ArticleMedical physics2025
Deep learning-based synthetic-CT-free photon dose calculation in MR-guided radiotherapy: A proof-of-concept study.
Article in Medical physics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Optimizing the delivery of radiotherapy with artificial intelligence.Nature reviews. Clinical oncology · 2026Review
- Deep learning-based synthetic-CT-free photon dose calculation in MR-guided radiotherapy: A proof-of-concept study.Medical physics · 2025Article
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Authors and funding
10 authors.
Funding
Abstract
backgroundIn magnetic resonance imaging (MRI)-guided online adaptive radiotherapy, MRI lacks tissue attenuation information necessary for accurate dose calculations. Although deep learning (DL)-based synthetic computed tomography (CT) generation models have been developed to obtain CT density information from MRI, they usually do not meet the requirement of real-time plan adaptation. PURPOSE: We propose a DL-based photon dose calculation method directly on 0.35 T MRI to skip synthetic CT generation and show its feasibility for prostate patient cases.
methodsThe 0.35 T planning MRI and deformed planning CT (registered to the planning MRI) of 34 prostate cancer patients treated with a 0.35 T magnetic resonance-linear accelerator (MR-Linac) were collected. The air cavities (ACs) in the abdominopelvic area of the deformed CT images were corrected based on manual AC contouring on the MRI. Monte Carlo (MC) dose simulations under a 0.35 T magnetic field were performed on the corrected CT images. All photon beams were simulated using a uniform field size of
resultsThe test results for all photon beams from the proposed models demonstrated a mean
conclusionsThe proposed method showed that DL-based dose calculation directly on MRI is feasible for prostate cases, which has the potential to simplify the procedure for MRI-only workflows and can be beneficial for real-time plan adaptation.
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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 Socratic graph.