AI powered CBCT for improved Combination Cancer Therapy (AC3T)
Beschreibung
The project enables a novel, combined, adaptive cancer therapy combining tumor treating field and radiation therapy due to significantly improved static (3D) and time-resolved (4D) low dose Cone Beam Computer Tomography images based on artificial intelligence image reconstruction algorithms.
Eckdaten
Projektleitung
Stellv. Projektleitung
Projektteam
Prof. Dr. Thilo Stadelmann, Prof. Dr. Rudolf Marcel Füchslin, Mohammadreza Amirian, Daniel Barco, Dr. Marc André Stadelmann, Martin Oswald, Ivo Herzig, Dr. Lijin Aryananda Blatter
Projektpartner
Varian Medical Systems Imaging Laboratory GmbH
Projektstatus
abgeschlossen, 05/2022 - 02/2025
Institut/Zentrum
Centre for Artificial Intelligence (CAI); Institut für Angewandte Mathematik und Physik (IAMP)
Drittmittelgeber
Innosuisse - Innovationsprojekt
Projektvolumen
1'473'000 CHF
Publikationen
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MInDI-3D : iterative deep learning in 3D for sparse-view cone beam computed tomography
2025 Barco, Daniel; Stadelmann, Marc; Oswald, Martin; Herzig, Ivo; Lichtensteiger, Lukas; Paysan, Pascal; Peterlik, Igor; Walczak, Michal; Menze, Bjoern; Schilling, Frank-Peter
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Artifact reduction in 3D and 4D cone-beam computed tomography images with deep learning - a review
2024 Amirian, Mohammadreza; Barco, Daniel; Herzig, Ivo; Schilling, Frank-Peter
