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X-ray imaging

Projection angle adapted distortion correction in high-speed image-intensifier based tomography

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.

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Mobile imaging of an object using penetrating radiation

J. Sijbers and De Beenhouwer, J., “Mobile imaging of an object using penetrating radiation”, U.S. Patent WO/2017/1783342017.

Approximated transport-of-intensity equation for coded-aperture x-ray phase-contrast imaging

M. Das and Liang, Z., “Approximated transport-of-intensity equation for coded-aperture x-ray phase-contrast imaging”, Optics Letters, vol. 39, no. 18, p. 5395, 2014.

Ring Artifact Reduction in Sinogram Space Using Deep Learning

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.

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Adjoint pairs of image warping operators for motion modeling in 4D-CT

J. Renders, Sijbers, J., and De Beenhouwer, J., “Adjoint pairs of image warping operators for motion modeling in 4D-CT”, in 6th International Conference on Image Formation in X-Ray Computed Tomography, 2020.

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BeadNet: a network for automated spherical marker detection in radiographs for geometry calibration

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.

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Nondestructive internal quality inspection of pear fruit by X-ray CT using machine learning

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.

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)

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.

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A Machine Learning Approach to Growth Direction Finding for Automated Planting of Bulbous Plants

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.

Tomography for in-line product inspection

T. De Schryver, Dierick, M., Van Hoorebeke, L., Verboven, P., Van Dael, M., and Sijbers, J., “Tomography for in-line product inspection”, U.S. Patent EP3106863A12015.

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