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Clinical implementation of a GPU-based simplified Monte Carlo method for a treatment planning system of proton beam therapy.

机译:用于质子束治疗的治疗计划系统的基于GPU的简化Monte Carlo方法的临床实现。

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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.
机译:我们在NVIDIA开发的计算机统一设备架构平台下,在图形处理单元(GPU)架构上实现了简化的蒙特卡洛(SMC)方法。基于GPU的SMC已临床应用于四名患有头颈癌,肺癌或前列腺癌的患者。在计算时间和差异方面,将结果与传统的基于CPU的SMC获得的结果进行了比较。在基于CPU和GPU的SMC计算中,计划目标体积区域中计算剂量的估计平均统计误差在0.5%rms之内。在统计误差范围内,基于GPU和CPU的SMC计算出的剂量分布相似。基于GPU的SMC的性能比基于CPU的SMC快12.30-16.00倍。对于临床案例,使用基于GPU的SMC进行每个光束布置的计算时间为9-67 s。结果证明了基于GPU的SMC在临床质子治疗计划中的成功应用。

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