Improving the performance of direct Monte Carlo optimization for large tumor volumes

Abstract

Direct Monte Carlo Optimization (DMCO) is a powerful method for dose optimization with Monte Carlo accuracy and direct aperture optimization with simulated annealing. Recently, we presented quasi intensity modulated arc therapy (qIMAT), a step-and-shoot technique that simulates a rotational technique by using a high number of beams and reducing the number of segments. In the present work, we applied a combination of both techniques to optimize an anal cancer case. Because of the limited memory of standard computers, two techniques for reducing the size of the inverse kernel (IK) were investigated. The standard deviation degradation technique (SDDT) and the reduced resolution technique (RRT) were applied to a 7-field IMRT plan on the CarPet phantom. Several IKs with an estimated standard deviation (SD) of the MC-calculation of 5%, 10% and 15% and another three IKs with voxel size of 4, 8 and 16 mm were calculated. All IKs were optimized with DMCO; after optimization, a final dose calculation with 5% SD and 4 mm resolution was carried out. SDDT was a better compromise between plan quality and IK-size reduction than RRT. PTV homogeneity and dose sparing to the OAR was almost identical for SDDT, while for RRT the quality was degraded by low resolution. Therefore, SDDT was applied to the anal cancer case. The IK-file of a quasi-IMAT plan with 30 beams was calculated with XVMC with 15% SD and a voxel size of 4 mm. After optimization with DMCO using one segment per beam, a final dose calculation with 2% variance was performed. By comparing the DVHs of qIMAT with a 7-field IMRT (commercial therapy planning system) and with a 7-field IMRT (DMCO), qIMAT showed considerably advantages over IMRT in OARs dose sparing. In this way, the DMCO optimization with qIMAT of complex cases with large treatment volumes, such as anal cancer, are possible. Furthermore, for anal cancer, the comparison of qIMAT with IMRT showed that qIMAT can improve the plan quality.