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1 May 2017 Finite element simulation of light transfer in turbid media under structured illumination
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Spatial-frequency domain (SFD) imaging technique allows to estimate the optical properties of biological tissues in a wide field of view. The technique is, however, prone to error in measurement because the two crucial assumptions used for deriving the analytical solution to diffusion approximation cannot be met perfectly in practical applications. This research was mainly focused on modeling light transfer in turbid media under the normal incidence of structured illumination using finite element method (FEM). Finite element simulations were performed for 50 simulation samples with different combinations of optical absorption and scattering coefficients for varying spatial frequencies, and the results were then compared with analytical method and Monte Carlo simulation. Relationships between diffuse reflectance and dimensionless absorption and dimensionless scattering coefficients were investigated. The results indicated that FEM provided reasonable results for diffuse reflectance, compared with the analytical method. Both FEM and analytical method overestimated the reflectance for μtr/fx values of greater than 2 and underestimated the reflectance for μtr/fx values of smaller than 2. Larger values of μ′sa yielded better estimations of diffuse reflectance than did those of smaller than 10. The reflectance increased nonlinearly with the dimensionless scattering, whereas the reflectance decreased linearly with the dimensionless absorption. It was also observed that diffuse reflectance was relatively stable and insensitive to μs′ when the dimensionless scattering was larger than 50. Overall results demonstrate that FEM is effective for modeling light transfer in turbid media and can be used to explore the effects of crucial parameters for the SFD imaging technique.
Conference Presentation
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Dong Hu, Renfu Lu, and Yibin Ying "Finite element simulation of light transfer in turbid media under structured illumination", Proc. SPIE 10217, Sensing for Agriculture and Food Quality and Safety IX, 102170B (1 May 2017);

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