PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Powiadomienia systemowe
  • Sesja wygasła!
  • Sesja wygasła!
Tytuł artykułu

Parallel, multi-purpose Monte Carlo code for simulation of light propagation in segmented tissues

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
We have developed a massive-parallel, multi-purpose Monte Carlo (MC) code for simulation of light propagation in complex structures modelled with e.g. magnetic resonance images. The code is designed to execute parallel threads on a Linux-based cluster of computers equipped with multiple graphical processing units (GPU) utilizing NVidia CUDA technology. We show steps one can take to implements such code itself. Furthermore, we provide methodology of building a MRI-based head model and populating it with realistic optical properties at excitation and fluorescence emission wavelengths during inflow of fluorescence agent (indocyanine green – ICG). The proposed code provides following original features: (i) Simulation of fluorescence light propagation in media with spatial distribution of multiple different fluorophores characterized by concentration, quantum yield and fluorescence lifetime. (ii) The fluorescence light tracking does not need extra photon tracking and works in parallel with tracking photons at the excitation wavelength, introducing execution time overhead of 0.02% only. (iii) Calculation of high-resolution spatial distributions of sensitivity factors mapping voxels absorption change to parameters measured on a model surface: statistical parameters (moments) of the distribution of time of flight of photons. (iv) Random number generators states are preserved between runs to greatly improve calculation time of the sensitivity factors. (v) Simulation of fluorescence lifetime wide field imaging in diffusively scattering media. The code is designed to handle big models, divided into e.g. 200 million voxels or more, which other methods struggle to handle. Simulations on a human head model with 0.3 mm voxel size require 2 GB of a GPU memory only. This is supported by developed non-standard floating-point data storage format. The proposed code is cross-validated with field-leading MC and finite element methods on the same hardware environment for varying model sizes and temporal resolutions. Tests revealed competitive execution time and high temporal resolution of boundary data for high spatial resolution of MRI-based head model.
Twórcy
  • Nalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Trojdena 4, 02-109 Warsaw, Poland
autor
  • Nalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Warsaw, Poland
Bibliografia
  • [1] Boas D, Culver J, Stott J, Dunn A. Three dimensional Monte Carlo code for photon migration through complex heterogeneous media including the adult human head. Opt Express 2002;10:159–70.
  • [2] Wang L, Jacques SL, Zheng L. MCML–Monte Carlo modeling of light transport in multi-layered tissues. Comput Methods Programs Biomed 1995;47:131–46.
  • [3] Zhu C, Liu Q. Review of Monte Carlo modeling of light transport in tissues. J Biomed Opt 2013;18:50902.
  • [4] Jacques S.L., Li T., Monte Carlo simulations of light transport in 3D heterogenous tissues (mcxyz.c)‘‘ (2013), retrieved http://omlc.org/software/mc/mcxyz/index.html.
  • [5] Liemert A, Kienle A. Analytical approach for solving the radiative transfer equation in two-dimensional layered media. J Quant Spectrosc Radiat Transfer 2012;113:559–64.
  • [6] Liemert A, Kienle A. Exact and efficient solution of the radiative transport equation for the semi-infinite medium. Sci Rep 2013;3:2018.
  • [7] Liemert A, Kienle A. Light diffusion in N-layered turbid media: frequency and time domains. J Biomed Opt 2010;15 025002.
  • [8] Dehghani H, Eames ME, Yalavarthy PK, Davis SC, Srinivasan S, Carpenter CM, et al. Near infrared optical tomography using NIRFAST: Algorithm for numerical model and image reconstruction. Commun Numer Methods Eng 2008;25:711–32.
  • [9] Jermyn M, Ghadyani H, Mastanduno MA, Turner W, Davis SC, Dehghani H, et al. Fast segmentation and high-quality three-dimensional volume mesh creation from medical images for diffuse optical tomography. J Biomed Opt 2013;18:86007.
