%0 Journal Article
%A Schneider, Moritz
%A Gutwein, Simon
%A Mönnich, David
%A Gani, Cihan
%A Fischer, Paul
%A Baumgartner, Christian F
%A Thorwarth, Daniela
%T Development and comprehensive clinical validation of a deep neural network for radiation dose modelling to enhance magnetic resonance imaging guided radiotherapy.
%J Physics & Imaging in Radiation Oncology
%V 33
%@ 2405-6316
%C Amsterdam [u. a.]
%I Elsevier Science
%M DKFZ-2025-00673
%P 100723
%D 2025
%X Online adaptive magnetic resonance imaging (MRI)-guided radiotherapy requires fast dose calculation algorithms to reduce intra-fraction motion uncertainties and improve workflow efficiency. While Monte-Carlo simulations are precise but computationally intensive, neural networks promise fast and accurate dose modelling in strong magnetic fields. This study aimed to train and evaluate a deep neural network for dose modelling in MRI-guided radiotherapy using a comprehensive clinical dataset.A dataset of 6595 clinical irradiation segments from 125 1.5 T MRI-Linac radiotherapy plans for various tumors sites was used. A 3D U-Net was trained with 3961 segments using 3D imaging data and field parameters as input, Root Mean Squared Error and a custom loss function, with full Monte-Carlo simulations as ground truth. For 2656 segments from 50 patients, gamma pass rates (γ-PR) for 3 mm/3
%K Artificial intelligence (Other)
%K Dose calculation (Other)
%K MRI-guided radiotherapy (Other)
%K Online adaptive radiotherapy (Other)
%F PUB:(DE-HGF)16
%9 Journal Article
%$ pmid:40093656
%2 pmc:PMC11908596
%R 10.1016/j.phro.2025.100723
%U https://inrepo02.dkfz.de/record/300196