{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["33"],"submitter":["Schneider M"],"pubmed_abstract":["<h4>Background and purpose</h4>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.<h4>Materials and methods</h4>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 "],"journal":["Physics and imaging in radiation oncology"],"pagination":["100723"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11908596"],"repository":["biostudies-literature"],"pubmed_title":["Development and comprehensive clinical validation of a deep neural network for radiation dose modelling to enhance magnetic resonance imaging guided radiotherapy."],"pmcid":["PMC11908596"],"pubmed_authors":["Monnich D","Thorwarth D","Gutwein S","Fischer P","Schneider M","Gani C","Baumgartner CF"],"additional_accession":[]},"is_claimable":false,"name":"Development and comprehensive clinical validation of a deep neural network for radiation dose modelling to enhance magnetic resonance imaging guided radiotherapy.","description":"<h4>Background and purpose</h4>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.<h4>Materials and methods</h4>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 ","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Jan","modification":"2026-07-15T11:13:30.787Z","creation":"2025-04-06T10:47:33.312Z"},"accession":"S-EPMC11908596","cross_references":{"pubmed":["40093656"],"doi":["10.1016/j.phro.2025.100723"]}}