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

arXiv: https://arxiv.org/abs/2505.24514 · HTML: https://mlsanity.com/paper/2505.24514/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 55 references that are in the map.
- First posted on arXiv 30 May 2025.

## Builds on (most cited first, 7 of 7)

- [A Haar Wavelet-Based Perceptual Similarity Index for Image Quality Assessment](https://mlsanity.com/paper/1607.06140/) (2016)
- [Model based learning for accelerated, limited-view 3D photoacoustic tomography](https://mlsanity.com/paper/1708.09832/) (2017)
- [Deep learning for biomedical photoacoustic imaging: A review](https://mlsanity.com/paper/2011.02744/) (2020)
- [So you think you can DAS? A viewpoint on delay-and-sum beamforming](https://mlsanity.com/paper/2007.11960/) (2020)
- [A Partially Learned Algorithm for Joint Photoacoustic Reconstruction and Segmentation](https://mlsanity.com/paper/1906.07499/) (2019)
- [Moving beyond simulation: data-driven quantitative photoacoustic imaging using tissue-mimicking phantoms](https://mlsanity.com/paper/2306.06748/) (2023)
- [A study on the adequacy of common IQA measures for medical images](https://mlsanity.com/paper/2405.19224/) (2024)

## Built on by (most cited first, 4 of 4)

- [Fast algorithms enabling optimization and deep learning for photoacoustic tomography in a circular detection geometry](https://mlsanity.com/paper/2510.24687/) (2025)
- [PhotIQA: A photoacoustic image data set with image quality ratings](https://mlsanity.com/paper/2507.03478/) (2025)
- [Application of a Virtual Imaging Framework for Investigating a Deep Learning-Based Reconstruction Method for 3D Quantitative Photoacoustic Computed Tomography](https://mlsanity.com/paper/2510.03431/) (2025)
- [Deep Image Prior for photoacoustic tomography can mitigate limited-view artifacts](https://mlsanity.com/paper/2604.19176/) (2026)

## Abstract (arXiv)

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.