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http://creativecommons.org/licenses/by/4.0/ + + + + Software archive + 10.5281/zenodo.10255220 + + + GitHub review issue + https://github.com/openjournals/joss-reviews/issues/5686 + + + + 10.21105/joss.05686 + https://joss.theoj.org/papers/10.21105/joss.05686 + + + https://joss.theoj.org/papers/10.21105/joss.05686.pdf + + + + + + Biomedical photoacoustic +imaging + Beard + Interface Focus + 4 + 1 + 10.1098/rsfs.2011.0028 + 2011 + Beard, P. (2011). Biomedical +photoacoustic imaging. Interface Focus, 1(4), 602–631. +https://doi.org/10.1098/rsfs.2011.0028 + + + Photoacoustic imaging as a tool to probe the +tumour microenvironment + Brown + Disease Models & +Mechanisms + 7 + 12 + 10.1242/dmm.039636 + 1754-8403 + 2019 + Brown, E., Brunker, J., & +Bohndiek, S. E. (2019). Photoacoustic imaging as a tool to probe the +tumour microenvironment. Disease Models & Mechanisms, 12(7), +dmm039636. https://doi.org/10.1242/dmm.039636 + + + Dataset for: PATATO: A Python Photoacoustic +Analysis Toolkit + Else + 10.17863/CAM.93181 + 2023 + Else, T., Groehl, J., Hacker, L., +& Bohndiek, S. (2023). Dataset for: PATATO: A Python Photoacoustic +Analysis Toolkit. +https://doi.org/10.17863/CAM.93181 + + + Learned spectral decoloring enables +photoacoustic oximetry + Gröhl + Scientific Reports + 1 + 11 + 10.1038/s41598-021-83405-8 + 2021 + Gröhl, J., Kirchner, T., Adler, T. +J., Hacker, L., Holzwarth, N., Hernández-Aguilera, A., Herrera, M. A., +Santos, E., Bohndiek, S. E., & Maier-Hein, L. (2021). Learned +spectral decoloring enables photoacoustic oximetry. Scientific Reports, +11(1), 6565. +https://doi.org/10.1038/s41598-021-83405-8 + + + SIMPA: An open-source toolkit for simulation +and image processing for photonics and acoustics + Gröhl + Journal of Biomedical Optics + 08 + 27 + 10.1117/1.JBO.27.8.083010 + 1083-3668 + 2022 + Gröhl, J., Dreher, K. K., +Schellenberg, M., Rix, T., Holzwarth, N., Vieten, P., Ayala, L., +Bohndiek, S. E., Seitel, A., & Maier-Hein, L. (2022). SIMPA: An +open-source toolkit for simulation and image processing for photonics +and acoustics. Journal of Biomedical Optics, 27(08), 083010. +https://doi.org/10.1117/1.JBO.27.8.083010 + + + Estimating blood oxygenation from +photoacoustic images: Can a simple linear spectroscopic inversion ever +work? + Hochuli + Journal of Biomedical Optics + 12 + 24 + 10.1117/1.JBO.24.12.121914 + 1083-3668 + 2019 + Hochuli, R., An, L., Beard, P. C., +& Cox, B. T. (2019). Estimating blood oxygenation from photoacoustic +images: Can a simple linear spectroscopic inversion ever work? Journal +of Biomedical Optics, 24(12), 1. +https://doi.org/10.1117/1.JBO.24.12.121914 + + + Multiple illumination learned spectral +decoloring for quantitative optoacoustic oximetry +imaging + Kirchner + https://doi.org/10.1117/1.JBO.26.8.085001 + 8 + 26 + 10.1117/1.JBO.26.8.085001 + 1083-3668 + 2021 + Kirchner, T., & Frenz, M. (2021). +Multiple illumination learned spectral decoloring for quantitative +optoacoustic oximetry imaging. +Https://Doi.org/10.1117/1.JBO.26.8.085001, 26(8), 085001. +https://doi.org/10.1117/1.JBO.26.8.085001 + + + A scalable open-source MATLAB toolbox for +reconstruction and analysis of multispectral optoacoustic tomography +data + O’Kelly + Scientific Reports 2021 11:1 + 1 + 11 + 10.1038/s41598-021-97726-1 + 2045-2322 + 0123456789 + 2021 + O’Kelly, D., Campbell, J., Gerberich, +J. L., Karbasi, P., Malladi, V., Jamieson, A., Wang, L., & Mason, R. +P. (2021). A scalable open-source MATLAB toolbox for reconstruction and +analysis of multispectral optoacoustic tomography data. Scientific +Reports 2021 11:1, 11(1), 1–18. +https://doi.org/10.1038/s41598-021-97726-1 + + + Assorted Spectra + Prahl + 2018 + Prahl, S. (2018). Assorted Spectra. +https://omlc.org/spectra/index.html. + + + Optoacoustic Imaging in +Inflammation + Regensburger + Biomedicines + 5 + 9 + 10.3390/biomedicines9050483 + 2227-9059 + 2021 + Regensburger, A. P., Brown, E., +Krönke, G., Waldner, M. J., & Knieling, F. (2021). Optoacoustic +Imaging in Inflammation. Biomedicines, 9(5), 483. +https://doi.org/10.3390/biomedicines9050483 + + + Negativity artifacts in back-projection based +photoacoustic tomography + Shen + Journal of Physics D: Applied +Physics + 7 + 54 + 10.1088/1361-6463/ABC37D + 0022-3727 + 2020 + Shen, K., Liu, S., Feng, T., Yuan, +J., Zhu, B., & Tian, C. (2020). Negativity artifacts in +back-projection based photoacoustic tomography. Journal of Physics D: +Applied Physics, 54(7), 074001. +https://doi.org/10.1088/1361-6463/ABC37D + + + Photoacoustic clinical +imaging + Steinberg + Photoacoustics + 14 + 10.1016/j.pacs.2019.05.001 + 2019 + Steinberg, I., Huland, D. M., +Vermesh, O., Frostig, H. E., Tummers, W. S., & Gambhir, S. S. +(2019). Photoacoustic clinical imaging. Photoacoustics, 14, 77–98. +https://doi.org/10.1016/j.pacs.2019.05.001 + + + Oxygen-enhanced and dynamic contrast-enhanced +optoacoustic tomography provide surrogate biomarkers of tumour vascular +function, hypoxia and necrosis. + Tomaszewski + Cancer Research + 20 + 78 + 10.1158/0008-5472.CAN-18-1033 + 0008-5472 + 2018 + Tomaszewski, M. R., Gehrung, M., +Joseph, J., Quiros Gonzalez, I., Disselhorst, J. A., & Bohndiek, S. +E. (2018). Oxygen-enhanced and dynamic contrast-enhanced optoacoustic +tomography provide surrogate biomarkers of tumour vascular function, +hypoxia and necrosis. Cancer Research, 78(20), canres.1033.2018. +https://doi.org/10.1158/0008-5472.CAN-18-1033 + + + Photoacoustic Tomography: In Vivo Imaging +from Organelles to Organs + Wang + Science + 6075 + 335 + 10.1126/science.1216210 + 0036-8075 + 2012 + Wang, L. V., & Hu, S. (2012). +Photoacoustic Tomography: In Vivo Imaging from Organelles to Organs. +Science, 335(6075), 1458–1462. +https://doi.org/10.1126/science.1216210 + + + Universal back-projection algorithm for +photoacoustic computed tomography + Xu + Physical Review E - Statistical, Nonlinear, +and Soft Matter Physics + 1 + 71 + 10.1103/PhysRevE.71.016706 + 2005 + Xu, M., & Wang, L. V. (2005). +Universal back-projection algorithm for photoacoustic computed +tomography. Physical Review E - Statistical, Nonlinear, and Soft Matter +Physics, 71(1). +https://doi.org/10.1103/PhysRevE.71.016706 + + + + + + From 8570e5cd2333e45fa1425923f01bf94577209a6c Mon Sep 17 00:00:00 2001 From: The Open Journals editorial robot <89919391+editorialbot@users.noreply.github.com> Date: Fri, 19 Jan 2024 09:43:18 +0000 Subject: [PATCH 3/6] Creating 10.21105.joss.05686.jats --- joss.05686/10.21105.joss.05686.jats | 707 ++++++++++++++++++++++++++++ 1 file changed, 707 insertions(+) create mode 100644 joss.05686/10.21105.joss.05686.jats diff --git a/joss.05686/10.21105.joss.05686.jats b/joss.05686/10.21105.joss.05686.jats new file mode 100644 index 0000000000..b0926b7c4d --- /dev/null +++ b/joss.05686/10.21105.joss.05686.jats @@ -0,0 +1,707 @@ + + +

