蒙特卡罗剂量计算用的解析直线加速器源模型。II。基于GPU的Monte Carlo软件包中的模型应用和自动源调试
An Analytic Linear Accelerator Source Model for Monte Carlo dose calculations. II. Model Utilization in a GPU-based Monte Carlo Package and Automatic Source Commissioning
摘要：
我们最近建立了一个基于GPU的MC剂量引擎的分析源模型。在本文中，我们提出了一种采样策略，有效地利用这个源模型在基于GPU的剂量计算。我们的源模型是基于相空间环（PSR）的概念。这种环形结构可以有效地解释射束旋转对称性，但由于矩形钳口设置而不适用于剂量计算。因此，我们首先将PSR源模型转换为其相空间let（PSL）表示。然后在剂量计算中，对不同类型的子源分别进行采样。源采样和粒子输运迭代。因此，同时采样和传输的粒子是相同类型的，并且在能量上接近，以减轻GPU线程发散。我们还提出了一种自动调试方法来调整模型，以更好地代表临床直线加速器。引入加权因子来调整PSR的相对权重，通过求解具有非负约束的二次最小化问题来确定。我们使用PSL文件测试了我们的模型相对于以前的源模型的效率增益。由于避免了长时间的数据读取和传输，效率提高了1.70 ~ 4.41。调试问题可在约20秒内解决。通过比较使用委托模型和未委托模型计算的剂量，并在临床Varian Truebeam 6 MV射束下的水体模中的不同开放野中进行测量，来测试其有效性。在深度剂量曲线上，建立区的平均一致距离从0.04~0.28 cm提高到0.04~0.12 cm，建立区后的均方根剂量差从0.32%~0.67%降低到0.21%~ 0.48%。对于横向剂量分布，内束的RMS差从0.31%~2.0%减小到0.06%~0.78%，外束的RMS差从0.20%~1.25%减小到0.10%~0.51%。
英文摘要：
We recently built an analytical source model for GPU-based MC dose engine. In this paper, we present a sampling strategy to efficiently utilize this source model in GPU-based dose calculation. Our source model was based on a concept of phase-space-ring (PSR). This ring structure makes it effective to account for beam rotational symmetry, but not suitable for dose calculations due to rectangular jaw settings. Hence, we first convert PSR source model to its phase-space let (PSL) representation. Then in dose calculation, different types of sub-sources were separately sampled. Source sampling and particle transport were iterated. So that the particles being sampled and transported simultaneously are of same type and close in energy to alleviate GPU thread divergence. We also present an automatic commissioning approach to adjust the model for a good representation of a clinical linear accelerator . Weighting factors were introduced to adjust relative weights of PSRs, determined by solving a quadratic minimization problem with a non-negativity constraint. We tested the efficiency gain of our model over a previous source model using PSL files. The efficiency was improved by 1.70 ~ 4.41, due to the avoidance of long data reading and transferring. The commissioning problem can be solved in ~20 sec. Its efficacy was tested by comparing the doses computed using the commissioned model and the uncommissioned one, with measurements in different open fields in a water phantom under a clinical Varian Truebeam 6MV beam. For the depth dose curves, the average distance-to-agreement was improved from 0.04~0.28 cm to 0.04~0.12 cm for build-up region and the root-mean-square (RMS) dose difference after build-up region was reduced from 0.32%~0.67% to 0.21%~0.48%. For lateral dose profiles, RMS difference was reduced from 0.31%~2.0% to 0.06%~0.78% at inner beam and from 0.20%~1.25% to 0.10%~0.51% at outer beam.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
