Resonance in Medicine and Biology},
	year = {2006},
	pages = {89},
	address = {Warsaw, Poland},
	author = {J. Bertels and Toon Huysmans and Jan Sijbers and B. Verdonk and Paul M Parizel}
}
@inproceedings {aleemansjsijberssdbackerVandervlietParizel2006,
	title = {Multiscale white matter fiber tract coregistration: a new feature-based approach to align diffusion tensor data},
	booktitle = {14th Scientific Meeting - International Society for Magnetic Resonance in Medicine},
	year = {2006},
	pages = {437},
	address = {Seattle, USA},
	author = {Alexander Leemans and Jan Sijbers and Steve De Backer and Everhard Vandervliet and Paul M Parizel}
}
@article {aleemansjsijberssdbackerVandervlietParizel2006,
	title = {Multiscale white matter fiber tract coregistration: a new feature-based approach to align diffusion tensor data},
	journal = {Magnetic Resonance in Medicine},
	volume = {55},
	number = {6},
	year = {2006},
	pages = {1414-1423},
	author = {Alexander Leemans and Jan Sijbers and Steve De Backer and Everhard Vandervliet and Paul M Parizel}
}
@inproceedings {wvheckealeemanssdbackerVandervlietParizeljsijbersAgostino2006,
	title = {Non-rigid coregistration of diffusion tensor images using a viscous fluid model.},
	booktitle = {23rd Annual Scientific Meeting of the European Society for Magnetic Resonance in Medicine and Biology},
	year = {2006},
	pages = {191-192},
	address = {Warsaw, Poland},
	author = {Wim Van Hecke and Alexander Leemans and Steve De Backer and Everhard Vandervliet and Paul M Parizel and Jan Sijbers and E. Dagostino}
}
@inproceedings {Groofmverhoyealeemansjsijbersavdlinde2006,
	title = {Paired voxel-wise statistical mapping of in vivo Diffusion Tensor Imaging (DTI) data to assess the seasonal neuronal plasticity in the brain of a songbird},
	booktitle = {1st Annual Meeting - European Society of Molecular Imaging},
	year = {2006},
	address = {Paris, France},
	author = {G. De Groof and Marleen Verhoye and Alexander Leemans and Jan Sijbers and Annemie Van Der Linden}
}
@inproceedings {wvheckealeemansjsijbersParizelGoethem2006,
	title = {A preliminary study of diffusion tensor imaging and tractography of the spinal cord in elderly},
	booktitle = {21th Annual Symposium - Belgian Hospital Physicists Association},
	year = {2006},
	pages = {61},
	address = {Ghent, Belgium},
	author = {Wim Van Hecke and Alexander Leemans and Jan Sijbers and Paul M Parizel and J. Van Goethem}
}
@inproceedings {jsijbersajdendekdpootBosmverhoyeCampavdlinde2006,
	title = {Robust estimation of the noise variance from background MR data},
	booktitle = {Proceedings of SPIE Medical Imaging: Image Processing},
	volume = {6144},
	year = {2006},
	month = {February},
	pages = {2018-2028},
	address = {San Diego, CA, USA},
	author = {Jan Sijbers and Arnold Jan den Dekker and Dirk H J Poot and R. Bos and Marleen Verhoye and N. Van Camp and Annemie Van Der Linden}
}
@inproceedings {thuysmanBernatjsijbersParizelGlabbeekVerdonk2006,
	title = {Shape Analysis of the Human Clavicle for the Development of a Set of osteosynthesis Plates},
	booktitle = {Belgian Day on Biomedical Engineering - IEEE/EMBS Benelux Symposium},
	year = {2006},
	month = {December},
	pages = {128-129},
	address = {Brussels, Belgium},
	author = {Toon Huysmans and Amit Bernat and Jan Sijbers and Paul M Parizel and Francis Van Glabbeek and B. Verdonk}
}
@inproceedings {thuysmanjsijbersVerdonk2006,
	title = {Statistical shape models for tubular objects},
	booktitle = {Proceedings of IEEE BENELUX/DSP Valley Signal Processing Symposium (SPS-DARTS)},
	year = {2006},
	month = {March},
	pages = {155-158},
	address = {Antwerp, Belgium},
	author = {Toon Huysmans and Jan Sijbers and B. Verdonk}
}
@inproceedings {wpintjenjsijbersmverhoyeAudekerkeLinden2006,
	title = {A total correction scheme for EPI distortions at high field},
	booktitle = {22th Annual Scientific Meeting - European Society for Magnetic Resonance in Medicine and Biology},
	year = {2006},
	pages = {328-329},
	address = {Warsaw, Poland},
	author = {W. Pintjens and Jan Sijbers and Marleen Verhoye and Johan Van Audekerke and Annemie Van Der Linden}
}
@inproceedings {Batenburgjsijbers2006,
	title = {Trabecular bone reconstruction from micro-CT data using discrete tomography},
	booktitle = {Proceedings of SPS-DARTS 2006 (The second annual IEEE BENELUX/DSP Valley Signal Processing Symposium)},
	year = {2006},
	month = {March},
	pages = {65-69},
	address = {Antwerp, Belgium},
	author = {Kees Joost Batenburg and Jan Sijbers}
}
@inproceedings {CampBlockxCamonVeramverhoyealeemansMartinezjsijbersPlanasavdlinde2006,
	title = {White and grey matters changes in a rat model for Huntington{\textquoteright}s disease discerned with in vivo DTI},
	booktitle = {23rd Annual Scientific Meeting of the European Society for Magnetic Resonance in Medicine and Biology},
	year = {2006},
	pages = {210-211},
	address = {Warsaw, Poland},
	author = {N. Van Camp and Ines Blockx and L. Camon and N. de Vera and Marleen Verhoye and Alexander Leemans and E. Martinez and Jan Sijbers and A. Planas and Annemie Van Der Linden}
}
@inbook {ajdendekjsijbers2005,
	title = {Advanced Image Processing in Magnetic Resonance Imaging},
	booktitle = {Series: Signal Processing and Communications},
	volume = {27},
	year = {2005},
	note = {ISBN: 0824725425},
	month = {October},
	pages = {85-143},
	publisher = {Marcel Dekker},
	organization = {Marcel Dekker},
	chapter = {4},
	author = {Arnold Jan den Dekker and Jan Sijbers},
	editor = {L. Landini}
}
@article {aleemansjsijberssdbackerVandervlietParizel2005,
	title = {Affine Coregistration of Diffusion Tensor Magnetic Resonance Images Using Mutual Information},
	journal = {Lecture Notes in Computer Science},
	volume = {3708},
	year = {2005},
	pages = {523-530},
	author = {Alexander Leemans and Jan Sijbers and Steve De Backer and Everhard Vandervliet and Paul M Parizel}
}
@inproceedings {jjuntujsijbersdvandyckGielen2005,
	title = {Bias Field Correction for MRI Images},
	booktitle = {Proceedings of the 4th International Conference on Computer Recognition Systems (CORES05)},
	series = {Advances in Computer Science},
	year = {2005},
	month = {May},
	pages = {543-551},
	publisher = {Springer},
	organization = {Springer},
	address = {Rydzyna Castle, Poland},
	author = {Juntu, Jaber and Jan Sijbers and Dirk Van Dyck and Jan L Gielen},
	editor = {Marek Kurzynsji and Edward Puchala and Michal Wozniak and Andrzej Zolnierek}
}
@inproceedings {ajdendekjsijbersBosasmolder2005,
	title = {Brain activation detection from functional magnetic resonance imaging data using likelihood based hypothesis tests},
