Circulation. 2002;106:2200-2206
Published online before print October 7, 2002, doi: 10.1161/01.CIR.0000035654.18341.5E
Published online before print October 7, 2002, doi: 10.1161/01.CIR.0000035654.18341.5E
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(Circulation. 2002;106:2200.)
© 2002 American Heart Association, Inc.
Clinical Investigation and Reports |
Coronary Plaque Classification With Intravascular Ultrasound Radiofrequency Data Analysis
From the Departments of Biomedical Engineering (A.N., B.D.K., D.G.V.) and Cardiology (E.M.T., P.S., S.E.N.), The Cleveland Clinic Foundation; and the Department of Biomedical Engineering (A.N.), Case Western Reserve University, Cleveland, Ohio.
Correspondence to D. Geoffrey Vince, PhD, Department of Biomedical Engineering/ND20, The Cleveland Clinic Foundation, 9500 Euclid Ave, Cleveland, OH 44195. E-mail vince{at}bme.ri.ccf.org
Background— Atherosclerotic plaque stability is related to histological composition. However, current diagnostic tools do not allow adequate in vivo identification and characterization of plaques. Spectral analysis of backscattered intravascular ultrasound (IVUS) data has potential for real-time in vivo plaque classification.
Methods and Results— Eighty-eight plaques from 51 left anterior descending coronary arteries were imaged ex vivo at physiological pressure with the use of 30-MHz IVUS transducers. After IVUS imaging, the arteries were pressure-fixed and corresponding histology was collected in matched images. Regions of interest, selected from histology, were 101 fibrous, 56 fibrolipidic, 50 calcified, and 70 calcified-necrotic regions. Classification schemes for model building were computed for autoregressive and classic Fourier spectra by using 75% of the data. The remaining data were used for validation. Autoregressive classification schemes performed better than those from classic Fourier spectra with accuracies of 90.4% for fibrous, 92.8% for fibrolipidic, 90.9% for calcified, and 89.5% for calcified-necrotic regions in the training data set and 79.7%, 81.2%, 92.8%, and 85.5% in the test data, respectively. Tissue maps were reconstructed with the use of accurate predictions of plaque composition from the autoregressive classification scheme.
Conclusions— Coronary plaque composition can be predicted through the use of IVUS radiofrequency data analysis. Autoregressive classification schemes performed better than classic Fourier methods. These techniques allow real-time analysis of IVUS data, enabling in vivo plaque characterization.
Key Words: atherosclerosis • coronary disease • Fourier analysis • plaque • ultrasonics
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