Monte Carlo simulation of photon migration in 3D turbid media accelerated by graphics processing units

By Qianqian Fang, David A. Boas. Optics Express, 2009

974 citations recorded by the source corpus.

We report a parallel Monte Carlo algorithm accelerated by graphics processing units (GPU) for modeling time-resolved photon migration in arbitrary 3D turbid media. By taking advantage of the massively parallel threads and low-memory latency, this algorithm allows many photons to be simulated simultaneously in a GPU. To further improve the computational efficiency, we explored two parallel random number generators (RNG), including a floating-point-only RNG based on a chaotic lattice. An efficient scheme for boundary reflection was implemented, along with the functions for time-resolved imaging. For a homogeneous semi-infinite medium, good agreement was observed between the simulation output and the analytical solution from the diffusion theory. The code was implemented with CUDA programming language, and benchmarked under various parameters, such as thread number, selection of RNG and memory access pattern. With a low-cost graphics card, this algorithm has demonstrated an acceleration ratio above 300 when using 1792 parallel threads over conventional CPU computation. The acceleration ratio drops to 75 when using atomic operations. These results render the GPU-based Monte Carlo simulation a practical solution for data analysis in a wide range of diffuse optical imaging applications, such as human brain or small-animal imaging.

Subfields: Biomedical Engineering, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/72418a969890621cfe99e470889ed0bedd0dba98

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In Biomedical Engineering

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

GPU computing in medical physics: a review. (2011, neutral): “D media 47 or multiple layers of tissue.”
Time domain functional NIRS imaging for human brain mapping (2014, positive): “With the advent of parallelization on graphical processing units (Alerstam et al., 2008b; Fang and Boas, 2009; Ren et al., 2010), computational times have been reduced by up to 100 times, and researchers have revived the use of Monte Carlo methods (Dehaes et al., 2011a; Sassaroli and Martelli,…”
Influence of skin blood flow on near-infrared spectroscopy signals measured on the forehead during a verbal fluency task (2011, neutral): “We used a free software package for the Monte Carlo simulation (Monte Carlo eXtream, Fang and Boas, 2009), which was performed with one hundred million photons.”
Quantitative spectroscopic photoacoustic imaging: a review. (2012, neutral): “μðnþ1Þ a ðxÞ 1⁄4 HðxÞ ΦðnÞ1⁄2x; μðnÞ a ðxÞ ; (38)”
Review of Monte Carlo modeling of light transport in tissues (2013, methodology): “Fang et al.100 reported a parallel MC algorithm accelerated by GPU for the simulation of time-resolved photon propagation in an arbitrary 3-”
A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology (2014, methodology): “Monte Carlo methods have been applied as well (Hayakawa et al., 2001) and became very attractive with the advent of GPU based computation (Fang and Boas, 2009), which allowed for parallel simulation of many photons using a desktop PC. Signal analysis methods to extract the functional brain activity.”