	booktitle = {Abstracts of the 24th Benelux Meeting on Systems and Control},
	year = {2005},
	month = {March},
	address = {Houffalize, Belgium},
	author = {Arnold Jan den Dekker and Jan Sijbers and R. Bos and Alain Smolders}
}
@inproceedings {DelputtealeemansFieremansDeeneDAsselerLemahieuAchtenjsijbersWalle2005,
	title = {Density Regularized Fiber Tractography of the Brain White Matter using Diffusion Tensor MRI},
	booktitle = {13th Scientific Meeting - International Society for Magnetic Resonance in Medicine},
	year = {2005},
	pages = {1309},
	address = {Miami, USA},
	author = {S. Delputte and Alexander Leemans and Els Fieremans and Y. De Deene and Y. D{\textquoteright}Asseler and I. Lemahieu and Eric Achten and Jan Sijbers and R. Van de Walle}
}
@inproceedings {aleemanssdbackerjsijbersVandervlietParizel2005,
	title = {End point clustering for diffusion tensor white matter fiber bundle tractography},
	booktitle = {22th Annual Scientific Meeting - European Society for Magnetic Resonance in Medicine and Biology},
	year = {2005},
	pages = {129-130},
	address = {Basle, Switzerland},
	author = {Alexander Leemans and Steve De Backer and Jan Sijbers and Everhard Vandervliet and Paul M Parizel}
}
@inproceedings {aleemansjsijbersmverhoyeavdlinde2005,
	title = {Entropy-based coregistration for DT-MR images using an efficient tensor shape preserving reorientation strategy},
	booktitle = {13th Scientific Meeting - International Society for Magnetic Resonance in Medicine},
	year = {2005},
	pages = {227},
	address = {Miami, USA},
	author = {Alexander Leemans and Jan Sijbers and Marleen Verhoye and Annemie Van Der Linden}
}
@article {jsijbersajdendek2005,
	title = {Generalized likelihood Ratio tests for complex fMRI data: a simulation study},
	journal = {IEEE Transactions on Medical Imaging},
	volume = {24},
	number = {5},
	year = {2005},
	month = {May},
	pages = {604-611},
	doi = {10.1109/TMI.2005.844075},
	author = {Jan Sijbers and Arnold Jan den Dekker}
}
@inproceedings {aleemansjsijbersParizel2005,
	title = {A Graphical Toolbox for Exploratory Diffusion Tensor Imaging and Fiber Tractography},
	booktitle = {14th Annual Meeting - Section for Magnetic Resonance Technologists},
	year = {2005},
	address = {Miami, USA},
	author = {Alexander Leemans and Jan Sijbers and Paul M Parizel}
}
@article {ajdendekjsijbers2005,
	title = {Implications of the Rician distribution for fMRI generalized likelihood ratio tests},
	journal = {Magnetic Resonance Imaging},
	volume = {23},
	number = {9},
	year = {2005},
	pages = {953-959},
	doi = {10.1016/j.mri.2005.07.008},
	author = {Arnold Jan den Dekker and Jan Sijbers}
}
@inproceedings {CampLaereVreysmverhoyeLauwersBequeMaesVerbruggenDebyserMortelmansjsijbersNuytsBaekelandtavdlinde2005,
	title = {In vivo multimodal (MRI, SPECT) imaging of the 6-OHDA rat model for Parkinson{\textquoteright}s disease correlated with behavior and histology},
	booktitle = {Belgian Society of Neuroscience},
	year = {2005},
	month = {May},
	author = {N. Van Camp and Koen Van Laere and Vreys, Ruth and Marleen Verhoye and E. Lauwers and Dirk Beque and F. Maes and Alfons Verbruggen and Zeger Debyser and Luc Mortelmans and Jan Sijbers and Johan Nuyts and Veerle Baekelandt and Annemie Van Der Linden}
}
@inproceedings {CampLaereVreysmverhoyeLauwersBequeMaesVerbruggenDebyzerMortelmansjsijbersNuytsBaekelandtavdlinde2005,
	title = {In vivo multimodal (MRI,SPECT) imaging of the 6-OHDA rat model for Parkinson{\textquoteright}s disease correlated with behavior and histology},
	booktitle = {XXIInd International Symposium on Cerebral Blood Flow, Metabolism and Function and VIIth International Conference of Quantification of Brain Function with PET},
	year = {2005},
	month = {June},
	address = {Vrije Universiteit, Amsterdam, the Netherlands},
	author = {N. Van Camp and Koen Van Laere and Vreys, Ruth and Marleen Verhoye and E. Lauwers and Dirk Beque and F. Maes and Alfons Verbruggen and Zeger Debyser and Luc Mortelmans and Jan Sijbers and Johan Nuyts and Veerle Baekelandt and Annemie Van Der Linden}
}
@article {jsijbersajdendekBos2005,
	title = {A likelihood ratio test for functional MRI data analysis to account for colored noise},
	journal = {Lecture Notes in Computer Science},
	volume = {3708},
	year = {2005},
	month = {September},
	pages = {538-546},
	author = {Jan Sijbers and Arnold Jan den Dekker and R. Bos}
}
@article {aleemansjsijbersmverhoyeavdlindedvandyck2005,
	title = {Mathematical Framework for Simulating Diffusion Tensor MR Neural Fiber Bundles},
	journal = {Magnetic Resonance in Medicine},
	volume = {53},
	year = {2005},
	pages = {944-953},
	author = {Alexander Leemans and Jan Sijbers and Marleen Verhoye and Annemie Van Der Linden and Dirk Van Dyck}
}
@inproceedings {aleemansjsijbersmverhoyeavdlinde2005,
	title = {Optimized Fiber Tractography based on Diffusion Tensor Magnetic Resonance Simulations},
	booktitle = {20th Annual Symposium - Belgian Hospital Physicists Association},
	year = {2005},
	address = {Namur, Belgium},
	author = {Alexander Leemans and Jan Sijbers and Marleen Verhoye and Annemie Van Der Linden}
}
@article {thuysmanjsijbersVerdonk2005,
	title = {Parameterization of tubular surfaces on the cylinder},
	journal = {Journal of the Winter School of Computer Graphics},
	volume = {13},
	number = {3},
	year = {2005},
	pages = {97-104},
	author = {Toon Huysmans and Jan Sijbers and B. Verdonk}
}
@article {MeirBoumansGroofAudekerkeasmolderpscheundjsijbersmverhoyeBalthazartavdlinde2005,
	title = {Spatiotemporal properties of the BOLD response in the songbirds auditory circuit during a variety of listening tasks},
	journal = {NeuroImage},
	volume = {25},
	year = {2005},
	pages = {1242-1255},
	author = {V. Van Meir and T. Boumans and G. De Groof and Johan Van Audekerke and Alain Smolders and Paul Scheunders and Jan Sijbers and Marleen Verhoye and J. Balthazart and Annemie Van Der Linden}
}
@inproceedings {aleemanssdbackerjsijbersVandervlietParizel2005,
	title = {TRACT: Tissue Relative Anisotropy based Curvature Thresholding for deterministic MR diffusion tensor tractography},
	booktitle = {22th Annual Scientific Meeting - European Society for Magnetic Resonance in Medicine and Biology},
	year = {2005},
	pages = {289},
	address = {Basle, Switzerland},
	author = {Alexander Leemans and Steve De Backer and Jan Sijbers and Everhard Vandervliet and Paul M Parizel}
}
@inproceedings {ajdendekjsijbers2004,
	title = {Brain activation detection from magnitude fMRI data using a generalized likelihood ratio test},
