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

2025-05-30

AI Summary

Quantitative full-reference evaluation of photoacoustic image reconstruction algorithms is hindered by the absence of ideal ground-truth reference images in vivo or in phantom experiments. To address this, we propose the first digital twin calibration framework tailored for photoacoustic imaging: a high-fidelity numerical twin model of tissue-mimicking phantoms and the imaging system is established, and domain gap reduction is achieved via joint experimental–simulation calibration, enabling rigorous full-reference assessment. We further pioneer the application of circular-scan Fourier-domain reconstruction to real measured data and systematically compare it against iterative time-reversal methods. Results demonstrate that calibration substantially reduces forward-model error; Fourier-domain reconstruction achieves comparable PSNR and SSIM to iterative methods while accelerating computation by over 90%. All experimental data, source code, and the digital twin model are publicly released.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Computer Vision: Computational Photography, Image & Video Synthesis
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Web applications in cross-disciplinary domains and verticals such as mixed reality, smart cities, and digital health

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.

Problem

• Quantitative comparison of photoacoustic image reconstruction algorithms is challenging
• Digital twins enable full-reference quality assessment without ideal reference images
• Evaluating Fourier transform-based algorithm's performance against iterative methods

Innovation

• Digital twins simulate tissue-mimicking phantoms and imaging systems
• Fourier transform-based algorithm tested on experimental data
• Full-reference quality assessment enables accurate image comparison