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Journal Article
E. Van de Casteele, Van Dyck, D., Sijbers, J., and Raman, E., A model-based correction method for beam hardening artefacts in X-ray microtomography, Journal of X-ray science and technology, vol. 12, pp. 53-57, 2004.PDF icon Download full paper (632.04 KB)
Q. Beirinckx, Bladt, P., van der Plas, M. C. E., van Osch, M. J. P., Jeurissen, B., den Dekker, A. J., and Sijbers, J., Model-based super-resolution reconstruction for pseudo-continuous Arterial Spin Labeling, NeuroImage, vol. 286, p. 120506, 2024.PDF icon Download paper (7.23 MB)PDF icon Download supplementary material (20.75 MB)
Q. Beirinckx, Jeurissen, B., Nicastro, M., Poot, D. H. J., Verhoye, M., den Dekker, A. J., and Sijbers, J., Model-based super-resolution reconstruction with joint motion estimation for improved quantitative MRI parameter mapping, Computerized Medical Imaging and Graphics, vol. 100, no. 102071, pp. 1-16, 2022.PDF icon Download paper (15.42 MB)PDF icon Download supplementary material (1.35 MB)
E. Bettens, Van Dyck, D., den Dekker, A. J., Sijbers, J., and van den Bos, A., Model-based two-object resolution from observations having counting statistics, Ultramicroscopy, vol. 77, pp. 37-48, 1999.PDF icon Download full paper (226 KB)
J. Cant, Palenstijn, W. J., Behiels, G., and Sijbers, J., Modeling blurring effects due to continuous gantry rotation: application to region of interest tomography, Medical Physics, vol. 42, no. 5, pp. 2709-2717, 2015.
H. Aerts, Schirner, M., Jeurissen, B., Van Roost, D., Achten, E., Ritter, P., and Marinazzo, D., Modeling brain dynamics in brain tumor patients using The Virtual Brain, eNeuro, 2018.
E. Nazemi, Six, N., Iuso, D., De Samber, B., Sijbers, J., and De Beenhouwer, J., Monte-Carlo-Based Estimation of the X-ray Energy Spectrum for CT Artifact Reduction, Applied Sciences, vol. 11, no. 7, 2021.PDF icon Download paper (4.96 MB)
J. Veraart, Poot, D. H. J., Van Hecke, W., Blockx, I., Van Der Linden, A., Verhoye, M., and Sijbers, J., More accurate estimation of diffusion tensor parameters using diffusion kurtosis imaging, Magnetic Resonance in Medicine, vol. 65, pp. 138-145, 2011.PDF icon Download paper (387.31 KB)
N. Van Camp, Vreys, R., Van Laere, K., Lauwers, E., Beque, D., Verhoye, M., Casteels, C., Verbruggen, A., Debyser, Z., Mortelmans, L., Sijbers, J., Nuyts, J., Baekelandt, V., and Van Der Linden, A., Morphologic and functional changes in the unilateral 6-hydroxydopamine lesion rat model for Parkinson's disease discerned with microSPECT and quantitative MRI., Magnetic Resonance Materials in Physics, Biology and Medicine, vol. 23, no. 2, pp. 65-75, 2010.
S. De Backer, Cornelissen, F., Lemeire, J., Nuydens, R., Meert, T., Schelkens, P., and Scheunders, P., Mosiacing of Fibered Fluorescence Microscopy Video, Lecture notes in Computer Science, vol. 5259, pp. 915-923, 2008.
V. Van Nieuwenhove, De Beenhouwer, J., Vlassenbroeck, J., Brennan, M., and Sijbers, J., MoVIT: A tomographic reconstruction framework for 4D-CT, Optics Express, vol. 25, no. 16, pp. 19236-19250, 2017.
J. - D. Tournier, Smith, R., Raffelt, D., Tabbara, R., Dhollander, T., Pietsch, M., Christiaens, D., Jeurissen, B., Yeh, C. - H., and Connelly, A., MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation., Neuroimage, p. 116137, 2019.
K. Rafiezadeh Sahi, Ghamisi, P., Rasti, B., Gloaguen, R., and Scheunders, P., MS2A-Net: multi-view spectral-spatial association network for hyperspectral image clustering, IEEE-JSTARS, Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 6518-6530, 2022.PDF icon ms2a-net_multiscale_spectralspatial_association_network_for_hyperspectral_image_clustering.pdf (12.33 MB)
A. Duijster, De Backer, S., and Scheunders, P., Multicomponent image restoration, an experimental study, Lecture Notes in Computer Science, vol. 4633, pp. 58-68, 2007.
J. Driesen and Scheunders, P., A Multicomponent Image Segmentation Framework, Lecture Notes in Computer Science, vol. 5259, pp. 589-600, 2008.
R. Heylen and Scheunders, P., Multi-dimensional pixel purity index for convex hull estimation and endmember extraction, IEEE Transactions on Geoscience and Remote Sensing, vol. 51, no. 7, pp. 4059-4069, 2013.
S. Cools, Ghysels, P., Van Aarle, W., Sijbers, J., and Vanroose, W., A multi-level preconditioned Krylov method for the efficient solution of algebraic tomographic reconstruction problems, Journal of Computational and Applied Mathematics, vol. 238, no. 1, pp. 1-16, 2015.
R. Heylen and Scheunders, P., A multilinear mixing model for nonlinear spectral unmixing, IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 1, pp. 240-251, 2016.
A. Dabravolski, Batenburg, K. J., and Sijbers, J., A Multiresolution Approach to Discrete Tomography Using DART, PLoS ONE, vol. 9, no. 9, 2014.PDF icon Download paper (6.13 MB)
A. Leemans, Sijbers, J., De Backer, S., Vandervliet, E., and Parizel, P. M., Multiscale white matter fiber tract coregistration: a new feature-based approach to align diffusion tensor data, Magnetic Resonance in Medicine, vol. 55, pp. 1414-1423, 2006.
B. Koirala, Rasti, B., Bnoulkacem, Z., Ribeiro, A. De Lima, Madriz, Y., Herrmann, E., Gestels, A., De Kerf, T., Lorenz, S., Fuchs, M., Janssens, K., Steenackers, G., Gloaguen, R., and Scheunders, P., A Multisensor Hyperspectral Benchmark Dataset For Unmixing of Intimate Mixtures, IEEE Sensors Journal, 2023.PDF icon a_multisensor_hyperspectral_benchmark_dataset_for_unmixing_of_intimate_mixtures_1_.pdf (22.13 MB)
M. Van Dael, Verboven, P., Dhaene, J., Van Hoorebeke, L., Sijbers, J., and Nicolai, B., Multisensor X-ray inspection of internal defects in horticultural products, Postharvest Biology and Technology, vol. 128, pp. 33–43, 2017.
G. Thoonen, Mahmood, Z., Peeters, S., and Scheunders, P., Multisource classification of color and hyperspectral images using color attribute profiles and composite decision fusion, Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of, vol. 5, pp. 510 - 521, 2012.
B. Jeurissen, Tournier, J. - D., Dhollander, T., Connelly, A., and Sijbers, J., Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data, NeuroImage, vol. 103, pp. 411–426, 2014.
B. Jeurissen and Szczepankiewicz, F., Multi-tissue spherical deconvolution of tensor-valued diffusion MRI., Neuroimage, vol. 245, p. 118717, 2021.

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