Transport-theory-based reconstruction algorithm for optical tomography

Currently available image reconstruction schemes for photon migration tomography are based on the diffusion approximation for light propagation in turbid media. We have shown in previous works that these schemes fail to describe the light propagation in very low scattering media such as the cerebrospinal fluid of the brain or the synovial fluid of finger joints. Therefore, we have developed a reconstruction algorithm that is based on the equation of radiative transfer. This equation describes the photon propagation in turbid media most accurately without any assumptions regarding the optical properties. Analytical solutions for complex geometries and heterogeneous media are not available. Thus, a numerical method is considered, which is based on a finite-difference formulation of the time-dependent transport equation. The reconstruction code consists of three major parts: (1) a forward model based that predicts detector readings based on the equation of radiative transfer, (2) an objective function that describes the differences between the measured and the predicted data, and (3) an updating scheme that uses the gradient of the objective function to perform a line minimization to get new guesses of the optical properties. Based on a new guess of the optical properties a new forward calculation is performed. The reconstruction process is completed when the minimum of the objective function is found.