OSA's Digital Library

Biomedical Optics Express

Biomedical Optics Express

  • Editor: Joseph A. Izatt
  • Vol. 4, Iss. 11 — Nov. 1, 2013
  • pp: 2667–2672

Accelerated rescaling of single Monte Carlo simulation runs with the Graphics Processing Unit (GPU)

Owen Yang and Bernard Choi  »View Author Affiliations


Biomedical Optics Express, Vol. 4, Issue 11, pp. 2667-2672 (2013)
http://dx.doi.org/10.1364/BOE.4.002667


View Full Text Article

Enhanced HTML    Acrobat PDF (918 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

Abstract: To interpret fiber-based and camera-based measurements of remitted light from biological tissues, researchers typically use analytical models, such as the diffusion approximation to light transport theory, or stochastic models, such as Monte Carlo modeling. To achieve rapid (ideally real-time) measurement of tissue optical properties, especially in clinical situations, there is a critical need to accelerate Monte Carlo simulation runs. In this manuscript, we report on our approach using the Graphics Processing Unit (GPU) to accelerate rescaling of single Monte Carlo runs to calculate rapidly diffuse reflectance values for different sets of tissue optical properties. We selected MATLAB to enable non-specialists in C and CUDA-based programming to use the generated open-source code. We developed a software package with four abstraction layers. To calculate a set of diffuse reflectance values from a simulated tissue with homogeneous optical properties, our rescaling GPU-based approach achieves a reduction in computation time of several orders of magnitude as compared to other GPU-based approaches. Specifically, our GPU-based approach generated a diffuse reflectance value in 0.08ms. The transfer time from CPU to GPU memory currently is a limiting factor with GPU-based calculations. However, for calculation of multiple diffuse reflectance values, our GPU-based approach still can lead to processing that is ~3400 times faster than other GPU-based approaches.

© 2013 Optical Society of America

OCIS Codes
(110.7050) Imaging systems : Turbid media
(170.3660) Medical optics and biotechnology : Light propagation in tissues
(170.5280) Medical optics and biotechnology : Photon migration
(200.4960) Optics in computing : Parallel processing

ToC Category:
Optics of Tissue and Turbid Media

History
Original Manuscript: June 28, 2013
Revised Manuscript: October 14, 2013
Manuscript Accepted: October 18, 2013
Published: October 29, 2013

Virtual Issues
Advances in Optics for Biotechnology, Medicine and Surgery (2013) Biomedical Optics Express

Citation
Owen Yang and Bernard Choi, "Accelerated rescaling of single Monte Carlo simulation runs with the Graphics Processing Unit (GPU)," Biomed. Opt. Express 4, 2667-2672 (2013)
http://www.opticsinfobase.org/boe/abstract.cfm?URI=boe-4-11-2667


