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Q. Beirinckx, Ramos-Llordén, G., Jeurissen, B., Poot, D. H. J., Parizel, P. M., Verhoye, M., Sijbers, J., and den Dekker, A. J., “Joint Maximum Likelihood estimation of motion and T1 parameters from magnetic resonance images in a super-resolution framework: a simulation study”, Fundamenta Informaticae, vol. 172, pp. 105–128, 2020.PDF icon Download paper (final author version) (2.15 MB)
N. Six, Renders, J., De Beenhouwer, J., and Sijbers, J., “Joint multi-contrast CT for edge illumination X-ray phase contrast imaging using split Barzilai-Borwein steps”, Optics Express, vol. 32, no. 2, pp. 1135-1150, 2024.PDF icon Download paper (13.42 MB)
E. Fransen, Dhooghe, R., Van Camp, G., Verhoye, M., Sijbers, J., Reyniers, E., Soriano, P., Kamiguchi, H., Willemsen, R., Koekoek, K. E., Zeeuw, D. C. I., De Deyn, P. P., Van Der Linden, A., Lemmon, V., Kooy, R. F., and Willems, P. J., “L1 knockout mice show dilated ventricles, vermis hypoplasia and impaired exploration patterns”, Human Molecular Genetics, vol. 7, pp. 999-1009, 1998.PDF icon Download paper (248.02 KB)
A. J. den Dekker, Poot, D. H. J., Bos, R., and Sijbers, J., “Likelihood based hypothesis tests for brain activation detection from MRI data disturbed by colored noise: a simulation study”, IEEE Transactions on Medical Imaging, vol. 28, pp. 287-296, 2009.
J. Sijbers, den Dekker, A. J., and Bos, R., “A likelihood ratio test for functional MRI data analysis to account for colored noise”, Lecture Notes in Computer Science, vol. 3708, pp. 538-546, 2005.PDF icon Download full paper (483.15 KB)
L. Emsell, Leemans, A., Langan, C., Van Hecke, W., Barker, G. J., McCarthy, P., Jeurissen, B., Sijbers, J., Sunaert, S., Cannon, D. M., and McDonald, C., “Limbic and callosal white matter changes in euthymic bipolar I disorder: an advanced diffusion MRI tractography study”, Biologicial Psychiatry, vol. 73, no. 2, pp. 194-201, 2013.
V. Van Nieuwenhove, Van Eyndhoven, G., Batenburg, K. J., Buls, N., Vandemeulebroucke, J., De Beenhouwer, J., and Sijbers, J., “Local Attenuation Curve Optimization (LACO) framework for high quality perfusion maps in low-dose cerebral perfusion CT”, Medical Physics, vol. 43, no. 12, pp. 6429-6438, 2016.
Y. Huybrechts, De Ridder, R., Bergen, D., De Samber, B., Boudin, E., Tonelli, F., Knapen, D., Vergauwen, L., Schepers, D., Van Dijck, E., Tong, Q., Verhulst, A., De Beenhouwer, J., Sijbers, J., Hammond, C., Forlino, A., Mortier, G., Coucke, P., P Witten, E., Kwon, R. Young, Willaert, A., Hendrickx, G., and Van Hul, W., “Loss of the Ubiquitin-Associated Domain of sqstm1/p62 in Zebrafish Causes a Phenotype Resembling Paget’s Disease of Bone”, Calcified Tissue International, vol. 116, no. 1, pp. 1-15, 2025.
V. Nguyen, De Beenhouwer, J., Sanctorum, J., Van Wassenbergh, S., Bazrafkan, S., Dirckx, J. J. J., and Sijbers, J., “A low-cost geometry calibration procedure for a modular cone-beam X-ray CT system”, Nondestructive Testing and Evaluation , vol. 35, no. 3, pp. 252-265, 2020.
B. G. Booth, Sijbers, J., and De Beenhouwer, J., “A Machine Learning Approach to Growth Direction Finding for Automated Planting of Bulbous Plants”, Scientific Reports, vol. 10, no. 661, 2020.
J. Juntu, Sijbers, J., De Backer, S., Rajan, J., and Van Dyck, D., “A Machine Learning Study of Several Classifiers Trained with Texture Analysis Features to Differentiate Benign from Malignant Soft Tissue Tumors in T1-MRI Images”, Journal of Magnetic Resonance Imaging, vol. 31, pp. 680–689, 2010.PDF icon Download paper (300.61 KB)
S. Jillings, Van Ombergen, A., Tomilovskaya, E., Rumshiskaya, A., Litvinova, L., Nosikova, I., Pechenkova, E., Rukavishnikov, I., Kozlovskaya, I. B., Manko, O., Danilichev, S., Sunaert, S., Parizel, P. M., Sinitsyn, V., Petrovichev, V., Laureys, S., Eulenburg, Pzu, Sijbers, J., Wuyts, F. L., and Jeurissen, B., “Macro- and microstructural changes in cosmonauts’ brains after long-duration spaceflight”, Science Advances, vol. 6, no. 36, p. eaaz9488, 2020.PDF icon Download paper (942.53 KB)
R. Delgado Y Palacios, Adriaan, C., Kim, H., Verhoye, M., Poot, D. H. J., Jouke, D., Van Audekerke, J., Benveniste, H., Sijbers, J., Wiborg, O., and Van Der Linden, A., “Magnetic resonance imaging and spectroscopy reveal differential hippocampal changes in anhedonic and resilient subtypes of the chronic mild stress rat model”, Biological psychiatry, vol. 70, pp. 449-457, 2011.
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)
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.
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. 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. 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)
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.
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. 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.
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.
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)

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