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Letter

An Automatic Unmixing Approach to Detect Tissue Chromophores from Multispectral Photoacoustic Imaging

1
FUJIFILM VisualSonics, 1114 AB Amsterdam, The Netherlands
2
Institute for Animal Science, Hannover Medical School, 30625 Hannover, Germany
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(11), 3235; https://doi.org/10.3390/s20113235
Received: 29 April 2020 / Revised: 3 June 2020 / Accepted: 4 June 2020 / Published: 6 June 2020
Multispectral photoacoustic imaging has been widely explored as an emerging tool to visualize and quantify tissue chromophores noninvasively. This modality can capture the spectral absorption signature of prominent tissue chromophores, such as oxygenated, deoxygenated hemoglobin, and other biomarkers in the tissue by using spectral unmixing methods. Currently, most of the reported image processing algorithms use standard unmixing procedures, which include user interaction in the form of providing the expected spectral signatures. For translational research with patients, these types of supervised spectral unmixing can be challenging, as the spectral signature of the tissues can differ with respect to the disease condition. Imaging exogenous contrast agents and accessing their biodistribution can also be problematic, as some of the contrast agents are susceptible to change in spectral properties after the tissue interaction. In this work, we investigated the feasibility of an unsupervised spectral unmixing algorithm to detect and extract the tissue chromophores without any a-priori knowledge and user interaction. The algorithm has been optimized for multispectral photoacoustic imaging in the spectral range of 680–900 nm. The performance of the algorithm has been tested on simulated data, tissue-mimicking phantom, and also on the detection of exogenous contrast agents after the intravenous injection in mice. Our finding shows that the proposed automatic, unsupervised spectral unmixing method has great potential to extract and quantify the tissue chromophores, and this can be used in any wavelength range of the multispectral photoacoustic images. View Full-Text
Keywords: photoacoustic; optoacoustic; spectral imaging; blind source separation; unsupervised unmixing photoacoustic; optoacoustic; spectral imaging; blind source separation; unsupervised unmixing
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MDPI and ACS Style

Grasso, V.; Holthof, J.; Jose, J. An Automatic Unmixing Approach to Detect Tissue Chromophores from Multispectral Photoacoustic Imaging. Sensors 2020, 20, 3235. https://doi.org/10.3390/s20113235

AMA Style

Grasso V, Holthof J, Jose J. An Automatic Unmixing Approach to Detect Tissue Chromophores from Multispectral Photoacoustic Imaging. Sensors. 2020; 20(11):3235. https://doi.org/10.3390/s20113235

Chicago/Turabian Style

Grasso, Valeria, Joost Holthof, and Jithin Jose. 2020. "An Automatic Unmixing Approach to Detect Tissue Chromophores from Multispectral Photoacoustic Imaging" Sensors 20, no. 11: 3235. https://doi.org/10.3390/s20113235

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