DIR3CT: Deep Image Reconstruction through X-Ray Projection-based 3D Learning of Computed Tomography Volumes
Description
Project DIR3CT aims at improving the image quality of CBCT images by deep learning (DL) the 3D reconstruction from X-ray images end-to-end. This enables a novel CBCT product to be used during radiation therapy and will allow the use of these images for adaptive treatment.
Key data
Projectlead
Dr. Frank-Peter Schilling, Dr. Stefan Scheib
Project team
Prof. Dr. Thilo Stadelmann, Mohammadreza Amirian, Prof. Dr. Rudolf Marcel Füchslin, Dr. Lukas Lichtensteiger, Dr. Javier Montoya, Dr. Peter Eggenberger Hotz, Ivo Herzig, Marco Morf, Dr. Pascal Paysan, Dr. Igor Peterlik
Project partners
Varian Medical Systems Imaging Laboratory GmbH
Project status
completed, 02/2020 - 05/2022
Institute/Centre
Institute of Computer Science (InIT); Centre for Artificial Intelligence (CAI); Institute of Applied Mathematics and Physics (IAMP)
Funding partner
Innovationsprojekt / Projekt Nr. 35244.1 IP-LS
Project budget
1'128'000 CHF
Publications
-
Mitigation of motion-induced artifacts in cone beam computed tomography using deep convolutional neural networks
2023 Amirian, Mohammadreza; Montoya-Zegarra, Javier A.; Herzig, Ivo; Eggenberger Hotz, Peter; Lichtensteiger, Lukas; Morf, Marco; Züst, Alexander; Paysan, Pascal; Peterlik, Igor; Scheib, Stefan; Füchslin, Rudolf Marcel; Stadelmann, Thilo; Schilling, Frank-Peter
-
Deep learning-based simultaneous multi-phase deformable image registration of sparse 4D-CBCT
2022 Herzig, Ivo; Paysan, Pascal; Scheib, Stefan; Züst, Alexander; Schilling, Frank-Peter; Montoya, Javier; Amirian, Mohammadreza; Stadelmann, Thilo; Eggenberger Hotz, Peter; Füchslin, Rudolf Marcel; Lichtensteiger, Lukas
School of Engineering
Technikumstrasse 71
8400 Winterthur