Faculty, Staff and Student Publications
Publication Date
7-1-2025
Journal
Physics and Imaging in Radiation Oncology
DOI
10.1016/j.phro.2025.100800
PMID
40687303
PMCID
PMC12273433
PubMedCentral® Posted Date
6-25-2025
PubMedCentral® Full Text Version
Post-print
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.
Keywords
Adaptive radiotherapy, Quality assurance, Monte Carlo, MR-Linac
Published Open-Access
yes
Recommended Citation
Li, Ruiqi; Zhao, Yao; Lin, Jingying; et al., "Feasibility of Monte Carlo-Based Patient-Specific Quality Assurance in 15 Tesla Magnetic Resonance-Guided Online Adaptive Radiotherapy: A Multi-Institutional Study" (2025). Faculty, Staff and Student Publications. 5174.
https://digitalcommons.library.tmc.edu/uthgsbs_docs/5174
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