DIR3CT: Deep Image Reconstruction through X-Ray Projection-based 3D Learning of Computed Tomography Volumes
Beschreibung
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.
Eckdaten
Projektleitung
Prof. Dr. Frank-Peter Schilling, Dr. Stefan Scheib
Projektteam
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
Projektpartner
Varian Medical Systems Imaging Laboratory GmbH
Projektstatus
abgeschlossen, 02/2020 - 05/2022
Institut/Zentrum
Institut für Informatik (InIT); Centre for Artificial Intelligence (CAI); Institut für Angewandte Mathematik und Physik (IAMP)
Drittmittelgeber
Innovationsprojekt / Projekt Nr. 35244.1 IP-LS
Projektvolumen
1'128'000 CHF
Publikationen
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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