    + + + + +Journal of Open Source Software +JOSS + +2475-9066 + +Open Journals + + + +5686 +10.21105/joss.05686 + +PATATO: a Python photoacoustic tomography analysis +toolkit + + + +https://orcid.org/0000-0002-2652-4190 + +Else +Thomas R. + + + + + +https://orcid.org/0000-0002-5332-4856 + +Gröhl +Janek + + + + + +https://orcid.org/0000-0001-5160-0952 + +Hacker +Lina + + + + + + +https://orcid.org/0000-0003-0371-8635 + +Bohndiek +Sarah E. + + + +* + + + +CRUK Cambridge Institute, University of Cambridge, United +Kingdom + + + + +Department of Physics, University of Cambridge, United +Kingdom + + + + +Department of Oncology, University of Oxford, United +Kingdom + + + + +* E-mail: + + +8 +6 +2023 + +9 +93 +5686 + +Authors of papers retain copyright and release the +work under a Creative Commons Attribution 4.0 International License (CC +BY 4.0) +2022 +The article authors + +Authors of papers retain copyright and release the work under +a Creative Commons Attribution 4.0 International License (CC BY +4.0) + + + +Python +Photoacoustics +Imaging +Optoacoustics +MSOT +Reconstruction +Backprojection +Unmixing + + + + + + Summary +