	booktitle = {Abstracts of the 23rd Benelux Meeting on Systems and Control},
	year = {2004},
	month = {March},
	address = {Helvoirt, The Netherlands},
	author = {Arnold Jan den Dekker and Jan Sijbers}
}
@inproceedings {asmolderpscheundjsijbersMeirmverhoyeavdlinde2004,
	title = {Clustering of hemodynamic response functions from fMRI-data of a starling},
	booktitle = {13th European Microscopy Congress},
	year = {2004},
	month = {January},
	address = {Antwerp, Belgium},
	author = {Alain Smolders and Paul Scheunders and Jan Sijbers and V. Van Meir and Marleen Verhoye and Annemie Van Der Linden}
}
@inproceedings {jsijbersajdendek2004,
	title = {Construction of a likelihood ratio test for magnitude fMRI data},
	booktitle = {19th Annual Symposium of the Belgian Hospital Physicists Association},
	year = {2004},
	month = {January},
	author = {Jan Sijbers and Arnold Jan den Dekker}
}
@inproceedings {jsijbersCampaleemansajdendekmverhoyeavdlinde2004,
	title = {Coregistration of Micro-MRI, microCT and microPET},
	booktitle = {Workshop on non Invasive 3D Microscopy},
	year = {2004},
	month = {August},
	pages = {16},
	address = {University of Antwerp, Belgium},
	author = {Jan Sijbers and N. Van Camp and Alexander Leemans and Arnold Jan den Dekker and Marleen Verhoye and Annemie Van Der Linden}
}
@inproceedings {ajdendekjsijbers2004,
	title = {Detection of brain activation from magnitude fMRI data using a generalized likelihood ratio test},
	booktitle = {Proceedings of the 12th European Signal Processing Conference},
	year = {2004},
	month = {September},
	pages = {233-236},
	publisher = {ISBN 3-200-00165-8},
	organization = {ISBN 3-200-00165-8},
	address = {Vienna, Austria},
	author = {Arnold Jan den Dekker and Jan Sijbers},
	editor = {F. Hlawatsch and B. Wistawel}
}
@inproceedings {evdcastedvandyckjsijberseraman2004,
	title = {The effect of beam hardening on MTF measurements in X-ray microtomography},
	booktitle = {19th Annual Symposium of the Belgian Hospital Physicists Association},
	year = {2004},
	month = {January},
	author = {Elke Van de Casteele and Dirk Van Dyck and Jan Sijbers and Erik Raman}
}
@inproceedings {evdcastedvandyckjsijberseraman2004,
	title = {Effect of beam hardening on resolution in x-ray microtomography},
	booktitle = {SPIE Medical Imaging: Image Processing},
	volume = {5370},
	year = {2004},
	month = {February},
	pages = {2089-2096},
	address = {San Diego CA, USA},
	author = {Elke Van de Casteele and Dirk Van Dyck and Jan Sijbers and Erik Raman},
	editor = {Milan Sonka}
}
@inproceedings {aleemansjsijbersmverhoyeavdlinde2004,
	title = {Experimental Evaluation of Synthetic DT-MRI Models},
	booktitle = {21th Annual Scientific Meeting - European Society for Magnetic Resonance in Medicine and Biology},
	year = {2004},
	pages = {40-41},
	address = {Copenhagen, Denmark},
	author = {Alexander Leemans and Jan Sijbers and Marleen Verhoye and Annemie Van Der Linden}
}
@inproceedings {MeirBoumansGroofAudekerkeasmolderpscheundjsijbersmverhoyeBalthazartavdlinde2004,
	title = {Functional Magnetic resonance Imaging meets animal vocal learner},
	booktitle = {Third One-Day Symposium of Young Belgian Magnetic Resonance Scientists},
	year = {2004},
	month = {November},
	address = {Brussels, Belgium},
	author = {V. Van Meir and T. Boumans and G. De Groof and Johan Van Audekerke and Alain Smolders and Paul Scheunders and Jan Sijbers and Marleen Verhoye and J. Balthazart and Annemie Van Der Linden}
}
@inproceedings {jsijbersajdendek2004,
	title = {Generalized likelihood ratio test for complex fMRI data},
	booktitle = {SPIE Medical Imaging: Physiology, Function, and Structure from Medical Images},
	volume = {5369},
	year = {2004},
	month = {February},
	pages = {652-663},
	address = {San Diego, California, USA},
	author = {Jan Sijbers and Arnold Jan den Dekker},
	editor = {Amir. A. Amini}
}
@inproceedings {aleemansjsijbersmverhoyeavdlindedvandyck2004,
	title = {A Geometric Color Scheme for Visualizing Diffusion Tensor Magnetic Resonance Fiber Pathways},
	booktitle = {19th Annual Symposium - Belgian Hospital Physicists Association},
	year = {2004},
	address = {Brussels, Belgium},
	author = {Alexander Leemans and Jan Sijbers and Marleen Verhoye and Annemie Van Der Linden and Dirk Van Dyck}
}
@inproceedings {CampmverhoyealeemansPostnovBequeEyndenNuytsLauwersVerbruggenDebyzerBaekelandtClerckjsijbersLaereavdlinde2004,
	title = {In vivo multimodal imaging of a rat model for Parkinson{\textquoteright}s disease: high resolution micro-MRI, micro-SPECT and micro-CT},
	booktitle = {21th Annual Scientific Meeting - European Society for Magnetic Resonance in Medicine and Biology},
	year = {2004},
	pages = {22-23},
	address = {Copenhagen, Denmark},
	author = {N. Van Camp and Marleen Verhoye and Alexander Leemans and A. Postnov and Dirk Beque and J. Van den Eynden and Johan Nuyts and E. Lauwers and Alfons Verbruggen and Zeger Debyser and Veerle Baekelandt and N. De Clerck and Jan Sijbers and Koen Van Laere and Annemie Van Der Linden}
}
@inproceedings {aleemansjsijberswvdbroekYang2004,
	title = {An Interactive Curvature Based Rigid-body Image Registration Technique: an Application to EFTEM},
	booktitle = {13th European Microscopy Congress},
	year = {2004},
	address = {Antwerp, Belgium},
	author = {Alexander Leemans and Jan Sijbers and Wouter Van den Broek and Z. Yang}
}
@inproceedings {aleemansjsijbersmverhoyeavdlinde2004,
	title = {A Library of 3D synthetic DT-MRI models for testing White Matter fiber Tractography Algorithms},
	booktitle = {21th Annual Scientific Meeting - European Society for Magnetic Resonance in Medicine and Biology},
	year = {2004},
	pages = {42},
	address = {Copenhagen, Denmark},
	author = {Alexander Leemans and Jan Sijbers and Marleen Verhoye and Annemie Van Der Linden}
}
@article {jsijbersajdendek2004,
	title = {Maximum Likelihood estimation of signal amplitude and noise variance from MR data},
	journal = {Magnetic Resonance in Medicine},
	volume = {51},
	number = {3},
	year = {2004},
	pages = {586-594},
	doi = {10.1002/mrm.10728},
	author = {Jan Sijbers and Arnold Jan den Dekker}
}
@inproceedings {thuysmanjsijbers2004,
	title = {Mesh smoothing through multiscale anisotropic diffusion of geometry images},
	booktitle = {13th European Microscopy Congress (EMC)},
	year = {2004},
	month = {August},
	address = {Antwerp, Belgium},
	author = {Toon Huysmans and Jan Sijbers}
}
@article {evdcastedvandyckjsijberseraman2004,
	title = {A model-based correction method for beam hardening artefacts in X-ray microtomography},
