@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}
}
@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}
}
@conference {2460,
	title = {Fixel-based analysis of multi-shell diffusion MRI reveals white matter changes after long-duration spaceflight},
	year = {2024},
	pages = {2057},
	author = {Ben Jeurissen and Steven Jillings and Giraldo, Diana L and Van Ombergen, Angelique and Tomilovskaya, Elena and Pechenkova, Ekaterina and Rukavishnikov, Ilya and Petrovichev, Victor and Sijbers, Jan and zu Eulenburg, Peter and Wuyts, Floris L}
}
@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}
}
@conference {2394,
	title = {Validation of abundance determination of granular mixture using radiative transfer and Bayesian MCMC},
	year = {2024},
	abstract = {We used the semi-analytical reflectance model from (Hapke, 2012), which is a good compromise between physical realism and efficient computation time. In Andrieu et al., 2022, we demonstrated that the usual gradient-descent method is not helpful because the non-linearities are so strong that the results mainly depend on the initialization. So the analysis is done by a Monte Carlo Bayesian approach to propagate the uncertainties from the measurement in reflectance to the parameters (Cruz Mermy et al., 2023). This operation is sometimes called inversion or assimilation.},
	url = {https://meetingorganizer.copernicus.org/EPSC2024/EPSC2024-902.html},
	author = {Fr{\'e}d{\'e}ric Schmidt and Bikram Koirala and Fran{\c c}ois Andrieu}
}
@conference {2291,
	title = {Accelerated Dual-contrast Three-dimensional Knee Magnetic Resonance Imaging Using Super-resolution Reconstructed Deep Learning-enhanced Two-dimensional Dixon Turbo Spin-echo Imaging},
	volume = {27},
	year = {2023},
	month = {05/2023},
	pages = {1-24},
	publisher = {Thieme Medical Publishers, Inc.},
	abstract = {Recent work on three-dimensional (3D) super-resolution reconstruction (SRR) of conventional two-dimensional (2D) turbo spin-echo (TSE) knee magnetic resonance imaging (MRI) shows that high-resolution isotropic knee MRI is technically feasible. Yet the use of acceleration techniques and contrast optimization are needed for clinical validation of this 3D method. With the advent of deep learning (DL) image reconstruction techniques, high acceleration of SRR input data is now achievable through extended use of simultaneous multislice and parallel imaging methods. Moreover, the combination of accelerated TSE with the Dixon method allows us to acquire fat-suppressed and non-fat-suppressed data within a single acquisition that further accelerates the dual-contrast SRR protocol.

This study evaluated the technical feasibility of 3D SRR MRI based on DL-enhanced highly accelerated 2D Dixon TSE MRI. It also compared image quality and diagnostic confidence of this dual-contrast 3D technique to (accelerated) conventional 2D TSE knee MRI. We hypothesized that the SRR method provides the required contrasts needed for comprehensive knee joint evaluation in a competitive acquisition time.},
	doi = {10.1055/s-0043-1770025},
	url = {https://www.thieme-connect.com/products/ejournals/abstract/10.1055/s-0043-1770025},
	author = {C{\'e}line Smekens and Floris Vanhevel and Quinten Beirinckx and Thomas Janssens and Pieter Van Dyck}
}
@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}
}
@conference {2461,
	title = {Iterative model-based Rician bias correction and its application to denoising in diffusion MRI},
	year = {2023},
	pages = {3795},
	author = {Tournier, Jacques-Donald and Ben Jeurissen and Christiaens, Daan}
}
@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}
}
@conference {2462,
	title = {Data-driven characterization of knee structures using non-negative matrix factorization of quantitative UTE Spiral VIBE MRI},
	year = {2022},
	author = {Smekens, C{\'e}line and Van Dyck, Pieter and Janssens, Thomas and Sijbers, Jan and Ben Jeurissen}
}
@conference {2463,
	title = {dtiRIM: A recurrent inference machine for diffusion tensor estimation},
	year = {2022},
	pages = {1981},
	author = {Sabidussi, E R and Klein, S and Ben Jeurissen and Poot, D H J}
}
@conference {2269,
	title = {Edge Illumination Phase Contrast Simulations Using the OptiX GPU Ray Tracing Engine},
	year = {2022},
	author = {Nicholas Francken and Pavel Paramonov and Jan Sijbers and Jan De Beenhouwer}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@conference {2464,
	title = {On false positive control in Fixel-Based Analysis},
	volume = {29},