  • [10] Wojtkiewicz S, Durduran T, Dehghani H. Time-resolved near infrared light propagation using frequency domain superposition. Biomed Opt Express 2018;9:41–54.
  • [11] Liu Q, Ramanujam N. Scaling method for fast Monte Carlo simulation of diffuse reflectance spectra from multilayered turbid media. J Opt Soc Am A 2007;24:1011–25.
  • [12] Yu L, Nina-Paravecino F, Kaeli DR, Fang Q. Scalable and massively parallel Monte Carlo photon transport simulations for heterogeneous computing platforms. J Biomed Opt 2018;23:1–4.
  • [13] LaRochelle E, Arce P, Pogue B, Monte Carlo modeling photon-tissue interaction using on-demand cloud infrastructure (2020).
  • [14] 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 2008;13 060504.
  • [15] Fang Q, Boas DA. Monte Carlo simulation of photon migration in 3D turbid media accelerated by graphics processing units. Opt Express 2009;17:20178–90.
  • [16] Fang Q. Mesh-based Monte Carlo method using fast ray-tracing in Plucker coordinates. Biomed Opt Express 2010;1:165–75.
  • [17] Fang Q, Kaeli DR. Accelerating mesh-based Monte Carlo method on modern CPU architectures. Biomed Opt Express 2012;3:3223–30.
  • [18] Yao R, Intes X, Fang Q. Generalized mesh-based Monte Carlo for wide-field illumination and detection via mesh retessellation. Biomed Opt Express 2016;7:171–84.
  • [19] Yan S, Tran AP, Fang Q. Dual-grid mesh-based Monte Carlo algorithm for efficient photon transport simulations in complex three-dimensional media. J Biomed Opt 2019;24:1–4.
  • [20] Fang Q, Yan S. Graphics processing unit-accelerated mesh-based Monte Carlo photon transport simulations. J Biomed Opt 2019;24 115002.
  • [21] Young-Schultz T, Brown S, Lilge L, Betz V. FullMonteCUDA: a fast, flexible, and accurate GPU-accelerated Monte Carlo simulator for light propagation in turbid media. Biomed Opt Express 2019;10:4711–26.
  • [22] Cassidy J, Nouri A, Betz V, Lilge L. High-performance, robustly verified Monte Carlo simulation with FullMonte. J Biomed Opt 2018;23 085001.
  • [23] Dehghani H, Arridge SR, Schweiger M, Delpy DT. Optical tomography in the presence of void regions. J Opt Soc Am A 2000;17:1659–70.
  • [24] Zoller CJ, Hohmann A, Forschum F, Geiger S, Geiger M, Ertl TP, et al. Parallelized Monte Carlo software to efficiently simulate the light propagation in arbitrarily shaped objects and aligned scattering media. J Biomed Opt 2018;23 065004.
  • [25] Li H, Zhang C, Feng X. Monte Carlo simulation of light scattering in tissue for the design of skin-like optical devices. Biomed Opt Express 2019;10:868–78.
  • [26] Doronin A, Lee HR, Novikova T, Vera N, Staforelli JP, Bykov A, Meglinski I, GPU-accelerated online Monte Carlo (MC) application for imitation of twisted light propagation in turbid tissue-like scattering media (Conference Presentation), SPIE BiOS (SPIE, 2020), Vol. 11234.
  • [27] Wang Y, Bai L. Accurate Monte Carlo simulation of frequency-domain optical coherence tomography. International Journal for Numerical Methods in Biomedical Engineering 2019;35e3177.
  • [28] Jacques SL. Coupling 3D Monte Carlo light transport in optically heterogeneous tissues to photoacoustic signal generation. Photoacoustics 2014;2:137–42.
  • [29] Powell S, Leung T. Highly parallel Monte-Carlo simulations of the acousto-optic effect in heterogeneous turbid media. J Biomed Opt 2012;17 045002.
  • [30] Yona G, Meitav N, Kahn I, Shoham S, Realistic numerical and analytical modeling of light scattering in brain tissue for optogenetic applications, eneuro 3, ENEURO.0059-0015.2015(2016).
