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High-Performance Monte Carlo Simulations for Photon Migration and Applications in Optical Brain Functional Imaging

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Handbook of Large-Scale Distributed Computing in Smart Healthcare

Part of the book series: Scalable Computing and Communications ((SCC))

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Abstract

The human brain is a complex biological organ that is extremely challenging to study. Non-invasive optical scanning has been effective in exploring brain functions and diagnosing brain diseases. However, given the complexity of the human brain anatomy, quantitative analysis of optical brain imaging data has been challenging due to the extensive computation needed to solve the generalized models. In this chapter, we discuss Monte Carlo eXtreme (MCX), a computationally efficient and numerically accurate Monte Carlo photon simulation package. Leveraging the benefits of GPU-based parallel computing. MCX allows researchers to use 3D anatomical scans from MRI or CT to perform accurate photon transport simulations. Compared to conventional Monte Carlo (MC) methods, MCX provides a dramatic speed improvement of two to three orders of magnitude, thanks largely to the massively parallel threads enabled by modern GPU architectures.

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References

  1. Alerstam, E., Svensson, T. & Andersson-Engels, S., 2008. Parallel computing with graphics processing units for high-speed Monte Carlo simulation of photon migration. Journal of Biomedical Optics, 13(6), p. 60504.

    Google Scholar 

  2. Boas, D. et al., 2002. Three dimensional Monte Carlo code for photon migration through complex heterogeneous media including the adult human head. Optics express, 10(3), pp. 159–170.

    Google Scholar 

  3. Caffini, M. et al., 2010. Validating an Anatomical Brain Atlas for Analyzing NIRS Measurements of Brain Activation. Biomedical Optics and 3-D Imaging, p. JMA87.

    Google Scholar 

  4. Cooper, R.J. et al., 2012. Validating atlas-guided DOT: A comparison of diffuse optical tomography informed by atlas and subject-specific anatomies. NeuroImage, 62(3), pp. 1999–2006.

    Google Scholar 

  5. Cox, D.D. & Savoy, R.L., 2003. Functional magnetic resonance imaging (fMRI) “brain reading”: detecting and classifying distributed patterns of fMRI activity in human visual cortex. Neuroimage, 19(2), pp. 261–270.

    Google Scholar 

  6. Custo, A. et al., 2010. Anatomical atlas-guided diffuse optical tomography of brain activation. NeuroImage, 49(1), pp. 561–567.

    Google Scholar 

  7. Diamos, G. et al., 2011. SIMD re-convergence at thread frontiers. Proceedings of the 44th Annual IEEE/ACM International Symposium on Microarchitecture - MICRO-44 ’11, p. 477.

    Google Scholar 

  8. Fang, Q., 2010. Mesh-based Monte Carlo method using fast ray-tracing in Plücker coordinates. Biomedical optics express, 1(1), pp. 165–75.

    Google Scholar 

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

    Google Scholar 

  10. Gao, H., Phan, L. & Lin, Y., 2012. Parallel multigrid solver of radiative transfer equation for photon transport via graphics processing unit. Journal of Biomedical Optics, 17(9), pp. 96004–1.

    Google Scholar 

  11. Gaster, B. et al., 2012. Heterogeneous Computing with OpenCL: Revised OpenCL 1.2, Newnes.

    Google Scholar 

  12. Gorshkov, A. V. & Kirillin, M.Y., 2012. Monte Carlo simulation of brain sensing by optical diffuse spectroscopy. Journal of Computational Science, 3(6), pp. 498–503.

    Google Scholar 

  13. Guo, Z., Cai, F. & He, S., 2013. Optimization for Brain Activity Monitoring With Near Infrared Light in a Four-Layered Model of the Human Head. Progress In Electromagnetics Research, 140(April), pp. 277–295.

    Google Scholar 

  14. Irani, F. et al., 2007. Functional near infrared spectroscopy (fNIRS): an emerging neuroimaging technology with important applications for the study of brain disorders. The Clinical neuropsychologist, 21(1), pp. 9–37.