	year = {2021},
	author = {Smith, R E and Christiaens, D and Ben Jeurissen and Pietsch, M and Vaughan, D N and Jackson, G and Tournier, J D}
}
@conference {2147,
	title = {Faster and better HARDI using FSE and holistic reconstruction},
	year = {2021},
	author = {Maarten Naeyaert and Vladimir Golkov and Daniel Cremers and Jan Sijbers and Marleen Verhoye}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@conference {2081,
	title = {3D Atomic Scale Quantification of Nanostructures and their Dynamics Using Model-based STEM},
	year = {2020},
	author = {Sandra Van Aert and Annick De Backer and De wael, A and Jarmo Fatermans and Friedrich, T and Ivan Lobato and O{\textquoteright}Leary, C M. and Varambhia, A and Thomas Altantzis and Jones, L and Arnold Jan den Dekker and Peter D Nellist and Sara Bals}
}
@conference {2091,
	title = {Bayesian model selection for atom column detection from ABF-ADF STEM images},
	year = {2020},
	author = {Fatermans, J. and Arnold Jan den Dekker and N Gauquelin and Jo Verbeeck and Sandra Van Aert}
}
@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}
}
@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}
}
@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}
}
@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}
}
@conference {2094,
	title = {Strategies for quantifying the 3D atomic structure and the dynamics of nanomaterials using model-based STEM},
	year = {2020},
	author = {Sandra Van Aert and Annick De Backer and De wael, A and Jarmo Fatermans and Arslan Irmak, E and Friedrich, T and Ivan Lobato and Jones, L and Arnold Jan den Dekker and Peter D Nellist and Sara Bals}
}
@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}
}
@conference {2101,
	title = {Tracking Off the Beaten Track},
	year = {2020},
	pages = {e975},
	address = {Sydney, Australia},
	author = {Ben Jeurissen}
}
@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}
}
@conference {1974,
	title = {Atom column detection from STEM images using the maximum a posteriori probability rule},
	year = {2019},
	author = {J Fatermans and Arnold Jan den Dekker and O{\textquoteright}Leary, C M. and Peter D Nellist and Sandra Van Aert}
}
@conference {1975,
	title = {Atom detection from electron microscopy images},
	year = {2019},
	pages = {15},
	author = {J Fatermans and Arnold Jan den Dekker and Sandra Van Aert}
}
@conference {1993,
	title = {Beyond the consensus: is sacrificing part of the PCASL scan time for measurement of labeling efficiency and T1 of blood beneficial?},
	year = {2019},
	author = {Piet Bladt and M.J.P van Osch and Eric Achten and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {1994,
	title = {Beyond the consensus: should measurement of T1 of blood and labeling efficiency be included and should a single- or multi-PLD protocol be used in a five-minute protocol for PCASL?},
	year = {2019},
	author = {Piet Bladt and M.J.P van Osch and Eric Achten and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {1992,
	title = {Beyond the consensus: what to include when 5 minutes are available for perfusion imaging by PCASL?},
	year = {2019},
	author = {Piet Bladt and M.J.P van Osch and Eric Achten and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {2038,
	title = {BQ-MINDED: Introducing Quantitative MRI in routine clinical practice},
	year = {2019},
	author = {Michele Nicastro and Maira Siqueira Pinto}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@conference {1990,
	title = {Investigation of keel versus knife edge pinhole profiles for a next-generation SPECT system dedicated to clinical brain imaging},
	year = {2019},
	month = {2019},
	author = {Benjamin Auer and Kesava Kalluri and Jan De Beenhouwer and Navid Zeraatkar and Arda K{\"o}nik and Philip H. Kuo and Lars R. Furenlid and Michael A. King}
}
@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}
}
@conference {Wuyts2019-xr,
	title = {Novel insight on effect and recovery of long-duration spaceflight on the ventricles of the space traveller{\textquoteright}s brain},
	volume = {2019},
	year = {2019},
	pages = {IAC{\textendash}19_A1_2_4_x51230},
	publisher = {International Astronautical Federation},
	author = {Floris L Wuyts and Steven Jillings and Angelique Van Ombergen and Ben Jeurissen and Tomilovskaya, Elena and Rumshiskaya, Alena and Litvinova, Liudmila and Nosikova, Inna and Pechenkova, Ekaterina and Rukavishnikov, Ilya and others}
}
@conference {1982,
	title = {Optimal design of a blended diffusion/relaxometry experiment},
	year = {2019},
