Dept. of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, United States of America.
Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD 20850, United States of America.
Phys Med Biol. 2022 Aug 30;67(17). doi: 10.1088/1361-6560/ac8716.
Monte Carlo (MC) codes are increasingly used for accurate radiotherapy dose calculation. In proton therapy, the accuracy of the dose calculation algorithm is expected to have a more significant impact than in photon therapy due to the depth-dose characteristics of proton beams. However, MC simulations come at a considerable computational cost to achieve statistically sufficient accuracy. There have been efforts to improve computational efficiency while maintaining sufficient accuracy. Among those, parallelizing particle transportation using graphic processing units (GPU) achieved significant improvements. Contrary to the central processing unit, a GPU has limited memory capacity and is not expandable. It is therefore challenging to score quantities with large dimensions requiring extensive memory. The objective of this study is to develop an open-source GPU-based MC package capable of scoring those quantities.We employed a hash-table, one of the key-value pair data structures, to efficiently utilize the limited memory of the GPU and score the quantities requiring a large amount of memory. With the hash table, only voxels interacting with particles will occupy memory, and we can search the data efficiently to determine their address. The hash-table was integrated with a novel GPU-based MC code, moqui.The developed code was validated against an MC code widely used in proton therapy, TOPAS, with homogeneous and heterogeneous phantoms. We also compared the dose calculation results of clinical treatment plans. The developed code agreed with TOPAS within 2%, except for the fall-off and regions, and the gamma pass rates of the results were >99% for all cases with a 2 mm/2% criteria.We can score dose-influence matrix and dose-rate on a GPU for a 3-field H&N case with 10 GB of memory using moqui, which would require more than 100 GB of memory with the conventionally used array data structure.
蒙特卡罗（MC）代码越来越多地用于精确的放射治疗剂量计算。在质子治疗中，由于质子束的深度剂量特性，预计剂量计算算法的准确性比光子治疗更为重要。然而，MC 模拟需要相当大的计算成本才能达到统计学上足够的准确性。已经有一些努力来提高计算效率，同时保持足够的准确性。其中，使用图形处理单元（GPU）并行化粒子输运取得了显著的改进。与中央处理器不同，GPU 的内存容量有限且不可扩展。因此，对于需要大量内存的高维数量进行评分具有挑战性。本研究的目的是开发一种能够对这些数量进行评分的基于开源 GPU 的 MC 软件包。我们采用了哈希表，这是一种键值对数据结构，以有效地利用 GPU 的有限内存并对需要大量内存的数量进行评分。有了哈希表，只有与粒子相互作用的体素才会占用内存，我们可以有效地搜索数据来确定它们的地址。哈希表与一种新的基于 GPU 的 MC 代码 moqui 集成。所开发的代码与质子治疗中广泛使用的 MC 代码 TOPAS 在同质和异质体模中进行了验证。我们还比较了临床治疗计划的剂量计算结果。除了下降区和区域外，所开发的代码与 TOPAS 的偏差在 2%以内，所有情况下的结果伽马通过率均大于 99%，符合 2 毫米/2%的标准。我们可以使用 moqui 在 GPU 上对 3 野头颈部病例进行剂量影响矩阵和剂量率评分，使用传统的数组数据结构需要超过 100GB 的内存。
