Finite element approximation for quantitative photoacoustic tomography in a diffusive regime

TL;DR

This paper develops a finite element method for reconstructing optical coefficients in quantitative photoacoustic tomography within a diffusive regime, providing error estimates and validating through numerical experiments.

Contribution

It introduces a novel numerical scheme combining output least squares and finite element discretization for inverse diffusivity problems in photoacoustic tomography, with rigorous error analysis.

Findings

Error estimates guide regularization and discretization choices.

Numerical experiments confirm theoretical accuracy.

Method effectively reconstructs optical coefficients from internal data.

Abstract

In this paper, we focus on the numerical analysis of quantitative photoacoustic tomography. Our goal is to reconstruct the optical coefficients, i.e., the diffusion and absorption coefficients, using multiple internal observational data. The foundation of our numerical algorithm lies in solving an inverse diffusivity problem and a direct problem associated with elliptic equations. The stability of the inverse problem depends critically on a non-zero condition in the internal observations, a condition that can be met using randomly chosen boundary excitation data. Utilizing these randomly generated boundary data, we implement an output least squares formulation combined with finite element discretization to solve the inverse problem. In this scenario, we provide a rigorous error estimate in $L^2(\Omega)$ norm for the numerical reconstruction using a weighted energy estimate, inspired by the analysis of a newly proposed conditional stability result. The resulting error estimate serves as a valuable guide for selecting appropriate regularization parameters and discretization mesh sizes according to the noise levels present in the data. Several numerical experiments are presented to support our theoretical results and illustrate the effectiveness of our numerical scheme.