    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 pre-processing, 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 GPU-accelerated 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/.

    +
    + + Statement of Need +

    Photoacoustic imaging (PAI) contrast arises from the absorption of + light pulses by tissue chromophores, such as haemoglobin, melanin, + lipids and water + (Beard, + 2011). The acoustic waves that arise from the photoacoustic + effect are then captured by a detector array, giving raw acoustic time + series data associated with each light pulse. These raw data are + typically subject to i) pre-processing e.g. by filtering; ii) + reconstruction into 2D or 3D visualisations; iii) spectral unmixing + processes, to decompose the range of molecules that contributed to the + absorption process; and iv) data visualisation and quantification, + including drawing of regions of interest (ROIs) to extract both static + and dynamic biomarker values + ([fig:overview]).

    + +

    An overview of the key features of PATATO. + Command line interfaces for the given feature are shown in red. + Example Python interfaces for each feature are shown in blue. +

    + +
    +

    The PAI data processing pipeline is computationally intensive and + the output values are highly susceptible to parameter changes + (Hochuli + et al., 2019; + Shen + et al., 2020). Unfortunately, existing PAI data analysis + typically relies on commercial software packages, or custom in-house + unpublished codebases. Commercial software is generally optimised for + image reconstruction from a specific instrument marketed by the + vendor, enabling only a limited subset of predefined analyses and + making analysis incompatible with open-access research mandates. + Similarly, closed-source code reduces the transparency and + reproducibility of research and impedes the widespread adoption of new + algorithms.

    +

    Some open access code for PAI backprojection and model-based + reconstruction is available (pyoat, + https://github.com/berkanlafci/pyoat + and RAFT + (O’Kelly + et al., 2021)). Still, implementation is restricted to a few + use cases, documentation is limited and GPU acceleration lacking. + Similar limitations exist for open access spectral processing code + (Gröhl + et al., 2021; + Kirchner + & Frenz, 2021).