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  1. R. Hennessy, S. L. Lim, M. K. Markey, and J. W. Tunnell, “Monte Carlo lookup table-based inverse model for extracting optical properties from tissue-simulating phantoms using diffuse reflectance spectroscopy,” J. Biomed. Opt.18(3), 037003 (2013). [CrossRef] [PubMed]
  2. A. M. Laughney, V. Krishnaswamy, T. B. Rice, D. J. Cuccia, R. J. Barth, B. J. Tromberg, K. D. Paulsen, B. W. Pogue, and W. A. Wells, “System analysis of spatial frequency domain imaging for quantitative mapping of surgically resected breast tissues,” J. Biomed. Opt.18(3), 036012 (2013). [CrossRef] [PubMed]
  3. A. Kim, M. Khurana, Y. Moriyama, and B. C. Wilson, “Quantification of in vivo fluorescence decoupled from the effects of tissue optical properties using fiber-optic spectroscopy measurements,” J. Biomed. Opt.15(6), 067006 (2010). [CrossRef] [PubMed]
  4. R. B. Saager, D. J. Cuccia, S. Saggese, K. M. Kelly, and A. J. Durkin, “Quantitative fluorescence imaging of protoporphyrin IX through determination of tissue optical properties in the spatial frequency domain,” J. Biomed. Opt.16(12), 126013 (2011). [CrossRef] [PubMed]
  5. A. K. Glaser, S. C. Kanick, R. Zhang, P. Arce, and B. W. Pogue, “A GAMOS plug-in for GEANT4 based Monte Carlo simulation of radiation-induced light transport in biological media,” Biomed. Opt. Express4(5), 741–759 (2013). [CrossRef] [PubMed]
  6. M. D. Keller, E. Vargis, N. de Matos Granja, R. H. Wilson, M. A. Mycek, M. C. Kelley, and A. Mahadevan-Jansen, “Development of a spatially offset Raman spectroscopy probe for breast tumor surgical margin evaluation,” J. Biomed. Opt.16(7), 077006 (2011). [CrossRef] [PubMed]
  7. C. K. Hayakawa, E. O. Potma, and V. Venugopalan, “Electric field Monte Carlo simulations of focal field distributions produced by tightly focused laser beams in tissues,” Biomed. Opt. Express2(2), 278–290 (2011). [CrossRef] [PubMed]
  8. M. S. Patterson, B. Chance, and B. C. Wilson, “Time resolved reflectance and transmittance for the non-invasive measurement of tissue optical properties,” Appl. Opt.28(12), 2331–2336 (1989). [CrossRef] [PubMed]
  9. L. Wang, S. L. Jacques, and L. Zheng, “MCML--Monte Carlo modeling of light transport in multi-layered tissues,” Comput. Methods Programs Biomed.47(2), 131–146 (1995). [CrossRef] [PubMed]
  10. S. T. Flock, B. C. Wilson, and M. S. Patterson, “Monte Carlo modeling of light propagation in highly scattering tissues--II: Comparison with measurements in phantoms,” IEEE Trans. Biomed. Eng.36(12), 1169–1173 (1989). [CrossRef] [PubMed]
  11. A. H. Hielscher, S. L. Jacques, L. Wang, and F. K. Tittel, “The influence of boundary conditions on the accuracy of diffusion theory in time-resolved reflectance spectroscopy of biological tissues,” Phys. Med. Biol.40(11), 1957–1975 (1995). [CrossRef] [PubMed]
  12. C. K. Hayakawa, J. Spanier, F. Bevilacqua, A. K. Dunn, J. S. You, B. J. Tromberg, and V. Venugopalan, “Perturbation Monte Carlo methods to solve inverse photon migration problems in heterogeneous tissues,” Opt. Lett.26(17), 1335–1337 (2001). [CrossRef] [PubMed]
  13. D. J. Cuccia, F. Bevilacqua, A. J. Durkin, and B. J. Tromberg, “Modulated imaging: quantitative analysis and tomography of turbid media in the spatial-frequency domain,” Opt. Lett.30(11), 1354–1356 (2005). [CrossRef] [PubMed]
  14. D. J. Cuccia, F. Bevilacqua, A. J. Durkin, F. R. Ayers, and B. J. Tromberg, “Quantitation and mapping of tissue optical properties using modulated imaging,” J. Biomed. Opt.14(2), 024012 (2009). [CrossRef] [PubMed]
  15. E. Alerstam, S. Andersson-Engels, and T. Svensson, “White Monte Carlo for time-resolved photon migration,” J. Biomed. Opt.13(4), 041304 (2008). [CrossRef] [PubMed]
  16. E. Alerstam, W. C. Lo, T. D. Han, J. Rose, S. Andersson-Engels, and L. Lilge, “Next-generation acceleration and code optimization for light transport in turbid media using GPUs,” Biomed. Opt. Express1(2), 658–675 (2010). [CrossRef] [PubMed]
  17. A. Badal and A. Badano, “Accelerating Monte Carlo simulations of photon transport in a voxelized geometry using a massively parallel graphics processing unit,” Med. Phys.36(11), 4878–4880 (2009). [CrossRef] [PubMed]
  18. T. S. Leung and S. Powell, “Fast Monte Carlo simulations of ultrasound-modulated light using a graphics processing unit,” J. Biomed. Opt.15(5), 055007 (2010). [CrossRef] [PubMed]
  19. T. M. Baran and T. H. Foster, “New Monte Carlo model of cylindrical diffusing fibers illustrates axially heterogeneous fluorescence detection: simulation and experimental validation,” J. Biomed. Opt.16(8), 085003 (2011). [CrossRef] [PubMed]
  20. A. Doronin and I. Meglinski, “Online object oriented Monte Carlo computational tool for the needs of biomedical optics,” Biomed. Opt. Express2(9), 2461–2469 (2011). [CrossRef] [PubMed]
  21. S. Liu, P. Li, and Q. Luo, “Fast blood flow visualization of high-resolution laser speckle imaging data using graphics processing unit,” Opt. Express16(19), 14321–14329 (2008). [CrossRef] [PubMed]
  22. O. Yang, D. Cuccia, and B. Choi, “Real-time blood flow visualization using the graphics processing unit,” J. Biomed. Opt.16(1), 016009 (2011). [CrossRef] [PubMed]
  23. J. Swartling, A. Pifferi, A. M. K. Enejder, and S. Andersson-Engels, “Accelerated Monte Carlo models to simulate fluorescence spectra from layered tissues,” J. Opt. Soc. Am. A20(4), 714–727 (2003). [CrossRef] [PubMed]
  24. A. Kienle and M. S. Patterson, “Determination of the optical properties of turbid media from a single Monte Carlo simulation,” Phys. Med. Biol.41(10), 2221–2227 (1996). [CrossRef] [PubMed]
  25. M. Martinelli, A. Gardner, D. Cuccia, C. Hayakawa, J. Spanier, and V. Venugopalan, “Analysis of single Monte Carlo methods for prediction of reflectance from turbid media,” Opt. Express19(20), 19627–19642 (2011). [CrossRef] [PubMed]
  26. O. Yang, “Real-Time Laser Speckle Imaging Software using the GPU,” (2011), http://choi.bli.uci.edu/software/realtime_lsi.html .
  27. S. Bélanger, M. Abran, X. Intes, C. Casanova, and F. Lesage, “Real-time diffuse optical tomography based on structured illumination,” J. Biomed. Opt.15(1), 016006 (2010). [CrossRef] [PubMed]

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.

Figures

Fig. 1
 

« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited