Publications

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Journal Article
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.
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 , In Press.
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)
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 (581.05 KB)
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.
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.
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. 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.
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.

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