A transit portal dosimetry method for respiratory gating quality assurance with a dynamic 3D printed tumor phantom

Backgrounds: Respiratory gating is one of the motion management techniques that is used to deliver radiation dose to a tumor at a specific position under free breathing. However, due to the dynamic feedback process of this approach, regular equipment quality assurance (QA) and patient-specific QA checks need to be performed. This work proposes a new QA methodology using electronic portal imaging detector (EPID) to determine the target localization accuracy of phase gating. Methods: QA tools comprising 3D printed spherical tumor phantoms, programmable stages, and an EPID detector are characterized and assembled. Algorithms for predicting portal dose (PD) through moving phantoms are developed and verified using gamma analysis for two spherical tumor phantoms (2 cm and 4 cm), two different 6 MV volumetric modulated arc therapy plans, and two different gating windows (30%–70% and 40%–60%). Comparison between the two gating windows is then performed using the Wilcoxon signed-rank test. An optimizer routine, which is used to determine the optimal window, based on maximal gamma passing rate (GPR), was applied to an actual breathing curve and breathing plan. This was done to ascertain if our method yielded a similar result with the actual gating window. Results: High GPRs of more than 97% and 91% were observed when comparing the predicted PD with the measured PD in moving phantom at 2 mm/2% and1 mm/1% levels, respectively. Analysis of gamma heatmaps shows an excellent agreement with the tumor phantom. The GPR of 40%–60% PD was significantly lower than that of the 30%–70%PD at the 1 mm/1% level (p=0.0064). At the 2 mm/2% level, no significant differences were observed. The optimizer routine could accurately predict the center of the gating window to within a 10% range. Conclusion: We have successfully performed and verified a new method for QA with the use of a moving phantom with EPID for phase gating with real-time position management. Published version This work was supported by SingHealth Duke-NUS Academic Medicine and National Health Innovation Centre Singapore Joint MedTech grant (AM-NHIC/JMT006/2020) and Duke-NUS Oncology Aca-demic Clinical Programme Proton Research Fund(08/FY2021/EX(SL)/92-A146, 08/FY2020/EX(SL)/76-A152) grants.