Quantitative photoacoustic tomography using forward and adjoint Monte Carlo models of radiance

TL;DR

This paper introduces a Monte Carlo-based framework for quantitative photoacoustic tomography that efficiently computes gradients for reconstructing optical properties, leveraging harmonic angular basis and parallel computing.

Contribution

It presents a novel 2D radiance Monte Carlo model with harmonic basis and a gradient-based optimization scheme for quantitative photoacoustic imaging.

Findings

Validated against analytic solutions in heterogeneous media

Demonstrated efficient gradient computation from harmonic coefficients

Framework suitable for high-performance parallel computing

Abstract

Forward and adjoint Monte Carlo (MC) models of radiance are proposed for use in model-based quantitative photoacoustic tomography. A 2D radiance MC model using a harmonic angular basis is introduced and validated against analytic solutions for the radiance in heterogeneous media. A gradient-based optimisation scheme is then used to recover 2D absorption and scattering coefficients distributions from simulated photoacoustic measurements. It is shown that the functional gradients, which are a challenge to compute efficiently using MC models, can be calculated directly from the coefficients of the harmonic angular basis used in the forward and adjoint models. This work establishes a framework for transport-based quantitative photoacoustic tomography that can fully exploit emerging highly parallel computing architectures.