	address = {Rotterdam, the Netherlands},
	author = {Annelinde E. Buikema and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {1959,
	title = {Quantifying 3D atomic structures of nanomaterials and their dynamics using model-based scanning transmission electron microscopy},
	year = {2019},
	author = {Sandra Van Aert and De wael, A and J Fatermans and Ivan Lobato and Annick De Backer and Jones, L and Arnold Jan den Dekker and Peter D Nellist}
}
@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}
}
@conference {1958,
	title = {Strategies for quantifying 3D atomic structures of nanomaterials and their dynamics using dose-efficient ADF STEM},
	year = {2019},
	author = {Sandra Van Aert and De wael, A and J Fatermans and Ivan Lobato and Annick De Backer and Jones, L and Arnold Jan den Dekker and Peter D Nellist}
}
@conference {Dhondt2019-jv,
	title = {Structural adaptations of cognitive emotional brain regions are linked to endogenous pain modulation: a psychophysical and brain imaging study in healthy people and in low back pain},
	year = {2019},
	pages = {399},
	author = {Dhondt, E and Ben Jeurissen and Danneels, L and Van Oosterwijck, J}
}
@conference {1983,
	title = {Super-resolution T1 mapping with integrated motion compensation in a joint maximum likelihood framework},
	volume = {32 (Suppl. 1)},
	number = {S14.05},
	year = {2019},
	publisher = {Magn Reson Mater Phy},
	abstract = {To date, 3D high resolution (HR) quantitative T1 mapping is not feasible in clinical practice due to prohibitively long acquisition times. Recent work has shown that super-resolution reconstruction (SRR), in which a 3D HR T1 map is directly estimated from a set of low through-plane resolution (LR) multi-slice (ms) T1-weighted (T1w) images with different slice orientations, can improve the trade-off between SNR, spatial resolution, and acquisition time. In that work, however, inter-image motion compensation for SRR is performed in a preprocessing step in which the transformation parameters of each LR image are updated after image registration. As a result, potential registration errors might propagate in the T1 estimation as no feedback mechanism is in place. Moreover, due to missing subvoxel accuracy no HR information is readily available during preprocessing. In the current work, we explore the potential of an improved SRR T1 mapping method that aims at more accurate T1 maps by combining T1 and motion estimation in a joint Maximum Likelihood estimation (jMLE) framework.
},
	doi = {10.1007/s10334-019-00754-2},
	author = {Quinten Beirinckx and Ben Jeurissen and Marleen Verhoye and Arnold Jan den Dekker and Jan Sijbers}
}
@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}
}
@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}
}
@conference {2006,
	title = {Automatic Detection of Surface Damages on Steel Structures using Near Infrared Hyperspectral Imaging},
	year = {2018},
	abstract = {Surface damages such as corrosion and coating delamination are very common on steel structures and, if not prevented, can accelerate degradation and eventually lead to failure of the whole structure. Hence, field monitoring is of utmost importance to guarantee the structural health and reduce the maintenance cost. Visual inspection is very perilous (especially for offshore structures), time-consuming, and largely depends on the experience of the inspector (Price \& Figueira 2017). Current non-destructive testing (NDT) techniques (e.g. based on ultrasonic or eddy current technology, acoustic emission ([Calabrese et al 2016; Wu et al 2016] or Electrical Resistance [ Li et al 2007; Mathiesen et al 2016]), require a direct contact with the surface, which causes difficulties when the structure or parts of it are not accessible.
Image processing techniques have been considered as a fast and easier alternative to contact-based techniques. These techniques are usually based on measuring the textural and morphological changes in digital images from the structures (Feliciano et al 2015; Li \& Cheng 2016). However, in reality other materials on steel structures such as dirt, paint, bird droppings, oil leakage, and biofouling can significantly complicate the inspection. Moreover, some effects of corrosion not necessarily reveal themselves in the visible wavelength region. Hyperspectral imaging outside the visible range is an advanced imaging technique that can reveal chemical-mineralogical surface changes that are not observable by the naked eye.
