DIR3CT: Deep Image Reconstruction through X-Ray Projection-based 3D Learning of Computed Tomography Volumes
Auf einen Blick
- Projektleiter/in : Dr. Stefan Scheib, Dr. Frank-Peter Schilling
- Projektteam : 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
- Projektvolumen : CHF 1'128'000
- Projektstatus : abgeschlossen
- Drittmittelgeber : Innosuisse (Innovationsprojekt / Projekt Nr. 35244.1 IP-LS)
- Kontaktperson : Frank-Peter Schilling
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.
Publikationen
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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,
2022.
Deep learning-based simultaneous multi-phase deformable image registration of sparse 4D-CBCT [Poster].
In:
AAPM Annual Meeting, Washington, DC, USA, 10-14 July 2022.
American Association of Physicists in Medicine.
S. e325-e326.
Verfügbar unter: https://doi.org/10.1002/mp.15769