Jan Sijbers

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J
J. S. Jorgensen and Sijbers, J., “Just enough physics”, in Computed Tomography: Algorithms, Insight, and Just Enough Theory , vol. 4, SIAM, 2021.
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J. Renders, De Beenhouwer, J., and Sijbers, J., “Mesh-based reconstruction of dynamic foam images using X-ray CT”, in International Conference on 3D Vision (3DV2021), 2021, pp. 1312-1320.
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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)
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J. Renders, Nguyen, A. - T., De Beenhouwer, J., and Sijbers, J., “Motion compensating X-ray micro-CT of diamonds in a processing stage”, 31st International Conference on Diamond and Carbon Materials. 2021.PDF icon Download poster (1.04 MB)
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B. Shafieizargar, Jeurissen, B., Poot, D. H. J., den Dekker, A. J., and Sijbers, J., “Multi-contrast multi-shot EPI for accelerated diffusion MRI”, in 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2021, pp. 3869-3872.
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T. Van De Looverbosch, Raeymaekers, E., Verboven, P., Sijbers, J., and Nicolai, B., “Non-destructive internal disorder detection of Conference pears by semantic segmentation of X-ray CT scans using deep learning”, Expert Systems with Applications, vol. 176, no. 114925, pp. 1-12, 2021.
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V. Anania, Jeurissen, B., Morez, J., Buikema, A. Eline, Billiet, T., Sijbers, J., and den Dekker, A. J., “Optimal experimental design for the T2-weighted diffusion kurtosis imaging free water elimination model”, ESMRMB 2021 Online 38th Annual Scientific Meeting 7–9 October 2021. Magn Reson Mater Phy, vol. 34. pp. S54-S55, 2021.
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M. Siqueira Pinto, Winzeck, S., Correia, M. M., Kornaropoulos, E. N., Menon, D. K., Glocker, B., den Dekker, A. J., Sijbers, J., Guns, P. - J., Van Dyck, P., and Newcombe, V. F. J., “Outcome prediction in Mild Traumatic Brain Injury patients using conventional and diffusion MRI via Support Vector Machine: A CENTER-TBI study”, ISMRM & SMRT Annual Meeting. 2021.
M. Siqueira Pinto, Winzeck, S., Richter, S., Correia, M. M., Kornaropoulos, E. N., Menon, D. K., Glocker, B., Guns, P. - J., den Dekker, A. J., Sijbers, J., Newcombe, V. F. J., and Van Dyck, P., “Outcome prediction of mild traumatic brain injury using support vector machine based on longitudinal MRdiffusion imaging from CENTER-TBI”, Magn Reson Mater Phy (ESMRMB), vol. 34. p. S54, 2021.
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B. G. Booth, Keijsers, N. L. W., and Sijbers, J., “Outlier detection for foot complaint diagnosis: modeling confounding factors using metric learning”, IEEE Intelligent Systems, vol. 36, no. 3, pp. 41-49, 2021.
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J. Sanctorum, Van Wassenbergh, S., Nguyen, V., De Beenhouwer, J., Sijbers, J., and Dirckx, J. J. J., “Projection-angle-dependent distortion correction in high-speed image-intensifier-based x-ray computed tomography”, Measurement Science and Technology, vol. 32, no. 035404, pp. 1-11, 2021.
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D. Giraldo, Sijbers, J., and Romero, E., “Quantification of cognitive impairment to characterize heterogeneity of patients at risk of developing Alzheimer's disease dementia”, Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, vol. 13, no. 1, p. e12237, 2021.
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M. A. Zampini, Sijbers, J., Verhoye, M., and Garipov, R., “RAMSES: Relaxation Alternate Mapping of Spoiled Echo Signals sequence for simultaneous accurate T1 and T2* mapping”, ISMRM & SMRT Annual Meeting. 2021.
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E. Ribeiro Sabidussi, Klein, S., Caan, M., Bazrafkan, S., den Dekker, A. J., Sijbers, J., Niessen, W. J., and Poot, D. H. J., “Recurrent Inference Machines as inverse problem solvers for MR relaxometry”, Medical Image Analysis, vol. 74, pp. 1-11, 2021.PDF icon Download paper (2.26 MB)
E. Ribeiro Sabidussi, Caan, M., Bazrafkan, S., den Dekker, A. J., Sijbers, J., Niessen, W. J., and Poot, D. H. J., “Recurrent Inference Machines as Inverse Problem Solvers for MR Relaxometry”, in MIDL 2021 - Medical Imaging with Deep Learning, 2021.
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M. Nicastro, Jeurissen, B., Beirinckx, Q., Smekens, C., Poot, D. H. J., Sijbers, J., and den Dekker, A. J., “Rotated or shifted sets of multi-slice MR images for super-resolution reconstruction? A Bayesian answer”, Magn Reson Mater Phy (ESMRMB), vol. 34. pp. S56-S57, 2021.
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F. Danckaers, Van Houtte, J., Booth, B. G., Verstreken, F., and Sijbers, J., “Statistical shape and pose model of the forearm for custom splint design”, in IEEE International Symposium on Biomedical Imaging (ISBI), 2021, pp. 1669-1672.PDF icon Download paper (1.54 MB)
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M. Naeyaert, “Strategies for efficient acquisition and reconstruction of structural and quantitative MRI”, 2021.
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P. Paramonov, Lumbeeck, L. - P., Sijbers, J., and De Beenhouwer, J., “A study of terahertz beam simulation with ray tracing for computed tomography”, in 2021 OSA Imaging and Applied Optics Congress, 2021.
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C. Smekens, Beirinckx, Q., Vanhevel, F., Van Dyck, P., den Dekker, A. J., Sijbers, J., Janssens, T., and Jeurissen, B., “Super-resolution T2* mapping of the knee using UTE Spiral VIBE MRI”, Proceedings of the International Society for Magnetic Resonance in Medicine (ISMRM), 29th Annual Meeting, An Online Experience. p. 3920, 2021.
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D. Frenkel, De Beenhouwer, J., and Sijbers, J., “Tabu-DART: a dynamic update strategy for the Discrete Algebraic Reconstruction Technique based on Tabu-search”, in International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2021.
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S. Bazrafkan, Van Nieuwenhove, V., Soons, J., De Beenhouwer, J., and Sijbers, J., “To Recurse or not to Recurse A Low Dose CT Study”, Progress in Artificial Intelligence, vol. 10, pp. 65–81, 2021.
P. Bladt, “Accurate and precise perfusion parameter estimation in pseudo-continuous arterial spin labeling MRI”, 2020.PDF icon Download PhD thesis (13.17 MB)
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P. Paramonov, Lumbeeck, L. - P., De Beenhouwer, J., and Sijbers, J., “Accurate terahertz simulation with ray tracing incorporating beam shape and refraction”, in IEEE ICIP , 2020, pp. 3035-3039.PDF icon Download paper (190.62 KB)
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D. Frenkel, De Beenhouwer, J., and Sijbers, J., “An adaptive probability map for the Discrete Algebraic Reconstruction Technique”, in 10th Conference on Industrial Computed Tomography (ICT 2020), 2020, pp. 1-6.

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