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Conference Paper
J. Sijbers, den Dekker, A. J., Verhoye, M., and Van Dyck, D., Optimal estimation of T2 maps from magnitude MR data, in Proceedings of SPIE Medical Imaging, San Diego, CA, USA, 1998, vol. 3338, pp. 384-390.PDF icon Download paper (1.04 MB)
C. Bossuyt, De Beenhouwer, J., and Sijbers, J., Optimization of a multi-source rectangular X-ray CT geometry for inline inspection, in SPIE Optical Engineering: Developments in X-Ray Tomography XIV , 2022, vol. 12242, p. 1224219 .
A. Leemans, Sijbers, J., Verhoye, M., and Van Der Linden, A., Optimized Fiber Tractography based on Diffusion Tensor Magnetic Resonance Simulations, in 20th Annual Symposium - Belgian Hospital Physicists Association, Namur, Belgium, 2005.
W. Van Hecke, Leemans, A., Vandervliet, E., Parizel, P. M., and Sijbers, J., An optimized tensor orientation strategy for non-rigid alignment of DT-MRI data, in Joint Annual Meeting ISMRM-ESMRMB, Berlin, 2007.
M. Kudzinava, Poot, D. H. J., Plaisier, A., and Sijbers, J., Optimized Workflow for Diffusion Kurtosis Imaging of Newborns, in ISBI, 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Chicago, USA, 2011, pp. 922-926.PDF icon Paper (286.57 KB)PDF icon Presentation (1.25 MB)
A. J. den Dekker, Sijbers, J., and Van Dyck, D., Optimizing the design of an HREM experiment so as to attain the highest resolution, in Proceedings of FEMMS98: Frontiers of Electron Microscopy in Material Science, Kloster Irsee, Germany, 1998.
D. H. J. Poot, Sijbers, J., and den Dekker, A. J., Optimizing the Diffusion Kurtosis imaging acquisition, in European Society for Magnetic Resonance in Medicine and Biology, Valencia, Spain, 2008.
A. J. den Dekker, Sijbers, J., and Van Dyck, D., Optimizing the setting of an electron microscope for highest resolution using statistical parameter estimation theory, in Workshop: Towards Atomic Resolution Analysis 98, Port Ludlow, WA, U.S.A, 1998.
G. De Groof, Verhoye, M., Leemans, A., Sijbers, J., and Van Der Linden, A., Paired voxel-wise statistical mapping of in vivo Diffusion Tensor Imaging (DTI) data to assess the seasonal neuronal plasticity in the brain of a songbird, in 1st Annual Meeting - European Society of Molecular Imaging, Paris, France, 2006.
G. Ramos-Llordén, Segers, H., Palenstijn, W. J., den Dekker, A. J., and Sijbers, J., Partially discrete magnetic resonance tomography, in 2015 IEEE International Conference on Image Processing (ICIP), 2015, pp. 1653-1657.
T. Roelandts, Batenburg, K. J., and Sijbers, J., PDART: A Partially Discrete Algorithm for the Reconstruction of Dense Particles, in 11th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully 3D), Potsdam, Germany, 2011, pp. 448-451.PDF icon Download full paper (1.74 MB)
N. Six, De Beenhouwer, J., and Sijbers, J., pDART: Discrete algebraic reconstruction using a polychromatic forward model, in The Fifth International Conference on Image Formation in X-Ray Computed Tomography, Salt Lake City, Utah, USA, 2018.PDF icon Download paper (1.61 MB)
S. Manhaeve, Van Nieuwenhove, V., and Sijbers, J., Performance and memory use trade-off in CPU and GPU implementations of a deformation operator for 4D-CT, in 8th Conference on Industrial Computed Tomography, Wels, Austria, 2018.
J. Sijbers and den Dekker, A. J., The performance of generalized likelihood ratio tests for complex functional MRI data in the presence of phase model misspecification, in European Society of Magnetic Resonance in Medicine (ESMRMB), Copenhagen, Denmark, 2004, p. 96.
T. B. A. de Carvalho, Sibaldo, M. A. A., Tsang, I. R., Cavalcanti, G. D. C., Tsang, I. J., and Sijbers, J., Pixel clustering for face recognition, in 5th Brazilian Conference on Intelligent Systems, Brazil, 2016, pp. 121-126.
W. Fortes, Sijbers, J., and Batenburg, K. J., Practical Error Bounds for Binary Tomography, in 1st International Conference on Tomography of Materials and Structures, 2013, pp. 97-100.PDF icon Download paper (165.74 KB)
W. Van Hecke, Leemans, A., Sijbers, J., Parizel, P. M., and Van Goethem, J., A preliminary study of diffusion tensor imaging and tractography of the spinal cord in elderly, in 21th Annual Symposium - Belgian Hospital Physicists Association, Ghent, Belgium, 2006, p. 61.
J. Sijbers, Scheunders, P., Van Dyck, D., and Raman, E., Proceedings of the Royal Microscopical Society, in Optimization of the SNR in NMR images using image sequences, London, UK, 1994, vol. 29, p. 232.
K. J. Batenburg, Palenstijn, W. J., and Sijbers, J., Projection and backprojection in tomography: design choices and considerations, in Workshop on Applications of Discrete Geometry and Mathematical Morphology, 2010, pp. 106-110.PDF icon Download paper (367.34 KB)
J. Sanctorum, Nguyen, V., Sijbers, J., Van Wassenbergh, S., and Dirckx, J. J. J., Projection angle adapted distortion correction in high-speed image-intensifier based tomography, in 6th International Conference on Image Formation in X-Ray Computed Tomography, 2020.PDF icon Download paper (818.64 KB)
A. Dabravolski, De Beenhouwer, J., and Sijbers, J., Projection-based polygon estimation in X-ray computed tomography, in 6th International Conference on Optical Measurement Techniques for Structures and Systems (OPTIMESS), 2016, pp. 41-50.PDF icon Download paper (615.72 KB)
E. Van de Casteele, Parizel, P. M., and Sijbers, J., Quantitative evaluation of ASiR image quality: an adaptive statistical iterative reconstruction technique, in SPIE Medical Imaging, 2012, vol. 8313.PDF icon Download abstract (453.01 KB)
A. J. den Dekker, Sijbers, J., and Van Dyck, D., Quantitative HREM: viewpoints on resolution, precision, and experimental design, in Acta Cryst. A55 Supplement, Abstract M11.OE.OO5, IUCR Glasgow, Scotland, 1999.
L. - P. Lumbeeck, Paramonov, P., Sijbers, J., and De Beenhouwer, J., The Radon Transform for Terahertz Computed Tomography Incorporating the Beam Shape, in IEEE ICIP, 2020, pp. 3040-3044.PDF icon Download paper (692.64 KB)
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.