Multi-feature-based plaque characterization in ex vivo MRI trained by registration to 3D histology

Abstract

We present a new method for automated characterization of atherosclerotic plaque composition in ex vivo MRI. It uses MRI intensities as well as four other types of features: smoothed, gradient magnitude and Laplacian images at several scales, and the distances to the lumen and outer vessel wall. The ground truth for fibrous, necrotic and calcified tissue was provided by histology and μCT in 12 carotid plaque specimens. Semi-automatic registration of a 3D stack of histological slices and μCT images to MRI allowed for 3D rotations and in-plane deformations of histology. By basing voxelwise classification on different combinations of features, we evaluated their relative importance. To establish whether training by 3D registration yields different results than training by 2D registration, we determined plaque composition using (1) a 2D slice-based registration approach for three manually selected MRI and histology slices per specimen, and (2) an approach that uses only the three corresponding MRI slices from the 3D-registered volumes. Voxelwise classification accuracy was best when all features were used (73.3 ± 6.3%) and was significantly better than when only original intensities and distance features were used (Friedman, p < 0.05). Although 2D registration or selection of three slices from the 3D set slightly decreased accuracy, these differences were non-significant.