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Background Absolute dosimetry measurement is an integral part of Treatment Planning System (TPS) commissioning and it involves measuring the integrated absorbed dose to water for all energies in a pencil beam scanning delivery system. During the commissioning of Singapore's first proton therapy center, a uniform scanned field with an Advanced Markus chamber method was employed for this measurement, and a large dose fluctuation of at least 5% was observed for 10% of the energy layers during repeated measurements. Purpose This study aims to understand the root cause of this fluctuation by relating the actual delivered spot information in the log file with the charge measurement by the ion chambers. Methods A dedicated pencil beam dose algorithm was developed, taking into account the log file parameters, to calculate the dose for a single energy layer in a homogeneous water phantom. Three energies, 70.2, 182.7, and 228.7 MeV were used in this study, with the 182.7 MeV energy exhibiting large dose fluctuation. The dose fluctuation was investigated as a function of detector's sizes (pinpoint 3D, Advanced Markus, PTW 34070, and PTW 34089) and water depth (2 , 6, and 20 cm). Twelve ion chambers measurements were performed for each chamber and depth. The comparison of the theoretically predicted integrated dose and the charge measurement served as a validation of the algorithm. Results About 5.9% and 9.6% dose fluctuation were observed in Advanced Markus and pinpoint 3D measurements at 2 cm depth for 182.7 MeV, while fluctuation of 1.6% and 1.1% were observed in Advanced Markus with 228.7 and 70.2 MeV at similar depth. Fluctuation of less than 0.1% was observed for PTW34070 and PTW 34089 for all energies. The fluctuation was found to diminish with larger spot size at 20 cm depth to 1.3% for 182.7 MeV. The theoretical and measured charge comparison showed a high linear correlation of R2>0.80${R^2} > 0.80$ for all datasets, indicating the fluctuation originated from the delivered spot characteristics. The cause of fluctuation was identified to be due to the spill change occurring close to the detector, and since the spot positional deviation profiles were different between two spills, this resulted in local hot spots between columns of spots. The actual position of spill change varies randomly during measurement, which led to a random occurrence of hot spot within the detector's sensitive volume and a fluctuating dose measurement. Conclusion This is the first report of a dose fluctuation greater than 5% in absolute dosimetry measurement with a uniform scanned field and the cause of the fluctuation has been conclusively determined. It is important to choose the MU and scanning pattern carefully to avoid spill change happening when the spot delivery is near the detector.

ObjectiveThis work aims to use machine learning models to predict gamma passing rate of portal dosimetry quality assurance with log file derived features. This allows daily treatment monitoring for patients and reduce wear and tear on EPID detectors to save cost and prevent downtime.Methods578 VMAT trajectory log files selected from prostate, lung and spine SBRT were used in this work. Four machine learning models were explored to identify the best performing regression model for predicting gamma passing rate within each sub-site and the entire unstratified data. Predictors used in these models comprised of hand-crafted log file-derived features as well as modulation complexity score. Cross validation was used to evaluate the model performance in terms of R2 and RMSE.ResultUsing gamma passing rate of 1%/1mm criteria and entire dataset, LASSO regression has a R2 of 0.121 ± 0.005 and RMSE of 4.794 ± 0.013%, SVM regression has a R2 of 0.605 ± 0.036 and RMSE of 3.210 ± 0.145%, Random Forest regression has a R2 of 0.940 ± 0.019 and RMSE of 1.233 ± 0.197%. XGBoost regression has the best performance with a R2 and RMSE value of 0.981 ± 0.015 and 0.652 ± 0.276%, respectively.ConclusionLog file-derived features can predict gamma passing rate of portal dosimetry with an average error of less than 2% using the 1%/1mm criteria. This model can potentially be applied to predict the patient specific QA results for every treatment fraction.

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% and 1 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.

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