Performance optimization of a tri-hybrid method for estimation of patient scatter into the EPID.

Abstract

On-treatment EPID images are contaminated with patient-generated scattered photons. If this component can be accurately estimated, its effect can be removed, and therefore a corresponding in vivo patient dose estimate will be more accurate. Our group previously developed a tri-hybrid (TH) algorithm to provide fast but accurate estimates of patient-generated photon scatter. The algorithm uses an analytical method to solve for singly-scattered photon fluence, a modified Monte Carlo hybrid method to solve for multiply-scattered photon fluence, and a pencil beam scatter kernel method to solve for electron interaction generated scattered photon fluence. However, for efficient clinical implementation, spatial and energy sampling must be optimized for speed while maintaining overall accuracy. In this work, the most significant sampling issues were examined, including spatial sampling settings for the patient voxel size, the number of Monte Carlo histories used in the modified hybrid MC method, scatter order sampling for the hybrid method, and also a range of energy spectrum sampling (i.e., energy bin sizes). The total predicted patient-scattered photon fluence entering the EPID was compared with full MC simulation (EGSnrc) for validation. Three phantoms were tested with 6 and 18 MV beam energies, field sizes of 4\xa0×\xa04, 10\xa0×\xa010, and 20\xa0×\xa020 cm2 , and source-to-imager distance of 140\xa0cm to develop a set of optimal sampling settings. With the recommended sampling, accuracy and precision of the total-scattered energy fluence of the TH patient scatter prediction method are within 0.9% and 1.2%, respectively, for all test cases compared with full MC simulation results. For the mean energy spectrum across the imaging plane, comparison of TH with full MC simulation showed 95% overlap. This study has optimized sampling settings so that they have minimal impact on patient scatter prediction accuracy while maintaining maximum execution speed, a critical step for future clinical implementation.