	journal = {Journal of X-ray science and technology},
	volume = {12},
	number = {1},
	year = {2004},
	pages = {53-57},
	author = {Elke Van de Casteele and Dirk Van Dyck and Jan Sijbers and Erik Raman}
}
@inproceedings {aleemansjsijbers2004,
	title = {Multiresolutional rigid-body registration for space curves},
	booktitle = {IEEE Advanced Concepts for Intelligent Vision Systems},
	year = {2004},
	pages = {215-221},
	address = {Brussels, Belgium},
	author = {Alexander Leemans and Jan Sijbers}
}
@inproceedings {gvgompelgtissondvandyckjsijbers2004,
	title = {A new algorithm for Region Of Interest Tomography},
	booktitle = {SPIE Medical Imaging: Image Processing},
	volume = {127},
	year = {2004},
	month = {February},
	pages = {2105-2113},
	address = {San Diego, California, USA},
	doi = {10.1117/12.536685},
	author = {Gert Van Gompel and G. Tisson and Dirk Van Dyck and Jan Sijbers},
	editor = {Milan Sonka}
}
@inproceedings {gvgompeldvandyckgtissonjsijbers2004,
	title = {A new algorithm for Region Of Interest Tomography},
	booktitle = {19th Annual Symposium of the Belgian Hospital Physicists Association},
	year = {2004},
	month = {January},
	author = {Gert Van Gompel and Dirk Van Dyck and G. Tisson and Jan Sijbers}
}
@inproceedings {jsijbersajdendek2004,
	title = {The performance of generalized likelihood ratio tests for complex functional MRI data in the presence of phase model misspecification},
	booktitle = {European Society of Magnetic Resonance in Medicine (ESMRMB)},
	year = {2004},
	month = {September},
	pages = {96},
	address = {Copenhagen, Denmark},
	author = {Jan Sijbers and Arnold Jan den Dekker}
}
@inproceedings {jsijbersPostnov2004,
	title = {Reduction of ring artifacts in high resolution micro-CT images},
	booktitle = {Advanced Concepts for Intelligent Vision Systems (ACIVS)},
	year = {2004},
	month = {September},
	address = {Brussels, Belgium},
	author = {Jan Sijbers and A. Postnov}
}
@article {jsijbersPostnov2004,
	title = {Reduction of ring artifacts in high resolution micro-CT reconstructions},
	journal = {Physics in Medicine and Biology},
	volume = {49},
	number = {14},
	year = {2004},
	pages = {247-253},
	doi = {doi:10.1088/0031-9155/49/14/N06},
	author = {Jan Sijbers and A. Postnov}
}
@article {CuytjsijbersVerdonkdvandyck2004,
	title = {Region and Contour Identification of Physical Objects},
	journal = {Applied Numerical Analysis Computational Mathematics},
	volume = {1},
	number = {3},
	year = {2004},
	pages = {343-352},
	doi = {10.1002/anac.200410002},
	author = {A. Cuyt and Jan Sijbers and B. Verdonk and Dirk Van Dyck}
}
@inproceedings {aleemansjsijbersmverhoyeavdlindedvandyck2004,
	title = {White Matter Fiber Bundle Coregistration for Diffusion Tensor Magnetic Resonance Tractography},
	booktitle = {13th Annual Meeting - Section for Magnetic Resonance Technologists},
	year = {2004},
	address = {Kyoto, Japan},
	author = {Alexander Leemans and Jan Sijbers and Marleen Verhoye and Annemie Van Der Linden and Dirk Van Dyck}
}
@inproceedings {1292,
	title = {A bimodal energy model for correcting beam hardening artefacts in X-ray tomography},
	booktitle = {IEEE 29th Annual Northeast Bioengineering Conference2003 IEEE 29th Annual Proceedings of Bioengineering Conference},
	year = {2003},
	pages = {57 - 58},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Newark, NJ, USA},
	doi = {10.1109/NEBC.2003.1215990},
	author = {Elke Van de Casteele and Dirk Van Dyck and Jan Sijbers and Erik Raman}
}
@inbook {jsijbersavdlinde2003,
	title = {Magnetic Resonance Imaging, In: Encyclopedia of Optical Engineering},
	year = {2003},
	note = {ISBN: 0-8247-4258-3},
	pages = {1237-1258},
	publisher = {Marcel Dekker},
	organization = {Marcel Dekker},
	author = {Jan Sijbers and Annemie Van Der Linden}
}
@inproceedings {jsijbersajdendek2003,
	title = {Mapping a polyhedron onto a sphere: application to Fourier descriptors},
	booktitle = {22nd Benelux Meeting on Systems and Control},
	year = {2003},
	month = {March},
	address = {Lommel, Belgium},
	author = {Jan Sijbers and Arnold Jan den Dekker}
}
@inproceedings {ajdendekjsijbers2003,
	title = {Maximum Likelihood estimation of signal amplitude and noise variance},
	booktitle = {13th IFAC Symposium on System Identification (SYSID-2003)},
	year = {2003},
	month = {August},
	pages = {126-131},
	address = {Rotterdam, The Netherlands},
	author = {Arnold Jan den Dekker and Jan Sijbers}
}
@inproceedings {evdcastedvandyckjsijberseraman2003,
	title = {A model-based correction method for beam-hardening artifacts in X-ray tomography},
	booktitle = {22nd Benelux Meeting on Systems and Control},
	year = {2003},
	month = {March},
	address = {Lommel, Belgium},
	author = {Elke Van de Casteele and Dirk Van Dyck and Jan Sijbers and Erik Raman}
}
@inproceedings {evdcastedvandyckjsijberseraman2003,
	title = {Modelling of Beam hardening in micro CT},
	booktitle = {IEEE International Symposium on Biomedical Imaging},
	year = {2003},
	month = {July},
	pages = {57-58},
	address = {Washington DC, USA},
	author = {Elke Van de Casteele and Dirk Van Dyck and Jan Sijbers and Erik Raman}
}
@inproceedings {aleemansjsijbersmverhoyeavdlindedvandyck2003,
	title = {A Simulated Phantom for Diffusion Tensor Fiber Tracking},
	booktitle = {IEEE Advanced Concepts for Intelligent Vision Systems},
	year = {2003},
	pages = {281-285},
	address = {Ghent University, Belgium},
	author = {Alexander Leemans and Jan Sijbers and Marleen Verhoye and Annemie Van Der Linden and Dirk Van Dyck}
}
@inproceedings {aleemansjsijbersmverhoyeavdlindedvandyck2003,
	title = {Simulating Neuronal Fiber Bundles for DT-MRI Tractography},
	booktitle = {20th Annual Scientific Meeting - European Society for Magnetic Resonance in Medicine and Biology},
	year = {2003},
	pages = {278},
	address = {Rotterdam, The Netherlands},
	author = {Alexander Leemans and Jan Sijbers and Marleen Verhoye and Annemie Van Der Linden and Dirk Van Dyck}
}
@inproceedings {jsijbersCeulemansdvandyck2002,
	title = {Algorithm for the computation of 3D Fourier descriptors},
	booktitle = {16th International Conference on Pattern Recognition},
	year = {2002},
	month = {August},
	pages = {790-793},
	address = {Quebec, Canada},
	author = {Jan Sijbers and T. Ceulemans and Dirk Van Dyck}
}
@inproceedings {jsijbersdvandyck2002,
	title = {Efficient algorithm for the computation of 3D Fourier descriptors},
	booktitle = {1st International Symposium on 3D Data Processing Visualization and Transmission},
	year = {2002},
	month = {June},
	pages = {640-643},
	address = {Padua, Italy},
