# Optoacoustic Model-Based Inversion Using Anisotropic Adaptive Total-Variation Regularization

## TL;DR

This paper introduces an adaptive anisotropic total-variation regularization framework for optoacoustic image reconstruction, improving boundary preservation and contrast in noisy or incomplete data scenarios.

## Contribution

The novel adaptive anisotropic total-variation regularization scheme enhances boundary preservation and contrast over traditional methods in optoacoustic tomography.

## Key findings

- Outperforms total-variation-$L_1$ scheme in simulations and experiments.
- Better preserves complex boundaries in blood-vessel images.
- Enhances image contrast in noisy and incomplete data conditions.

## Abstract

In optoacoustic tomography, image reconstruction is often performed with incomplete or noisy data, leading to reconstruction errors. Significant improvement in reconstruction accuracy may be achieved in such cases by using nonlinear regularization schemes, such as total-variation minimization and $L_1$-based sparsity-preserving schemes. In this paper, we introduce a new framework for optoacoustic image reconstruction based on adaptive anisotropic total-variation regularization, which is more capable of preserving complex boundaries than conventional total-variation regularization. The new scheme is demonstrated in numerical simulations on blood-vessel images \textcolor{black} {as well as on experimental data} and is shown to be more capable than the total-variation-$L_1$ scheme in enhancing image contrast.