Quantitative segmentation of principal carotid atherosclerotic lesion components by feature space analysis based on...

The purpose of this paper is to evaluate the capability of feature space analysis (FSA) for quantifying the relative volumes of principal components (thrombus, calcification, fibrous, normal intima, and lipid) of atherosclerotic plaque tissue in multicontrast magnetic resonance images (mc-MRI) acquired in a setup resembling clinical conditions ex vivo. Utilizing endogenous contrast, proton density, T1-weighted, and T2-weighted images were acquired for 13 carotid endarterectomy (CEA) tissues under near-clinical conditions (human 1.5 T GE Excite scanner with sequence parameters comparable to an in vivo acquisition). An FSA algorithm was utilized to segment and quantify the principal components of atherosclerotic plaques. Pilot in vivo mc-MRI images were analyzed in the same way as the ex vivo images for exploring the possible adaptation of this technique to in vivo imaging. Relative abundance of principal plaque components in CEA tissues as determined by mc-MRI/FSA were compared to those measured by histology. Mean differences +/- standard deviations were 5.8 +/- 4.1% for thrombus, 1.5 +/-1.4 % for calcification, 4.0 +/-2.8% for fibrous, 8.2 +/- 10% for normal intima, and 2.4 +/- 2.2% for lipid. Reasonable quantitative agreement between the classification results obtained with FSA and histological data was obtained for near-clinical imaging conditions. Combination of mc-MRI and FSA may have an application for determining atherosclerotic lesion composition and monitoring treatment in vivo.