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Poster Hall B0 European Conferences on Biomedical Optics (ECBO) > Diffuse Optical Spectroscopy and Imaging > Lunch Break and Poster Session - Monday
12:45-14:15 h | Hall B0 Hall B0, ICM
Subjects: Biophotonics and Medical Engineering
Monte Carlo (MC) method is regarded as the gold standard for modeling the light transport in biological tissues. Due to the stochastic nature of the MC method, many photon packets need to be processed to obtain an adequate quality of the simulated reflectance. The number of required photon packets further increases if the numerical aperture of the detection scheme is low. Consequently, extensively long simulation times may be required to obtain adequate quality of the reflectance for such detection schemes. In this paper we propose an efficient regression model that maps reflectance simulated at the maximum acceptance angle of 90° to the reflectance observed at a much smaller realistic acceptance angle. The regression model was evaluated for a multimodal optical fiber probe with a numerical aperture (NA) of 0.22 and acceptance angle of 9.5°. The results of validation on spatially resolved reflectance and inverse models for estimation of optical properties show that the regression models are accurate and do not introduce additional errors into the spatially resolved reflectance or the optical properties estimated by appropriate inverse models from the regressed reflectance. The extended acceptance angle can be used to reduce the MC simulation noise and/or to speed up the simulations by reducing the number of photon packets while preserving the quality of the simulated reflectance. The results show that the number of photon packets can be reduced by 50-fold without introducing additional errors into the simulated reflectance or optical properties estimated by the inverse models.