Background-Recent studies demonstrated that in vivo and ex vivo MRI can characterize the components of the carotid atherosclerotic plaque, such as fibrous tissue, lipid/necrotic core, calcium, hemorrhage, and thrombus. The purpose of this study was to determine whether in vivo high-resolution multicontrast MRI could accurately classify human carotid atherosclerotic plaque according to the American Heart Association classification. Methods and Results-Sixty consecutive patients (mean age 70 years; 54 males) scheduled for carotid endarterectomy were imaged with a 1.5-T scanner after informed consent was obtained. A standardized protocol was used to obtain 4 different contrast-weighted images (time of flight and T1-, PD-, and T2-weighted) of the carotid arteries. Best voxel size was 0.25ϫ0.25ϫ1 mm 3 . Carotid plaques were removed intact and processed for histological examination. Both MR images and histological sections were independently reviewed, categorized, and compared. Overall, the classification obtained by MRI and the American Heart Association classifications showed good agreement, with Cohen's (95% CI) of 0.74 (0.67 to 0.82) and weighted of 0.79. The sensitivity and specificity, respectively, of MRI classification were as follows: type I-II lesions, 67% and 100%; type III lesions, 81% and 98%; type IV-V lesions, 84% and 90%; type VI lesions, 82% and 91%; type VII lesions, 80% and 94%; and type VIII lesions, 56% and 100%. Conclusions-In vivo high-resolution multicontrast MRI is capable of classifying intermediate to advanced atherosclerotic lesions in the human carotid artery and is also capable of distinguishing advanced lesions from early and intermediate atherosclerotic plaque.
Dynamic contrast-enhanced MRI of atherosclerotic vessels after contrast agent injection may provide unique information regarding lesion structure and vulnerability. The high-resolution images necessary for viewing lesion substructures, however, are often corrupted by patient motion and low signal-to-noise ratios, making pixel-level analyses difficult. This article presents a postprocessing method that enables pixel-level analysis of dynamic images by eliminating motion and enhancing image quality. Noise and motion correction are performed using optimal statistical methods under the assumption that noise and contrast agent dynamics are random processes. The method is demonstrated and validated on dynamic images of atherosclerotic plaques in human carotid arteries. Magn Reson Med 47:1211-1217, 2002.
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