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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Bibliographic Details
Published in:Scientific reports 2023-03, Vol.13 (1), p.4119-4119, Article 4119
Main Authors: Cano, Camilo, Matos, Catarina, Gholampour, Amir, van Sambeek, Marc, Lopata, Richard, Wu, Min
Format: Article
Language:English
Subjects:
639/166/985
692/700/1421
Biological samples
Carotid Arteries - diagnostic imaging
Carotid Arteries - pathology
Diagnostic Imaging
Hemorrhage
Histology
Humanities and Social Sciences
Humans
Lipids
multidisciplinary
Photoacoustic Techniques - methods
Plaque, Atherosclerotic - diagnostic imaging
Plaque, Atherosclerotic - pathology
Science
Science (multidisciplinary)
Spectrum analysis
Spectrum Analysis - methods
Surgery
Tissues
Wavelengths
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Summary: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.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-023-31343-y