(Sunday, 7/29/2018) 3:00 PM - 6:00 PM
Room: Exhibit Hall
Purpose: To establish a Monte Carlo model of an active scanning proton therapy treatment beam installed at the Maryland Proton Treatment Center (MPTC).
Methods: We used TOPAS MC code to simulate the active scanning proton therapy beam. First, we matched the simulated energy and energy distribution of the depth dose curves to those measured at MPTC. Next, we matched the beam divergence by using spot size measurements around the isocenter and simulations to account for the intrinsic beam divergence and divergence due to scatter in air. Parameterized functions were created for the facility to convert the patient-specific DICOM plans to match the measured energy and energy distribution, spot size, and absolute dose. Finally, automated post-processing scripts were created to combine voxel-based dose into organ-based dose based on the DICOM RT structure file. The established methods were then applied to normal tissue dose calculations for a medulloblastoma proton treatment plans created on one- and five-year-old ICRP phantoms.
Results: Energy and optical parameters of the system were modeled and matched using a set of proton depth-dose profiles and spot sizes measurements (Figure 1) for 19 therapeutic energies. Preliminary calculations of organ doses for the five-year-old phantom are shown in Table 1.
Conclusion: Utilizing the beam model established in this study, it is possible to reconstruct patient-specific normal tissue doses under active scanning proton therapy in MPTC. This allows for not only calculating the main dose by the proton beam, but also the doses produced by scatter radiation not typically accounted for during treatment planning. In addition, our beam model also makes it possible to investigate the varying LET and the differences that it may produce between TPS dose and actual RBE dose delivered.
Funding Support, Disclosures, and Conflict of Interest: This work was funded by the intramural program of the National Institutes of Health, National Cancer Institute, Division of Cancer Epidemiology and Genetics. The contents are solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
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