GPU-based Monte Carlo radiotherapy dose calculation using phase-space sources

蒙特卡罗方法 计算机科学 计算科学 相空间 图形处理单元 物理 相(物质) 计算物理学 并行计算 数学 量子力学 热力学 统计
作者
R Townson,Xun Jia,Zhen Tian,Yan Jiang Graves,Sergei Zavgorodni,Steve Jiang
出处
期刊:Physics in Medicine and Biology [IOP Publishing]
卷期号:58 (12): 4341-4356 被引量:36
标识
DOI:10.1088/0031-9155/58/12/4341
摘要

A novel phase-space source implementation has been designed for GPU-based Monte Carlo dose calculation engines. Due to the parallelized nature of GPU hardware, it is essential to simultaneously transport particles of the same type and similar energies but separated spatially to yield a high efficiency. We present three methods for phase-space implementation that have been integrated into the most recent version of the GPU-based Monte Carlo radiotherapy dose calculation package gDPM v3.0. The first method is to sequentially read particles from a patient-dependent phase-space and sort them on-the-fly based on particle type and energy. The second method supplements this with a simple secondary collimator model and fluence map implementation so that patient-independent phase-space sources can be used. Finally, as the third method (called the phase-space-let, or PSL, method) we introduce a novel strategy to pre-process patient-independent phase-spaces and bin particles by type, energy and position. Position bins located outside a rectangular region of interest enclosing the treatment field are ignored, substantially decreasing simulation time. The three methods were validated in absolute dose against BEAMnrc/DOSXYZnrc and compared using gamma-index tests (2%/2mm above the 10% isodose). It was found that the PSL method has the optimal balance between accuracy and efficiency and thus is used as the default method in gDPM v3.0. Using the PSL method, open fields of 4x4, 10x10 and 30x30 cm2 in water resulted in gamma passing rates of 99.96%, 99.92% and 98.66%, respectively. Relative output factors agreed within 1%. An IMRT patient plan using the PSL method resulted in a passing rate of 97%, and was calculated in 50 seconds using a single GPU compared to 8.4 hours (per CPU) for BEAMnrc/DOSXYZnrc.

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