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Tomography

Computed tomography (CT)

cuPARE: Parametric Reconstruction of Curved Fibres from Glass fibre-reinforced Composites

T. Elberfeld, Fröhler, B., Heinzl, C., Sijbers, J., and De Beenhouwer, J., “cuPARE: Parametric Reconstruction of Curved Fibres from Glass fibre-reinforced Composites”, Nondestructive Testing and Evaluation, 2022.

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Fiber orientation estimation by constrained spherical deconvolution of the anisotropic edge illumination x-ray dark field signal

B. Huyge, Jeurissen, B., De Beenhouwer, J., and Sijbers, J., “Fiber orientation estimation by constrained spherical deconvolution of the anisotropic edge illumination x-ray dark field signal”, in SPIE: Developments in X-Ray Tomography XIV, 2022, vol. 12242, p. 122420V .

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Edge Illumination Phase Contrast Simulations Using the OptiX GPU Ray Tracing Engine

N. Francken, Paramonov, P., Sijbers, J., and De Beenhouwer, J., “Edge Illumination Phase Contrast Simulations Using the OptiX GPU Ray Tracing Engine”, 5th International Conference on Tomography of Materials & Structures (ICTMS). 2022.

Virtual grating approach for Monte Carlo simulations of edge illumination-based x-ray phase contrast imaging

J. Sanctorum, Sijbers, J., and De Beenhouwer, J., “Virtual grating approach for Monte Carlo simulations of edge illumination-based x-ray phase contrast imaging”, Optics Express, vol. 31, no. 21, pp. 38695-38708, 2022.

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Alternative grating designs for cone-beam edge illumination X-ray phase contrast imaging

P. - J. Vanthienen, Sanctorum, J., Huyge, B., Six, N., Sijbers, J., and De Beenhouwer, J., “Alternative grating designs for cone-beam edge illumination X-ray phase contrast imaging”, in Proc. SPIE 12242, Developments in X-Ray Tomography XIV, San Diego, USA, 2022, vol. 12242, p. 122420Z.

Tabu-DART: A dynamic update strategy for efficient discrete algebraic reconstruction

D. Frenkel, Six, N., De Beenhouwer, J., and Sijbers, J., “Tabu-DART: A dynamic update strategy for efficient discrete algebraic reconstruction”, The Visual Computer, vol. 39, pp. 4671–4683, 2023.

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Probability of Detection applied to X-ray inspection using numerical simulations

M. Yosifov, Reiter, M., Heupl, S., Gusenbauer, C., Fröhler, B., R. Gutierrez, F. -, De Beenhouwer, J., Sijbers, J., Kastner, J., and Heinzl, C., “Probability of Detection applied to X-ray inspection using numerical simulations”, Nondestructive Testing and Evaluation, vol. 37, no. 5, pp. 536-551, 2022.

Evaluation of deeply supervised neural networks for 3D pore segmentation in additive manufacturing

D. Iuso, Chatterjee, S., Heylen, R., Cornelissen, S., De Beenhouwer, J., and Sijbers, J., “Evaluation of deeply supervised neural networks for 3D pore segmentation in additive manufacturing”, in SPIE Optical Engineering: Developments in X-Ray Tomography XIV , 2022, vol. 12242, p. 122421K.

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Joint reconstruction of attenuation, refraction and dark field X-ray phase contrasts using split Barzilai-Borwein steps

N. Six, Renders, J., De Beenhouwer, J., and Sijbers, J., “Joint reconstruction of attenuation, refraction and dark field X-ray phase contrasts using split Barzilai-Borwein steps”, in SPIE Optical Engineering: Developments in X-Ray Tomography XIV , 2022, vol. 12242, p. 122420O.

Optimization of a multi-source rectangular X-ray CT geometry for inline inspection

C. Bossuyt, De Beenhouwer, J., and Sijbers, J., “Optimization of a multi-source rectangular X-ray CT geometry for inline inspection”, in SPIE Optical Engineering: Developments in X-Ray Tomography XIV , 2022, vol. 12242, p. 1224219 .

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