Log in

X-ray imaging

Analysis of flat fields in edge illumination phase contrast imaging

B. Huyge, Sanctorum, J., Six, N., De Beenhouwer, J., and Sijbers, J., “Analysis of flat fields in edge illumination phase contrast imaging”, in 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), Nice, France, 2021, pp. 1310-1313.

Files:

FleXCT: a Flexible X-ray CT scanner with 10 degrees of freedom

B. De Samber, Renders, J., Elberfeld, T., Maris, Y., Sanctorum, J., Six, N., Liang, Z., De Beenhouwer, J., and Sijbers, J., “FleXCT: a Flexible X-ray CT scanner with 10 degrees of freedom”, Optics Express, vol. 29, no. 3, pp. 3438-3457, 2021.

Files:

To Recurse or not to Recurse A Low Dose CT Study

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.

Full-field X-ray orientation imaging using convex optimization and a discrete representation of six-dimensional position - orientation space

N. Roberto Vigano, “Full-field X-ray orientation imaging using convex optimization and a discrete representation of six-dimensional position - orientation space”, 2015.

X-ray phase contrast simulation for grating-based interferometry using GATE

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.

Files:

Extreme Sparse X-ray Computed Laminography Via Convolutional Neural Networks

L. F. Alves Pereira, De Beenhouwer, J., Kastner, J., and Sijbers, J., “Extreme Sparse X-ray Computed Laminography Via Convolutional Neural Networks”, in ICTAI 2020, 2020.

Files:

Projection-angle-dependent distortion correction in high-speed image-intensifier-based x-ray computed tomography

J. Sanctorum, Van Wassenbergh, S., Nguyen, V., De Beenhouwer, J., Sijbers, J., and Dirckx, J. J. J., “Projection-angle-dependent distortion correction in high-speed image-intensifier-based x-ray computed tomography”, Measurement Science and Technology, vol. 32, no. 035404, pp. 1-11, 2021.

Ringing Artefact Removal From Sparse View Tomosynthesis using Deep Neural Networks

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.

Files:

Analysis and Comparison of Algorithms for the Tomographic Reconstruction of Curved Fibres

B. Fröhler, Elberfeld, T., Möller, T., Hege, H. - C., De Beenhouwer, J., Sijbers, J., Kastner, J., and Heinzl, C., “Analysis and Comparison of Algorithms for the Tomographic Reconstruction of Curved Fibres”, Nondestructive Testing and Evaluation, vol. 35, no. 3, pp. 328-341, 2020.

A low-cost geometry calibration procedure for a modular cone-beam X-ray CT system

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.

Pages