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一种用于基于GPU的蒙特卡罗剂量计算的解析线性加速器源模型。

An analytic linear accelerator source model for GPU-based Monte Carlo dose calculations.

作者信息

Tian Zhen, Li Yongbao, Folkerts Michael, Shi Feng, Jiang Steve B, Jia Xun

机构信息

Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.

出版信息

Phys Med Biol. 2015 Oct 21;60(20):7941-67. doi: 10.1088/0031-9155/60/20/7941. Epub 2015 Sep 29.

Abstract

Recently, there has been a lot of research interest in developing fast Monte Carlo (MC) dose calculation methods on graphics processing unit (GPU) platforms. A good linear accelerator (linac) source model is critical for both accuracy and efficiency considerations. In principle, an analytical source model should be more preferred for GPU-based MC dose engines than a phase-space file-based model, in that data loading and CPU-GPU data transfer can be avoided. In this paper, we presented an analytical field-independent source model specifically developed for GPU-based MC dose calculations, associated with a GPU-friendly sampling scheme. A key concept called phase-space-ring (PSR) was proposed. Each PSR contained a group of particles that were of the same type, close in energy and reside in a narrow ring on the phase-space plane located just above the upper jaws. The model parameterized the probability densities of particle location, direction and energy for each primary photon PSR, scattered photon PSR and electron PSR. Models of one 2D Gaussian distribution or multiple Gaussian components were employed to represent the particle direction distributions of these PSRs. A method was developed to analyze a reference phase-space file and derive corresponding model parameters. To efficiently use our model in MC dose calculations on GPU, we proposed a GPU-friendly sampling strategy, which ensured that the particles sampled and transported simultaneously are of the same type and close in energy to alleviate GPU thread divergences. To test the accuracy of our model, dose distributions of a set of open fields in a water phantom were calculated using our source model and compared to those calculated using the reference phase-space files. For the high dose gradient regions, the average distance-to-agreement (DTA) was within 1 mm and the maximum DTA within 2 mm. For relatively low dose gradient regions, the root-mean-square (RMS) dose difference was within 1.1% and the maximum dose difference within 1.7%. The maximum relative difference of output factors was within 0.5%. Over 98.5% passing rate was achieved in 3D gamma-index tests with 2%/2 mm criteria in both an IMRT prostate patient case and a head-and-neck case. These results demonstrated the efficacy of our model in terms of accurately representing a reference phase-space file. We have also tested the efficiency gain of our source model over our previously developed phase-space-let file source model. The overall efficiency of dose calculation was found to be improved by ~1.3-2.2 times in water and patient cases using our analytical model.

摘要

最近,在图形处理单元(GPU)平台上开发快速蒙特卡罗(MC)剂量计算方法引起了很多研究兴趣。出于准确性和效率的考虑,一个好的直线加速器(linac)源模型至关重要。原则上,对于基于GPU的MC剂量引擎而言,解析源模型应比基于相空间文件的模型更受青睐,因为这样可以避免数据加载和CPU - GPU数据传输。在本文中,我们提出了一种专门为基于GPU的MC剂量计算开发的与场无关的解析源模型,并结合了一种对GPU友好的采样方案。我们提出了一个关键概念,即相空间环(PSR)。每个PSR包含一组类型相同、能量相近且位于上准直器上方相空间平面上一个窄环内的粒子。该模型对每个初级光子PSR、散射光子PSR和电子PSR的粒子位置、方向和能量的概率密度进行了参数化。采用一个二维高斯分布模型或多个高斯分量模型来表示这些PSR的粒子方向分布。开发了一种方法来分析参考相空间文件并推导相应模型参数。为了在GPU上的MC剂量计算中有效使用我们的模型,我们提出了一种对GPU友好的采样策略,该策略确保同时采样和传输的粒子类型相同且能量相近,以减轻GPU线程发散。为了测试我们模型的准确性,使用我们的源模型计算了水模体中一组开放射野的剂量分布,并与使用参考相空间文件计算的结果进行比较。对于高剂量梯度区域,平均距离一致性(DTA)在1毫米以内,最大DTA在2毫米以内。对于相对低剂量梯度区域,均方根(RMS)剂量差异在1.1%以内,最大剂量差异在1.7%以内。输出因子的最大相对差异在0.5%以内。在IMRT前列腺患者病例和头颈部病例中,采用2%/2毫米标准的三维伽马指数测试通过率超过98.5%。这些结果证明了我们的模型在准确表示参考相空间文件方面的有效性。我们还测试了我们的源模型相对于我们之前开发的相空间let文件源模型的效率提升。发现在水模体和患者病例中,使用我们的解析模型,剂量计算的总体效率提高了约1.3 - 2.2倍。

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