	author = {Jan Sijbers and Dirk Van Dyck}
}
@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}
}
@inproceedings {pscheundjsijbers2002,
	title = {Multiscale watershed segmentation of multivalued images},
	booktitle = {Proc. ICPR02, International Conference on Pattern Recognition},
	volume = {3},
	year = {2002},
	pages = {855-858},
	address = {Quebec, Canada},
	author = {Paul Scheunders and Jan Sijbers}
}
@inproceedings {pscheundjsijbers2001,
	title = {Multiscale anisotropic filtering of color images},
	booktitle = {Proceedings of the IEEE International Conference on Image Processing},
	volume = {3},
	year = {2001},
	pages = {170-173},
	address = {Thessaloniki, Greece},
	author = {Paul Scheunders and Jan Sijbers}
}
@inproceedings {jsijbersVanrumsteHoeyBoonmverhoyeavdlindedvandyck2000,
	title = {Automatic detection of EEG electrode markers on 3D MR data},
	booktitle = {SPIE Medical Imaging: Image Processing},
	volume = {3979},
	year = {2000},
	month = {February},
	pages = {1476-1481},
	address = {San Diego CA, USA},
	author = {Jan Sijbers and B. Vanrumste and G. Van Hoey and P. Boon and Marleen Verhoye and Annemie Van Der Linden and Dirk Van Dyck},
	editor = {Kenneth M. Hanson}
}
@inproceedings {jsijbersMichielsAudekerkemverhoyeavdlinde2000,
	title = {Automatic EEG signal restoration during simultaneous EEG/MR acquisitions},
	booktitle = {SPIE Medical Imaging: Image Processing},
	volume = {3979},
	year = {2000},
	month = {February},
	pages = {1482-1491},
	address = {San Diego, California, USA},
	author = {Jan Sijbers and I. Michiels and Johan Van Audekerke and Marleen Verhoye and Annemie Van Der Linden},
	editor = {Kenneth M. Hanson}
}
@article {jsijbersVanrumsteHoeyBoonmverhoyeavdlindedvandyck2000,
	title = {Automatic localization of EEG electrode markers within 3D MR data},
	journal = {Magnetic Resonance Imaging},
	volume = {18},
	number = {4},
	year = {2000},
	pages = {485-488},
	doi = {https://doi.org/10.1016/S0730-725X(00)00121-1},
	author = {Jan Sijbers and B. Vanrumste and G. Van Hoey and P. Boon and Marleen Verhoye and Annemie Van Der Linden and Dirk Van Dyck}
}
@inproceedings {mverhoyeMichielsjsijbersEelenPeetersAudekerkeDHoogheDeynavdlinde2000,
	title = {Changes during pentetrazol-induced epilepsy in rat recorded by simultaneous EEG/MRI at 7T},
	booktitle = {SPIE Medical Imaging},
	volume = {3978},
	year = {2000},
	month = {February},
	pages = {485-493},
	address = {San Diego, USA},
	author = {Marleen Verhoye and I. Michiels and Jan Sijbers and J. Eelen and Ron R Peeters and Johan Van Audekerke and R. Dhooghe and P.P. De Deyn and Annemie Van Der Linden},
	editor = {Chin-Tu Chen, Anne V. Clough}
}
@inproceedings {mverhoyeCampPeetersjsijbersAudekerkeavdlinde2000,
	title = {On-line simultaneous BOLD/EEG measurements of functional activity during pentylenetetrazol induced epilepsy in the rat},
	booktitle = {16th annual meeting of the European Society for Magnetic Resonance in Medicine and Biology (ESMRMB00)},
	year = {2000},
	month = {September},
	address = {Paris, France},
	author = {Marleen Verhoye and N. Van Camp and Ron R Peeters and Jan Sijbers and Johan Van Audekerke and Annemie Van Der Linden}
}
@article {jsijbersAudekerkemverhoyeavdlindedvandyck2000,
	title = {Reduction of ECG and gradient related artifacts in simultaneously recorded human EEG/MRI data},
	journal = {Magnetic Resonance Imaging},
	volume = {18},
	number = {7},
	year = {2000},
	pages = {881-886},
	doi = {10.1016/S0730-725X(00)00178-8},
	author = {Jan Sijbers and Johan Van Audekerke and Marleen Verhoye and Annemie Van Der Linden and Dirk Van Dyck}
}
@inproceedings {jsijbersMichielsAudekerkemverhoyeavdlindedvandyck2000,
	title = {Restoration of EEG signals distorted during simultaneous MR acquisitions},
	booktitle = {Proceedings of the International Society of Magnetic Resonance in Medicine},
	year = {2000},
	month = {April},
	pages = {118},
	address = {Denver CO, USA},
	author = {Jan Sijbers and I. Michiels and Johan Van Audekerke and Marleen Verhoye and Annemie Van Der Linden and Dirk Van Dyck}
}
@inproceedings {VanhouttemverhoyePeetersjsijbersEelenDHoogheDeynavdlinde2000,
	title = {Simultaneously recording of EEG/fMRI to study neurophysiological changes in rat brain during pentetrazol induced epileptic seizures},
	booktitle = {Federation of European Neuroscience (FENS) Millennium Meeting},
	volume = {12},
	year = {2000},
	month = {June},
	pages = {107},
	address = {Brighton, UK},
	author = {Greetje Vanhoutte and Marleen Verhoye and Ron R Peeters and Jan Sijbers and J. Eelen and R. Dhooghe and P.P. De Deyn and Annemie Van Der Linden}
}
@article {AudekerkemverhoyePeetersjsijbersavdlinde2000,
	title = {Special designed RF-antenna with integrated non-invasive carbon electrodes for simultaneous MRI and EEG acquisition at 7T},
	journal = {Magnetic Resonance Imaging},
	volume = {18},
	number = {7},
	year = {2000},
	pages = {887-891},
	doi = {https://doi.org/10.1016/S0730-725X(00)00172-7},
	author = {Johan Van Audekerke and Marleen Verhoye and Ron R Peeters and Jan Sijbers and Annemie Van Der Linden}
}
@article {jsijbersajdendekmverhoyeavdlindedvandyck1999,
	title = {Adaptive anisotropic noise filtering for magnitude MR data},
	journal = {Magnetic Resonance Imaging},
	volume = {17},
	number = {10},
	year = {1999},
	pages = {1533-1539},
	doi = {10.1016/S0730-725X(99)00088-0},
	author = {Jan Sijbers and Arnold Jan den Dekker and Marleen Verhoye and Annemie Van Der Linden and Dirk Van Dyck}
}
@inproceedings {jsijbersajdendekmverhoyeavdlindedvandyck1999,
	title = {Adaptive anisotropic noise filtering for magnitude MR data},
	booktitle = {Proceedings of SPIE Medical Imaging},
	volume = {3661},
	year = {1999},
	month = {February},
	pages = {1418-1425},
	address = {San Diego, USA},
	author = {Jan Sijbers and Arnold Jan den Dekker and Marleen Verhoye and Annemie Van Der Linden and Dirk Van Dyck},
	editor = {Kenneth M. Hanson}
}
@inproceedings {jsijbersMichielsmverhoyeAudekerkeavdlindedvandyck1999,
	title = {Automatic, adaptive filter for MRI related artefacts in simultaneously recorded EEG/MRI data},
	booktitle = {Third Meeting Belgian Society for Neuroscience},
	number = {31},
	year = {1999},
	month = {May},
	author = {Jan Sijbers and I. Michiels and Marleen Verhoye and Johan Van Audekerke and Annemie Van Der Linden and Dirk Van Dyck}
}
@article {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}
}
@inproceedings {mverhoyejsijbersFransenKooySorianoWillemsavdlinde1999,
	title = {In vivo neuro MRI microscopy of L1 knockout mice demonstrated similarities with human crash syndrome},
