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Tomography

Computed tomography (CT)

Grating designs for cone beam edge illumination X-ray phase contrast imaging: a simulation study

P. - J. Vanthienen, Sanctorum, J., Huyge, B., Six, N., Sijbers, J., and De Beenhouwer, J., “Grating designs for cone beam edge illumination X-ray phase contrast imaging: a simulation study”, Optics Express, vol. 31, no. 17, pp. 28051-28064, 2023.

Enhancing industrial inspection with efficient edge illumination x-ray phase contrast simulations

N. Francken, Paramonov, P., Sijbers, J., and De Beenhouwer, J., “Enhancing industrial inspection with efficient edge illumination x-ray phase contrast simulations”, in IEEE EUROCON 2023 -20th International Conference on Smart Technologies, Torino, Italy, 2023.

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eurocon_2023.pdf

Fiber Orientation Estimation from X-ray Dark Field Images of Fiber Reinforced Polymers using Constrained Spherical Deconvolution

B. Huyge, Sanctorum, J., Jeurissen, B., De Beenhouwer, J., and Sijbers, J., “Fiber Orientation Estimation from X-ray Dark Field Images of Fiber Reinforced Polymers using Constrained Spherical Deconvolution”, Polymers, vol. 15, no. 13, p. 2887, 2023.

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Toward denoising of 3D CT scans with few data

Z. Liang, Van Heteren, A., Sijbers, J., and De Beenhouwer, J., “Toward denoising of 3D CT scans with few data”, in e-Journal of Nondestructive Testing, 2023, vol. 28, no. 3.

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Defect detectability analysis via Probability of defect detection between traditional and deep learning methods in numerical simulations

M. Yosifov, Weinberger, P., Reiter, M., Fröhler, B., De Beenhouwer, J., Sijbers, J., Kastner, J., and Heinzl, C., “Defect detectability analysis via Probability of defect detection between traditional and deep learning methods in numerical simulations”, in e-Journal of Nondestructive Testing, 2023, vol. 28, no. 3.

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The Deep Steerable Convolutional Framelet Network for Suppressing Directional Artifacts in X-ray Tomosynthesis

L. F. Alves Pereira, De Beenhouwer, J., and Sijbers, J., “The Deep Steerable Convolutional Framelet Network for Suppressing Directional Artifacts in X-ray Tomosynthesis”, in 31st European Signal Processing Conference, EUSIPCO, 2023.

Parametric Fiber Analysis for Glass Fiber-reinforced Composite Tomographic Images

T. Elberfeld, “Parametric Fiber Analysis for Glass Fiber-reinforced Composite Tomographic Images”, 2023.

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Download PhD thesis

Towards material and process agnostic features for the classification of pore types in metal additive manufacturing

M. Vandecasteele, Heylen, R., Iuso, D., Thanki, A., Philips, W., Witvrouw, A., Verhees, D., and Booth, B. G., “Towards material and process agnostic features for the classification of pore types in metal additive manufacturing”, Materials & Design, vol. 227, p. 111757, 2023.

Sparse-view Medical Tomosynthesis via Mixed Scale Dense Convolutional Framelet Networks

L. F. Alves Pereira, De Beenhouwer, J., and Sijbers, J., “Sparse-view Medical Tomosynthesis via Mixed Scale Dense Convolutional Framelet Networks”, in IEEE International Symposium on Biomedical Imaging (ISBI), 2023, pp. 880-884.

3D X-ray radiography-based inspection of manufactured objects

A. Presenti, “3D X-ray radiography-based inspection of manufactured objects”, 2022.

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