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13 March 2024 Massively parallel Monte Carlo simulations of light propagation in anisotropic scattering media by open-source PyXOpto engine
Peter Naglič, Ernesto Pini, Lorenzo Pattelli, Miran Bürmen
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Proceedings Volume PC12856, Biomedical Applications of Light Scattering XIV; PC1285608 (2024) https://doi.org/10.1117/12.3001838
Event: SPIE BiOS, 2024, San Francisco, California, United States
Abstract
In many biological tissues such as muscles, dental enamel and mucosa that exhibit macroscopic and/or microscopic spatially anisotropic structures the nature of light scattering becomes anisotropic. This well-known property of tissues is usually neglected, which is rooted in the fact that the available open-source numerical solutions to the radiative transfer equation based on the stochastic Monte Carlo (MC) method do not allow simulations with anisotropic optical properties. In this contribution, we present an extension to our massively parallel PyXOpto (https://github.com/xopto/pyxopto) simulation engine that enables highly efficient and user-friendly MC simulations for layered or voxelated sample geometries with anisotropic scattering properties, both in the steady state and time-resolved domain.
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Peter Naglič, Ernesto Pini, Lorenzo Pattelli, and Miran Bürmen "Massively parallel Monte Carlo simulations of light propagation in anisotropic scattering media by open-source PyXOpto engine", Proc. SPIE PC12856, Biomedical Applications of Light Scattering XIV, PC1285608 (13 March 2024); https://doi.org/10.1117/12.3001838
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KEYWORDS
Monte Carlo methods
Scattering media
Simulations
Tissues
Biological samples
Biomedical optics
Diffuse optical imaging
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