ArticlePhysics in medicine and biology2021
DeepMC: a deep learning method for efficient Monte Carlo beamlet dose calculation by predictive denoising in magnetic resonance-guided radiotherapy.
Article in Physics in medicine and biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A unified deep-learning framework for enhanced patient-specific quality assurance of intensity-modulated radiation therapy plans.Medical physics · 2025Article
- Monte Carlo Simulations in Nanomedicine: Advancing Cancer Imaging and Therapy.Nanomaterials (Basel, Switzerland) · 2025Review
- Development and comprehensive clinical validation of a deep neural network for radiation dose modelling to enhance magnetic resonance imaging guided radiotherapy.Physics and imaging in radiation oncology · 2025Article
- A generalization performance study on the boosting radiotherapy dose calculation engine based on super-resolution.Zeitschrift fur medizinische Physik · 2024Article
- A deep learning based dynamic arc radiotherapy photon dose engine trained on Monte Carlo dose distributions.Physics and imaging in radiation oncology · 2024Article
- Sub-second photon dose prediction via transformer neural networks.Medical physics · 2023Article
- Prediction of dose deposition matrix using voxel features driven machine learning approach.The British journal of radiology · 2023Article
- Advances in Automated Treatment Planning.Seminars in radiation oncology · 2022Review
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Authors and funding
5 authors.
Funding
Abstract
Emerging magnetic resonance (MR) guided radiotherapy affords significantly improved anatomy visualization and, subsequently, more effective personalized treatment. The new therapy paradigm imposes significant demands on radiation dose calculation quality and speed, creating an unmet need for the acceleration of Monte Carlo (MC) dose calculation. Existing deep learning approaches to denoise the final plan MC dose fail to achieve the accuracy and speed requirements of large-scale beamlet dose calculation in the presence of a strong magnetic field for online adaptive radiotherapy planning. Our deep learning dose calculation method, DeepMC, addresses these needs by predicting low-noise dose from extremely noisy (but fast) MC-simulated dose and anatomical inputs, thus enabling significant acceleration. DeepMC simultaneously reduces MC sampling noise and predicts corrupted dose buildup at tissue-air material interfaces resulting from MR-field induced electron return effects. Here we demonstrate our model's ability to accelerate dose calculation for daily treatment planning by a factor of 38 over traditional low-noise MC simulation with clinically meaningful accuracy in deliverable dose and treatment delivery parameters. As a post-processing approach, DeepMC provides compounded acceleration of large-scale dose calculation when used alongside established MC acceleration techniques in variance reduction and graphics processing unit-based MC simulation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.