National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba 277-8577, Japan.
Phys Med Biol. 2011 Nov 21;56(22):N287-94. doi: 10.1088/0031-9155/56/22/N03. Epub 2011 Oct 28.
We implemented the simplified Monte Carlo (SMC) method on graphics processing unit (GPU) architecture under the computer-unified device architecture platform developed by NVIDIA. The GPU-based SMC was clinically applied for four patients with head and neck, lung, or prostate cancer. The results were compared to those obtained by a traditional CPU-based SMC with respect to the computation time and discrepancy. In the CPU- and GPU-based SMC calculations, the estimated mean statistical errors of the calculated doses in the planning target volume region were within 0.5% rms. The dose distributions calculated by the GPU- and CPU-based SMCs were similar, within statistical errors. The GPU-based SMC showed 12.30-16.00 times faster performance than the CPU-based SMC. The computation time per beam arrangement using the GPU-based SMC for the clinical cases ranged 9-67 s. The results demonstrate the successful application of the GPU-based SMC to a clinical proton treatment planning.
我们在 NVIDIA 开发的计算机统一设备架构平台下,将简化蒙特卡罗(SMC)方法应用于图形处理单元(GPU)架构。基于 GPU 的 SMC 已在四位患有头颈部、肺部或前列腺癌的患者中进行了临床应用。我们将基于 GPU 和基于 CPU 的 SMC 的计算结果与计算时间和差异进行了比较。在基于 CPU 和 GPU 的 SMC 计算中,在计划靶区区域中计算剂量的估计平均统计误差在 0.5%rms 以内。基于 GPU 和 CPU 的 SMC 计算的剂量分布相似,在统计误差范围内。基于 GPU 的 SMC 的性能比基于 CPU 的 SMC 快 12.30-16.00 倍。使用基于 GPU 的 SMC 对临床病例进行每次射束布置的计算时间为 9-67 秒。结果表明,基于 GPU 的 SMC 已成功应用于临床质子治疗计划。