A global inverse-problem approach to quantitative photo-switching optoacoustic mesoscopy

TL;DR

This paper introduces a comprehensive physics-based inverse-problem framework for photo-switching optoacoustic mesoscopy, enabling robust and high-quality image reconstruction from acoustic signals.

Contribution

It presents a novel unified inverse approach with hybrid regularization and matrix-free models, improving robustness and image quality over existing methods.

Findings

Outperforms two-step and unregularized methods in noise robustness.

Achieves higher-quality images in numerical experiments.

Uses a combined inverse problem with hybrid regularization.

Abstract

In this paper, we propose a global framework that includes a detailed model of the photo-switching and acoustic processes for photo-switching optoacoustic mesoscopy, based on the underlying physics. We efficiently implement two forward models as matrix-free linear operators and join them as one forward operator. Then, we reconstruct the concentration maps directly from the temporal series of acoustic signals through the resolution of one combined inverse problem. For robustness against noise and clean unmixing results, we adopt a hybrid regularization technique composed of the $l_1$ and total-variation regularizers applied to two different spaces. We use a proximal-gradient algorithm to solve the minimization problem. Our numerical results show that our regularized one-step approach is the most robust in terms of noise and experimental setup. It consistently achieves higher-quality images, as compared to two-step or unregularized methods.