    +

    We developed PATATO to enhance the transparency, reproducibility + and consistency of PAI data analysis. PATATO is designed to be fast, + extendable, and compatible with different data formats and systems, + enabling users to easily go beyond the limited capabilities of + commercial software packages and have complete control of their data + processing pipeline. By providing an extendable platform for + reproducible analysis, we hope to improve the uptake and dissemination + of new analysis algorithms across both application-focused users and + computational researchers, a goal that has garnered significant + community support through the International Photoacoustic + Standardization Consortium + (Gröhl + et al., 2022).

    + +

    An outline of the software architecture of PATATO + showing the key features. Abstract base classes are shown in + bold italics. Classes are shown in boxes in bold. Functions are + shown with a normal font. Command line functions are shown in bold + without a box. +

    + +
    +
    + + Software Pipeline +

    PATATO is written in Python (currently supporting versions 3.9, + 3.10 and 3.11) incorporating the strengths of standard numerical + programming libraries, including NumPy, SciPy and matplotlib to access + fast, well-tested numerical algorithms. PATATO can run without + advanced hardware, as GPU dependencies are optional and memory + requirements minimal, promoting accessibility and flexibility for the + maximum number of users.

    +

    Photoacoustic data can be large in size + ( + + ≫ + 1 GB), impeding data import and data sharing. To enable fast handling + of large datasets, PATATO implements batch processing and stores + output in an HDF5 format, which allows seamless transfer of large data + sets between fixed storage and memory. With HDF5, users can transfer + data from PATATO to other tools and programming languages. PATATO + includes dedicated wrappers for a number of data sources, e.g., for + the IPASC data format + (Gröhl + et al., 2022), while also enabling user-defined wrappers.

    +

    PATATO features a modular design + ([fig:softwareoverview]) + reflecting the four main steps of PAI data processing + ([fig:overview]). + Raw time-series pre-processing and backprojection + (Xu + & Wang, 2005) are implemented using JAX, a high-performance + numerical computing library that enables GPU acceleration. JAX uses + the same code for CPUs and GPUs, removing potential inconsistencies + between different platforms. PATATO enables linear spectral unmixing + based on the NumPy matrix pseudo-inverse + (Hochuli + et al., 2019), including reference optical absorption spectra + for common chromophores such as deoxyhaemoglobin, oxyhaemoglobin and + melanin, and the contrast agent indocyanine green (ICG) + (Prahl, + 2018). Users can adapt existing algorithms for any part of the + processing pipeline or implement their own algorithms by extending the + appropriate class.

    +
    + + Strengths +

    PATATO is designed to be user-friendly. Users are not required to + write code to use PATATO for typical image processing tasks. We also + include command line tools for data importation, speed of sound + optimisation, reconstruction, unmixing, temporal analysis, and region + of interest analysis. Graphical user interfaces based on matplotlib + are provided. Custom data processing algorithms can easily be added + and examples are presented in the documentation.

    +
    + + Limitations and caveats +

    Spectral analysis in PATATO is currently limited to linear spectral + unmixing. This is an approximate method that does not account for + changes in light fluence.

    +

    Current data examples are restricted to 2D PAI, however, the + reconstruction and analysis algorithms do support 3D data.

    +
    + + Example results +

    To demonstrate the main features and enable benchmarking of + different algorithms, we have included a selection of data sets + (Else + et al., 2023) with PATATO that were collected using a + cylindrical-array pre-clinical (small animal) PAI system and a + handheld clinical PAI system (MSOT inVision 256 and MSOT Acuity Echo + respectively; both iThera Medical GmbH, Munich, Germany). Animal + procedures were conducted under project licence PE12C2B96 and personal + licence I33984279, issued under the United Kingdom Animals (Scientific + Procedures) Act, 1986, and were approved locally under compliance form + number CFSB2022. Detailed methods for this procedure have been + published previously + (Tomaszewski + et al., 2018).