This paper studied the feasibility of detecting coating damages on steel components using hyperspectral imaging. Two different samples of industrial components, provided by ENGIE Laborelec, were scanned with a hyperspectral camera: a part of a flame tube of a heavy-duty gas turbine as well as an artificially corroded sample of coated steel. The images were collected with a snapshot hyperspectral camera in the visible and Near Infrared range (manufactured by IMEC) and analyzed using a k-means clustering method. The outputs showed that despite the visual similarities between coating layers, corroded, and base material, hyperspectral imaging was capable to distinguish between them. Spectral plots showed that differences in spectral behavior of corroded and coated materials were more apparent when the wavelength shifts from the visible towards the near-infrared range. While further investigation is needed, the current results demonstrated that hyperspectral image analysis is a promising technology to complement or even replace digital image analysis within the visible range for characterizing steel surfaces in situ.},
	author = {Zohreh Zahiri and Bart Ribbens and Steve Vanlanduit and Paul Scheunders}
}
@conference {1890,
	title = {Bayesian analysis of noisy scanning transmission electron microscopy images for single atom detection},
	year = {2018},
	pages = {95},
	author = {J Fatermans and Arnold Jan den Dekker and M{\"u}ller-Caspary, K and Ivan Lobato and Sandra Van Aert}
}
@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}
}
@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}
}
@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}
}
@conference {1957,
	title = {Maximising dose efficiency in quantitative STEM to reveal the 3D atomic structure of nanomaterials},
	year = {2018},
	author = {Sandra Van Aert and J Fatermans and Annick De Backer and van den Bos, K. H. W. and O{\textquoteright}Leary, C M. and M{\"u}ller-Caspary, K and Jones, L and Ivan Lobato and B{\'e}ch{\'e}, A and Arnold Jan den Dekker and Sara Bals and Peter D Nellist}
}
@conference {1891,
	title = {The maximum a posteriori probability rule to detect single atoms from low signal-to-noise ratio scanning transmission electron microscopy images},
	year = {2018},
	author = {J Fatermans and Arnold Jan den Dekker and M{\"u}ller-Caspary, K and Ivan Lobato and Sandra Van Aert}
}
@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}
}
@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}
}
@conference {Jeurissen2018-af,
	title = {Spherical deconvolution of diffusion MRI data with tensor-valued encodings},
	year = {2018},
	pages = {1559},
	author = {Ben Jeurissen and Szczepankiewicz, Filip}
}
@conference {Dhondt2018-hg,
	title = {Structural alterations of cognitive emotional brain regions are linked to the presence of spinal sensitization in low back pain},
	year = {2018},
	author = {Dhondt, Evy and Ben Jeurissen and Danneels, Lieven and Van Oosterwijck, Jessica}
}
@conference {Jeurissen2018-zz,
	title = {Super-resolution for spherical deconvolution of multi-shell diffusion MRI data},
	year = {2018},
	pages = {36},
	author = {Ben Jeurissen and Ramos-Llord{\'e}n, Gabriel and Floris Vanhevel and Paul M Parizel and Jan Sijbers}
}
@conference {Van_Dyck2018-st,
	title = {Super-resolution Reconstruction of Knee MRI},
	year = {2018},
	pages = {5184},
	author = {Pieter Van Dyck and Floris Vanhevel and De Smet, E and Paul M Parizel and Jan Sijbers and Ben Jeurissen}
}
@conference {Adnan2017-uz,
	title = {The chronification of pain: are peripheral muscle dysfunction linked to central alteration in the brain},
	year = {2017},
	author = {Adnan, Rahmat and Dhondt, Evy and Danneels, Lieven and Hodges, Paul and Ben Jeurissen and Van Oosterwijck, Jessica}
}
@conference {Adnan2017-sq,
	title = {The chronification of pain : are peripheral muscle dysfunctions linked to central alteration in the brain?},
	year = {2017},
	author = {Adnan, Rahmat and Dhondt, Evy and Danneels, Lieven and Hodges, Paul and Ben Jeurissen and Van Oosterwijck, Jessica}
}
@conference {1889,
	title = {Detection of atomic columns from noisy STEM images},
	year = {2017},
	pages = {445-446},
	author = {J Fatermans and M{\"u}ller-Caspary, K and Arnold Jan den Dekker and Sandra Van Aert}
}
@conference {Van_Dyck2017-jo,
	title = {Diffusion Tensor Imaging of the Anterior Cruciate Ligament Graft},
	year = {2017},
	pages = {377},
	author = {Pieter Van Dyck and De Smet, Eline and Froeling, Martijn and Peter Verdonk and Torfs, Micha{\"e}l and Pim Pullens and Jan Sijbers and Paul M Parizel and Ben Jeurissen}
}
@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}
}
@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}
}
@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}
}
@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}
}
@conference {1760,
	title = {Screw fixation of simulated scaphoid waist fractures: a biomechanical comparison of two screw lengths.},
	year = {2017},
	abstract = {Background: 
Sixty percent of carpal bone fractures affect the scaphoid. These fractures typically occur in a young and male population, with a mean age of 25 years. Eighty percent of scaphoid fractures are located at the waist and the majority of these fractures is nondisplaced and stable. The poor healing capacity of scaphoid fractures, frequently causes delayed- or nonunion, leading to impaired function, early degenerative changes and chronic wrist pain.. Adequate treatment is therefore essential.
