Linearized Reconstruction for Diffuse Optical Spectroscopic Imaging

TL;DR

This paper introduces a linearized, group sparsity-based reconstruction method for diffuse optical spectroscopic imaging that improves simultaneous recovery of tissue optical properties, even with modeling errors, using multi-wavelength data.

Contribution

The paper proposes a novel linearization and group sparsity approach for diffuse optical imaging that enhances simultaneous reconstruction of absorption and diffusion coefficients.

Findings

Effective recovery of optical properties with multi-wavelength data.

Robustness to boundary modeling errors demonstrated.

Numerical experiments validate the method's performance.

Abstract

In this paper, we present a novel reconstruction method for diffuse optical spectroscopic imaging with a commonly used tissue model of optical absorption and scattering. It is based on linearization and group sparsity, which allows recovering the diffusion coefficient and absorption coefficient simultaneously, provided that their spectral profiles are incoherent and a sufficient number of wavelengths are judiciously taken for the measurements. We also discuss the reconstruction for imperfectly known boundary and show that with the multi-wavelength data, the method can reduce the influence of modelling errors and still recover the absorption coefficient. Extensive numerical experiments are presented to support our analysis.