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A Python Toolkit for the analysis of photoacoustic tomography data.

Project description

Journal of open source software status Documentation Status License: MIT PyPI version Build

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PATATO: PhotoAcoustic Tomography Analysis TOolkit

Documentation

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).

Getting Started

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 patato

Or, using pip:

pip install patato

To setup support for GPU-based reconstruction, please follow the installation guide in the documentation.

Citing PATATO

To cite PATATO, please reference our article in the Journal of Open Source software, here.

Documentation, examples and contributing

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.

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