Edge-promoting reconstruction of absorption and diffusivity in optical tomography

TL;DR

This paper introduces an edge-promoting reconstruction algorithm for optical tomography that effectively estimates internal absorption and diffusivity by leveraging an edge-prior and iterative linearization, validated through 3D simulations.

Contribution

It presents a novel iterative method combining lagged diffusivity and linearization with an edge-prior for improved optical property reconstruction.

Findings

Effective reconstruction of optical properties in simulated 3D data

Edge-prior enhances boundary delineation of inhomogeneities

Method outperforms traditional techniques in accuracy

Abstract

In optical tomography a physical body is illuminated with near-infrared light and the resulting outward photon flux is measured at the object boundary. The goal is to reconstruct internal optical properties of the body, such as absorption and diffusivity. In this work, it is assumed that the imaged object is composed of an approximately homogeneous background with clearly distinguishable embedded inhomogeneities. An algorithm for finding the maximum a posteriori estimate for the absorption and diffusion coefficients is introduced assuming an edge-preferring prior and an additive Gaussian measurement noise model. The method is based on iteratively combining a lagged diffusivity step and a linearization of the measurement model of diffuse optical tomography with priorconditioned LSQR. The performance of the reconstruction technique is tested via three-dimensional numerical experiments with simulated measurement data.