  • [31] Dupont C, Baert G, Mordon S, Vermandel M. Parallelized Monte-Carlo dosimetry using graphics processing units to model cylindrical diffusers used in photodynamic therapy: From implementation to validation. Photodiagn Photodyn Ther 2019;26:351–60.
  • [32] Periyasamy V, Pramanik M. Advances in Monte Carlo simulation for light propagation in tissue. IEEE Rev Biomed Eng 2017;10:122–35.
  • [33] ‘‘Monte Carlo eXtreme: GPU-based Monte Carlo Simulations”, retrieved http://mcx.sourceforge.net/cgi-bin/index.cgi.
  • [34] Selb J, Ogden TM, Dubb J, Fang Q, Boas DA. 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. J Biomed Opt 2014;19:16010.
  • [35] Gerega A, Milej D, Weigl W, Botwicz M, Zolek N, Kacprzak M, et al. Multiwavelength time-resolved detection of fluorescence during the inflow of indocyanine green into the adult’s brain. J Biomed Opt 2012;17 087001.
  • [36] Luo Z, Deng Y, Wang K, Lian L, Yang X, Luo Q. Decoupled fluorescence Monte Carlo model for direct computation of fluorescence in turbid media. J Biomed Opt 2015;20:25002.
  • [37] Gerega A, Zolek N, Soltysinski T, Milej D, Sawosz P, Toczylowska B, et al. Wavelength-resolved measurements of fluorescence lifetime of indocyanine green. J Biomed Opt 2011;16 067010.
  • [38] Philip J, Carlsson K. Theoretical investigation of the signal-to-noise ratio in fluorescence lifetime imaging. J Opt Soc Am A 2003;20:368–79.
  • [39] Sawosz P, Kacprzak M, Zolek N, Weigl W, Wojtkiewicz S, Maniewski R, et al. Optical system based on time-gated, intensified charge-coupled device camera for brain imaging studies. J Biomed Opt 2010;15 066025.
  • [40] Sawosz P, Wojtkiewicz S, Kacprzak M, Zieminska E, Morawiec M, Maniewski R, et al. Towards in-vivo assessment of fluorescence lifetime: imaging using time-gated intensified CCD camera. Biocybern Biomed Eng 2018;38:966–74.
  • [41] Eggebrecht AT, Ferradal SL, Robichaux-Viehoever A, Hassanpour MS, Dehghani H, Snyder AZ, et al. Mapping distributed brain function and networks with diffuse optical tomography. Nat Photonics 2014;8:448–54.
  • [42] Kacprzak M, Liebert A, Sawosz P, Zolek N, Maniewski R. Time-resolved optical imager for assessment of cerebral oxygenation. J Biomed Opt 2007;12 034019.
  • [43] Liebert A, Wabnitz H, Steinbrink J, Obrig H, Moller M, Macdonald R, et al. Time-resolved multidistance nearinfrared spectroscopy of the adult head: intracerebral and extracerebral absorption changes from moments of distribution of times of flight of photons. Appl Opt 2004;43:3037–47.
  • [44] Liebert A, Wabnitz H, Zolek N, Macdonald R. Monte Carlo algorithm for efficient simulation of time-resolved fluorescence in layered turbid media. Opt Express 2008;16:13188–202.
  • [45] Milej D, Gerega A, Kacprzak M, Sawosz P,Weigl W, Maniewski R, et al. Time-resolved multi-channel optical system for assessment of brain oxygenation and perfusion by monitoring of diffuse reflectance and fluorescence. Opto-Electron Rev 2014;22:55–67.
  • [46] Jacques SL. Optical properties of biological tissues: a review. Phys Med Biol 2013;58:R37–61.
  • [47] Lee J-H, Shin S-J, Istook C. Analysis of human head shapes in the United States. Int J Human Ecol 2006;7.
