Literature DB >> 21361695

Hyperspectral imaging of atherosclerotic plaques in vitro.

Eivind L P Larsen1, Lise L Randeberg, Elisabeth Olstad, Olav A Haugen, Astrid Aksnes, Lars O Svaasand.   

Abstract

Vulnerable plaques constitute a risk for serious heart problems, and are difficult to identify using existing methods. Hyperspectral imaging combines spectral- and spatial information, providing new possibilities for precise optical characterization of atherosclerotic lesions. Hyperspectral data were collected from excised aorta samples (n = 11) using both white-light and ultraviolet illumination. Single lesions (n = 42) were chosen for further investigation, and classified according to histological findings. The corresponding hyperspectral images were characterized using statistical image analysis tools (minimum noise fraction, K-means clustering, principal component analysis) and evaluation of reflectance/fluorescence spectra. Image analysis combined with histology revealed the complexity and heterogeneity of aortic plaques. Plaque features such as lipids and calcifications could be identified from the hyperspectral images. Most of the advanced lesions had a central region surrounded by an outer rim or shoulder-region of the plaque, which is considered a weak spot in vulnerable lesions. These features could be identified in both the white-light and fluorescence data. Hyperspectral imaging was shown to be a promising tool for detection and characterization of advanced atherosclerotic plaques in vitro. Hyperspectral imaging provides more diagnostic information about the heterogeneity of the lesions than conventional single point spectroscopic measurements.

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Year:  2011        PMID: 21361695     DOI: 10.1117/1.3540657

Source DB:  PubMed          Journal:  J Biomed Opt        ISSN: 1083-3668            Impact factor:   3.170


  9 in total

1.  Green light may improve diagnostic accuracy of nailfold capillaroscopy with a simple digital videomicroscope.

Authors:  Harm H A Weekenstroo; Bart M W Cornelissen; Hein J Bernelot Moens
Journal:  Rheumatol Int       Date:  2014-12-16       Impact factor: 2.631

2.  Blind source separation of ex-vivo aorta tissue multispectral images.

Authors:  July Galeano; Sandra Perez; Yonatan Montoya; Deivid Botina; Johnson Garzón
Journal:  Biomed Opt Express       Date:  2015-04-06       Impact factor: 3.732

Review 3.  Medical hyperspectral imaging: a review.

Authors:  Guolan Lu; Baowei Fei
Journal:  J Biomed Opt       Date:  2014-01       Impact factor: 3.170

Review 4.  Single-Cell Analysis Using Hyperspectral Imaging Modalities.

Authors:  Nishir Mehta; Shahensha Shaik; Ram Devireddy; Manas Ranjan Gartia
Journal:  J Biomech Eng       Date:  2018-02-01       Impact factor: 2.097

5.  Optimization of wavelength selection for multispectral image acquisition: a case study of atrial ablation lesions.

Authors:  Huda Asfour; Shuyue Guan; Narine Muselimyan; Luther Swift; Murray Loew; Narine Sarvazyan
Journal:  Biomed Opt Express       Date:  2018-04-16       Impact factor: 3.732

6.  Label-free hyperspectral imaging and deep-learning prediction of retinal amyloid β-protein and phosphorylated tau.

Authors:  Xiaoxi Du; Yosef Koronyo; Nazanin Mirzaei; Chengshuai Yang; Dieu-Trang Fuchs; Keith L Black; Maya Koronyo-Hamaoui; Liang Gao
Journal:  PNAS Nexus       Date:  2022-08-19

7.  Tongue tumor detection in medical hyperspectral images.

Authors:  Zhi Liu; Hongjun Wang; Qingli Li
Journal:  Sensors (Basel)       Date:  2011-12-23       Impact factor: 3.576

Review 8.  Hyperspectral imaging solutions for brain tissue metabolic and hemodynamic monitoring: past, current and future developments.

Authors:  Luca Giannoni; Frédéric Lange; Ilias Tachtsidis
Journal:  J Opt       Date:  2018-03-22       Impact factor: 2.516

9.  Electrostatically Tuned Optical Filters Based on Hybrid Plasmonic-Dielectric Thin Films for Hyperspectral Imaging.

Authors:  Ahmed Abdelghfar; Mohamed A Mousa; Bassant M Fouad; Ahmed H Saad; Noha Anous; Noha Gaber
Journal:  Micromachines (Basel)       Date:  2021-06-29       Impact factor: 2.891

  9 in total

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