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International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems

IEA/AIE 2016: Trends in Applied Knowledge-Based Systems and Data Science pp 493–502Cite as

A Recent Study on Hardware Accelerated Monte Carlo Modeling of Light Propagation in Biological Tissues

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9799))

Abstract

The Monte Carlo (MC) method is the gold standard in photon migration through 3D media with spatially varying optical proper-ties. MC offers excellent accuracy, easy-to-program and straightforward parallelization. In this study we summarize the recent advances in accelerating simulations of light propagation in biological tissues. The systematic literature review method is involved selecting the relevant studies for the research. With this approach research questions regarding the acceleration techniques are formulated and additional selection criteria are applied. The selected studies are analyzed and the research questions are answered. We discovered that there are several possibilities for accelerating the MC code and the CUDA platform is used in more than \(60\,\)% of all studies. We also discovered that the trend in GPU acceleration with CUDA has continued in last two years.

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References

  1. Wang, L.V., Hsin-i, W.: Biomedical Optics: Principles and Imaging. Wiley, Hoboken (2007)

    Google Scholar 

  2. Welch, A.J., Van Gemert, M.J.C.: Optical-Thermal Response of Laser-Irradiated Tissue. Springer, New York (2011)

    Book  Google Scholar 

  3. Ancora, D., Zacharopoulos, A., Ripoll, J., Zacharakis, G.: Light propagation through weakly scattering media: a study of Monte Carlo vs. diffusion theory with application to neuroimaging. 9538(Mc), 95380G (2015)

    Google Scholar 

  4. Wang, L., Jacquesa, S.L., Zhengb, L.: MCML - Monte Carlo modeling of light transport in multi-layered tissues. Biomedicine 2607(713), 131–146 (1995)

    Google Scholar 

  5. nVidia.: Cuda C programming guide (2015)

    Google Scholar 

  6. Alerstam, E., Svensson, T., Andersson-Engels, S.: Parallel computing with graphics processing units for high-speed Monte Carlo simulation of photon migration. J. Biomed. Opt. 13(6), 060504 (2008)

    Article  Google Scholar 

  7. Alerstam, E., Lo, W.C.Y., Han, T.D., Rose, J., Andersson-Engels, S., Lilge, L.: Next-generation acceleration and code optimization for light transport in turbid media using GPUs. Biomed. Opt. Express 1(2), 658–675 (2010)

    Article  Google Scholar 

  8. Achimugu, P., Selamat, A., Ibrahim, R., Mahrin, M.N.: A systematic literature review of software requirements prioritization research. Inf. Softw. Technol. 56(6), 568–585 (2014)

    Article  Google Scholar 

  9. Fang, Q., Boas, D.A.: Monte Carlo simulation of photon migration in 3D turbid media accelerated by graphics processing units. Opt. Express 17(22), 20178–20190 (2009)

    Article  Google Scholar 

  10. Fang, Q.: Mesh-based Monte Carlo method using fast ray-tracing in Plücker coordinates. Biomed. Opt. Express 1(1), 165 (2010)

    Article  Google Scholar 

  11. Ren, N., Liang, J., Xiaochao, Q., Li, J., Bingjia, L., Tian, J.: GPU-based Monte Carlo simulation for light propagation in complex heterogeneous tissues. Opt. Express 18(7), 6811–6823 (2010)

    Article  Google Scholar 

  12. Doronin, A., Meglinski, I.: Online object oriented Monte Carlo computational tool for the needs of biomedical optics. Biomed. Opt. Express 2(9), 2461 (2011)

    Article  Google Scholar 

  13. Shen, H., Wang, G.: A tetrahedron-based inhomogeneous Monte Carlo optical simulator. Phys. Med. Biol. 55(4), 947–962 (2010)

    Article  Google Scholar 

  14. Lo, W.C.Y.: Hardware acceleration of a Monte Carlo simulation for photodynamic therapy treatment planning by copyright c 2009 by William Chun Yip Lo. Master’s thesis. University of Toronto (2009)

    Google Scholar 

  15. Zołek, N.S., Liebert, A., Maniewski, R.: Optimization of the Monte Carlo code for modeling of photon migration in tissue. Comput. Methods Programs Biomed. 84(1), 50–57 (2006)

    Article  Google Scholar 

  16. Chen, J., Fang, Q., Intes, X.: Mesh-based Monte Carlo method in time-domain widefield fluorescence molecular tomography. J. Biomed. Opt. 17(10), 1060091 (2012)

    Article  Google Scholar 

  17. Martinsen, P., Blaschke, J., Künnemeyer, R., Jordan, R.: Accelerating Monte Carlo simulations with an NVIDIA graphics processor. Comput. Phys. Commun. 180(10), 1983–1989 (2009)

    Article  Google Scholar 

  18. Carbone, N., Di Rocco, H., Iriarte, D.I., Pomarico, J.A.: Solution of the direct problem in turbid media with inclusions using Monte Carlo simulations implemented in graphics processing units: new criterion for processing transmittance data. J. Biomed. Opt. 15(3), 035002 (2010)

    Article  Google Scholar 

  19. Selb, J., Zimmermann, B.B., Martino, M., Ogden, T., Boas, D.A..: Functional brain imaging with a supercontinuum time-domain NIRS system. In: SPIE BiOS, vol. 8578, no. 1, 857807–857807-9 (2013)

    Google Scholar 

  20. D’Alessandro, B., Dhawan, A.P.: Voxel-based, parallel simulation of light in skin tissue for the reconstruction of subsurface skin lesion volumes. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, no. 2, pp. 8448–8451 (2011)