  • [48] Zolek NS, Wojtkiewicz S, Liebert A. Correction of anisotropy coefficient in original Henyey Greenstein phase function for Monte Carlo simulations of light transport in tissue. Biocybern Biomed Eng 2008;28:59–73.
  • [49] Wojtkiewicz S, Liebert A, Rix H, Zolek N, Maniewski R. Laser-Doppler spectrum decomposition applied for the estimation of speed distribution of particles moving in a multiple scattering medium. Phys Med Biol 2009;54:679–97.
  • [50] Wang L, Jacques SL, Monte Carlo Multi-Layered (MCML) (1995), retrieved https://omlc.org/software/mc/mcml/.
  • [51] Wojtkiewicz S, Liebert A, Rix H, Sawosz P, Maniewski R. Estimation of scattering phase function utilizing laser Doppler power density spectra. Phys Med Biol 2013;58:937–55.
  • [52] Philip R, Penzkofer A, Bäumler W, Szeimies RM, Abels C. Absorption and fluorescence spectroscopic investigation of indocyanine green. J Photochem Photobiol, A 1996;96:137–48.
  • [53] Mourant JR, Freyer JP, Hielscher AH, Eick AA, Shen D, Johnson TM. Mechanisms of light scattering from biological cells relevant to noninvasive optical-tissue diagnostics. Appl Opt 1998;37:3586–93.
  • [54] Wabnitz H, Taubert DR, Mazurenka M, Steinkellner O, Jelzow A, Macdonald R, et al. Performance assessment of time-domain optical brain imagers, part 1: basic instrumental performance protocol. J Biomed Opt 2014;19 086010.
  • [55] Nvidia, retrieved http://docs.nvidia.com/cuda/curand.
  • [56] Hwu W-m W. GPU Computing Gems Emerald Edition. Morgan Kaufmann Publishers Inc.; 2011. p. 886.
  • [57] Yao R, Intes X, Fang Q. Direct approach to compute Jacobians for diffuse optical tomography using perturbation Monte Carlo-based photon **#x0201C;replay**#x0201D. Biomed Opt Express 2018;9:4588–603.
  • [58] ‘‘SPM - Statistical Parametric Mapping” (2017/01/27), retrieved http://www.fil.ion.ucl.ac.uk/spm/.
  • [59] Prahl S, Tabulated Molar Extinction Coefficient for Hemoglobin in Water (2020), retrieved https://omlc.org/spectra/hemoglobin/summary.html.
  • [60] Bamett AH, Culver JP, Sorensen AG, Dale A, Boas DA. Robust inference of baseline optical properties of the human head with three-dimensional segmentation from magnetic resonance imaging. Appl Opt 2003;42:3095–108.
  • [61] Dehghani H, White BR, Zeff BW, Tizzard A, Culver JP. Depth sensitivity and image reconstruction analysis of dense imaging arrays for mapping brain function with diffuse optical tomography. Appl Opt 2009;48:D137–143.
  • [62] Torricelli A, Pifferi A, Taroni P, Giambattistelli E, Cubeddu R. In vivo optical characterization of human tissues from 610 to 1010 nm by time-resolved reflectance spectroscopy. Phys Med Biol 2001;46:2227–37.
  • [63] Jager M, Kienle A. Non-invasive determination of the absorption coefficient of the brain from time-resolved reflectance using a neural network. Phys Med Biol 2011;56: N139–144.
  • [64] Comelli D, Bassi A, Pifferi A, Taroni P, Torricelli A, Cubeddu R, et al. In vivo time-resolved reflectance spectroscopy of the human forehead. Appl Opt 2007;46:1717–25.
  • [65] Strangman G, Franceschini MA, Boas DA. Factors affecting the accuracy of near-infrared spectroscopy concentration calculations for focal changes in oxygenation parameters. NeuroImage 2003;18:865–79.
  • [66] Strangman GE, Zhang Q, Li Z. Scalp and skull influence on near infrared photon propagation in the Colin27 brain template. NeuroImage 2014;85(Part 1):136–49.
