Development and comprehensive clinical validation of a deep neural network for radiation dose modelling to enhance magnetic resonance imaging guided radiotherapy

Background and purpose: 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. Materials and methods: 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 (gamma-PR) for 3 mm/3%, 2 mm/2%, and 1 mm/1% criteria were calculated to assess dose modelling accuracy. Performance was also tested in a standardized water phantom to evaluate basic radiation physics properties. Results: The neural network accurately modeled dose distributions in both patient and water phantom settings. Median (range) gamma-PR of 97.7% (87.5-100.0%), 89.1% (69.7-99.4%), and 60.8% (38.5-82.1%) were observed for treatment plans, and 97.1% (55.5-100.0%), 88.8% (38.8-99.7%), and 61.7% (17.9-94.4%) for individual segments, across the three criteria. Conclusion: High median gamma-PR and accurate modelling in both water phantom and clinical data demonstrate the high potential of neural networks for dose modelling. However, instances of lower gamma-PR highlight the need for comprehensive test data, improved robustness and future built-in uncertainty estimation.