Model-based temporal unmixing towards quantitative photo-switching optoacoustic tomography

TL;DR

This paper introduces a model-based variational approach for unmixing and imaging photo-switching reporters in optoacoustic tomography, enabling better separation of signals and quantitative analysis in live tissue imaging.

Contribution

It presents a novel mathematical framework and iterative algorithm that improve unmixing accuracy by modeling physical switching mechanisms and using regularization techniques.

Findings

Successfully disentangles overlapping labels in phantom and mouse experiments.

Recovers continuous maps of reporter quantities with high robustness to noise.

Enhances quantitative photo-switching imaging in optoacoustic tomography.

Abstract

Optoacoustic (OA) imaging combined with reversibly photoswitchable proteins has emerged as a promising technology for the high-sensitivity and multiplexed imaging of cells in live tissues in preclinical research. Through carefully-designed illumination schedules of ON and OFF laser pulses, the resulting OA signal is a multiplex of different reporter species and the background. We propose a model-based variational framework to computationally unmix and image different species of photo-switching reporters using optoacoustic tomography. It is based on a detailed mathematical description of the photo-switching mechanism, which models how relevant physical parameters such as the kinetic constants and light fluence impact the switching signal. We introduce an algorithm that operates on images, as opposed to traditional pixelwise approaches. It takes the form of an iterative inversion combined with tailored $\ell_1$ and total-variation regularization to increase the robustness to noise and to improve the unmixing quality. We show that our method is able to disentangle multiple spatially overlapping labels and to recover continuous maps of quantities of interest on controlled phantoms and mice experiments.