Metropolis Monte Carlo simulation scheme for fast scattered X-ray photon calculation in CT

Yuan Xu, Yusi Chen, Zhen Tian, Xun Jia, Linghong Zhou

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


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%.

Original languageEnglish (US)
Pages (from-to)1262-1275
Number of pages14
JournalOptics Express
Issue number2
StatePublished - Jan 21 2019

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics


Dive into the research topics of 'Metropolis Monte Carlo simulation scheme for fast scattered X-ray photon calculation in CT'. Together they form a unique fingerprint.

Cite this