Current Optics and Photonics

Optical Society of Korea (한국광학회)
권호 표지
DOI QR코드

DOI QR Code

GPU-based Monte Carlo Photon Migration Algorithm with Path-partition Load Balancing

  • Jeon, Youngjin (Department of Electronics and Information Engineering, Korea University Sejong Campus) ;
  • Park, Jongha (Department of Electronics and Information Engineering, Korea University Sejong Campus) ;
  • Hahn, Joonku (Department of Electronics and Information Engineering, Korea University Sejong Campus) ;
  • Kim, Hwi (Department of Electronics and Information Engineering, Korea University Sejong Campus)
  • Received : 2021.06.23
  • Accepted : 2021.09.23
  • Published : 2021.12.25
Download PDF
⟨ Previous Next ⟩

Abstract

A parallel Monte Carlo photon migration algorithm for graphics processing units that implements an improved load-balancing strategy is presented. Conventional parallel Monte Carlo photon migration algorithms suffer from a computational bottleneck due to their reliance on a simple load-balancing strategy that does not take into account the different length of the mean free paths of the photons. In this paper, path-partition load balancing is proposed to eliminate this computational bottleneck based on a mathematical formula that parallelizes the photon path tracing process, which has previously been considered non-parallelizable. The performance of the proposed algorithm is tested using three-dimensional photon migration simulations of a human skin model.

Keywords

Acknowledgement

This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2020-0-00981, Development of Digital Holographic Metrology Technology for Phase Retrieval).

References

  1. L. V. Wang and H.-I. Wu, Biomedical Optics: Principles and Imaging (John Wiley & Sons, NJ, USA. 2007).
  2. L. Wang and S. L. Jacques, Monte Carlo Modeling of Light Transport in Multi-layered Tissues in Standard C (M. D. Anderson Cancer Center, University of Texas, USA. 1992).
  3. S. Prahl, "Monte Carlo light scattering programs," (Oregon Medical Laser Center, Published date: 2018), https://omlc.org/software/mc/ (Accessed date: January 2018).
  4. N. Cao, M. Ortner, and A. Nehorai, "Solutions for diffuse optical tomography using the Feynman-Kac formula and interacting particle method," Proc. SPIE 6434, 643402 (2007). https://doi.org/10.1117/12.699067
  5. S. Pauli, R. N. Gantner, P. Arbenz, and A. Adelmann, "Multilevel Monte Carlo for the Laplace equation," BIT Numer. Math. 55, 1125-1143 (2015). https://doi.org/10.1007/s10543-014-0543-8
  6. M. Qassem and P. Kyriacou, "Reflectance near-infrared measurements for determining changes in skin barrier function and scattering in relation to moisturizer application," J. Biomed. Opt. 20, 095008 (2015). https://doi.org/10.1117/1.jbo.20.9.095008
  7. Y. Miyamae, Y. Yamakawa, M. Kawabata, and Y. Ozaki, "A noninvasive method for assessing interior skin damage caused by chronological aging and photoaging based on near-infrared diffuse reflection spectroscopy," Appl. Spectrosc. 62, 677-681 (2008). https://doi.org/10.1366/000370208784658156
  8. Y. Miyamae, M. Kawabata, Y. Yamakawa, J. Tsuchiya, and Y. Ozaki, "Non-invasive estimation of skin thickness by near infrared diffuse reflection spectroscopy-separate determination of epidermis and dermis thickness," J. Near Infrared Spectrosc. 20, 617-622 (2012). https://doi.org/10.1255/jnirs.1024
  9. M. Miyazawa and M. Sonoyama, "Second derivative near infrared studies on the structural characterisation of proteins," J. Near Infrared Spectrosc. 6, A253-A257 (1998). https://doi.org/10.1255/jnirs.204
  10. A. Lorber, K. Faber, and B. R. Kowalski, "Net analyte signal calculation in multivariate calibration," Anal. Chem. 69, 1620-1626 (1997). https://doi.org/10.1021/ac960862b
  11. B. Nadler and R. Coifman, "Partial least squares, Beer's law and the net analyte signal: statistical modeling and analysis," J. Chemom. 19, 45-54 (2005). https://doi.org/10.1002/cem.906
  12. S. A. Prahl, "A Monte Carlo model of light propagation in tissue," Proc. SPIE 10305, 1030509 (1989). https://doi.org/10.1117/12.2283590
  13. T. J. Farrell, M. S. Patterson, and B. Wilson, "A diffusion theory model of spatially resolved, steady-state diffuse reflectance for the noninvasive determination of tissue optical properties in vivo," Med. Phys. 19, 879-888 (1992). https://doi.org/10.1118/1.596777
  14. L. Wang, S. Ren, and X. Chen, "Comparative evaluations of the Monte Carlo-based light propagation simulation packages for optical imaging," J. Innov. Opt. Health Sci. 11, 1750017 (2018). https://doi.org/10.1142/S1793545817500171
  15. V. Periyasamy and M. Pramanik, "Monte Carlo simulation of light transport in turbid medium with embedded object-spherical, cylindrical, ellipsoidal or cuboidal objects embedded within multilayered tissues," J. Biomed. Opt. 19, 045003 (2014). https://doi.org/10.1117/1.JBO.19.4.045003
  16. H. Shen and G. Wang, "A tetrahedron-based inhomogeneous Monte Carlo optical simulator," Phys. Med. Biol. 55, 947-962 (2010). https://doi.org/10.1088/0031-9155/55/4/003
  17. H. Shen and G. Wang, "A study on tetrahedron-based inhomogeneous Monte Carlo optical simulation," Biomed. Opt. Express 2, 44-57 (2011). https://doi.org/10.1364/BOE.2.000044
  18. Q. Fang, "Mesh-based Monte Carlo method using fast raytracing in Plucker coordinates," Biomed. Opt. Express 1, 165-175 (2010). https://doi.org/10.1364/BOE.1.000165
  19. E. Alerstam, T. Svensson, and S. Andersson-Engel, "Parallel computing with graphics processing units for high-speed Monte Carlo simulation of photon migration," J. Biomed. Opt. 13, 060504 (2008). https://doi.org/10.1117/1.3041496
  20. E. Alerstam, W. C. Y. Lo, T. D. Han, J. Ross, S. Andersson-Engels, and L. Lilge, "Next-generation acceleration and code optimization for light transport in turbid media using GPUs," Biomed. Opt. Express 1, 658-675 (2010). https://doi.org/10.1364/BOE.1.000658
  21. Q. Fang, and D. A. Boas, "Monte Carlo simulation of photon migration in 3D turbid media accelerated by graphics processing units," Opt. Express 17, 20178-20190 (2009). https://doi.org/10.1364/OE.17.020178
  22. L. Yu, F. Nina-Paravecino, D. R. Kaeli, and Q. Fang, "Scalable and massively parallel Monte Carlo photon transport simulations for heterogeneous computing platforms," J. Bio. Opt. 23, 010504 (2018).