Scaphoid fractures can be managed with a cast or with screw fixation.  The latter offers a shorter time of immobilisation with a faster return to function, whilst having the same long-term outcome. Ideal screw length is an area of discussion. Theoretically, longer screws provide more stability, but there is a higher risk of protrusion of the screw in the cartilage resulting in more adverse effects. 

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

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

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

Conclusions: These data suggest that in oblique waist fracture, fixation with a long or a short screw are equivalent in strength. Given the higher risk of complications with longer screws, a shorter screw is advisable and has no negative impact on the stability of the construct.
},
	author = {Maarten Ouwendijk and Niek Slingerland and Job van Nistelrooij and Toon Huysmans and Elke Van de Casteele and Francis Van Glabbeek and Geert Meermans and Frederik Verstreken}
}
@conference {1764,
	title = {Solving the Free Water Elimination Estimation Problem by Incorporating T2 Relaxation Properties},
	year = {2017},
	author = {Quinten Collier and Jelle Veraart and Arnold Jan den Dekker and Floris Vanhevel and Paul M Parizel and Jan Sijbers}
}
@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}
}
@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}
}
@conference {1888,
	title = {Bayesian model-order selection in electron microscopy to detect atomic columns in noisy images},
	year = {2016},
	pages = {53},
	author = {J Fatermans and Sandra Van Aert and Arnold Jan den Dekker}
}
@conference {1643,
	title = {Building 3D Statistical Shape Models of Vegetables and Fruit},
	year = {2016},
	abstract = {Introduction
Statistical shape modeling is a promising approach to map out the variability of a population. By adapting the parameters of the shape model, a new, realistic surface can be obtained. In this work, the framework for surface registration and building a statistical shape model of fruit is described.

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

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

Applications
Possible applications of a statistical shape model of fruit are predicting the final size of the fruit, search for correlations between stages of growth, estimate the volume from a single view and evaluation of the effect of the fruit shape on airflow characteristics to obtain cooling uniformity.},
	author = {Femke Danckaers and Toon Huysmans and Jan Sijbers}
}
@conference {1694,
	title = {Building a statistical shape model of the interior and exterior of the bell pepper},
	volume = {28-30 November},
	year = {2016},
	address = {Vienna, Austria},
	author = {Femke Danckaers and Toon Huysmans and Seppe Rogge and Mattias Van Dael and Pieter Verboven and Bart Nicolai and Jan Sijbers}
}
@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}
}
@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}
}
@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}
}
@conference {Kenney2016-ju,
	title = {Lateralisation of the arcuate fasciculus in psychosis \& the role in verbal learning \& auditory verbal hallucinations},
	volume = {26},
	year = {2016},
	pages = {S76{\textendash}S77},
	publisher = {Elsevier Science Bv},
	address = {Po Box 211, 1000 Ae Amsterdam, Netherlands},
	author = {Kenney, J and McInerney, S and McPhilemy, G and Najt, P and Scanlon, C and Arndt, S and Scherz, E and Byrne, F and Leemans, A and Ben Jeurissen and Donohoe, G and Hallahan, B and McDonald, C and Cannon, D}
}
@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}
}
@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}
}
@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}
}
@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}
}
@conference {1613,
	title = {3D evaluation of clavicle fractures},
	year = {2015},
	author = {Van Tongel, Alexander and Toussaint,, Arnaud and Lieven De Wilde and Toon Huysmans}
}
@conference {1589,
	title = {A 4D CT reconstruction algorithm for fast liquid flow imaging},
	year = {2015},
