Coupled Elastic-Acoustic Modelling for Quantitative Photoacoustic Tomography

TL;DR

This paper develops a Bayesian method to improve quantitative photoacoustic tomography near the skull by compensating for elastic wave effects, enhancing the accuracy of chromophore concentration estimates.

Contribution

It introduces a Bayesian approximation error approach to account for elastic wave effects in photoacoustic imaging, improving estimation accuracy.

Findings

Bayesian approach improves posterior estimates of initial pressure.

Compensation for elastic wave effects enhances chromophore concentration accuracy.

Uncertainty quantification supports more reliable imaging near bone structures.

Abstract

Quantitative photoacoustic tomography (qPAT) is an imaging technique aimed at estimating chromophore concentrations inside tissues from photoacoustic images, which are formed by combining optical information and ultrasonic propagation. The application of qPAT as a transcranial imaging modality is complicated by shear waves that can be produced when ultrasound waves travel from soft tissue to bone. Because of this, the estimation of chromophores distributions near the skull can be problematic. In this paper, we take steps towards compensating for aberrations of the recorded photoacoustic signals caused by elastic wave propagation. With photoacoustic data simulated in a coupled elastic-acoustic domain, we conduct inversions in a purely acoustic domain. Estimation of the posterior density of the initial pressure is achieved by inversion under the Bayesian framework. We utilize the Bayesian approximation error approach to compensate for the modelling errors arising from approximating a coupled elastic-acoustic domain with a purely fluid domain. The resulting reconstructions and corresponding uncertainty estimates are then used to evaluate the posterior density of the optical absorption parameter. In the sense of the posterior uncertainty, the results show that the Bayesian approximation error approach yields a more feasible estimate for the posterior model of the initial pressure which, in turn, yields a more feasible estimate for the posterior model of the absorption coefficient.