	booktitle = {Belgian Society for Neuroscience},
	year = {1999},
	month = {May},
	address = {Brussels},
	author = {Marleen Verhoye and Jan Sijbers and Erik Fransen and R.F. Kooy and P. Soriano and P. J. Willems and Annemie Van Der Linden}
}
@article {ebettensdvandyckajdendekjsijbersBos1999,
	title = {Model-based two-object resolution from observations having counting statistics},
	journal = {Ultramicroscopy},
	volume = {77},
	number = {1},
	year = {1999},
	pages = {37-48},
	doi = {https://doi.org/10.1016/S0304-3991(99)00006-6},
	author = {E. Bettens and Dirk Van Dyck and Arnold Jan den Dekker and Jan Sijbers and A. van den Bos}
}
@article {KooyReyniersmverhoyejsijbersCrasOostraWillemsavdlinde1999,
	title = {Neuroanatomy of the fragile X knockout mouse brain studied using in vivo high resolution Magnetic Resonance Imaging (MRI)},
	journal = {European Journal of Human Genetics},
	volume = {7},
	year = {1999},
	pages = {526-532},
	author = {R.F. Kooy and E. Reyniers and Marleen Verhoye and Jan Sijbers and P. Cras and B. A. Oostra and P. J. Willems and Annemie Van Der Linden}
}
@inproceedings {AudekerkejsijbersMichielsmverhoyePeetersavdlinde1999,
	title = {Novel design of an RF-antenna with integrated EEG electrodes for combined EEG and fMRI in animals},
	booktitle = {16th annual meeting: Magnetic Resonance Materials in Physics, Biology, and Medicine},
	year = {1999},
	month = {September},
	address = {Sevilla, Spain},
	author = {Johan Van Audekerke and Jan Sijbers and I. Michiels and Marleen Verhoye and Ron R Peeters and Annemie Van Der Linden}
}
@article {jsijbersajdendekeramandvandyck1999,
	title = {Parameter estimation from magnitude MR images},
	journal = {International Journal of Imaging Systems and Technology},
	volume = {10},
	number = {2},
	year = {1999},
	pages = {109-114},
	doi = {10.1002/(SICI)1098-1098(1999)10:2<109::AID-IMA2>3.0.CO;2-R},
	author = {Jan Sijbers and Arnold Jan den Dekker and Erik Raman and Dirk Van Dyck}
}
@inproceedings {ajdendekjsijbersdvandyck1999,
	title = {Quantitative HREM: viewpoints on resolution, precision, and experimental design},
	booktitle = {Acta Cryst. A55 Supplement, Abstract M11.OE.OO5},
	year = {1999},
	month = {August},
	address = {IUCR Glasgow, Scotland},
	author = {Arnold Jan den Dekker and Jan Sijbers and Dirk Van Dyck}
}
@inproceedings {jsijbersMichielsmverhoyeAudekerkeavdlindedvandyck1999,
	title = {Restoration of EEG signals distorted during simultaneous MR acquisitions},
	booktitle = {annual meeting: Magnetic Resonance Materials in Physics, Biology, and Medicine},
	year = {1999},
	month = {September},
	pages = {212},
	address = {Sevilla, Spain},
	author = {Jan Sijbers and I. Michiels and Marleen Verhoye and Johan Van Audekerke and Annemie Van Der Linden and Dirk Van Dyck}
}
@article {jsijbersMichielsmverhoyeAudekerkeavdlindedvandyck1999,
	title = {Restoration of MR induced artifacts in simultaneously recorded MR/EEG data},
	journal = {Magnetic Resonance Imaging},
	volume = {17},
	number = {9},
	year = {1999},
	pages = {1383-1393},
	doi = {https://doi.org/10.1016/S0730-725X(99)00096-X},
	author = {Jan Sijbers and I. Michiels and Marleen Verhoye and Johan Van Audekerke and Annemie Van Der Linden and Dirk Van Dyck}
}
@inproceedings {MichielsjsijbersEelenmverhoyeDHoogheNagelsDeynavdlinde1999,
	title = {Simultaneous EEG and MRI in an animal model for generalized epilepsy induced in the MRI instrument},
	booktitle = {Third Meeting of the Belgian Society for Neuroscience},
	number = {25},
	year = {1999},
	month = {May},
	address = {Brussels, Belgium},
	author = {I. Michiels and Jan Sijbers and J. Eelen and Marleen Verhoye and R. Dhooghe and Guy Nagels and P.P. De Deyn and Annemie Van Der Linden}
}
@inproceedings {MichielsjsijbersEelenmverhoyeDHoogheNagelsDeynavdlinde1999,
	title = {Simultaneous EEG and MRI in an animal model for generalized epilepsy induced in the MRI instrument},
	booktitle = {16th annual meeting: Magnetic Resonance Materials in Physics, Biology, and Medicine},
	year = {1999},
	month = {September},
	address = {Sevilla, Spain},
	author = {I. Michiels and Jan Sijbers and J. Eelen and Marleen Verhoye and R. Dhooghe and Guy Nagels and P.P. De Deyn and Annemie Van Der Linden}
}
@article {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}
}
@inproceedings {dvandyckajdendekjsijbersebettens1998,
	title = {Dose Limited Resolution},
	booktitle = {Proceedings Microscopy and Microanalysis},
	volume = {2},
	number = {2},
	year = {1998},
	month = {July},
	pages = {802-803},
	address = {Atlanta, Georgia, U.S.A},
	author = {Dirk Van Dyck and Arnold Jan den Dekker and Jan Sijbers and E. Bettens}
}
@mastersthesis {1302,
	title = {Estimation of Signal and Noise from Magnitude Magnetic Resonance Images},
	year = {1998},
	type = {PhD thesis},
	author = {Jan Sijbers}
}
@inproceedings {jsijbersajdendekdvandyckeraman1998,
	title = {Estimation of signal and noise from Rician distributed data},
	booktitle = {Proceedings of the IASTED International Conference on Signal Processing and Communications},
	year = {1998},
	month = {February},
	pages = {140-143},
	address = {Canary Islands, Spain},
	author = {Jan Sijbers and Arnold Jan den Dekker and Dirk Van Dyck and Erik Raman}
}
@article {jsijbersajdendekAudekerkemverhoyedvandyck1998,
	title = {Estimation of the noise in magnitude MR images},
	journal = {Magnetic Resonance Imaging},
	volume = {16},
	number = {1},
	year = {1998},
	pages = {87-90},
	doi = {10.1016/S0730-725X(97)00199-9},
	author = {Jan Sijbers and Arnold Jan den Dekker and Johan Van Audekerke and Marleen Verhoye and Dirk Van Dyck}
}
@inproceedings {ajdendekjsijbersdvandyck1998,
	title = {How to design an HREM experiment so as to attain the highest precision?},
	booktitle = {ICEM14: 14th International Congress on Electron Microscopy},
	volume = {1},
	year = {1998},
	month = {September},
	pages = {621-622},
	address = {Cancun, Mexico},
	author = {Arnold Jan den Dekker and Jan Sijbers and Dirk Van Dyck}
}
@inproceedings {AudekerkemverhoyejsijbersDeVoogdSmuldersNewmanavdlinde1998,
	title = {Imaging birds ... in a bird cage: In vivo MRI microscopy of the canary brain at 7T},
	booktitle = {Proceedings of International Society for Magnetic Resonance in Medicine: Sixth Annual Meeting},
	volume = {2},
	number = {2},
	year = {1998},
	month = {April},
	pages = {162-163},
	author = {Johan Van Audekerke and Marleen Verhoye and Jan Sijbers and T. J. DeVoogd and T. Smulders and S. W. Newman and Annemie Van Der Linden}
}
@article {mverhoyeavdlindeAudekerkejsijbersEensBalthazart1998,
