THE COMPARISON OF 2D DOSE PATIENT-SPECIFIC QUALITY ASSURANCE BETWEEN MONTE CARLO-CONVOLUTION AND MODIFIED CLARKSON INTEGRATION ALGORITHM

Akbar Azzi; Supriyanto Ardjo Pawiro; Terry Mart

doi:10.21009/SPEKTRA.082.01

Authors

Akbar Azzi Department of Physics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Indonesia
Supriyanto Ardjo Pawiro Department of Physics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Indonesia
Terry Mart Department of Physics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Indonesia

DOI:

https://doi.org/10.21009/SPEKTRA.082.01

Keywords:

patient-specific quality assurance, log files, 2D dose calculation algorithm

Abstract

A sophisticated machine of radiotherapy treatment process follows the complexity of the quality assurance (QA) measurement. Non-measurement QA becomes one of the solutions to reduce the medical physicists’ workload. However, this method has not been clinically established. This study compared two non-measurement methods of patient-specific quality assurance (PSQA) to find the feasible algorithm for the adaptive radiotherapy process. Monte Carlo-based (MC) PSQA used a phase space file of the medical linear accelerator (Linac) to obtain the photon energy fluence and forward projected to the isoplane. In contrast, Modified Clarkson Integration-based (MCI) used a non-uniform fluence map in the isoplane. For the modulated intensity, we used a pair of the dynamic log files of the multileaf-collimator (MLC) and then employed them in the algorithms. The dose distributions of MC and MCI methods were compared to the treatment planning system (TPS) using gamma index analysis. We found that the gamma pass rates (GPR) for MC-TPS and MCI-TPS were 99.54% and 99.57%, respectively. Further, the dose distribution in the off-axis region for the MCI method showed lesser accuracy due to the higher secondary dose contribution. The linac log file information can be used and calculated into a 2D dose distribution using both MC and MCI methods, providing high-accuracy results.

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