Abstract
Objective: In this study, near-infrared Raman spectroscopy (NIRS) was used for evaluation of human atherosclerotic lesions using a simple algorithm based on discriminant analysis. The Mahalanobis distance was used to classify the clustered spectral features extracted from NIRS of a total of 111 arterial fragments of human coronary arteries. Background Data: Raman spectroscopy has been used for diagnosis of a variety of diseases. For real-time applications, it is important to have a simple algorithm that could perform fast data acquisition and analysis. The ultimate goal is to obtain a feasible diagnosis, which discriminates various atherosclerotic lesions with high sensitivities and specificities. Materials and Methods: Non-atherosclerotic (NA) arteries, atherosclerotic plaques without calcification (NC), and atherosclerotic plaques with classification (C) were obtained and scanned with an NIR Raman spectrometer with 830-nm laser excitation. An algorithm based on the discriminant analysis using the Mahalanobis distance of the clustered spectral features was used for tissue classification into three categories: Na, NC, and C. Results: Human coronary arteries exhibit different spectral signatures depending on different bio-chemicals present in each tissue type such as collagen, cholesterol, and calcium hydroxyapatite, respectively. It is shown that our algorithm has a maximum sensitivity and specificity of 85% and 89%, respectively, for the diagnosis of the NA tissue type, 85% and 89% for the NC tissue type, and 100% and 100% for the C tissue type. Conclusion: An algorithm (with a minimum of mathematical and computational requirements) based on the discriminant analysis of spectral features has been developed to classify atherosclerotic lesions with high sensitivities and specificities.
Get full access to this article
View all access options for this article.