Abstract
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%.
Highlights
Monte Carlo (MC) simulation [1,2] was first proposed in the 1940s to investigate radiation shielding and distances that neutrons travel through various materials
It has become one of the most important numerical algorithms to handle problems in biomedical field [3,4,5,6] that requires particle transport simulations in a high degree of accuracy. It is commonly considered as the golden standard particle transport simulation approach [7] because of faithful computations based on fundamental physics principles and flexibility to handle simulation geometry
Particle travels from one interaction site to another followed by scattering toward a certain direction governed by differential cross section (DCS) functions
Summary
Monte Carlo (MC) simulation [1,2] was first proposed in the 1940s to investigate radiation shielding and distances that neutrons travel through various materials. Existing MC packages for particle transport simulation commonly perform computations via a particle-by-particle scheme [11,12,13,14,15] In this scheme, particle travels from one interaction site to another followed by scattering toward a certain direction governed by differential cross section (DCS) functions. Driven by the desire to improve efficiency by solving this problem, we propose an innovative MC scheme with a path-by-path sampling method realized through a Metropolis algorithm [20,21,22] It is fundamentally different from the conventional approach sampling a particle and following it sequentially through a series of physical interactions.
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