BACKGROUND: Recent studies have shown that chemical composition and morphology, rather than anatomy (degree of stenosis), determine atherosclerotic plaque instability and predict disease progression. Current clinical diagnostic techniques provide accurate assessment of plaque anatomy, but have limited capability to assess plaque morphology in vivo. Here we describe a technique for a morphology-based diagnosis of atherosclerosis in the coronary arteries using Raman spectroscopy that can potentially be performed in vivo using optical fiber technology. METHODS: Raman tissue spectra were collected from normal and atherosclerotic coronary artery samples in different stages of disease progression (n=165) from explanted transplant recipient hearts (n=16). Raman spectra from the elastic laminae (EL), collagen fibers (CF), smooth muscle cells (SMC), adventitial adipocytes (AA) or fat cells, foam cells (FC), necrotic core (NC), cholesterol crystals (CC), beta-carotene containing crystals (beta-C), and calcium mineralizations (CM) were used as basis spectra in a linear least squares-minimization (LSM) model to calculate the contribution of these morphologic structures to the coronary artery tissue spectra. RESULTS: We developed a diagnostic algorithm that used the fit-contributions of the various morphologic structures to classify 97 coronary artery samples in an initial calibration data set as either nonatherosclerotic, calcified plaque, or noncalcified atheromatous plaque. The algorithm was subsequently tested prospectively in a second validation data set, and correctly classified 64 (94%) of 68 coronary artery samples. CONCLUSIONS: Raman spectroscopy provides information about the morphologic composition of intact human coronary artery without the need for excision and microscopic examination. In the future, it may be possible to use this technique to analyze the morphologic composition of atherosclerotic coronary artery lesions and assess plaque instability and disease progression in vivo.
BACKGROUND: Recent studies have shown that chemical composition and morphology, rather than anatomy (degree of stenosis), determine atherosclerotic plaque instability and predict disease progression. Current clinical diagnostic techniques provide accurate assessment of plaque anatomy, but have limited capability to assess plaque morphology in vivo. Here we describe a technique for a morphology-based diagnosis of atherosclerosis in the coronary arteries using Raman spectroscopy that can potentially be performed in vivo using optical fiber technology. METHODS: Raman tissue spectra were collected from normal and atherosclerotic coronary artery samples in different stages of disease progression (n=165) from explanted transplant recipient hearts (n=16). Raman spectra from the elastic laminae (EL), collagen fibers (CF), smooth muscle cells (SMC), adventitial adipocytes (AA) or fat cells, foam cells (FC), necrotic core (NC), cholesterol crystals (CC), beta-carotene containing crystals (beta-C), and calcium mineralizations (CM) were used as basis spectra in a linear least squares-minimization (LSM) model to calculate the contribution of these morphologic structures to the coronary artery tissue spectra. RESULTS: We developed a diagnostic algorithm that used the fit-contributions of the various morphologic structures to classify 97 coronary artery samples in an initial calibration data set as either nonatherosclerotic, calcified plaque, or noncalcified atheromatous plaque. The algorithm was subsequently tested prospectively in a second validation data set, and correctly classified 64 (94%) of 68 coronary artery samples. CONCLUSIONS: Raman spectroscopy provides information about the morphologic composition of intact human coronary artery without the need for excision and microscopic examination. In the future, it may be possible to use this technique to analyze the morphologic composition of atherosclerotic coronary artery lesions and assess plaque instability and disease progression in vivo.
Authors: Obrad R Sćepanović; Maryann Fitzmaurice; Arnold Miller; Chae-Ryon Kong; Zoya Volynskaya; Ramachandra R Dasari; John R Kramer; Michael S Feld Journal: J Biomed Opt Date: 2011 Jan-Feb Impact factor: 3.170
Authors: Carlos J de Lima; Leonardo M Moreira; Juliana P Lyon; Antonio B Villaverde; Marcos T T Pacheco Journal: Lasers Med Sci Date: 2008-09-09 Impact factor: 3.161
Authors: T Anthony Anderson; Jeon Woong Kang; Tatyana Gubin; Ramachandra R Dasari; Peter T C So Journal: Anesthesiology Date: 2016-10 Impact factor: 7.892