Publications

Export 1318 results:
Author Type [ Year(Asc)]
2021
J. Van Houtte, Sijbers, J., and Zheng, G., “Graphical User Interface for Joint Space Width Assessment by Optical Marker Tracking”, in 4th International Conference on Bio-engineering for Smart Technologies, 2021.
C. Smekens, Beirinckx, Q., Vanhevel, F., Van Dyck, P., den Dekker, A. J., Sijbers, J., Janssens, T., and Jeurissen, B., “High-resolution T2* mapping of the knee based on UTE Spiral VIBE MRI”, Magn Reson Mater Phy, vol. 34. pp. S53-S54, 2021.
M. Zare, M. Helfroush, S., Kazemi, K., and Scheunders, P., “Hyperspectral and multispectral image fusion using coupled non-negative tucker tensor decomposition”, Remote Sensing, vol. 13, no. 2930, 2021.PDF icon remotesensing-13-02930.pdf (3.79 MB)
T. Hu, Li, W., Liu, N., Tao, R., Zhang, F., and Scheunders, P., “Hyperspectral Image Restoration Using Adaptive Anisotropy Total Variation and Nuclear Norms”, IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 2, pp. 1516-1533, 2021.PDF icon tgrs_2020.pdf (5.71 MB)
S. Jillings, “The impact of long-duration spaceflight on brain structure and function”, University of Antwerp, 2021.
M. Ljubenović, Zhuang, L., De Beenhouwer, J., and Sijbers, J., “Joint Deblurring and Denoising of THz Time-Domain Images”, IEEE Access, vol. 9, pp. 162-176, 2021.PDF icon Download paper (2.38 MB)
J. S. Jorgensen and Sijbers, J., “Just enough physics”, in Computed Tomography: Algorithms, Insight, and Just Enough Theory , vol. 4, SIAM, 2021.
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.
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)
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)
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.
K. Rafiezadeh Sahi, Ghamisi, P., Jackisch, R., Rasti, B., Scheunders, P., and Gloaguen, R., “A Multi-Sensor Subspace-Based Clustering Algorithm Using RGB and Hyperspectral Data”, in Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2021.
B. Jeurissen and Szczepankiewicz, F., “Multi-tissue spherical deconvolution of tensor-valued diffusion MRI.”, Neuroimage, vol. 245, p. 118717, 2021.
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.
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.
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.
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.
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.
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.
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.
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.
B. Koirala, Zahiri, Z., Lamberti, A., and Scheunders, P., “Robust supervised method for nonlinear spectral unmixing accounting for endmember variability”, IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 9, pp. 7434-7448, 2021.PDF icon ieee_version.pdf (3.76 MB)
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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