State-estimation approach to the nonstationary optical tomography problem

Abstract

We propose a new numerical approach to the nonstationary optical (diffusion) tomography (OT) problem. The assumption in the method is that the absorption and/or diffusion coefficients are nonstationary in the sense that they may exhibit significant changes during the time that is needed to measure data for one traditional image frame. In the proposed method, the OT problem is formulated as a state-estimation problem. Within the state-estimation formulation, the absorption and/or diffusion coefficients are considered a stochastic process. The objective is to estimate a sequence of states for the process when the state evolution model for the process, the observation model for OT experiments, and data on the exterior boundary are given. In the proposed method, the state estimates are computed by using Kalman filtering techniques. The performance of the proposed method is evaluated on the basis of synthetic data. The simulations also illustrate that further improvements to the results in nonstationary applications can be obtained by adjustment of the measurement protocol.