PATATO is an Open-Source project to enable the analysis of photoacoustic (PA) imaging data in a transparent, reproducible and extendable way. We provide efficient, GPU-optimised implementations of common PA algorithms written around standard Python libraries, including filtered backprojection, model-based reconstruction and spectral unmixing.
The tool supports many file formats, such as the International Photoacoustic Standardisation Consortium (IPASC) data format, and it can be extended to support custom data formats. We hope that this toolkit can enable faster and wider dissemination of analysis techniques for PA imaging and provide a useful tool to the community.
- Please report any bugs or issues you find to our GitHub repository
- Please do get involved! Contact Thomas Else (telse@ic.ac.uk).
In order to use PATATO, you must have a Python environment set up on your computer. We recommend using uv (https://docs.astral.sh/uv/) to run Python. This will help you to avoid dependency conflicts. You can alternatively use Anaconda or virtual environments.
We currently recommend running PATATO on Python version 3.12.
You can install patato with uv like so:
uv add patatoOr, using pip:
pip install patatoTo setup support for GPU-based reconstruction, please follow the installation guide in the documentation.
To cite PATATO, please reference our article in the Journal of Open Source software, here.
Documentation for PATATO can be found at https://patato.readthedocs.io/en/latest/?badge=latest.
Copyright (c) Thomas Else 2022-25. Distributed under a MIT License.