Single-stage reconstruction algorithm for quantitative photoacoustic tomography

TL;DR

This paper introduces a single-stage nonlinear inverse algorithm for quantitative photoacoustic tomography that directly reconstructs optical parameters from acoustic data, improving accuracy over traditional two-stage methods.

Contribution

The paper proposes a novel single-stage reconstruction approach for quantitative photoacoustic tomography, coupling radiative transfer and acoustic equations, with Tikhonov regularization and proximal gradient methods.

Findings

Single-stage algorithm improves reconstruction quality.

Comparable computational cost to existing methods.

Numerical results validate the effectiveness of the approach.

Abstract

The development of efficient and accurate image reconstruction algorithms is one of the cornerstones of computed tomography. Existing algorithms for quantitative photoacoustic tomography currently operate in a two-stage procedure: First an inverse source problem for the acoustic wave propagation is solved, whereas in a second step the optical parameters are estimated from the result of the first step. Such an approach has several drawbacks. In this paper we therefore propose the use of single-stage reconstruction algorithms for quantitative photoacoustic tomography, where the optical parameters are directly reconstructed from the observed acoustical data. In that context we formulate the image reconstruction problem of quantitative photoacoustic tomography as a single nonlinear inverse problem by coupling the radiative transfer equation with the acoustic wave equation. The inverse problem is approached by Tikhonov regularization with a convex penalty in combination with the proximal gradient iteration for minimizing the Tikhonov functional. We present numerical results, where the proposed single-stage algorithm shows an improved reconstruction quality at a similar computational cost.