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首页> 外文期刊>Physics in medicine and biology. >A GPU implementation of EGSnrc's Monte Carlo photon transport for imaging applications.
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A GPU implementation of EGSnrc's Monte Carlo photon transport for imaging applications.

机译：EGSnrc的蒙特卡洛光子传输的GPU实现，用于成像应用。

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摘要

EGSnrc is a well-known Monte Carlo simulation package for coupled electron-photon transport that is widely used in medical physics application. This paper proposes a parallel implementation of the photon transport mechanism of EGSnrc for graphics processing units (GPUs) using NVIDIA's Compute Unified Device Architecture (CUDA). The implementation is specifically designed for imaging applications in the diagnostic energy range and does not model electrons. No approximations or simplifications of the original EGSnrc code were made other than using single floating-point precision instead of double precision and a different random number generator. To avoid performance penalties due to the random nature of the Monte Carlo method, the simulation was divided into smaller steps that could easily be performed in a parallel fashion suitable for GPUs. Speedups of 20 to 40 times for 64(3) to 256(3) voxels were observed while the accuracy of the simulation was preserved. A detailed analysis of the differences between the CUDA simulation and the original EGSnrc was conducted. The two simulations were found to produce equivalent results for scattered photons and an overall systematic deviation of less than 0.08% was observed for primary photons.
机译：EGSnrc是著名的蒙特卡洛模拟软件包，用于电子-光子耦合传输，已广泛用于医学物理学应用。本文提出了使用NVIDIA Compute Unified Device Architecture（CUDA）的图形处理单元（GPU）的EGSnrc光子传输机制的并行实现。该实现是专为诊断能量范围内的成像应用而设计的，并且不对电子建模。除了使用单浮点精度代替双精度和使用不同的随机数生成器外，没有对原始EGSnrc代码进行任何近似或简化。为了避免由于蒙特卡洛方法的随机性而导致的性能损失，将模拟分为较小的步骤，可以轻松地以适合于GPU的并行方式执行该步骤。观察到64（3）到256（3）体素的加速比是20到40倍，同时保留了模拟的准确性。对CUDA模拟与原始EGSnrc之间的差异进行了详细分析。发现这两个模拟对于散射光子产生了相同的结果，并且对于初级光子观察到的整体系统偏差小于0.08％。

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