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Journal Article
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)
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)
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)
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)
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)
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.
B. Rasti, Koirala, B., Scheunders, P., and Chanussot, J., MiSiCNet: Minimum Simplex Convolutional Network for Deep Hyperspectral Unmixing, IEEE Transactions on Geoscience and Remote Sensing, vol. 60, no. 5522815, 2022.PDF icon misicnet_ieee_tgrs_author_version.pdf (5.57 MB)
B. Rast, Koirala, B., Scheunders, P., and Chanussot, J., MiSiCNet: Minimum Simplex Convolutional Network for Deep Hyperspectral Unmixing, IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-15, 2022.PDF icon ieee_journal_misicnet.pdf (11.02 MB)
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.
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)
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.
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)
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)
B. Koirala, Zahiri, Z., and Scheunders, P., A Machine Learning Framework for Estimating Leaf Biochemical Parameters From Its Spectral Reflectance and Transmission Measurements, IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 10, pp. 7393-7405, 2020.PDF icon final_version_leaf_parameter_estimation.pdf (2.66 MB)

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