Complementary roles of GPU-accelerated Monte Carlo and ArcCHECK in TomoTherapy quality assurance

The complex dose delivery mechanisms of TomoTherapy (TOMO) demand rigorous patient-specific quality assurance (PSQA). This study systematically evaluates the relationship between ArcCHECK measurements and GPU-accelerated Monte Carlo (GPU-MC) calculations for nasopharyngeal carcinoma (NPC) TOMO plans across multiple gamma criteria, aiming to delineate their respective strengths and inform an optimized verification strategy. A retrospective analysis was conducted on 317 TOMO plans for NPC, each optimized using the Accuray Precision Treatment Planning System. Patient-specific dose verification was performed using ArcCHECK measurements and independent MC calculations implemented through PlanQA. Gamma passing rates (GPRs) were evaluated under nine different conditions, including both same-criterion and cross-criterion comparisons. To assess differences, agreement, and correlations between methods, statistical analyses were conducted using the Wilcoxon signed-rank test, Bland-Altman analysis, and Spearman correlation. Under identical Gamma criteria, there was no statistically significant difference in GPR between ArcCHECK and MC. However, cross-criterion comparisons revealed marked discrepancies, highlighting the criterion-dependent nature of GPR outcomes. Lenient standards typically exhibit good consistency and relatively minor deviations. Furthermore, the correlations among all combinations of these standards can be considered negligible. The comparable performance of GPU-MC and ArcCHECK under conventional criteria (3%/3 mm, 3%/2 mm) validates ArcCHECK's established role in verifying delivery fidelity. Crucially, under the stringent 2%/2 mm criterion where ArcCHECK passing rates decline, MC provides critical diagnostic power to differentiate between discrepancies originating from the dose calculation algorithm and those arising from the physical delivery process. For TOMO PSQA, GPU-MC and ArcCHECK are complementary. An integrated approach leveraging both methods is therefore recommended.