Diffuse optical tomography using a linear matrix inequality algorithm in an admissible solution approach

Diffuse optical tomography (DOT) is an emerging medical imaging modality offering the possibility of recovering the distribution of optical absorption and scattering coefficients, and from them localize metabolic parameters such as oxygen saturation or neural activity, using nonionizing near-IR light. However it requires solving a badly ill-posed inverse problem. In this article we discuss the formulation of the DOT problem that uses an efficient interior-point-type optimization algorithm to find a DOT inverse solution in an admissible solution scenario. We present simulation results verifying the effectiveness of the approach.