Comparative evaluations of the Monte Carlo-based light propagation simulation packages for optical imaging

Lin Wang; Shenghan Ren; Xueli Chen

doi:10.1142/s1793545817500171

[1] V. Ntziachristos, J. Ripoll, L. V. Wang, R. Weissleder, “Looking and listening to light: The evolution of whole-body photonic imaging," Nat. Biotechnol. 23(3), 313 320 (2005).

[2] R. Weissleder, M. Pittet, “Imaging in the era of molecular oncology," Nature 452(7187), 580 589 (2008).

[3] K. Liu, Y. Lu, J. Tian et al., “Evaluation of the simplified spherical harmonics approximation in bioluminescence tomography through heterogeneous mouse models," Opt. Express 18(20), 20988 21002 (2010).

[4] X. He, H. Guo, J. Yu, X. Zhang, Y. Hou “Effective and robust approach for fluorescence molecular tomography based on CoSaMP and SP3 model," J. Innov. Opt. Heal. Sci. 9(6), 1650024 (2016).

[5] L. Wang, S. Jacques, L. Zheng, “MCML Monte Carlo modeling of light transport in multi-layered tissues," Comput. Methods Programs Biomed. 47(2), 131-146 (1995).

[6] V. Periyasamy, 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(4), 045003 (2014).

[7] V. Periyasamy, S. Sanchita, D. Gagan, A. Freek, S. Umapathy M. Pramanik, “Experimentally validated Raman Monte Carlo simulation for a cuboid object to obtain Raman spectroscopic signatures for hidden material," J. Raman Spectrosc. 46(7), 669-676 (2015).

[8] K. Sivasubramanian, V. Periyasamy, K. Wen, M. Pramanik, “Optimizing light delivery through fiber bundle in photoacoustic imaging with clincial ultrasound system: Monte Carlo simulation and experimental validation," J. Biomed. Opt. 22(4), 041008 (2016).

[9] D. Boas, J. Culver, J. Stott, A. Dunn, “Three dimensional Monte Carlo code for photon migration through complex heterogeneous media including the adult human head," Opt. Express 10(3), 159-170 (2002).

[10] E. Margallo-Balbas, P. French, “Shape based Monte Carlo code for light transport in complex heterogeneous tissues," Opt. Express, 15(21), 14086-14098 (2007).

[11] N. Ren, J. Liang, X. Qu, J. Li, B. Lu, J. Tian, “GPU-based Monte Carlo simulation for light propagation in complex heterogeneous tissues," Opt. Express, 18(7), 6811-6823 (2010).

[12] S. Ren, X. Chen, H. Wang, X. Qu, G. Wang, J. Liang, J. Tian, “Molecular optical simulation environment (MOSE): A platform for the simulation of light propagation in turbid media," PLOS ONE, 8(4), e61304 (2013).

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

[14] Q. Fang, “Mesh-based Monte Carlo method using fast ray-tracing in Plucker coordinates," Biomed. Opt. Exp. 1(1), 165-175 (2010).

[15] T. J. Pfefer, J. Barton, E. Chan, M. Ducros, “A three-dimensional modular adaptable grid numerical model for light propagation during laser irradiation of skin tissue," IEEE J. Sel. Top. Quant. 2(4), 934-942 (1996).

[16] D. Li, B. Chen, W. Ran et al., “Selection of voxel size and photon number in voxel based Monte Carlo method: Criteria and applications," J. Biomed. Opt. 20(9), 095014 (2015).

[17] Y. Zhang, B. Chen, D. Li, G. Wang, “E±cient and accurate simulation of light propagation in biotissues using the three dimensional geometric Monte Carlo method," Numer. Heat Trans. A, Appl. 68(8), 827-846 (2015).

[18] H. Jia, B. Chen, D. Li, Y. Zhang, Boundary discretization in the numerical simulation of light propagation in skin tissue: Problem and strategy, J. Biomed. Opt. 20(2), 025007 (2015).

[19] T. Li, Y. Zhao, Y. Sun, K. Li, Effects of wavelength, beam type and size on cerebral low-level laser therapy by a Monte Carlo study on visible Chinese human, J. Innov. Opt. Heal. Sci. 8(1), 1540002 (2015).

[20] Y. Liu, H. Wang, Y. Liu, W. Li, Z. Qian, Monte Carlo and phantom study in the brain edema models, J. Innov. Opt. Heal. Sci. 10(3), 1650050 (2017).

[21] K. Zhou, J. Tian, Q. Zhang, X. Meng, K. Yang, Q. Ren, Simulation and quantitative analysis of fluorescence intensity distribution based on the Monte Carlo method, J. Innov. Opt. Heal. Sci. 8(6), 1550038 (2015).

[22] T. Li, Y. Li, Y. Sun, M. Duan, L. Peng, Effect of head model on Monte Carlo modeling of spatial sensitivity distribution for functional near-infrared spectroscopy, J. Innov. Opt. Heal. Sci. 8(5), 1550024 (2015).

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

[24] H. Shen, G. Wang, “A study on tetrahedron-based inhomogeneous Monte Carlo optical simulation," Biomed. Opt. Express 2(1), 44-57 (2011).

[25] Q. Fang, D. Boas, Tetrahedral mesh generation from volumetric binary and grayscale images, 2009 IEEE Int. Symp. Biomedical Imaging: from Nano to Macro, Vols. 1 and 2, pp. 1142 1145, Boston, MA (2009).

[26] J. Chen, Q. Fang, X. Intes, “Mesh-based Monte Carlo method in time-domain widefield fluorescence molecular tomography," J. Biomed. Opt. 17(10), 106009 (2012).

[27] R. Yao, X. Intes, Q. Fang, “Generalized mesh-based Monte Carlo for wide-field illumination and detection via mesh retessellation," Biomed. Opt. Express, 7(1), 171-184 (2016).

[28] B. Dogdas, D. Stout, A. F. Chatziioannou, R. M. Leahy, “Digimouse: A 3D whole body mouse atlas from CT and cryosection data," Phys. Med. Biol. 52(3), 577-587 (2007).

[29] G. Alexandrakis, F. R. Rannou, A. F. Chatziioannou, “Tomographic bioluminescence imaging by use of a combined optical-PET (OPET) system: A computer simulation feasibility study," Phys. Med. Biol. 50(17), 4225-4241 (2005).

[30] E. Alerstam, W. Lo, T. Han, J. Rose, S. Andersson- Engels, L. Lilge, “Next-generation acceleration and code optimization for light transport in turbid media using GPUs," Biomed. Opt. Express, 1(2), 658-675 (2010).

[31] Q. Fang, D. Boas, “Monte Carlo simulation of photon migration in 3D turbid media accelerated by graphics processing units," Opt. Express, 17(22), 20178-20190 (2009).