    +

    The typical photoacoustic analysis procedure in PATATO can be + illustrated using the mouse dataset described above. In this study, + mice were implanted with tumours and photoacoustic images were + acquired to interrogate the blood perfusion of the tumours. We + perturbed the distribution of absorbing molecules in the mouse by + changing the breathing gas to oxygen, thereby causing a change in the + blood oxygenation, and by injecting the contrast agent indocyanine + green (ICG). PATATO allows the streamlined analysis of such datasets + ([fig:mice]). + Firstly, we reconstructed the photoacoustic images by backprojection. + We then drew polygon regions of interest around three regions of the + mouse: the two implanted tumours and the spine + ([fig:mice] A). To + obtain maps of the blood oxygenation ( + + sO2), + total haemoglobin (THb), and ICG content we applied linear spectral + unmixing ([fig:mice] + B). Plots of the + + sO2 + and + + ICG + levels in the three regions over time were made and the enhancement + level was calculated + ([fig:mice] C). Maps + of the signal enhancement ( + + ΔsO2 + or + + ΔICG) + were then made, revealing useful biomarkers related to hypoxia and + blood perfusion respectively + ([fig:mice]) + (Tomaszewski + et al., 2018).

    + +

    An example of the analysis process in PATATO using + a two-dimensional cross-sectional image of a tumour-bearing + mouse. Regions of interest were drawn around key features of + the image, the spine and both tumours (A). We applied linear + spectral unmixing, giving maps of the blood oxygenation + ( + + sO2) + and total haemoglobin (THb) (B). We acquired dynamic imaging data in + response to changing breathing gas or contrast agent administration, + showing the effects of oxygen enhancement or contrast enhancement + respectively (C). We calculated two enhancement metrics, + + + ΔsO2 + and + + ΔICG + (D). Scale bar = 5 mm. +

    + +
    +
    + + Future developments +

    PATATO will be developed on a continuous basis, and we welcome + collaborative contributions from the PAI community, particularly to + implement wrappers for different data formats and in adding image + reconstruction and analysis tools. Contributions are particularly + encouraged for model-based reconstructions and advanced spectral + unmixing tools.

    +
    + + Acknowledgements +

    We would like to thank Mariam-Eleni Oraiopoulou, Ellie Bunce and + Thierry Lefebvre for their helpful feedback as early users of the + toolkit. This work was funded by: Cancer Research UK (SB, TRE; + C9545/A29580); the MedAccel program of the National Physical + Laboratory financed by the Department for Business, Energy and + Industrial Strategy’s Industrial Strategy Challenge Fund (LH); and the + Walter Benjamin Stipendium of the Deutsche Forschungsgemeinschaft + (JG). We would like to thank + Elly + Pugh for design of the PATATO logo.

