Feasibility of Monte Carlo-based patient-specific quality assurance in 1.5 Tesla magnetic resonance-guided online adaptive radiotherapy: a multi-institutional study - Ruiqi Li; Yao Zhao; Jingying Lin; Tsuicheng Chiu; Weiguo Lu; Jinzhong Yang; Mu-Han Lin (Physics and Imaging in Radiation Oncology, 2025)
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.