ArticlePhysics and imaging in radiation oncology2025
Development and comprehensive clinical validation of a deep neural network for radiation dose modelling to enhance magnetic resonance imaging guided radiotherapy.
Article in Physics and imaging in radiation oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Uncertainty estimation for reliable neural network-based radiotherapy dose modelling using Monte Carlo dropout, mean variance estimation and deep ensemble.Physics and imaging in radiation oncology · 2026Article
- Deep learning-based synthetic-CT-free photon dose calculation in MR-guided radiotherapy: A proof-of-concept study.Medical physics · 2025Article
- A proof-of-concept study of direct magnetic resonance imaging-based proton dose calculation for brain tumors via neural networks with Monte Carlo-comparable accuracy.Physics and imaging in radiation oncology · 2025Article
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7 authors.
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Abstract
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 (γ-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) γ-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 γ-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 γ-PR highlight the need for comprehensive test data, improved robustness and future built-in uncertainty estimation.
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