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Dataset for: PATATO: A Python Photoacoustic Analysis Toolkit


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Authors

Else, Thomas 
Groehl, Janek 
Hacker, Lina 

Description

======================================================================== Example Data for PATATO: Python Photoacoustic Tomography Analysis toolkit

Thomas Else, Sarah Bohndiek (seb53@cam.ac.uk),

CRUK Cambridge Institute and Department of Physics, University of Cambridge.

Data collected between October 2020 and December 2022 in Cambridge, United Kingdom.

Please note: All animal procedures used to acquire the data described below were conducted in accordance with project (PE12C2B96) and personal licenses (I33984279) issued under the United Kingdom Animals (Scientific Procedures) Act, 1986, and were approved locally under compliance form number CFSB2022.

 

Description of Files

These datasets were collected using two different commercial photoacoustic imaging systems, details of which can be found on the vendor website. More information regarding the experimental acquisition of these files can be found via the PATATO Python toolkit repository, which is freely available on GitHub (https://github.com/tomelse/patato)

 

clinical_phantom.hdf5: Data collected 02/08/2022. A photoacoustic imaging dataset containing raw data from a scan taken on a tissue-mimicking phantom. The data were acquired using the iThera Medical GmbH MSOT Acuity CE device. The file format is HDF5.

 

preclinical_phantom.hdf5: Data collected 02/09/2021 A photoacoustic imaging dataset containing raw data from a scan taken on a tissue-mimicking phantom. The data were acquired using the iThera Medical GmbH MSOT inVision system. The file format is HDF5.

 

invivo_oe.hdf5: Data collected 01/10/2020. A photoacoustic imaging dataset containing raw data from a scan of a mouse, with oxygen-enhanced imaging whereby the breathing gas of the mouse was changed during the scan time. The data were acquired using the iThera Medical MSOT inVision system. The file format is HDF5.

 

invivo_dce.hdf5: Data collected 01/10/2020. A photoacoustic imaging dataset containing raw data from a scan of a mouse, with dynamic-contrast enhanced imaging using indocyanine green, whereby the contrast agent was introduced intravenously during the scan time. The data were acquired using the iThera Medical GmbH MSOT inVision system. The file format is HDF5.

 

ithera_invivo_oe.zip: The same as invivo_oe.hdf5 but in a different format. The zip file contains imaging data in a proprietary format provided by the device manufacturer. It can be loaded using proprietary software such as iThera ViewMSOT, or by converting it to an open format.

 

ithera_invivo_dce.zip: The same as invivo_dce.hdf5 but in a different format. The zip file contains imaging data in a proprietary format provided by the device manufacturer. It can be loaded using proprietary software such as iThera ViewMSOT, or by converting it to an open format.

Version

Software / Usage instructions

The HDF5 files above contain a number of datasets. To access the raw time series data, use the "raw_data" dataset. To access the reconstructed images, use the "recons" dataset. The scan metadata is also included, such as the acquisition geometry and the wavelengths used, these are in the "GEOMETRY" and "wavelengths" dataset respectively. These files can be loaded easily using the PATATO Python toolkit, which is freely available on GitHub (https://github.com/tomelse/patato). Alternatively, they can be accessed using standard HDF5 file tools. All units are SI (metres, seconds, metres per second etc.). The HDF5 files were generated from the corresponding iThera Medical GmbH file format using the PATATO toolkit.

Keywords

photoacoustic imaging

Publisher

Sponsorship
Cancer Research UK (C14303/A17197)
National Physical Laboratory (NPL) (unknown)
National Physical Laboratory (NPL) (unknown)
National Physical Laboratory (NPL) (unknown)
EPSRC (EP/V027069/1)
Acquisition of this data was supported by: Cancer Research UK (C9545/A29580) (TE and SEB); 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 Deutsche Forschungsgemeinschaft through a Walter Benjamin Stipendium (JG).
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