Abstract
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.
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General scientific summary. Presently, accurate dose evaluations in terms of local control, tumour recurrence and normal tissue complications have not been fully carried out, and require further investigation to realize highly precise proton beam therapy. Therefore, more accurate dose calculations are required. In clinical use, its computation time is also a significant factor in addition to its accuracy. To solve both problems, we implemented the simplified Monte Carlo (SMC) method on graphics processing unit (GPU) architecture under the computer unified device architecture platform. The GPU-based SMC was applied for four patients with head and neck, lung or prostate cancer. The computational time per beam arrangement using the GPU-based SMC for the clinical cases ranged from 9–67 s. The results demonstrate the successful application of the GPU-based SMC to clinical proton treatment planning. We can apply the GPU-based SMC to optimization of treatment plans and four-dimensional treatment analysis, which requires enormous dose calculations.