Log in

Tomography

Computed tomography (CT)

A Trajectory Based Bottom-Up Volume Reconstruction Method for Atom Probe Tomography

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.

An adaptive probability map for the Discrete Algebraic Reconstruction Technique

D. Frenkel, De Beenhouwer, J., and Sijbers, J., “An adaptive probability map for the Discrete Algebraic Reconstruction Technique”, in 10th Conference on Industrial Computed Tomography (ICT 2020), 2020, pp. 1-6.

Deep learning-based 2D-3D sample pose estimation for X-ray 3DCT

A. Presenti, Bazrafkan, S., Sijbers, J., and De Beenhouwer, J., “Deep learning-based 2D-3D sample pose estimation for X-ray 3DCT”, in 10th Conference on Industrial Computed Tomography (ICT 2020), 2020.

poly-DART: A discrete algebraic reconstruction technique for polychromatic X-ray CT

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.

Files:

Deep learning based missing wedge artefact removal for electron tomography

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.

Aortic root sizing for transcatheter aortic valve implantation using a shape model parameterisation

B. Bosmans, Huysmans, T., Lopes, P., Verhoelst, E., Dezutter, T., de Jaegere, P., Sijbers, J., Vander Sloten, J., and Bosmans, J., “Aortic root sizing for transcatheter aortic valve implantation using a shape model parameterisation”, Medical & Biological Engineering & Computing, vol. 57, no. 10, pp. 2081–2092, 2019.

Files:

Mixed-Scale Dense Convolutional Neural Network based Improvement of Glass Fiber-reinforced Composite CT Images

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.

Fiber assignment by continuous tracking for parametric fiber reinforced polymer reconstruction

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.

Files:

A Visual Tool for the Analysis of Algorithms for Tomographic Fiber Reconstruction in Materials Science

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.

An Interactive Visual Comparison Tool for 3D Volume Datasets represented by Nonlinearly Scaled 1D Line Plots through Space-filling Curves

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.

Files:

Pages