AI powered CBCT for improved Combination Cancer Therapy (AC3T)
Description
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.
Key data
Projectlead
Deputy Projectlead
Project team
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
Project partners
Varian Medical Systems Imaging Laboratory GmbH
Project status
completed, 05/2022 - 02/2025
Institute/Centre
Centre for Artificial Intelligence (CAI); Institute of Applied Mathematics and Physics (IAMP)
Funding partner
Innosuisse - Innovationsprojekt
Project budget
1'473'000 CHF
Publications
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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
