ArticlePhysics and imaging in radiation oncology2025
Feasibility of Monte Carlo-based patient-specific quality assurance in 1.5 Tesla magnetic resonance-guided online adaptive radiotherapy: a multi-institutional study.
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. Not yet cited in PubMed.
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Abstract
Introduction: To evaluate the feasibility of Monte Carlo (MC)-based patient-specific quality assurance (PSQA) for MR-guided online adaptive radiotherapy and to explore the potential to eliminate the post-delivery measurement-based PSQA. Material and methods: A total of 113 cases from two institutions, treated on MR-Linac machines, were included in the study. A customized GPU-accelerated, Monte Carlo-based secondary dose verification software (ART2Dose) was developed and integrated into the QA workflow, accounting for a 1.5 Tesla magnetic field. PSQA included ArcCheck (AC) delivery QA and online MC calculation-based QA. Reference plans underwent offline validation with AC and MC, while adapt-to-shape (ATS) plans were processed through MC and post-delivery QA. Gamma pass rates (GPR) with 3 %/2mm criteria were compared statistically across methods. Radcalc was applied to compare point dose difference with MC. Results: MC QA achieved GPRs of 97.5 % ± 2.0 % and 97.1 % ± 2.9 % for reference and ATS plans, comparable to AC QA (97.6 % ± 2.0 % and 96.9 % ± 3.0 %). Wilcoxon signed-rank test showed statistically significant differences between reference and ATS plan QA (p < 0.05), but a Pearson correlation coefficient of 0.76 confirmed a linear relationship for MC GPR. Lung cases exhibited lower GPRs with MC compared to AC QA. MC QA demonstrated supaireerior point dose agreement with TPS (1.7 % ± 1.2 %) compared to RadCalc (4.1 % ± 1.7 %). No significant differences were observed between institutions. Conclusion: MC-based QA is a robust tool for adaptive QA workflows in 1.5-T MR-Linac systems. It enhances efficiency and potentially supports the elimination of post-delivery measurement-based QA for adaptive plans.
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