Publications

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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.
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
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)
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.
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.
M. Nauwynck, Bazrafkan, S., Van Heteren, A., De Beenhouwer, J., and Sijbers, J., “Ring Artifact Reduction in Sinogram Space Using Deep Learning”, in 6th International Conference on Image Formation in X-Ray Computed Tomography, 2020.PDF icon Download paper (2.49 MB)
S. Bazrafkan, Van Nieuwenhove, V., Soons, J., De Beenhouwer, J., and Sijbers, J., “Ringing Artefact Removal From Sparse View Tomosynthesis using Deep Neural Networks”, in The 6th International Conference on Image Formation in X-Ray Computed Tomography, 2020, pp. 380-383.PDF icon Download paper (570.79 KB)
2019
J. Van Houtte, Bazrafkan, S., Vandenberghe, F., Zheng, G., and Sijbers, J., “A Deep Learning Approach to Horse Bone Segmentation from Digitally Reconstructed Radiographs”, in International Conference on Image Processing Theory, Tools, and Applications, 2019.
E. Ribeiro Sabidussi, Nicastro, M., Bazrafkan, S., Beirinckx, Q., Jeurissen, B., Sijbers, J., den Dekker, A. J., Klein, S., and Poot, D. H. J., “A deep learning approach to T1 mapping in quantitative MRI”, 36th Annual Scientific Meeting of the European Society for Magnetic Resonance in Medicine & Biology (ESMRMB), Rotterdam, The Netherlands, vol. 32 (Suppl. 1). Magn Reson Mater Phy, 2019.
J. Rimpelainen, Bazrafkan, S., Sijbers, J., and De Beenhouwer, J., “Deep learning based missing wedge artefact removal for electron tomography”, Microscopy Conference, Berlin, Germany. pp. 660-661, 2019.
T. Elberfeld, Bazrafkan, S., De Beenhouwer, J., and Sijbers, J., “Mixed-Scale Dense Convolutional Neural Network based Improvement of Glass Fiber-reinforced Composite CT Images”, 4th International Conference on Tomography of Materials & Structures. 2019.