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Blind spectral unmixing for characterization of plaque composition based on multispectral photoacoustic imaging

To improve the assessment of carotid plaque vulnerability, a comprehensive characterization of their composition is paramount. Multispectral photoacoustic imaging (MSPAI) can provide plaque composition based on their absorption spectra. However, although various spectral unmixing methods have been d...

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Autores principales: Cano, Camilo, Matos, Catarina, Gholampour, Amir, van Sambeek, Marc, Lopata, Richard, Wu, Min
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10011570/
https://www.ncbi.nlm.nih.gov/pubmed/36914717
http://dx.doi.org/10.1038/s41598-023-31343-y
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author Cano, Camilo
Matos, Catarina
Gholampour, Amir
van Sambeek, Marc
Lopata, Richard
Wu, Min
author_facet Cano, Camilo
Matos, Catarina
Gholampour, Amir
van Sambeek, Marc
Lopata, Richard
Wu, Min
author_sort Cano, Camilo
collection PubMed
description To improve the assessment of carotid plaque vulnerability, a comprehensive characterization of their composition is paramount. Multispectral photoacoustic imaging (MSPAI) can provide plaque composition based on their absorption spectra. However, although various spectral unmixing methods have been developed to characterize different tissue constituents, plaque analysis remains a challenge since its composition is highly complex and diverse. In this study, we employed an adapted piecewise convex multiple-model endmember detection method to identify carotid plaque constituents. Additionally, we explore the selection of the imaging wavelengths in linear models by conditioning the coefficient matrix and its synergy with our unmixing approach. We verified our method using plaque mimicking phantoms and performed ex-vivo MSPAI on carotid endarterectomy samples in a spectral range from 500 to 1300 nm to identify the main spectral features of plaque materials for vulnerability assessment. After imaging, the samples were processed for histological analysis to validate the photoacoustic decomposition. Results show that our approach can perform spectral unmixing and classification of highly heterogeneous biological samples without requiring an extensive fluence correction, enabling the identification of relevant components to assess plaque vulnerability.
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spelling pubmed-100115702023-03-15 Blind spectral unmixing for characterization of plaque composition based on multispectral photoacoustic imaging Cano, Camilo Matos, Catarina Gholampour, Amir van Sambeek, Marc Lopata, Richard Wu, Min Sci Rep Article To improve the assessment of carotid plaque vulnerability, a comprehensive characterization of their composition is paramount. Multispectral photoacoustic imaging (MSPAI) can provide plaque composition based on their absorption spectra. However, although various spectral unmixing methods have been developed to characterize different tissue constituents, plaque analysis remains a challenge since its composition is highly complex and diverse. In this study, we employed an adapted piecewise convex multiple-model endmember detection method to identify carotid plaque constituents. Additionally, we explore the selection of the imaging wavelengths in linear models by conditioning the coefficient matrix and its synergy with our unmixing approach. We verified our method using plaque mimicking phantoms and performed ex-vivo MSPAI on carotid endarterectomy samples in a spectral range from 500 to 1300 nm to identify the main spectral features of plaque materials for vulnerability assessment. After imaging, the samples were processed for histological analysis to validate the photoacoustic decomposition. Results show that our approach can perform spectral unmixing and classification of highly heterogeneous biological samples without requiring an extensive fluence correction, enabling the identification of relevant components to assess plaque vulnerability. Nature Publishing Group UK 2023-03-13 /pmc/articles/PMC10011570/ /pubmed/36914717 http://dx.doi.org/10.1038/s41598-023-31343-y Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Cano, Camilo
Matos, Catarina
Gholampour, Amir
van Sambeek, Marc
Lopata, Richard
Wu, Min
Blind spectral unmixing for characterization of plaque composition based on multispectral photoacoustic imaging
title Blind spectral unmixing for characterization of plaque composition based on multispectral photoacoustic imaging
title_full Blind spectral unmixing for characterization of plaque composition based on multispectral photoacoustic imaging
title_fullStr Blind spectral unmixing for characterization of plaque composition based on multispectral photoacoustic imaging
title_full_unstemmed Blind spectral unmixing for characterization of plaque composition based on multispectral photoacoustic imaging
title_short Blind spectral unmixing for characterization of plaque composition based on multispectral photoacoustic imaging
title_sort blind spectral unmixing for characterization of plaque composition based on multispectral photoacoustic imaging
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10011570/
https://www.ncbi.nlm.nih.gov/pubmed/36914717
http://dx.doi.org/10.1038/s41598-023-31343-y
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