MRI is emerging as a promising modality for monitoring carotid atherosclerosis. Multiple MR contrast weightings are required for identification of plaque constituents. In this study, eight MR contrast weightings with proven potential for plaque characterization were used to image carotid endarterectomy specimens. A classification technique was developed to create a tissue-specific map by incorporating information from all MR contrast weightings. The classifier was validated by comparison with micro-CT (calcification only) and with matched histological slices registered to MR images using a nonlinear warping algorithm (other components). A pathologist who was blinded to the classifier results manually segmented digitized histological images. The sensitivity of the classifier, as determined by pixel-by-pixel comparison with the pathologist's segmentation and micro-CT, was 60.4% for fibrous tissue, 83.9% for necrosis, 97.6% for calcification, and 65.2% for loose connective tissue. The corresponding values for specificity were 87.9%, 75.0%, 98.3%, and 94.9%, respectively. In conclusion, multicontrast MRI was successfully used in conjunction with a supervised classification algorithm to identify plaque components in endarterectomy specimens. Furthermore, this methodology will provide a framework for comparing different classification algorithms, and determining which combination of MR contrasts will be most valuable for in vivo plaque imaging.
Copyright 2003 Wiley-Liss, Inc.