Room: AAPM ePoster Library
Purpose: Accurate treatment planning and dose delivery are crucial to ensuring effective radiation therapy treatments. We aim to develop a true MC model that can be implemented in our clinic as part of our routine patient-specific quality assurance.
Methods: A MC model of the Elekta VersaHD linear accelerator was developed for 6 MV photon beams using the EGSnrc/BEAMnrc user codes and doses were calculated in DOSXYZnrc. PDDs, beam profiles, and output factors were verified against commissioning data. A tool was created in MATLAB that allows for DICOM RP files to be generated based on the parameters of the plan delivered to the patient from Elekta’s high-definition machine log files. Four previously treated patients’ IMRT QA plans were selected for recalculation using MC with log file-based plans. They were compared to the IMRT QA measurements made with the PTW Octavius phantom and evaluated in Verisoft with a gamma criterion of 3%/2mm. The relevant gamma index statistics were compared with those from the patients’ initial treatment plans in the Pinnacle Treatment Planning System (TPS).
Results: All PDDs, profiles, and output factors agreed with commissioning data within 1%/1mm and had less than 1% statistical uncertainty. All patient plans recalculated with MC using machine log files had less than 3% statistical uncertainty. The four plans recalculated with MC had an average gamma passing rate of 97.9% when compared to measurements. On average, our MC model showed better agreement with measurements than the Pinnacle TPS when calculating dose in a homogenous phantom.
Conclusion: A MC model of the Elekta VersaHD linear accelerator has been tested and verified against commissioning data. The results of our MC calculations are in good agreement with the measured data. The results of this work allow for continued quality assurance of patient plan delivery throughout treatment, past the initial IMRT QA.
Funding Support, Disclosures, and Conflict of Interest: The project described was supported by the National Center for Advancing Translational Sciences, National Institutes of Health (Grant TL1 TR002647). This work is also supported by the Cancer Prevention and Research Institute of Texas (CPRIT) Research Training Award (RP170345).