Digital twins enable full-reference quality assessment of photoacoustic image reconstructions

TL;DR

This paper introduces a digital twin framework for quantitatively assessing photoacoustic image reconstruction quality, enabling comparison of algorithms with full-reference metrics in vivo and phantom studies.

Contribution

The paper presents a novel digital twin approach for calibration and comparison of photoacoustic reconstruction algorithms, including a new Fourier transform-based method tested on experimental data.

Findings

Digital twins enable accurate assessment of reconstruction algorithms.

The Fourier transform-based algorithm performs comparably to iterative time reversal.

The proposed method reduces computational costs while maintaining accuracy.

Abstract

Quantitative comparison of the quality of photoacoustic image reconstruction algorithms remains a major challenge. No-reference image quality measures are often inadequate, but full-reference measures require access to an ideal reference image. While the ground truth is known in simulations, it is unknown in vivo, or in phantom studies, as the reference depends on both the phantom properties and the imaging system. We tackle this problem by using numerical digital twins of tissue-mimicking phantoms and the imaging system to perform a quantitative calibration to reduce the simulation gap. The contributions of this paper are two-fold: First, we use this digital-twin framework to compare multiple state-of-the-art reconstruction algorithms. Second, among these is a Fourier transform-based reconstruction algorithm for circular detection geometries, which we test on experimental data for the first time. Our results demonstrate the usefulness of digital phantom twins by enabling assessment of the accuracy of the numerical forward model and enabling comparison of image reconstruction schemes with full-reference image quality assessment. We show that the Fourier transform-based algorithm yields results comparable to those of iterative time reversal, but at a lower computational cost. All data and code are publicly available on Zenodo: https://doi.org/10.5281/zenodo.15388429.