PATATO: a Python photoacoustic tomography analysis
toolkit
toolkit
File(s)
Published version
Author(s)
Else, Thomas R
Gröhl, Janek
Hacker, Lina
Bohndiek, Sarah E
Type
Journal Article
Abstract
Photoacoustic imaging (PAI) is an emerging scalable imaging technology that combines the
high contrast of optical imaging with the spatiotemporal resolution of ultrasound (Beard, 2011).
Using light absorption by endogenous molecules, such as haemoglobin in red blood cells, PAI
can reveal the emergence of diseases ranging from inflammation to cancer in both preclinical
animal models and in patients (Brown et al., 2019; Regensburger et al., 2021; Steinberg
et al., 2019; Wang & Hu, 2012). Extracting accurate photoacoustic imaging biomarkers,
such as blood oxygen saturation, from raw data requires a robust image reconstruction and
analysis process, which is challenging due to the high dimensionality of the data across
spatial, spectral and temporal domains. Here we introduce PATATO, a Python toolkit
that offers fast implementations of commonly-used data analysis methods, including preprocessing, reconstruction and temporal data analysis, via a user-friendly command-line
interface and Python API. The toolkit uses JAX, a modern machine learning tool, for GPUaccelerated pre-processing and image reconstruction, and NumPy for easy integration with other
commonly used Python libraries. PATATO is open-source, hosted on GitHub and PyPi, and
distributed under an MIT licence. We have designed PATATO to be modular and extendable
to accommodate different data types, reconstruction methods, and custom analyses for specific
scientific questions. We welcome contributions, bug reports, and feedback. Detailed examples,
documentation, and an API reference are available at https://patato.readthedocs.io/en/latest/.
high contrast of optical imaging with the spatiotemporal resolution of ultrasound (Beard, 2011).
Using light absorption by endogenous molecules, such as haemoglobin in red blood cells, PAI
can reveal the emergence of diseases ranging from inflammation to cancer in both preclinical
animal models and in patients (Brown et al., 2019; Regensburger et al., 2021; Steinberg
et al., 2019; Wang & Hu, 2012). Extracting accurate photoacoustic imaging biomarkers,
such as blood oxygen saturation, from raw data requires a robust image reconstruction and
analysis process, which is challenging due to the high dimensionality of the data across
spatial, spectral and temporal domains. Here we introduce PATATO, a Python toolkit
that offers fast implementations of commonly-used data analysis methods, including preprocessing, reconstruction and temporal data analysis, via a user-friendly command-line
interface and Python API. The toolkit uses JAX, a modern machine learning tool, for GPUaccelerated pre-processing and image reconstruction, and NumPy for easy integration with other
commonly used Python libraries. PATATO is open-source, hosted on GitHub and PyPi, and
distributed under an MIT licence. We have designed PATATO to be modular and extendable
to accommodate different data types, reconstruction methods, and custom analyses for specific
scientific questions. We welcome contributions, bug reports, and feedback. Detailed examples,
documentation, and an API reference are available at https://patato.readthedocs.io/en/latest/.
Date Issued
2024-01-19
Date Acceptance
2024-01-01
Citation
Journal of Open Source Software, 2024, 9 (93)
ISSN
2475-9066
Publisher
Journal of Open Source Software
Journal / Book Title
Journal of Open Source Software
Volume
9
Issue
93
Copyright Statement
Authors of papers retain copyright and release the work under a Creative Commons Attribution 4.0 International License (CC BY 4.0)
License URL
Identifier
10.21105/joss.05686
Publication Status
Published
Article Number
5686
Date Publish Online
2024-01-19