    Google Scholar 

  15. Kaeli, D.R. et al., 2015. Heterogeneous Computing with OpenCL 2.0, Morgan Kaufmann.

    Google Scholar 

  16. Mantor, M. & Houston, M., 2011. AMD Graphics Core Next. AMD Fusion Developer Summit.

    Google Scholar 

  17. Marsaglia, G., 2003. Xorshift RNGs. Journal of Statistical Software, 8(14), pp. 1–6.

    Google Scholar 

  18. Munshi, A., 2009. The opencl specification. In 2009 IEEE Hot Chips 21 Symposium (HCS). pp. 1–314.

    Google Scholar 

  19. NVIDIA, 2013. CUDA Math API., p. 23.

    Google Scholar 

  20. NVIDIA, 2012. Kepler GK110. Whitepaper.

    Google Scholar 

  21. NVIDIA, 2015. Multi Processing Service.

    Google Scholar 

  22. NVIDIA, 2016. NVIDIA GeForce GTX 1080. Whitepaper, pp. 1–52.

    Google Scholar 

  23. NVIDIA, 2014. NVIDIA GeForce GTX 980 Featuring Maxwell, The Most Advanced GPU Ever Made., pp. 1–32.

    Google Scholar 

  24. NVIDIA, 2008. Programming guide.

    Google Scholar 

  25. Patterson, D., 2009. The top 10 innovations in the new NVIDIA Fermi architecture, and the top 3 next challenges. NVIDIA Whitepaper, pp. 3–10.

    Google Scholar 

  26. Perdue, K.L., Fang, Q. & Diamond, S.G., 2012. Quantitative assessment of diffuse optical tomography sensitivity to the cerebral cortex using a whole-head probe. Physics in Medicine and Biology, 57(10), pp. 2857–2872.

    Google Scholar 

  27. Perlman, S.B., Huppert, T.J. & Luna, B., 2015. Functional Near-Infrared Spectroscopy Evidence for Development of Prefrontal Engagement in Working Memory in Early Through Middle Childhood. Cerebral cortex (New York, N.Y.: 1991), p. bhv139.

    Google Scholar 

  28. Prabhu Verleker, A. et al., 2015. An empirical approach to estimate near-infra-red photon propagation and optically induced drug release in brain tissues., 9308, p. 93080T.

    Google Scholar 

  29. Prabhu Verleker, A. et al., 2014. An Optical Therapeutic Protocol to treat brain metastasis by mapping NIR activated drug release: A Pilot Study., pp. 14–16.

    Google Scholar 

  30. Selb, J. et al., 2014. Comparison of a layered slab and an atlas head model for Monte Carlo fitting of time-domain near-infrared spectroscopy data of the adult head. Journal of biomedical optics, 19(1), p. 16010.

    Google Scholar 

  31. Ukidave, Y., Li, X. & Kaeli, D., 2016. Mystic: Predictive Scheduling for GPU Based Cloud Servers Using Machine Learning. Proceedings - 2016 IEEE 30th International Parallel and Distributed Processing Symposium, IPDPS 2016, pp. 353–362.

    Google Scholar 

  32. Wu, H. et al., 2012. Characterization and transformation of unstructured control flow in bulk synchronous GPU applications. International Journal of High Performance Computing Applications, 26(2), pp. 170–185.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Northeastern University, Boston, USA

    Fanny Nina-Paravecino, Leiming Yu & David Kaeli

  2. Department of Bioengineering, Northeastern University, Boston, USA

    Qianqian Fang

Authors
  1. Fanny Nina-Paravecino
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  2. Leiming Yu
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  3. Qianqian Fang
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  4. David Kaeli
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Corresponding author

Correspondence to David Kaeli .

Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, North Dakota State University , Fargo, North Dakota, USA

    Samee U. Khan

  2. School of Information Technologies, University of Sydney, Sydney, New South Wales, Australia

    Albert Y. Zomaya

  3. Department of Computer Science, COMSATS Institute of Information Technology, Islamabad, Pakistan

    Assad Abbas

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Nina-Paravecino, F., Yu, L., Fang, Q., Kaeli, D. (2017). High-Performance Monte Carlo Simulations for Photon Migration and Applications in Optical Brain Functional Imaging. In: Khan, S., Zomaya, A., Abbas, A. (eds) Handbook of Large-Scale Distributed Computing in Smart Healthcare. Scalable Computing and Communications. Springer, Cham. https://doi.org/10.1007/978-3-319-58280-1_4

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  • DOI: https://doi.org/10.1007/978-3-319-58280-1_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-58279-5

  • Online ISBN: 978-3-319-58280-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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