	abstract = {  The study of fluid flow through solid matter by computed tomography (CT) imaging has a broad range of applications, ranging from oil extraction to scientific research on fluid dynamics. Current techniques are often limited by a low temporal/spatial resolution. In this talk, a new iterative CT reconstruction algorithm for improved temporal/spatial resolution in the imaging of fluid flowing through solid matter is introduced. The proposed algorithm exploits prior knowledge in two ways. Firstly, the time-varying object is assumed to consist of stationary (the solid matter) and dynamic regions (the fluid flow). Secondly, the attenuation of a particular voxel in the dynamic region is modeled by a piecewise constant function over time (i.e., the voxel consists of fluid or air). Experiments on simulation data and on a real neutron tomography dataset demonstrate that the proposed approach can significantly increase the temporal resolution in comparison to conventional algorithms.},
	author = {Van Eyndhoven, Geert and Kees Joost Batenburg and Kazantsev, Daniil and Vincent Van Nieuwenhove and Lee, Peter D. and Katherine J Dobson and Jan Sijbers}
}
@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}
}
@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}
}
@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}
}
@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}
}
@conference {Van_Ombergen2015-nk,
	title = {A first insight in regional brain changes after parabolic flight: a voxel-based morphometry study},
	year = {2015},
	pages = {1258},
	author = {Angelique Van Ombergen and Ben Jeurissen and Floris Vanhevel and Loeckx, Dirk and Dousset, Vincent and Paul M Parizel and Floris L Wuyts}
}
@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}
}
@conference {1660,
	title = {Human In Vivo Myeloarchitecture Using Whole-Brain Diffusion MRI},
	year = {2015},
	pages = {1679},
	address = {Toronto, Ontario, Canada},
	author = {Fernando Calamante and Ben Jeurissen and Robert Elton Smith and Jacques-Donald Tournier and Alan Connelly}
}
@conference {1661,
	title = {Inversion Recovery DTI In Vivo at 7T in the Human Brain},
	year = {2015},
	pages = {567},
	address = {Toronto, Ontario, Canada},
	author = {Silvia De Santis and Ben Jeurissen and Derek K. Jones and Yaniv Assaf and Alard Roebroek}
}
@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}
}
@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}
}
@conference {jeurissen2015processing,
	title = {Processing multi-shell diffusion MRI data using MRtrix3},
	year = {2015},
	author = {Ben Jeurissen}
}
@conference {Jeurissen2015-ge,
	title = {Processing multi-shell diffusion MRI data using MRtrix3},
	year = {2015},
	publisher = {Frontiers in Neuroinformatics},
	author = {Ben Jeurissen}
}
@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}
}
@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 {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}
}
@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 {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 {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 {1662,
	title = {Time to Move On: An FOD-Based DEC Map to Replace DTI{\textquoteright}s Trademark DEC FA},
	year = {2015},
	pages = {1027},
	author = {Thijs Dhollander and Robert Elton Smith and Jacques-Donald Tournier and Ben Jeurissen and Alan Connelly}
}
@conference {1595,
	title = {Tissue-type segmentation using non-negative matrix factorization of multi-shell diffusion-weighted MRI images},
	volume = {23},
	year = {2015},
	pages = {349},
	author = {Ben Jeurissen and Jacques-Donald Tournier and Jan Sijbers}
}
@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}
}
@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}
}
@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}
}
@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}
}
@conference {roine2014ismrm,
	title = {Improving fiber orientation estimation in constrained spherical deconvolution under non-white matter partial volume effects},
	year = {2014},
	month = {05/2014},
	pages = {4492},
	author = {Timo Roine and Ben Jeurissen and Wilfried Philips and Alexander Leemans and Jan Sijbers}
}
@conference {1486,
	title = {Influence of Correspondence Method on Statistical Model Based Shape Prediction},
	year = {2014},
	author = {Christophe Van Dijck and Wirix-Speetjens, Roel and Toon Huysmans and Femke Danckaers and Jan Sijbers and Vander Sloten, Jos}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@conference {1554,
	title = {Structural brain network analysis in families multiply affected with bipolar 1 disorder},
	year = {2014},
	address = {Athens, Greece},
	author = {Forde, Natalie J and Scanlon, Cathy and Louise Emsell and O{\textquoteright}Donoghue, S and Chaddock, Chris and Alexander Leemans and Ben Jeurissen and Dara M. Cannon and Murray, R M and Colm McDonald}