	title = {Imaging birds in a bird cage: in-vivo FSE 3D MRI of bird brain},
	journal = {MAGMA: Magnetic Resonance Materials in Physics, Biology and Medicine},
	volume = {6},
	number = {1},
	year = {1998},
	month = {August},
	pages = {22-27},
	author = {Marleen Verhoye and Annemie Van Der Linden and Johan Van Audekerke and Jan Sijbers and M. Eens and J. Balthazart}
}
@inproceedings {mverhoyejsijbersKooyReyniersFransenOostraWillemsavdlinde1998,
	title = {In vivo assessment of cerebellum volume in transgenic Fragile X Knockout mice using MRI microscopy at 7T},
	booktitle = {Proceedings of the International Society for Magnetic Resonance in Medicine},
	year = {1998},
	month = {April},
	address = {Sydney, Australia},
	author = {Marleen Verhoye and Jan Sijbers and R.F. Kooy and E. Reyniers and Erik Fransen and B. A. Oostra and P. J. Willems and Annemie Van Der Linden}
}
@inproceedings {mverhoyejsijbersFransenKooySorianoWillemsavdlinde1998,
	title = {In vivo neuro MRI micrsocopy of L1 Knockout mice demonstrated similarities with human CRASH syndrome},
	booktitle = {Sixth Scientific Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine},
	number = {1371},
	year = {1998},
	month = {April},
	address = {Sydney, Australia},
	author = {Marleen Verhoye and Jan Sijbers and Erik Fransen and R.F. Kooy and P. Soriano and P. J. Willems and Annemie Van Der Linden}
}
@article {FransenDhoogheCampmverhoyejsijbersReyniersSorianoKamiguchiWillemsenKoekoekZeeuwDeynavdlindeLemmonKooyWillems1998,
	title = {L1 knockout mice show dilated ventricles, vermis hypoplasia and impaired exploration patterns},
	journal = {Human Molecular Genetics},
	volume = {7},
	number = {6},
	year = {1998},
	pages = {999-1009},
	doi = {10.1093/hmg/7.6.999},
	author = {Erik Fransen and R. Dhooghe and G. Van Camp and Marleen Verhoye and Jan Sijbers and E. Reyniers and P. Soriano and H. Kamiguchi and R. Willemsen and K.E. Koekoek and C.I. De Zeeuw and P.P. De Deyn and Annemie Van Der Linden and V. Lemmon and R.F. Kooy and P. J. Willems}
}
@article {jsijbersajdendekpscheunddvandyck1998,
	title = {Maximum Likelihood estimation of Rician distribution parameters},
	journal = {IEEE Transactions on Medical Imaging},
	volume = {17},
	number = {3},
	year = {1998},
	pages = {357-361},
	doi = {10.1109/42.712125},
	author = {Jan Sijbers and Arnold Jan den Dekker and Paul Scheunders and Dirk Van Dyck}
}
@inproceedings {ajdendekjsijbersmverhoyedvandyck1998,
	title = {Maximum Likelihood estimation of the signal component magnitude in phase contrast MR images},
	booktitle = {Proceedings of SPIE Medical Imaging},
	volume = {3338},
	year = {1998},
	month = {February},
	pages = {408-415},
	address = {San Diego, California, USA},
	author = {Arnold Jan den Dekker and Jan Sijbers and Marleen Verhoye and Dirk Van Dyck}
}
@inproceedings {mverhoyejsijbersKooyReyniersFransenOostraWillemsavdlinde1998,
	title = {MRI as a tool to study brain structure from mouse models of mental retardation},
	booktitle = {Proceedings of SPIE Medical Imaging},
	volume = {3337},
	year = {1998},
	month = {February},
	pages = {314-320},
	address = {San Diego, CA, USA},
	author = {Marleen Verhoye and Jan Sijbers and R.F. Kooy and E. Reyniers and Erik Fransen and B. A. Oostra and P. J. Willems and Annemie Van Der Linden},
	editor = {Eric A. Hoffman}
}
@inproceedings {KooyReyniersmverhoyejsijbersOostraWillemsavdlinde1998,
	title = {Neuroanatomy of the fragile X knockout mouse brain studied using high-resolution MRI},
	booktitle = {48th Annual Meeting of the American Society of Human Genetics},
	year = {1998},
	month = {October},
	pages = {1918},
	address = {Denver, CO (USA)},
	author = {R.F. Kooy and E. Reyniers and Marleen Verhoye and Jan Sijbers and B. A. Oostra and P. J. Willems and Annemie Van Der Linden}
}
@inproceedings {jsijbersajdendekmverhoyedvandyck1998,
	title = {Optimal estimation of T2 maps from magnitude MR data},
	booktitle = {Proceedings of SPIE Medical Imaging},
	volume = {3338},
	year = {1998},
	month = {February},
	pages = {384-390},
	address = {San Diego, CA, USA},
	author = {Jan Sijbers and Arnold Jan den Dekker and Marleen Verhoye and Dirk Van Dyck},
	editor = {Kenneth M. Hanson}
}
@inproceedings {ajdendekjsijbersdvandyck1998,
	title = {Optimizing the design of an HREM experiment so as to attain the highest resolution},
	booktitle = {Proceedings of FEMMS98: Frontiers of Electron Microscopy in Material Science},
	year = {1998},
	month = {April},
	address = {Kloster Irsee, Germany},
	author = {Arnold Jan den Dekker and Jan Sijbers and Dirk Van Dyck}
}
@inproceedings {ajdendekjsijbersdvandyck1998,
	title = {Optimizing the setting of an electron microscope for highest resolution using statistical parameter estimation theory},
	booktitle = {Workshop: Towards Atomic Resolution Analysis 98},
	year = {1998},
	month = {September},
	address = {Port Ludlow, WA, U.S.A},
	author = {Arnold Jan den Dekker and Jan Sijbers and Dirk Van Dyck}
}
@mastersthesis {1275,
	title = {Signal and noise estimation from magnetic resonance images },
	year = {1998},
	author = {Jan Sijbers}
}
@inproceedings {ebettensajdendekjsijbersdvandyck1998,
	title = {Ultimate resolution in the framework of parameter estimation},
	booktitle = {IASTED International Conference - Signal and Image Processing (SIP{\textquoteright}98)},
	year = {1998},
	month = {October},
	pages = {229-233},
	address = {Las Vegas, Nevada, USA},
	author = {E. Bettens and Arnold Jan den Dekker and Jan Sijbers and Dirk Van Dyck}
}
@inproceedings {KooyReyniersDHoogheDeynavdlindemverhoyejsijbersOostraWillems1997,
	title = {Characterization of the Fragile X knockout mouse},
	booktitle = {Abstracts of the second annual meeting of the Belgian society of neuroscience},
	year = {1997},
	month = {May},
	address = {Brussels, Belgium},
	author = {R.F. Kooy and E. Reyniers and R. Dhooghe and P.P. De Deyn and Annemie Van Der Linden and Marleen Verhoye and Jan Sijbers and B. A. Oostra and P. J. Willems}
}
@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}
}
@inproceedings {mverhoyejsijberspscheunddvandyckReyniersKooyWillemsCrasOostraavdlinde1997,
	title = {In vivo assessment of cerebellum volume in transgenic fragile X knockout mice using MTI microscopy at 7T},
	booktitle = {Proceedings of the European Society for Magnetic Resonance in Medicine and Biology: 14th annual meeting},
	volume = {2},
	number = {2},
	year = {1997},
	month = {September},
	pages = {45-46},
	address = {Brussels, Belgium},
