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2020
V. Nguyen, De Beenhouwer, J., Bazrafkan, S., Hoang, A. - T., Van Wassenbergh, S., and Sijbers, J., “BeadNet: a network for automated spherical marker detection in radiographs for geometry calibration”, in 6th International Conference on Image Formation in X-Ray Computed Tomography, 2020, pp. 518-521.PDF icon Download paper (2.16 MB)
M. Ljubenović, Bazrafkan, S., De Beenhouwer, J., and Sijbers, J., “CNN-based Deblurring of Terahertz Images”, in Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP), 2020, vol. 4, pp. 323-330.PDF icon Download paper (16.31 MB)
T. Peeters, Vleugels, J., Verwulgen, S., Danckaers, F., Huysmans, T., Sijbers, J., and De Bruyne, G., “A Comparative Study Between Three Measurement Methods to Predict 3D Body Dimensions Using Shape Modelling”, in Advances in Additive Manufacturing, Modeling Systems and 3D Prototyping, Cham, 2020, vol. 975, pp. 464–470.
T. Peeters, Vleugels, J., Verwulgen, S., Danckaers, F., Huysmans, T., Sijbers, J., and De Bruyne, G., “A Comparative Study Between Three Measurement Methods to Predict 3D Body Dimensions Using Shape Modelling”, in Advances in Additive Manufacturing, Modeling Systems and 3D Prototyping, Cham, 2020, vol. 975, pp. 464–470.
T. Peeters, Vleugels, J., Verwulgen, S., Danckaers, F., Huysmans, T., Sijbers, J., and De Bruyne, G., “A Comparative Study Between Three Measurement Methods to Predict 3D Body Dimensions Using Shape Modelling”, in Advances in Additive Manufacturing, Modeling Systems and 3D Prototyping, Cham, 2020, vol. 975, pp. 464–470.
P. Bladt, den Dekker, A. J., Clement, P., Achten, E., and Sijbers, J., “The costs and benefits of estimating T1 of tissue alongside cerebral blood flow and arterial transit time in pseudo-continuous arterial spin labeling”, NMR in Biomedicine, vol. 33, no. 12, pp. 1-17, 2020.PDF icon Download paper (16.44 MB)
A. Presenti, Bazrafkan, S., Sijbers, J., and De Beenhouwer, J., “Deep learning-based 2D-3D sample pose estimation for X-ray 3DCT”, in 10th Conference on Industrial Computed Tomography (ICT 2020), 2020.
P. Van Dyck, Billiet, T., Desbuquoit, D., Verdonk, P., Heusdens, C. H., Roelant, E., Sijbers, J., and Froeling, M., “Diffusion tensor imaging of the anterior cruciate ligament graft following reconstruction: a longitudinal study”, European Radiology, vol. 34, pp. 6673–6684, 2020.
W. Keustermans, Huysmans, T., Schmelzer, B., Sijbers, J., and Dirckx, J. J. J., “The effect of nasal shape on the thermal conditioning of inhaled air: Using clinical tomographic data to build a large-scale statistical shape model”, Computers in Biology and Medicine, vol. 117, no. 103600, pp. 1-13, 2020.
B. De Samber, De Rycke, R., De Bruyne, M., Kienhuis, M., Sandblad, L., Bohic, S., Cloetens, P., Urban, C., Polerecky, L., and Vincze, L., “Effect of sample preparation techniques upon single cell chemical imaging: A practical comparison between synchrotron radiation based X-ray fluorescence (SR-XRF) and Nanoscopic Secondary Ion Mass Spectrometry (nano-SIMS)”, Analytica Chimica Acta, vol. 1106, pp. 22-32, 2020.PDF icon Download paper (4.01 MB)
B. De Samber, De Rycke, R., De Bruyne, M., Kienhuis, M., Sandblad, L., Bohic, S., Cloetens, P., Urban, C., Polerecky, L., and Vincze, L., “Effect of sample preparation techniques upon single cell chemical imaging: A practical comparison between synchrotron radiation based X-ray fluorescence (SR-XRF) and Nanoscopic Secondary Ion Mass Spectrometry (nano-SIMS)”, Analytica Chimica Acta, vol. 1106, pp. 22-32, 2020.PDF icon Download paper (4.01 MB)
B. De Samber, De Rycke, R., De Bruyne, M., Kienhuis, M., Sandblad, L., Bohic, S., Cloetens, P., Urban, C., Polerecky, L., and Vincze, L., “Effect of sample preparation techniques upon single cell chemical imaging: A practical comparison between synchrotron radiation based X-ray fluorescence (SR-XRF) and Nanoscopic Secondary Ion Mass Spectrometry (nano-SIMS)”, Analytica Chimica Acta, vol. 1106, pp. 22-32, 2020.PDF icon Download paper (4.01 MB)
L. F. Alves Pereira, De Beenhouwer, J., Kastner, J., and Sijbers, J., “Extreme Sparse X-ray Computed Laminography Via Convolutional Neural Networks”, in ICTAI 2020, 2020.PDF icon Download paper (2.5 MB)
M. Siqueira Pinto, Paolella, R., Billiet, T., Van Dyck, P., Guns, P. - J., Jeurissen, B., Ribbens, A., den Dekker, A. J., and Sijbers, J., “Harmonisation of Brain Diffusion MRI: Concepts and Methods”, Frontiers in Neuroscience , vol. 14, pp. 1-17, 2020.PDF icon Download paper (2.61 MB)
V. Anania, Billiet, T., Jeurissen, B., Ribbens, A., den Dekker, A. J., and Sijbers, J., “Improved voxel-wise quantification of diffusion and kurtosis metrics in the presence of noise and intensity outliers”, 12th Annual Meeting ISMRM Benelux Chapter, Arnhem, The Netherlands. 2020.
B. Shafieizargar, Jeurissen, B., den Dekker, A. J., and Sijbers, J., “Joint estimation of phase and diffusion tensor parameters from multi-shot k-q-space data: a proof of concept”, International Society for Magnetic Resonance in Medicine (ISMRM), vol. 28. 2020.
B. Shafieizargar, Jeurissen, B., den Dekker, A. J., and Sijbers, J., “Joint estimation of phase and diffusion tensor parameters from multi-shot k-q-space data: a proof of concept”, ISMRM-Benelux, vol. 12. 2020.
Q. Beirinckx, Ramos-Llordén, G., Jeurissen, B., Poot, D. H. J., Parizel, P. M., Verhoye, M., Sijbers, J., and den Dekker, A. J., “Joint Maximum Likelihood estimation of motion and T1 parameters from magnetic resonance images in a super-resolution framework: a simulation study”, Fundamenta Informaticae, vol. 172, pp. 105–128, 2020.PDF icon Download paper (final author version) (2.15 MB)
B. Auer, Kalluri, K., De Beenhouwer, J., Zeraatkar, N., Momsen, N., Kuo, P. H., Furenlid, L. R., and King, M. A., “Keel-Edge Height Selection for Improved Multi-Pinhole 123I Brain SPECT Imaging”, Journal of Nuclear Medicine, vol. 61, p. 573, 2020.PDF icon Download paper (127.73 KB)
V. Nguyen, De Beenhouwer, J., Sanctorum, J., Van Wassenbergh, S., Bazrafkan, S., Dirckx, J. J. J., and Sijbers, J., “A low-cost geometry calibration procedure for a modular cone-beam X-ray CT system”, Nondestructive Testing and Evaluation , vol. 35, no. 3, pp. 252-265, 2020.
V. Nguyen, De Beenhouwer, J., Sanctorum, J., Van Wassenbergh, S., Bazrafkan, S., Dirckx, J. J. J., and Sijbers, J., “A low-cost geometry calibration procedure for a modular cone-beam X-ray CT system”, Nondestructive Testing and Evaluation , vol. 35, no. 3, pp. 252-265, 2020.
B. G. Booth, Sijbers, J., and De Beenhouwer, J., “A Machine Learning Approach to Growth Direction Finding for Automated Planting of Bulbous Plants”, Scientific Reports, vol. 10, no. 661, 2020.
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)
N. Six, Renders, J., Sijbers, J., and De Beenhouwer, J., “Newton-Krylov Methods For Polychromatic X-Ray CT”, in 2020 IEEE International Conference on Image Processing (ICIP), Abu Dhabi, 2020, pp. 3045-3049.
T. Van De Looverbosch, Bhuiyan, H. Rahman, Verboven, P., Dierick, M., Van Loo, D., De Beenhouwer, J., Sijbers, J., and Nicolai, B., “Nondestructive internal quality inspection of pear fruit by X-ray CT using machine learning”, Food Control, vol. 113, no. 107170, pp. 1-13, 2020.

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