}
@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}
}
@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 {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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@conference {1557,
	title = {Recursive calibration of the fiber response function for spherical deconvolution diffusion ODF sharpening},
	year = {2013},
	address = {Podstrana, Croatia},
	author = {Chantal M W Tax and Ben Jeurissen and Vos, Sjoerd B. and Viergever, Max A. and Alexander Leemans}
}
@conference {1398,
	title = {Region-Based SIRT algorithm for the reconstrcution of phase bins in dynamic micro-CT},
	year = {2013},
	author = {Van Eyndhoven, Geert and Kees Joost Batenburg and Jan Sijbers}
}
@conference {1556,
	title = {Robust fiber response function estimation for deconvolution based diffusion MRI methods},
	volume = {21},
	year = {2013},
	pages = {3149},
	address = {Salt Lake City, Utah},
	author = {Chantal M W Tax and Ben Jeurissen and Viergever, Max A. and Alexander Leemans}
}
@conference {1555,
	title = {Robust fiber response function estimation for deconvolution based diffusion MRI methods},
	volume = {5},
	year = {2013},
	pages = {55},
	address = {Rotterdam, The Netherlands},
	author = {Chantal M W Tax and Ben Jeurissen and Viergever, Max A. and Alexander Leemans}
}
@conference {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}
}
@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}
}
@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}
}
@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}
}
@conference {1366,
	title = {Discrete algebraic reconstruction in MRI: a simulation study},
	volume = {25},
	year = {2012},
	pages = {480-481},
	keywords = {compressed sensing, DART, iterative reconstruction, prior knowledge},
	doi = {10.1007/s10334-012-0324-9},
	author = {Quinten Collier and Segers, Hilde and Jan Sijbers}
}
@conference {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}
}
@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}
}
@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}
}
@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}
}
@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 {1552,
	title = {Constrained spherical deconvolution based tractography and cognition in Alzheimer{\textquoteright}s disease},
	year = {2011},
	address = {Paris, France},
	author = {Reijmer, Yael D and Alexander Leemans and Heringa, Sophie M and Wielaard, Ilse and Ben Jeurissen and Koek, Huiberdina L and Biessels, Geert Jan}
}
@conference {1549,
	title = {Constrained spherical deconvolution based tractography and cognition in Alzheimer{\textquoteright}s disease},
	volume = {5},
	year = {2011},
	address = {Lille, France},
	author = {Reijmer, Yael D and Alexander Leemans and Heringa, Sophie M and Wielaard, Ilse and Ben Jeurissen and Koek, Huiberdina L and Biessels, Geert Jan}
}
@conference {1551,
	title = {Constrained spherical deconvolution based tractography and cognition in Alzheimer{\textquoteright}s disease},
	year = {2011},
	address = {Qu{\'e}bec, Canada},
	author = {Reijmer, Yael D and Alexander Leemans and Heringa, Sophie M and Wielaard, Ilse and Ben Jeurissen and Koek, Huiberdina L and Biessels, Geert Jan}
}
@conference {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}
}
@conference {1245,
	title = {Denoising magnitude MRI using an adaptive NLML method},
	year = {2011},
	pages = {383},
	address = {Leipzig, Germay},
	author = {Jeny Rajan and Johan Van Audekerke and Marleen Verhoye and Annemie Van Der Linden and Jan Sijbers}
}
@conference {Kudzinava_2011_ESMRMB,
	title = {Denoising of DKI images: effect on feasibility and accuracy of kurtosis parameters},
	year = {2011},
	month = {October},
	pages = {236},
	address = {Leipzig, Germany},
	abstract = {Diffusion Kurtosis Imaging (DKI) is a new MRI technique, which quantifies non-Gaussianity of water diffusion. DKI requires high b-values, resulting in a low SNR of acquired diffusion weighted images (DWIs). Generally, kurtosis tensors are estimated from these DWIs using Weighted Least Squares (WLS) or Maximum Likelihood (ML) methods. In this work, we apply the Restricted Local Maximum Likelihood (RLML) denoising algorithm to remove noise from DWIs prior to WLS estimation. Then, we compare the RLML+WLS approach to the WLS and ML methods in terms of feasibility, that is the percentage of estimated tensors that satisfy certain DKI model constraints; and in terms of accuracy of calculated values of Mean Kurtosis.},