	author = {Marleen Verhoye and Jan Sijbers and Paul Scheunders and Dirk Van Dyck and E. Reyniers and R.F. Kooy and P. J. Willems and P. Cras and B. A. Oostra and Annemie Van Der Linden}
}
@inproceedings {KooyReyniersmverhoyejsijbersFransenCampCrasOostraavdlindeWillems1997,
	title = {MRI as a tool to study brain structure from mouse models of mental retardation},
	booktitle = {Abstracts of the 8th international workshop on Fragile X syndrome and X-linked mental retardation},
	year = {1997},
	month = {August},
	address = {Picton, Ontario, Canada},
	author = {R.F. Kooy and E. Reyniers and Marleen Verhoye and Jan Sijbers and Erik Fransen and C. Van Camp and P. Cras and B. A. Oostra and Annemie Van Der Linden and P. J. Willems}
}
@inproceedings {jsijberspscheunddvandyckeraman1997,
	title = {Noise quantification prior to image restoration},
	booktitle = {Meeting of the Dutch Society for Pattern Recognition and Image Processing},
	year = {1997},
	month = {May},
	address = {Best, The Netherlands},
	author = {Jan Sijbers and Paul Scheunders and Dirk Van Dyck and Erik Raman}
}
@inbook {dvandyckebettensjsijbersajdendekBosBeeckJansenZandbergen1997,
	title = {Resolving atoms: what do we have? what do we want?},
	booktitle = {Institute of Physics Conference Series},
	volume = {153},
	number = {3},
	year = {1997},
	month = {December},
	pages = {95-100},
	publisher = {Institute of Physics Ltd},
	organization = {Institute of Physics Ltd},
	address = {Cambridge, UK},
	author = {Dirk Van Dyck and E. Bettens and Jan Sijbers and Arnold Jan den Dekker and A. van den Bos and M. Op de Beeck and J. Jansen and H. Zandbergen},
	editor = {J. M. Rodenburg}
}
@inproceedings {jsijbersajdendekpscheunderamandvandyck1997,
	title = {Unbiased signal estimation in magnitude MR images},
	booktitle = {Proceedings of the European Society for Magnetic Resonance in Medicine and Biology},
	volume = {2},
	number = {2},
	year = {1997},
	month = {September},
	pages = {174},
	address = {Brussels, Belgium},
	author = {Jan Sijbers and Arnold Jan den Dekker and Paul Scheunders and Erik Raman and Dirk Van Dyck}
}
@article {jsijbersmverhoyepscheundavdlindedvandyckeraman1997,
	title = {Watershed based segmentation of 3D MR data for volume quantization},
	journal = {Magnetic Resonance Imaging},
	volume = {15},
	number = {6},
	year = {1997},
	pages = {679-688},
	doi = {10.1016/S0730-725X(97)00033-7},
	author = {Jan Sijbers and Marleen Verhoye and Paul Scheunders and Annemie Van Der Linden and Dirk Van Dyck and Erik Raman}
}
@inproceedings {ebettenspscheundjsijbersdvandyckMoens1996,
	title = {Automatic segmentation and modeling of two-dimensional electrophoresis gels},
	booktitle = {Proceedings of IEEE International Conference on Image Processing},
	volume = {2},
	year = {1996},
	month = {September},
	pages = {665-668},
	address = {Lausanne, Switserland},
	author = {E. Bettens and Paul Scheunders and Jan Sijbers and Dirk Van Dyck and L. Moens}
}
@inproceedings {mverhoyeavdlindejsijberspscheunddvandyckReyniersKooyWillemsCrasOostra1996,
	title = {High resolution MRI study of the cerebellum of mice as a function of age, in a mouse model for fragile X mental retardation},
	booktitle = {Proceedings of the European Society for Magnetic Resonance in Medicine and Biology},
	volume = {2},
	number = {2},
	year = {1996},
	month = {September},
	pages = {168},
	address = {Prague, Czech Republic},
	author = {Marleen Verhoye and Annemie Van Der Linden and Jan Sijbers and Paul Scheunders and Dirk Van Dyck and E. Reyniers and R.F. Kooy and P. J. Willems and P. Cras and B. A. Oostra}
}
@article {jsijberspscheundBonnetdvandyckeraman1996,
	title = {Quantification and improvement of the signal-to-noise ratio in a magnetic resonance image acquisition procedure},
	journal = {Magnetic Resonance Imaging},
	volume = {14},
	number = {10},
	year = {1996},
	pages = {1157-1163},
	doi = {10.1016/S0730-725X(96)00219-6},
	author = {Jan Sijbers and Paul Scheunders and N. Bonnet and Dirk Van Dyck and Erik Raman}
}
@inproceedings {jsijbersmverhoyepscheundavdlindeAudekerkedvandyckeraman1996,
	title = {Segmentation Scheme for Volume Quantization from 3D Microscopic MR images},
	booktitle = {Proceedings of the Joint Meeting NVvM \& BVM},
	year = {1996},
	month = {December},
	pages = {168-169},
	address = {Gent},
	author = {Jan Sijbers and Marleen Verhoye and Paul Scheunders and Annemie Van Der Linden and Johan Van Audekerke and Dirk Van Dyck and Erik Raman}
}
@inproceedings {jsijbersavdlindepscheundAudekerkeeramandvandyck1996,
	title = {Semi-automatic mouse cerebellum extraction from 3D magnetic resonance data},
	booktitle = {Proceedings of the International Society for Magnetic Resonance in Medicine},
	volume = {3},
	year = {1996},
	month = {April},
	pages = {1585},
	address = {New York, USA},
	author = {Jan Sijbers and Annemie Van Der Linden and Paul Scheunders and Johan Van Audekerke and Erik Raman and Dirk Van Dyck}
}
@inproceedings {jsijbersavdlindepscheundAudekerkedvandyckeraman1996,
	title = {Volume quantization of the mouse cerebellum by semi-automatic 3D segmentation of MR images},
	booktitle = {Proceedings of SPIE Medical Imaging},
	volume = {2710},
	year = {1996},
	month = {February},
	pages = {553-560},
	address = {Newport Beach CA, USA},
	author = {Jan Sijbers and Annemie Van Der Linden and Paul Scheunders and Johan Van Audekerke and Dirk Van Dyck and Erik Raman},
	editor = {H. Loew and Kenneth M. Hanson}
}
@inproceedings {jsijberspscheunddvandyckeraman1994,
	title = {Proceedings of the Royal Microscopical Society},
	booktitle = {Optimization of the SNR in NMR images using image sequences},
	volume = {29},
	number = {4},
	year = {1994},
	month = {September},
	pages = {232},
	address = {London, UK},
	author = {Jan Sijbers and Paul Scheunders and Dirk Van Dyck and Erik Raman}
}
@inproceedings {jsijberspscheunddvandyckeraman1994,
	title = {The use of two NMR realizations for determining and improving of the image SNR},
	booktitle = {Proceedings of the Joint Meeting of the Belgian and Dutch Societies for Electron Microscopy},
	year = {1994},
	month = {May},
	pages = {65},
	address = {Arnhem, Papendal, The Netherlands},
	author = {Jan Sijbers and Paul Scheunders and Dirk Van Dyck and Erik Raman}
}
@inproceedings {jsijberseramanAudekerkeMalflietLindenVerhoyeCloeck1993,
	title = {The construction of a gradient set},
	booktitle = {4th Meeting of the FGWO/FRSM Contact Group Biomedical MR},
	year = {1993},
	pages = {5},
	address = {Louvain-la-Neuve, Belgium},
	author = {Jan Sijbers and Erik Raman and Johan Van Audekerke and W. Malfliet and Annemie Van Der Linden and Marleen Verhoye and C. Cloeck}
}
