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. Stefan Scheib, Prof. Dr. Frank-Peter Schilling
Project team
Mohammadreza Amirian, Dr. Peter Eggenberger Hotz, Prof. Dr. Rudolf Marcel Füchslin, Ivo Herzig, Dr. Lukas Lichtensteiger, Dr. Javier Montoya, Marco Morf, Dr. Pascal Paysan, Dr. Igor Peterlik, Prof. Dr. Thilo Stadelmann
Project partners
Varian Medical Systems Imaging Laboratory GmbH
Project status
completed, 02/2020 - 05/2022
Funding partner
Innovationsprojekt / Projekt Nr. 35244.1 IP-LS
Project budget
1'128'000 CHF
Publications
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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
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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
