Anatomy-Matched Multi-Variant Stochastic Optoacoustic Numerical Breast Phantoms and Simulated OAT Measurement Data

This dataset is a subset of a companion dataset to the manuscript:
Seonyeong Park, Gangwon Jeong, Umberto Villa, Mark A. Anastasio, "A Virtual Imaging Framework for Three-Dimensional Quantitative Optoacoustic Tomography Using Stochastic Numerical Breast Phantoms," arXiv preprint arXiv:2510.00189 (2025) https://doi.org/10.48550/arXiv.2510.00189

This subset was specifically used in the following publication:
Refik Mert Cam, Seonyeong Park, Umberto Villa, Mark A. Anastasio, "Application of a Virtual Imaging Framework for Investigating a Deep Learning-based Reconstruction Method for 3D Quantitative Photoacoustic Computed Tomography," Photoacoustics 100792 (2025) https://doi.org/10.1016/j.pacs.2025.100792

The dataset contains 64 sets of three-dimensional (3D) numerical breast phantoms (NBPs) for use in virtual imaging studies of optoacoustic tomography (OAT), along with the corresponding simulated multi-wavelength optical fluence distributions, induced initial pressure distributions, and OAT measurement data. Each set corresponds to a distinct breast anatomy and includes four anatomy-matched variants: (i) a healthy breast with Fitzpatrick skin tone 1, and (ii-iv) lesion-inserted breasts with Fitzpatrick skin tones 1, 3, and 5.

More detailed information is provided in the accompanying README.txt file.