A depth-dependent, transverse shift-invariant operator for fast iterative 3D photoacoustic tomography in planar geometry

TL;DR

This paper introduces a fast, FFT-based forward model for 3D photoacoustic tomography that leverages transverse shift invariance to significantly accelerate iterative image reconstruction.

Contribution

It develops a novel depth-dependent, shift-invariant operator that reduces computational cost by avoiding PDE solves during iterative reconstruction in planar geometries.

Findings

Achieves up to 100x acceleration in iterative reconstructions.

Validates the model against PDE solvers with matched discretization.

Demonstrates effectiveness on experimental all-optical photoacoustic datasets.

Abstract

Iterative model-based image reconstruction in photoacoustic tomography (PAT) enables principled incorporation of detector physics, object-related priors, and complex acquisition strategies. However, for three-dimensional (3D) imaging scenario, the computational cost is often dominated by repeatedly solving wave equations. We propose a fast forward model for planar detection geometries that exploits transverse shift invariance. This symmetry enables to compute the full acoustic field from a 3D object, as a result of a set of 2D convolutions with depth-dependent impulse responses. This formulation yields a FFT-based forward operator and its corresponding discrete adjoint operator, making iterative reconstruction faster without calling partial differential equation (PDE) solvers at each iteration. We validate the model against commonly used PDE solver under matched discretization and boundary settings, and demonstrate accelerations of up to 2 orders of magnitude for iterative reconstructions from experimental all-optical photoacoustic datasets.