Jan Sijbers

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V
A. Van Ombergen, Jillings, S., Jeurissen, B., Tomilovskaya, E., Rumshiskaya, A., Litvinova, L., Nosikova, I., Pechenkova, E. V., Rukavishnikov, I., Manko, O., Danylichev, S., R. Rühl, M., Kozlovskaya, I. B., Sunaert, S., Parizel, P. M., Sinitsyn, V., Laureys, S. S. L., Sijbers, J., Eulenburg, Pzu, and Wuyts, F. L., “Brain ventricular volume changes induced by long-duration spaceflight”, Proceedings of the National Academy of Sciences, vol. 116, no. 21, pp. 10531-10536, 2019.
P
A. Presenti, Sijbers, J., den Dekker, A. J., and De Beenhouwer, J., “CAD-based defect inspection with optimal view angle selection based on polychromatic X-ray projection images”, in 9th Conference on Industrial Computed Tomography, Padova, Italy, 2019, pp. 1-5.PDF icon ict2019_full_paper_55.pdf (216.2 KB)
V
M. Van Dael, Verboven, P., Zanella, A., Sijbers, J., and Nicolai, B., “Combination of shape and X-ray inspection for apple internal quality control: in silico analysis of the methodology based on X-ray computed tomography”, Postharvest Biology and Technology, vol. 148, pp. 218-227, 2019.
M
J. Morez, Sijbers, J., and Jeurissen, B., “A comparison of response function tensor models for multi-tissue spherical deconvolution”, 36th Annual Scientific Meeting of the European Society for Magnetic Resonance in Medicine & Biology, Rotterdam, The Netherlands, vol. 32 (Suppl. 1). pp. S138–S139, 2019.
V
J. Van Houtte, Bazrafkan, S., Vandenberghe, F., Zheng, G., and Sijbers, J., “A Deep Learning Approach to Horse Bone Segmentation from Digitally Reconstructed Radiographs”, in International Conference on Image Processing Theory, Tools, and Applications, 2019.
S
E. Ribeiro Sabidussi, Nicastro, M., Bazrafkan, S., Beirinckx, Q., Jeurissen, B., Sijbers, J., den Dekker, A. J., Klein, S., and Poot, D. H. J., “A deep learning approach to T1 mapping in quantitative MRI”, 36th Annual Scientific Meeting of the European Society for Magnetic Resonance in Medicine & Biology (ESMRMB), Rotterdam, The Netherlands, vol. 32 (Suppl. 1). Magn Reson Mater Phy, 2019.
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J. Rimpelainen, Bazrafkan, S., Sijbers, J., and De Beenhouwer, J., “Deep learning based missing wedge artefact removal for electron tomography”, Microscopy Conference, Berlin, Germany. pp. 660-661, 2019.
S
S. Scataglini, Danckaers, F., Huysmans, T., Sijbers, J., and Andreoni, G., “Design smart clothing using digital human models”, in DHM and Posturography, Academic Press, 2019, pp. 683-698.
D
F. Danckaers, “The Development of 3D Statistical Shape Models for Diverse Applications”, 2019.
B
A. E. Buikema, den Dekker, A. J., and Sijbers, J., “Diffusion time dependence in the mid-time regime: a simulation study using PGSE.”, 27th Annual Meeting of the ISMRM. Montreal, Canada, 2019.
J
S. Jillings, Van Ombergen, A., Tomilovskaya, E., Laureys, S., P Eulenburg, zu, Sunaert, S., Sijbers, J., Wuyts, F. L., and Jeurissen, B., “Diffusion-weighted imaging reveals structural brain changes in cosmonauts after long-duration spaceflight”, 36th Annual Scientific Meeting of the European Society for Magnetic Resonance in Medicine & Biology, Rotterdam, The Netherlands, vol. 32 (Suppl. 1). p. Magn Reson Mater Phy. 2019; 32(Suppl. 1):S101, 2019.
P
A. Presenti, Sijbers, J., and De Beenhouwer, J., “Dynamic angle selection for few-view X-ray inspection of CAD based objects”, in Proc. SPIE, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully3D), 2019, vol. 11072.
B
A. E. Buikema, den Dekker, A. J., and Sijbers, J., “Effect of diffusion time dependence on parameter estimation in the clinical time frame: a simulation study using PGSE”, 11th Annual Meeting of the ISMRM Benelux Chapter. Leiden, the Netherlands, 2019.
J
E. Janssens, Sijbers, J., Dierick, M., and De Beenhouwer, J., “Fast detection of cracks in ultrasonically welded parts by inline X-ray inspection”, in 9th Conference on Industrial Computed Tomography, Padova, Italy, 2019.
E
T. Elberfeld, De Beenhouwer, J., and Sijbers, J., “Fiber assignment by continuous tracking for parametric fiber reinforced polymer reconstruction”, in 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully3D), 2019, vol. 11072.PDF icon Download paper (5.15 MB)
J
B. Jeurissen, Westin, C. - F., Sijbers, J., and Szczepankiewicz, F., “Improved precision and accuracy in q-space trajectory imaging by model-based super-resolution reconstruction”, Proc Intl Soc Mag Reson Med 27. p. 556, 2019.
W
J. Weissenböck, Fröhler, B., Gröller, E., Sanctorum, J., De Beenhouwer, J., Sijbers, J., Karunakaran, S. Ayalur, Hoeller, H., Kastner, J., and Heinzl, C., “An Interactive Visual Comparison Tool for 3D Volume Datasets represented by Nonlinearly Scaled 1D Line Plots through Space-filling Curves”, in 9th Conference on Industrial Computed Tomography, Padova, Italy, 2019.PDF icon Download paper (1.22 MB)
N
V. Nguyen, De Beenhouwer, J., Sanctorum, J., Van Wassenbergh, S., Aerts, P., Dirckx, J. J. J., and Sijbers, J., “A low-cost and easy-to-use phantom for cone-beam geometry calibration of a tomographic X-ray system”, in 9th Conference on Industrial Computed Tomography, Padova, Italy, 2019.PDF icon Download paper (1.93 MB)
K
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.
S
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.
E
T. Elberfeld, Bazrafkan, S., De Beenhouwer, J., and Sijbers, J., “Mixed-Scale Dense Convolutional Neural Network based Improvement of Glass Fiber-reinforced Composite CT Images”, 4th International Conference on Tomography of Materials & Structures. 2019.
S
S. Scataglini, Danckaers, F., Haelterman, R., Huysmans, T., and Sijbers, J., “Moving Statistical Body Shape Models Using Blender”, in Proceedings of the 20th Congress of the International Ergonomics Association (IEA 2018), Cham, 2019, pp. 28–38.
H
T. Huysmans, Danckaers, F., Vleugels, J., Lacko, D., De Bruyne, G., Verwulgen, S., and Sijbers, J., “Multi-patch B-Spline Statistical Shape Models for CAD-Compatible Digital Human Modeling”, in Advances in Human Factors in Simulation and Modeling, Cham, 2019, pp. 179–189.
N
M. Naeyaert, Roose, D., Mai, Z., Keliris, A. J., Sijbers, J., Van Der Linden, A., and Verhoye, M., “Normalized Averaged Range (nAR), a Robust Quantification Method for MPIO-content”, Journal of Magnetic Resonance, vol. 300, pp. 18-27, 2019.
B
A. E. Buikema, den Dekker, A. J., and Sijbers, J., “Optimal design of a blended diffusion/relaxometry experiment”, 36th Annual Scientific Meeting of the ESMRMB. Rotterdam, the Netherlands, 2019.

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