  • [67] Bevilacqua F, Piguet D, Marquet P, Gross JD, Tromberg BJ, Depeursinge C. In vivo local determination of tissue optical properties: applications to human brain. Appl Opt 1999;38:4939–50.
  • [68] Sawosz P, Wojtkiewicz S, Kacprzak M, Weigl W, Borowska-Solonynko A, Krajewski P, et al. Human skull translucency: post mortem studies. Biomed Opt Express 2016;7:5010–20.
  • [69] Okada E, Delpy DT. Near-infrared light propagation in an adult head model. I. Modeling of low-level scattering in the cerebrospinal fluid layer. Appl Opt 2003;42:2906–14.
  • [70] Milej D, Abdalmalak A, Desjardins L, Ahmed H, Lee T-Y, Diop M, et al. Quantification of blood-brain barrier permeability by dynamic contrast-enhanced NIRS. Sci Rep 2017;7:1702.
  • [71] Weigl W, Milej D, Gerega A, Toczylowska B, Kacprzak M, Sawosz P, et al. Assessment of cerebral perfusion in post-traumatic brain injury patients with the use of ICG-bolus tracking method. NeuroImage 2014;85(Pt 1):555–65.
  • [72] Liebert A, Wabnitz H, Obrig H, Erdmann R, Moller M, Macdonald R, et al. Non-invasive detection of fluorescence from exogenous chromophores in the adult human brain. NeuroImage 2006;31:600–8.
  • [73] Milej D, He L, Abdalmalak A, Baker W, Anazodo UC, Dolui S, Kavuri VC, Diop M, Pavlosky W, Balu R, Detre JA, Kofke A, Yodh AG, Lawrence KS. Quantification of cerebral blood flow in adults by dynamic contrast-enhanced NIRS: validation against MRI. Biophotonics Congress: Biomedical Optics Congress 2018 (Microscopy/Translational/Brain/OTS), OSA Technical Digest. Optical Society of America; 2018. BF2C.2.
  • [74] Weigl W, Milej D, Gerega A, Toczyłowska B, Sawosz P, Kacprzak M, et al. Confirmation of brain death using optical methods based on tracking of an optical contrast agent: assessment of diagnostic feasibility. Sci Rep 2018;8:7332.
  • [75] Landsman ML, Kwant G, Mook GA, Zijlstra WG. Light-absorbing properties, stability, and spectral stabilization of indocyanine green. J Appl Physiol 1976;40:575–83.
  • [76] Benson RC, Kues HA. Fluorescence properties of indocyanine green as related to angiography. Phys Med Biol 1978;23:159–63.
  • [77] Leung TS, Tachtsidis I, Tisdall M, Smith M, Delpy DT, Elwell CE. Theoretical investigation of measuring cerebral blood flow in the adult human head using bolus Indocyanine Green injection and near-infrared spectroscopy. Appl Opt 2007;46:1604–14.
  • [78] Patterson MS, Pogue BW. Mathematical model for time-resolved and frequency-domain fluorescence spectroscopy in biological tissues. Appl Opt 1994;33:1963–74.
  • [79] ‘‘GitHub - nirfaster/NIRFASTer: Open source software for multi-modal optical molecular imaging” (2020), retrieved https://github.com/nirfaster/NIRFASTer.
  • [80] ‘‘CGAL, Computational Geometry Algorithms Library”, retrieved http://www.cgal.org.
  • [81] Leino A, Pulkkinen A, Tarvainen T. ValoMC: a Monte Carlo software and MATLAB toolbox for simulating light transport in biological tissue. OSA Continuum 2019;2:957.
  • [82] Marti D, Aasbjerg RN, Andersen PE, Hansen AK. MCmatlab: an open-source, user-friendly, MATLAB-integrated three-dimensional Monte Carlo light transport solver with heat diffusion and tissue damage. J Biomed Opt 2018;23 121622.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-94b998de-206d-43b4-b870-e030743133a7
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.