    Google Scholar 

  21. D’Alessandro, B., Dhawan, A.P.: Transillumination imaging for blood oxygen saturation estimation of skin lesions. IEEE Trans. Biomed. Eng. 59(9), 2660–2667 (2012)

    Article  Google Scholar 

  22. D’Alessandro, B., Dhawan, A.P.: 3-D volume reconstruction of skin lesions for melanin and blood volume estimation and lesion severity analysis. IEEE Trans. Med. Imaging 31(11), 2083–2092 (2012)

    Article  MathSciNet  Google Scholar 

  23. Doronin, A., Meglinski, I.: GPU-accelerated object-oriented Monte Carlo modeling of photon migration in turbid media. In: Proceedings of SPIE 7999, Saratov Fall Meeting 2010: Optical Technologies in Biophysics and Medicine XII, vol. 7999 (2010)

    Google Scholar 

  24. Doronin, A., Meglinski, I.: Monte Carlo simulation of photon migration in turbid random media based on the object-oriented programming paradigm. In: Proceedings of SPIE - The International Society for Optical Engineering (2011)

    Google Scholar 

  25. Doronin, A., Meglinski, I.: Peer-to-peer Monte Carlo simulation of photon migration in topical applications of biomedical optics. J. Biomed. Opt. 17(9), 0905041 (2012)

    Article  Google Scholar 

  26. Doronin, A., Meglinski, I.: Using peer-to-peer network for on-line Monte Carlo computation of fluence rate distribution. In: Proceedings of SPIE 8699, Saratov Fall Meeting 2012: Optical Technologies in Biophysics and Medicine XIV and Laser Physics and Photonics XIV, vol. 8699, p. 869909 (2013)

    Google Scholar 

  27. Hennig, G., Stepp, H., Sroka, R., Beyer, W.: Comparison of an accelerated weighted fluorescence Monte Carlo simulation method with reference methods in multi-layered turbid media. Appl. Opt. 52(5), 1066–1075 (2013)

    Article  Google Scholar 

  28. Cai, F.: Using graphics processing units to accelerate perturbation Monte Carlo simulation in a turbid medium. J. Biomed. Opt. 17(4), 040502 (2012)

    Article  Google Scholar 

  29. Yi, X., Chen, W., Linhui, W., Zhang, W., Li, J., Wang, X., Zhang, L., Zhao, H., Gao, F.: Towards diffuse optical tomography of arbitrarily heterogeneous turbid medium using GPU-accelerated Monte-Carlo forward calculation. In: Proceedings of SPIE 8574, Multimodal Biomedical Imaging VIII, vol. 8574, p. 857400 (2013)

    Google Scholar 

  30. Bjorgan, A., Milanic, M., Randeberg, L.L.: Estimation of skin optical parameters for real-time hyperspectral imaging applications. J. Biomed. Opt. 19(6), 066003 (2014)

    Article  Google Scholar 

  31. Leung, T.S., Powell, S.: Fast Monte Carlo simulations of ultrasound-modulated light using a graphics processing unit. J. Biomed. Opt. 15(5), 055007 (2014)

    Article  Google Scholar 

  32. Chen, Y.-W., Tseng, S.-H.: Efficient construction of robust artificial neural networks for accurate determination of superficial sample optical properties. Biomed. Opt. Express 6(3), 747 (2015)

    Article  Google Scholar 

  33. Qianqian, F., Kaeli, D.R.: Accelerating mesh-based Monte Carlo method on modern CPU architectures. Biomed. Opt. Express 3(12), 3223–3230 (2012)

    Article  Google Scholar 

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Acknowledgment

This work and the contribution were supported by project Smart Solutions for Ubiquitous Computing Environments FIM, University of Hradec Kralove, Czech Republic (under ID: UHK-FIM-SP-2016-2102). The work was also supported by project 16-13967S.

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Authors and Affiliations

  1. Faculty of Informatics and Management, Center for Basic and Applied Research, University of Hradec Kralove, Hradec Králové, Czech Republic

    Jakub Mesicek, Ondrej Krejcar, Ali Selamat & Kamil Kuca

  2. Biomedical Research Center, University Hospital Hradec Kralove, Hradec Králové, Czech Republic

    Jakub Mesicek & Kamil Kuca

  3. Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, Malaysia

    Ali Selamat

Authors
  1. Jakub Mesicek
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  2. Ondrej Krejcar
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  3. Ali Selamat
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  4. Kamil Kuca
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Corresponding author

Correspondence to Ondrej Krejcar .

Editor information

Editors and Affiliations

  1. Iwate Prefectural University , Iwate, Japan

    Hamido Fujita

  2. Department Computer Science, Texas State University, San Marcos, Texas, USA

    Moonis Ali

  3. Universiti Teknologi Malaysis (UTM), Bahru, Malaysia

    Ali Selamat

  4. Iwate Prefectural University , Iwate, Japan

    Jun Sasaki

  5. Iwate Prefectural University , Iwate, Japan

    Masaki Kurematsu

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© 2016 Springer International Publishing Switzerland

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Mesicek, J., Krejcar, O., Selamat, A., Kuca, K. (2016). A Recent Study on Hardware Accelerated Monte Carlo Modeling of Light Propagation in Biological Tissues. In: Fujita, H., Ali, M., Selamat, A., Sasaki, J., Kurematsu, M. (eds) Trends in Applied Knowledge-Based Systems and Data Science. IEA/AIE 2016. Lecture Notes in Computer Science(), vol 9799. Springer, Cham. https://doi.org/10.1007/978-3-319-42007-3_43

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  • DOI: https://doi.org/10.1007/978-3-319-42007-3_43

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42006-6

  • Online ISBN: 978-3-319-42007-3

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