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2021
S. Hosseinnejad, Bosch, E. G. T., Kohr, H., Lazić, I., Zharinov, V., Franken, E., Sijbers, J., and De Beenhouwer, J., “3D atomic resolution tomography from iDPC-STEM images using multiple atom model prior”, Microscopy Conference. 2021.PDF icon Download abstract (534.35 KB)
M. Nicastro, Jeurissen, B., Beirinckx, Q., Smekens, C., Poot, D. H. J., Sijbers, J., and den Dekker, A. J., “Comparison of MR acquisition strategies for super-resolution reconstruction using the Bayesian Mean Squared Error”, in International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2021.
R. Paolella, de la Rosa, E., Sima, D. M., Dive, D., Durand-Dubief, F., Sappey-Marinier, D., Jeurissen, B., Sijbers, J., and Billiet, T., “Decoding Multiple Sclerosis EDSS disability scores from MRI using Deep Learning”, 38th Annual Scientific Meeting Congress of the European Society for Magnetic Resonance in Medicine and Biology, vol. 34. pp. S57-S58, 2021.
P. Van Dyck, Froeling, M., Heusdens, C. H. W., Sijbers, J., Ribbens, A., and Billiet, T., “Diffusion Tensor Imaging of the Anterior Cruciate Ligament Following Primary Repair with Internal Bracing: a Longitudinal Study”, Journal of Orthopaedic Research , vol. 39, pp. 1318–1330, 2021.
A. De Luca, Ianus, A., Leemans, A., Palombo, M., Shemesh, N., Zhang, H., Alexander, D. C., Nilsson, M., Froeling, M., Biessels, G. - J., Zucchelli, M., Frigo, M., Albay, E., Sedlar, S., Alimi, A., Deslauriers-Gauthier, S., Deriche, R., Fick, R., Afzali, M., Pieciak, T., Bogusz, F., Aja-Fernandez, S., Ozarslan, E., Jones, D. K., Chen, H., Jin, M., Zhang, Z., Wang, F., Nath, V., Parvathaneni, P., Morez, J., Sijbers, J., Jeurissen, B., Shreyas,, Fadnavis,, Endres, S., Rokem, A., Garyfallidis, E., Sanchez, I., Prchkovska, V., Rodrigues, P., Landman, B. A., and Schilling, K. G., “On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: chronicles of the MEMENTO challenge”, NeuroImage, vol. 240, no. 118367, 2021.
A. De Luca, Ianus, A., Leemans, A., Palombo, M., Shemesh, N., Zhang, H., Alexander, D. C., Nilsson, M., Froeling, M., Biessels, G. - J., Zucchelli, M., Frigo, M., Albay, E., Sedlar, S., Alimi, A., Deslauriers-Gauthier, S., Deriche, R., Fick, R., Afzali, M., Pieciak, T., Bogusz, F., Aja-Fernandez, S., Ozarslan, E., Jones, D. K., Chen, H., Jin, M., Zhang, Z., Wang, F., Nath, V., Parvathaneni, P., Morez, J., Sijbers, J., Jeurissen, B., Shreyas,, Fadnavis,, Endres, S., Rokem, A., Garyfallidis, E., Sanchez, I., Prchkovska, V., Rodrigues, P., Landman, B. A., and Schilling, K. G., “On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: chronicles of the MEMENTO challenge”, NeuroImage, vol. 240, no. 118367, 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.
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.
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.
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.
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.
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.
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)
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.
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.
2020
S. Van Aert, De Backer, A., De wael, A., Fatermans, J., Friedrich, T., Lobato, I., O'Leary, C. M., Varambhia, A., Altantzis, T., Jones, L., den Dekker, A. J., Nellist, P. D., and Bals, S., “3D Atomic Scale Quantification of Nanostructures and their Dynamics Using Model-based STEM”, Microscopy & Microanalysis 2020 (online), Milwaukee, United States. 2020.
P. Bladt, “Accurate and precise perfusion parameter estimation in pseudo-continuous arterial spin labeling MRI”, 2020.PDF icon Download PhD thesis (13.17 MB)
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)
G. Araizi-Kanoutas, Geessinck, J., Gauquelin, N., Smit, S., Verbeeck, X. H., Mishra, S. K., Bencok, P., Schlueter, C., Lee, T. - L., Krishnan, D., Fatermans, J., Verbeeck, J., Rijnders, G., Koster, G., and Golden, M. S., “Co valence transformation in isopolar LaCoO3/LaTiO3 perovskite heterostructures via interfacial engineering”, Phys. Rev. Materials, vol. 4, 2020.
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.
V. Varkarakis, Bazrafkan, S., and Corcoran, P., “Deep neural network and data augmentation methodology for off-axis iris segmentation in wearable headsets”, Science Direct Elsevier Neural Networks, vol. 121, pp. 101-121, 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.

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