We discuss a novel numerical approach to dynamic optical diffusion tomography (OT) problem. The assumption in the method is that the optical properties of the target are non-stationary 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, OT problem is formulated as a state estimation problem. Within the state estimation formulation, the optical properties of the target are considered as 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 using Kalman filtering techniques. The performance of the proposed method is evaluated with simulations.
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