A technique for generating phase-space-based Monte Carlo beamlets in radiotherapy applications

Abstract

As radiotherapy treatment planning moves toward Monte Carlo (MC) based dose calculation methods, the MC beamlet is becoming an increasingly common optimization entity. At present, methods used to produce MC beamlets have utilized a particle source model (PSM) approach. In this work we outline the implementation of a phase-space-based approach to MC beamlet generation that is expected to provide greater accuracy in beamlet dose distributions. In this approach a standard BEAMnrc phase space is sorted and divided into beamlets with particles labeled using the inheritable particle history variable. This is achieved with the use of an efficient sorting algorithm, capable of sorting a phase space of any size into the required number of beamlets in only two passes. Sorting a phase space of five million particles can be achieved in less than 8 s on a single-core 2.2 GHz CPU. The beamlets can then be transported separately into a patient CT dataset, producing separate dose distributions (doselets). Methods for doselet normalization and conversion of dose to absolute units of Gy for use in intensity modulated radiation therapy (IMRT) plan optimization are also described.