Jan De Beenhouwer

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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.
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.
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)
B. Auer, Kalluri, K., De Beenhouwer, J., Doty, K., Zeraatkar, N., Kuo, P. H., Furenlid, L. R., and King, M. A., Reconstruction using Depth of Interaction Information of Curved and Flat Detector Designs for Quantitative Multi-Pinhole Brain SPECT, Journal of Nuclear Medicine, vol. 61, p. 103, 2020.PDF icon snm2020_recon_curved.pdf (103.23 KB)
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)
J. Sanctorum, De Beenhouwer, J., and Sijbers, J., X-ray phase contrast simulation for grating-based interferometry using GATE, Optics Express, vol. 28, no. 22, pp. 33390-33412, 2020.PDF icon Download paper (2.5 MB)
2019
A. Presenti, Sijbers, J., den Dekker, A. J., and De Beenhouwer, J., CAD-based defect inspection with optimal view angle selection based on polychromatic X-ray projection images, in 9th Conference on Industrial Computed Tomography, Padova, Italy, 2019, pp. 1-5.PDF icon ict2019_full_paper_55.pdf (216.2 KB)
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.
A. Presenti, Sijbers, J., and De Beenhouwer, J., Dynamic angle selection for few-view X-ray inspection of CAD based objects, in Proc. SPIE, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully3D), 2019, vol. 11072.
E. Janssens, Sijbers, J., Dierick, M., and De Beenhouwer, J., Fast detection of cracks in ultrasonically welded parts by inline X-ray inspection, in 9th Conference on Industrial Computed Tomography, Padova, Italy, 2019.
T. Elberfeld, De Beenhouwer, J., and Sijbers, J., Fiber assignment by continuous tracking for parametric fiber reinforced polymer reconstruction, in 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully3D), 2019, vol. 11072.PDF icon Download paper (5.15 MB)
J. Weissenböck, Fröhler, B., Gröller, E., Sanctorum, J., De Beenhouwer, J., Sijbers, J., Karunakaran, S. Ayalur, Hoeller, H., Kastner, J., and Heinzl, C., An Interactive Visual Comparison Tool for 3D Volume Datasets represented by Nonlinearly Scaled 1D Line Plots through Space-filling Curves, in 9th Conference on Industrial Computed Tomography, Padova, Italy, 2019.PDF icon Download paper (1.22 MB)
B. Auer, Zeraatkar, N., De Beenhouwer, J., Kalluri, K., Kuo, P. H., Furenlid, L. R., and King, M. A., Investigation of a Monte Carlo simulation and an analytic-based approach for modeling the system response for clinical I-123 brain SPECT imaging, in 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2019, vol. 11072, pp. 187 – 190.
B. Auer, Kalluri, K., De Beenhouwer, J., Zeraatkar, N., Könik, A., Kuo, P. H., Furenlid, L. R., and King, M. A., Investigation of keel versus knife edge pinhole profiles for a next-generation SPECT system dedicated to clinical brain imaging, 2nd International Conference on Monte Carlo Techniques for Medical Applications. 2019.
V. Nguyen, De Beenhouwer, J., Sanctorum, J., Van Wassenbergh, S., Aerts, P., Dirckx, J. J. J., and Sijbers, J., A low-cost and easy-to-use phantom for cone-beam geometry calibration of a tomographic X-ray system, in 9th Conference on Industrial Computed Tomography, Padova, Italy, 2019.PDF icon Download paper (1.93 MB)
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.
N. Six, De Beenhouwer, J., and Sijbers, J., poly-DART: A discrete algebraic reconstruction technique for polychromatic X-ray CT, Optics Express, vol. 27, no. 23, pp. 33427-33435, 2019.PDF icon Download paper (997.08 KB)
B. Auer, De Beenhouwer, J., Zeraatkar, N., Kuo, P. H., Furenlid, L. R., and King, M. A., Preliminary investigation of attenuation and scatter correction strategies for a next-generation SPECT system dedicated to quantitative clinical brain imaging, in IEEE Nuclear Science Symposium and Medical Imaging Conference, Manchester, UK, 2019.
A. Könik, Auer, B., De Beenhouwer, J., Kalluri, K., Zeraatkar, N., Furenlid, L. R., and King, M. A., Primary, scatter, and penetration characterizations of parallel-hole and pinhole collimators for I-123 SPECT, Physics in Medicine & Biology, vol. 64, no. 24, p. 245001, 2019.PDF icon i123_spectra_final_revision_11_7_19.pdf (7.63 MB)
J. Sanctorum, De Beenhouwer, J., Weissenböck, J., Heinzl, C., Kastner, J., and Sijbers, J., Simulated grating-based x-ray phase contrast images of CFRP-like objects, in 9th Conference on Industrial Computed Tomography, Padova, Italy, 2019, pp. 1-8.PDF icon Download paper (706.73 KB)
B. Fröhler, Melo, Lda Cunha, Weissenböck, J., Kastner, J., Möller, T., Hege, H. - C., Gröller, E., Sanctorum, J., De Beenhouwer, J., Sijbers, J., and Heinzl, C., Tools for the Analysis of Datasets from X-Ray Computed Tomography based on Talbot-Lau Grating Interferometry, in 9th Conference on Industrial Computed Tomography, Padova, Italy, 2019.PDF icon Download paper (1.07 MB)
Y. - T. Ling, Cools, S., De Beenhouwer, J., Sijbers, J., and Vandervorst, W., A Trajectory Based Bottom-Up Volume Reconstruction Method for Atom Probe Tomography, European Atom Probe Workshop. 2019.
B. Fröhler, Elberfeld, T., Möller, T., Weissenböck, J., De Beenhouwer, J., Sijbers, J., Hege, H. - C., Kastner, J., and Heinzl, C., A Visual Tool for the Analysis of Algorithms for Tomographic Fiber Reconstruction in Materials Science, Computer Graphics Forum, vol. 38, no. 3, pp. 273-283, 2019.

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