	keywords = {denoising, diffusion kurtosis imaging, maximum likelihood estimation},
	author = {Maryna Kudzinava and Jeny Rajan and Jan Sijbers}
}
@conference {1306,
	title = {Denoising SENSE reconstructed MR images},
	year = {2011},
	author = {Jeny Rajan and Jan Sijbers}
}
@conference {Kudzinava_2011_WMSG,
	title = {Diffusion and kurtosis parameters in white matter of premature newborns},
	year = {2011},
	month = {August},
	pages = {21},
	address = {Reykjavik, Iceland},
	abstract = {Diffusion Tensor Imaging (DTI) is often used to study brain maturation processes and white matter (WM) injuries in premature newborns. However, DTI assumes that water diffusion in biological tissues is free. In reality, there is always structural hindrance in WM, which only increases with age due to myelination. Diffusional Kurtosis Imaging (DKI) relaxes the assumption of free diffusion and allows estimation of conventional diffusion parameters plus new kurtosis measures, which are related to tissue complexity. In this work, we present and discuss the first statistics of DKI parameters in WM of two prematurely born infants.},
	keywords = {anisotropy, diffusion, kurtosis, premature newborns, white matter},
	author = {Maryna Kudzinava and A. Plaisier and J. Dudink and Jan Sijbers}
}
@conference {R.jveraartG.H.mverhoyeP.jsijbersavdlinde2011,
	title = {Diffusion kurtosis abnormalities in pre-symptomatic (alpha)-synycleunopathy mouse model},
	year = {2011},
	month = {January},
	address = {Hoeven, The Netherlands},
	author = {Rafael  Delgado Y Palacios and Jelle Veraart and Greetje Vanhoutte and H. Schell and Marleen Verhoye and P. Kahle and Jan Sijbers and Annemie Van Der Linden}
}
@conference {1550,
	title = {Diffusion tensor imaging and cognition in Alzheimer{\textquoteright}s disease: the influence of crossing fibers},
	year = {2011},
	address = {Lunteren, The Netherlands},
	author = {Wielaard, Ilse and Reijmer, Yael D and Alexander Leemans and Heringa, Sophie M and Ben Jeurissen and Koek, Huiberdina L and Biessels, Geert Jan}
}
@conference {1307,
	title = {Distortion correction of DKI data: affine approach},
	year = {2011},
	month = {December},
	abstract = {In this work, the performance of FMRIB{\textquoteright}s Linear Registration Tool (FLIRT) is evaluated with respect to motion and distortion correction of diffusion kurtosis imaging (DKI) data. FLIRT is tested on two DKI datasets heavily distorted by motion and eddy currents artefacts. Six different criteria are used for evaluation. Our results show that distortion correction using FLIRT substantially improves the original data. However, being an affine registration tool, FLIRT cannot cope with the amount of non-linear distortion present in DKI datasets. Hence, we conclude that, for correction of DKI data, non-linear registration techniques are more suitable.},
	keywords = {medical imaging},
	author = {Maryna Kudzinava and Alexander Leemans and Wilfried Philips and Daniele Perrone and Jan Aelterman and J. Dudink and Jan Sijbers}
}
@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}
}
@conference {M.J.bjeurissP.J.K.J.aleemans2011,
	title = {Fiber architecture of the female pelvic floor: An exploratory investigation using different diffusion MRI tractography algorithms},
	year = {2011},
	month = {May},
	address = {Montreal, Canada},
	author = {Martijn Froeling and Strijkers G. J. and Ben Jeurissen and van der Paardt M. P. and J. Stoker and K. Nicolay and A. J. Nederveen and Alexander Leemans}
}
@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}
}
@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}
}
@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}
}
@conference {1204,
	title = {Ultra-High Resolution Electron Tomography for Materials Science: A Roadmap},
	year = {2011},
	address = {Nashville, TN, United States},
	doi = {http://dx.doi.org/10.1017/S143192761100554X},
	author = {Kees Joost Batenburg and Sara Bals and Sandra Van Aert and Roelandts, Tom and Jan Sijbers}
}
@conference {1553,
	title = {White Matter Tract Deficits in Schizophrenia},
	volume = {7},
	year = {2011},
	address = {Dublin, Ireland},
	author = {Forde, Natalie J and Ellison-Wright, I and Nathan, Pradeep J and Zaman, R and Dudas, R and Agius, M and Fernandez-Egea, E and Alexander Leemans and Ben Jeurissen and Scanlon, Cathy and Colm McDonald and Dara M. Cannon}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
