Perturbation Monte Carlo Method for Quantitative Photoacoustic Tomography

TL;DR

This paper introduces a perturbation Monte Carlo method for solving the inverse problem in quantitative photoacoustic tomography, enabling accurate estimation of optical parameters like absorption and scattering in biological tissues.

Contribution

It presents a novel perturbation Monte Carlo approach that improves the robustness and gradient formation for Bayesian inverse problems in photoacoustic tomography.

Findings

Accurately estimates spatial distributions of absorption and scattering.

Qualitatively good and quantitatively accurate parameter estimates.

Applicable to realistic biological tissue parameters.

Abstract

Quantitative photoacoustic tomography aims at estimating optical parameters from photoacoustic images that are formed utilizing the photoacoustic effect caused by the absorption of an externally introduced light pulse. This optical parameter estimation is an ill-posed inverse problem, and thus it is sensitive to measurement and modeling errors. In this work, we propose a novel way to solve the inverse problem of quantitative photoacoustic tomography based on the perturbation Monte Carlo method. Monte Carlo method for light propagation is a stochastic approach for simulating photon trajectories in a medium with scattering particles. It is widely accepted as an accurate method to simulate light propagation in tissues. Furthermore, it is numerically robust and easy to implement. Perturbation Monte Carlo maintains this robustness and enables forming gradients for the solution of the inverse problem. We validate the method and apply it in the framework of Bayesian inverse problems. The simulations show that the perturbation Monte Carlo method can be used to estimate spatial distributions of both absorption and scattering parameters simultaneously. These estimates are qualitatively good and quantitatively accurate also in parameter scales that are realistic for biological tissues.