Optimizing Quantitative Photoacoustic Imaging Systems: The Bayesian Cram\'er-Rao Bound Approach

TL;DR

This paper presents a novel Bayesian Cramér-Rao bound approach for optimal experimental design in quantitative photoacoustic computed tomography, addressing the challenges of system design and inverse problem complexity.

Contribution

It introduces a new computational method for PDE-based system design using Bayesian CRB, incorporating priors and variational adjoint techniques for efficiency.

Findings

The approach effectively guides system design in a 2D numerical study.

First application of Bayesian CRB for PDE-governed imaging systems.

Demonstrates computational efficiency and independence from specific estimators.

Abstract

Quantitative photoacoustic computed tomography (qPACT) is an emerging medical imaging modality that carries the promise of high-contrast, fine-resolution imaging of clinically relevant quantities like hemoglobin concentration and blood-oxygen saturation. However, qPACT image reconstruction is governed by a multiphysics, partial differential equation (PDE) based inverse problem that is highly non-linear and severely ill-posed. Compounding the difficulty of the problem is the lack of established design standards for qPACT imaging systems, as there is currently a proliferation of qPACT system designs for various applications and it is unknown which ones are optimal or how to best modify the systems under various design constraints. This work introduces a novel computational approach for the optimal experimental design (OED) of qPACT imaging systems based on the Bayesian Cram\'er-Rao bound (CRB). Our approach incorporates several techniques to address challenges associated with forming the bound in the infinite-dimensional function space setting of qPACT, including priors with trace-class covariance operators and the use of the variational adjoint method to compute derivatives of the log-likelihood function needed in the bound computation. The resulting Bayesian CRB based design metric is computationally efficient and independent of the choice of estimator used to solve the inverse problem. The efficacy of the bound in guiding experimental design was demonstrated in a numerical study of qPACT design schemes under a stylized two-dimensional imaging geometry. To the best of our knowledge, this is the first work to propose Bayesian CRB based design for systems governed by PDEs.