# Application of a Virtual Imaging Framework for Investigating a Deep Learning-Based Reconstruction Method for 3D Quantitative Photoacoustic Computed Tomography

arXiv: https://arxiv.org/abs/2510.03431 · HTML: https://mlsanity.com/paper/2510.03431/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 72 references that are in the map.
- First posted on arXiv 3 Oct 2025.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [CS2-Net: Deep Learning Segmentation of Curvilinear Structures in Medical Imaging](https://mlsanity.com/paper/2010.07486/) (2020)
- [Theory of Curriculum Learning, with Convex Loss Functions](https://mlsanity.com/paper/1812.03472/) (2018)
- [Moving beyond simulation: data-driven quantitative photoacoustic imaging using tissue-mimicking phantoms](https://mlsanity.com/paper/2306.06748/) (2023)
- [Estimation of blood oxygenation with learned spectral decoloring for quantitative photoacoustic imaging (LSD-qPAI)](https://mlsanity.com/paper/1902.05839/) (2019)
- [Eigenspectra optoacoustic tomography achieves quantitative blood oxygenation imaging deep in tissues](https://mlsanity.com/paper/1511.05846/) (2015)
- [O-Net: A Convolutional Neural Network for Quantitative Photoacoustic Image Segmentation and Oximetry](https://mlsanity.com/paper/1911.01935/) (2019)
- [ProxNF: Neural Field Proximal Training for High-Resolution 4D Dynamic Image Reconstruction](https://mlsanity.com/paper/2403.03860/) (2024)
- [Digital twins enable full-reference quality assessment of photoacoustic image reconstructions](https://mlsanity.com/paper/2505.24514/) (2025)
- [Spatiotemporal Image Reconstruction to Enable High-Frame Rate Dynamic Photoacoustic Tomography with Rotating-Gantry Volumetric Imagers](https://mlsanity.com/paper/2310.00529/) (2023)
- [Scalable and massively parallel Monte Carlo photon transport simulations for heterogeneous computing platforms](https://mlsanity.com/paper/1711.03244/) (2017)
- [Direct Quantitative Photoacoustic Tomography for realistic acoustic media](https://mlsanity.com/paper/1810.11685/) (2018)
- [Photoacoustic digital brain: numerical modelling and image reconstruction via deep learning](https://mlsanity.com/paper/2109.09127/) (2021)

## Abstract (arXiv)

Quantitative photoacoustic computed tomography (qPACT) is a promising imaging modality for estimating physiological parameters such as blood oxygen saturation. However, developing robust qPACT reconstruction methods remains challenging due to computational demands, modeling difficulties, and experimental uncertainties. Learning-based methods have been proposed to address these issues but remain largely unvalidated. Virtual imaging (VI) studies are essential for validating such methods early in development, before proceeding to less-controlled phantom or in vivo studies. Effective VI studies must employ ensembles of stochastically generated numerical phantoms that accurately reflect relevant anatomy and physiology. Yet, most prior VI studies for qPACT relied on overly simplified phantoms. In this work, a realistic VI testbed is employed for the first time to assess a representative 3D learning-based qPACT reconstruction method for breast imaging. The method is evaluated across subject variability and physical factors such as measurement noise and acoustic aberrations, offering insights into its strengths and limitations.