ArticlePhysics and imaging in radiation oncology2024
A deep learning based dynamic arc radiotherapy photon dose engine trained on Monte Carlo dose distributions.
Article in Physics and imaging in radiation oncology, 2024. 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.
- Interpretable graph-conditioned CNNs for dose-volume histogram prediction in radiotherapy.Medical physics · 2026Article
- 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 prediction of Monte Carlo dose distribution for heavy ion therapy.Physics and imaging in radiation oncology · 2025Article
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2 authors.
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Abstract
Background and purpose: Despite hardware acceleration, state-of-the-art Monte Carlo (MC) dose engines require considerable computation time to reduce stochastic noise. We developed a deep learning (DL) based dose engine reaching high accuracy at strongly reduced computation times. Materials and methods: Radiotherapy treatment plans and computed tomography scans were collected for 350 treatments in a variety of tumor sites. Dose distributions were computed using a MC dose engine for Results: DL dose evaluation was on average 82 times faster than MC computation at a 1 % accuracy setting. In voxels receiving at least 10 % of the maximum dose the overall global gamma pass rate using a 2 % and 2 mm criterion was 99.6 %, while mean local gamma values were accurate within 2 %. In the high dose region over 50 % of maximum the mean local gamma approached a 1 % accuracy. Conclusions: A DL based dose engine was implemented, able to accurately reproduce MC computed dynamic arc radiotherapy dose distributions at high speed.
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