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

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Τίτλος: Blind spectral unmixing for characterization of plaque composition based on multispectral photoacoustic imaging
Συγγραφείς: Cano, Camilo, Matos, Catarina, Gholampour, Amir, van Sambeek, Marc, Lopata, Richard, Wu, Min
Πηγή: Cano, C, Matos, C, Gholampour, A, van Sambeek, M, Lopata, R & Wu, M 2023, 'Blind spectral unmixing for characterization of plaque composition based on multispectral photoacoustic imaging', Scientific Reports, vol. 13, no. 1, 4119. https://doi.org/10.1038/s41598-023-31343-y
Έτος έκδοσης: 2023
Θεματικοί όροι: Humans, Photoacoustic Techniques/methods, Diagnostic Imaging, Plaque, Atherosclerotic/diagnostic imaging, Spectrum Analysis/methods, Carotid Arteries/diagnostic imaging
Περιγραφή: 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.
Τύπος εγγράφου: article in journal/newspaper
Περιγραφή αρχείου: application/pdf
Γλώσσα: English
ISSN: 2045-2322
Relation: info:eu-repo/semantics/altIdentifier/pmid/36914717; info:eu-repo/semantics/altIdentifier/pissn/2045-2322
DOI: 10.1038/s41598-023-31343-y
Διαθεσιμότητα: https://research.tue.nl/en/publications/bd4dda27-b1e9-40a8-8030-706c4baf29b9
https://doi.org/10.1038/s41598-023-31343-y
https://pure.tue.nl/ws/files/345969304/s41598-023-31343-y.pdf
https://www.scopus.com/pages/publications/85150087572
Rights: info:eu-repo/semantics/openAccess
Αριθμός Καταχώρησης: edsbas.3F371A69
Βάση Δεδομένων: BASE
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  – Url: https://dx.doi.org/doi:10.1038/s41598-023-31343-y
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  Data: Cano, C, Matos, C, Gholampour, A, van Sambeek, M, Lopata, R & Wu, M 2023, 'Blind spectral unmixing for characterization of plaque composition based on multispectral photoacoustic imaging', Scientific Reports, vol. 13, no. 1, 4119. https://doi.org/10.1038/s41598-023-31343-y
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  Data: 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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      – SubjectFull: Diagnostic Imaging
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      – SubjectFull: Plaque
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      – SubjectFull: Spectrum Analysis/methods
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