Metropolis Monte Carlo simulation scheme for fast scattered X-ray photon calculation in CT.
Metropolis Monte Carlo simulation scheme for fast scattered X-ray photon calculation in CT.
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DOI：
10.1364/oe.27.001262
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发表时间：
2019-01
期刊：
影响因子：
3.8
通讯作者：
Yuan Xu;Yusi Chen;Z. Tian;X. Jia;Linghong Zhou
中科院分区：
文献类型：
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作者：
Yuan Xu;Yusi Chen;Z. Tian;X. Jia;Linghong Zhou
蒙特卡罗(MC)方法能够精确地模拟物理相互作用和模拟几何结构，被认为是最准确的粒子输运模拟方法。通常，MC模拟是以逐个粒子的方式执行的。在CT探测器上计算散射X射线光子信号等问题中，传统的模拟方案效率较低，这主要是由于模拟了大量的光子而没有到达探测器。因此，花在这些光子上的计算资源被浪费了。为了解决这一问题，本研究提出了一种基于GPU的Metropolis MC(GMMC)，并提出了一种新的逐路径模拟方案，并以CT散射X射线光子计算为例验证了该方法的有效性。与传统的MC方法不同，gMMC使用Metropolis-Hasting算法对从X射线源到探测器的整个光子路径进行采样。逐路径模拟方案确保了每个采样事件对感兴趣信号的贡献，从而提高了整体效率。我们将gMMC与内部开发的基于GPU的MC工具gMCDRR进行基准比较，gMCDRR以传统的逐个粒子的方式执行模拟。GMMC在单纯幻象病例中达到加速倍数37~48倍，在真实病例中达到20~34倍。用gMCDRR和gMMC计算的结果与3%的平均误差吻合较好。
Monte Carlo (MC) method is commonly considered as the most accurate approach for particle transport simulation because of its capability to precisely model physics interactions and simulation geometry. Conventionally, MC simulation is performed in a particle-by-particle fashion. In certain problems such as computing scattered X-ray photon signal at a detector of CT, the conventional simulation scheme suffers from low efficiency mainly due to the fact that abundant photons are simulated but do not reach the detector. The computational resources spent on those photons are therefore wasted. To solve this problem, this study develops a novel GPU-based Metropolis MC (gMMC) with a novel path-by-path simulation scheme and demonstrates its effectiveness in an example problem of scattered X-ray photon calculation in CT. In contrast to the conventional MC approach, gMMC samples an entire photon path extending from the X-ray source to the detector using Metropolis-Hasting algorithm. The path-by-path simulation scheme ensures contribution of every sampled event to the signal of interest, improving overall efficiency. We benchmark gMMC against an in-house developed GPU-based MC tool, gMCDRR, which performs simulations in the conventional particle-by-particle fashion. gMMC reaches speed up factors of 37~48 times in simple phantom cases and 20-34 times in real patient cases. The results calculated by gMCDRR and gMMC agree well with average differences < 3%.