    +
    + + + + + + + BeardPaul + + Biomedical photoacoustic imaging + Interface Focus + The Royal Society + 201108 + 20190930 + 1 + 4 + 10.1098/rsfs.2011.0028 + 602 + 631 + + + + + + BrownEmma + BrunkerJoanna + BohndiekSarah E. + + Photoacoustic imaging as a tool to probe the tumour microenvironment + Disease Models & Mechanisms + The Company of Biologists + 201907 + 20191015 + 12 + 7 + 1754-8403 + 10.1242/dmm.039636 + dmm039636 + + + + + + + ElseThomas + GroehlJanek + HackerLina + BohndiekSarah + + Dataset for: PATATO: A Python Photoacoustic Analysis Toolkit + Department of Physics + 2023 + 20230217 + 10.17863/CAM.93181 + + + + + + GröhlJanek + KirchnerThomas + AdlerTim J. + HackerLina + HolzwarthNiklas + Hernández-AguileraAdrián + HerreraMildred A. + SantosEdgar + BohndiekSarah E. + Maier-HeinLena + + Learned spectral decoloring enables photoacoustic oximetry + Scientific Reports + Springer Science and Business Media LLC + 202112 + 20210415 + 11 + 1 + 10.1038/s41598-021-83405-8 + 33753769 + 6565 + + + + + + + GröhlJanek + DreherKris K. + SchellenbergMelanie + RixTom + HolzwarthNiklas + VietenPatricia + AyalaLeonardo + BohndiekSarah E. + SeitelAlexander + Maier-HeinLena + + SIMPA: An open-source toolkit for simulation and image processing for photonics and acoustics + Journal of Biomedical Optics + SPIE + 202204 + 20230216 + 27 + 08 + 1083-3668 + 10.1117/1.JBO.27.8.083010 + 35380031 + 083010 + + + + + + + HochuliRoman + AnLu + BeardPaul C. + CoxBenjamin T. + + Estimating blood oxygenation from photoacoustic images: Can a simple linear spectroscopic inversion ever work? + Journal of Biomedical Optics + 201912 + 20200108 + 24 + 12 + 1083-3668 + 10.1117/1.JBO.24.12.121914 + 1 + + + + + + + KirchnerThomas + FrenzMartin + + Multiple illumination learned spectral decoloring for quantitative optoacoustic oximetry imaging + https://doi.org/10.1117/1.JBO.26.8.085001 + SPIE + 202108 + 20230216 + 26 + 8 + 1083-3668 + 10.1117/1.JBO.26.8.085001 + 34350736 + 085001 + + + + + + + O’KellyDevin + CampbellJames + GerberichJeni L. + KarbasiPaniz + MalladiVenkat + JamiesonAndrew + WangLiqiang + MasonRalph P. + + A scalable open-source MATLAB toolbox for reconstruction and analysis of multispectral optoacoustic tomography data + Scientific Reports 2021 11:1 + Nature Publishing Group + 202110 + 20230120 + 11 + 1 + 0123456789 + 2045-2322 + 10.1038/s41598-021-97726-1 + 34615891 + 1 + 18 + + + + + + PrahlScott + + Assorted Spectra + https://omlc.org/spectra/index.html + 2018 + 20230216 + + + + + + RegensburgerAdrian P. + BrownEmma + KrönkeGerhard + WaldnerMaximilian J. + KnielingFerdinand + + Optoacoustic Imaging in Inflammation + Biomedicines + Multidisciplinary Digital Publishing Institute + 202104 + 20210519 + 9 + 5 + 2227-9059 + 10.3390/biomedicines9050483 + 483 + + + + + + + ShenKang + LiuSongde + FengTing + YuanJie + ZhuBenpeng + TianChao + + Negativity artifacts in back-projection based photoacoustic tomography + Journal of Physics D: Applied Physics + IOP Publishing + 202011 + 20230120 + 54 + 7 + 0022-3727 + 10.1088/1361-6463/ABC37D + 074001 + + + + + + + SteinbergIdan + HulandDavid M. + VermeshOphir + FrostigHadas E. + TummersWillemieke S. + GambhirSanjiv S. + + Photoacoustic clinical imaging + Photoacoustics + Elsevier GmbH + 201906 + 20200602 + 14 + 10.1016/j.pacs.2019.05.001 + 77 + 98 + + + + + + TomaszewskiMichal R. + GehrungMarcel + JosephJames + Quiros GonzalezIsabel + DisselhorstJonathan A. + BohndiekSarah E. + + Oxygen-enhanced and dynamic contrast-enhanced optoacoustic tomography provide surrogate biomarkers of tumour vascular function, hypoxia and necrosis. + Cancer Research + American Association for Cancer Research Inc. + 201808 + 20191025 + 78 + 20 + 0008-5472 + 10.1158/0008-5472.CAN-18-1033 + canres.1033.2018 + + + + + + + WangLihong V + HuSong + + Photoacoustic Tomography: In Vivo Imaging from Organelles to Organs + Science + American Association for the Advancement of Science + 201203 + 20190930 + 335 + 6075 + 0036-8075 + 10.1126/science.1216210 + 22442475 + 1458 + 1462 + + + + + + XuMinghua + WangLihong V + + Universal back-projection algorithm for photoacoustic computed tomography + Physical Review E - Statistical, Nonlinear, and Soft Matter Physics + 2005 + 20200113 + 71 + 1 + 10.1103/PhysRevE.71.016706 + + + + +
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