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Journal Article
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., 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.
J. Driesen and Scheunders, P., A Multicomponent Image Segmentation Framework, Lecture Notes in Computer Science, vol. 5259, pp. 589-600, 2008.
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. - 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.
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.
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.
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.
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)
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.
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.
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)
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 (581.05 KB)
P. Kempeneers, Zarco-Tejada, P. J., North, P. R. J., De Backer, S., Delalieux, S., Sepulcre-Canto, G., Morales, F., van Aardt, J., Sagardoy, R., Coppin, P., and Scheunders, P., Model inversion for chlorophyll estimation in open canopies from hyperspectral imagery, International Journal of Remote Sensing, vol. 29, pp. 5093-5111, 2008.
I. Blockx, De Groof, G., Verhoye, M., Van Audekerke, J., Raber, K., Poot, D. H. J., Sijbers, J., Osmand, A. P., Von Hörsten, S., and Van Der Linden, A., Microstructural changes observed with DKI in a transgenic Huntington rat model: Evidence for abnormal neurodevelopment., NeuroImage, vol. 59, pp. 957-67, 2012.
J. Sanctorum, Adriaens, D., Dirckx, J. J. J., Sijbers, J., Van Ginneken, C., Aerts, P., and Van Wassenbergh, S., Methods for characterization and optimisation of measuring performance of stereoscopic x-ray systems with image intensifiers, Measurement Science and Technology, vol. 30, no. 10, 2019.
W. Van den Broek, Rosenauer, A., Sijbers, J., Van Dyck, D., and Van Aert, S., A memory efficient method for fully three-dimensional object reconstruction with HAADF STEM Ultramicroscopy, Ultramicroscopy, vol. 141, pp. 22–31, 2014.
J. Gao, Liang, Z., Soper, D. E., Lai, H. - L., Nadolsky, P. M., and Yuan, C. - P., MEKS: A program for computation of inclusive jet cross sections at hadron colliders, Computer Physics Communications, vol. 184, no. 6, pp. 1626 - 1642, 2013.
B. Goris, De Beenhouwer, J., De Backer, A., Zanaga, D., Batenburg, K. J., Sánchez-Iglesias, A., Liz-Marzán, L. M., Van Aert, S., Bals, S., Sijbers, J., and Van Tendeloo, G., Measuring Lattice Strain in Three Dimensions through Electron Microscopy, Nano Letters, vol. 15, no. 10, pp. 6996–7001, 2015.
J. Sijbers and den Dekker, A. J., Maximum Likelihood estimation of signal amplitude and noise variance from MR data, Magnetic Resonance in Medicine, vol. 51, pp. 586-594, 2004.PDF icon Download full paper (295.12 KB)
J. Sijbers, den Dekker, A. J., Scheunders, P., and Van Dyck, D., Maximum Likelihood estimation of Rician distribution parameters, IEEE Transactions on Medical Imaging, vol. 17, pp. 357-361, 1998.PDF icon Download paper (106.26 KB)
J. Rajan, Jeurissen, B., Verhoye, M., Van Audekerke, J., and Sijbers, J., Maximum likelihood estimation based denoising of magnetic resonance images using restricted local neighborhoods, Physics in Medicine and Biology, vol. 56, pp. 5221-5234, 2011.PDF icon Download full paper (643.93 KB)
J. Fatermans, Van Aert, S., and den Dekker, A. J., The maximum a posteriori probability rule for atom column detection from HAADF STEM images, Ultramicroscopy, vol. 201, pp. 81-91, 2019.
W. Keustermans, Huysmans, T., Schmelzer, B., Sijbers, J., and Dirckx, J. J. J., Matlab® toolbox for semi-automatic segmentation of the human nasal cavity based on active shape modeling, Computers in Biology and Medicine, vol. 105, pp. 27-38, 2019.
A. Leemans, Sijbers, J., Verhoye, M., Van Der Linden, A., and Van Dyck, D., Mathematical Framework for Simulating Diffusion Tensor MR Neural Fiber Bundles, Magnetic Resonance in Medicine, vol. 53, pp. 944-953, 2005.PDF icon Download full paper (1.55 MB)