OPTICAL PROPERTY CHARACTERIZATION BASED ON A PHASE FUNCTION APPROXIMATION MODEL

Recently, we generalized the Delta-Eddington phase function and applied it to the radiative transfer equation for modeling the photon propagation in biological tissue. The resultant phase approximation model was shown to be highly accurate with a wide range of optical properties, including the strongly absorbing and weakly scattering media. In this paper, we propose phase-approximation-based method for estimating the optical parameters. Specifically, we design an iterative algorithm to take advantage of both the global search ability of the differential evolution algorithm and the efficiency of the conjugate gradient method. Then, we demonstrate the feasibility and merits of the proposed method in both numerical simulation and phantom experiments.

Modeling photon propagation in biological tissues using a generalized Delta-Eddington phase function

Photon propagation in biological tissue is commonly described by the radiative transfer equation, while the phase function in the equation represents the scattering characteristics of the medium and has significant influence on the precision of solution and the efficiency of computation. In this work, we present a generalized Delta-Eddington phase function to simplify the radiative transfer equation to an integral equation with respect to photon fluence rate. Comparing to the popular diffusion approximation model, the solution of the integral equation is highly accurate to model photon propagation in the biological tissue over a broad range of optical parameters. This methodology is validated by Monte Carlo simulation.

Flux vector formulation for photon propagation in the biological tissue

We present a generalized delta-Eddington phase function to simplify the radiative transfer equation to an integral equation with respect to the photon flux vector. The solution of the integral equation is highly accurate to model the photon propagation in the biological tissue over a broad range of optical parameters, especially in the visible light spectrum where the diffusion approximation breaks down. The methodology is validated in the Monte Carlo simulation and can be applied in various optical imaging applications.