Image reconstruction with uncertainty quantification in photoacoustic tomography

Abstract

Photoacoustic tomography is a hybrid imaging method that combines optical contrast and ultrasound resolution. The goal of photoacoustic tomography is to resolve an initial pressure distribution from detected ultrasound waves generated within an object due to an illumination of a short light pulse. In this work, a Bayesian approach to photoacoustic tomography is described. The solution of the inverse problem is derived and computation of the point estimates for image reconstruction and uncertainty quantification is described. The approach is investigated with simulations in different detector geometries, including limited view setup, and with different detector properties such as ideal point-like detectors, finite size detectors, and detectors with a finite bandwidth. The results show that the Bayesian approach can be used to provide accurate estimates of the initial pressure distribution, as well as information about the uncertainty of the estimates.