Towards Automated Quantification of Vessel Wall Composition Using MRI

BACKGROUND: MRI can be used to generate fat fraction (FF) and R2* data, which have been previously shown to characterize the plaque compositional features lipid-rich necrotic core (LRNC) and intraplaque hemorrhage (IPH) in the carotid arteries (CAs). Previously, these data were extracted from CA plaques using time-consuming manual analyses. PURPOSE: To design and demonstrate a method for segmenting the CA and extracting data describing the composition of the vessel wall. STUDY TYPE: Prospective. SUBJECTS: 31 subjects from the Swedish CArdioPulmonary bioImage Study (SCAPIS). FIELD STRENGTH/SEQUENCES: W) quadruple inversion recovery, contrast-enhanced MR angiography (CE-MRA), and 4-point Dixon data were acquired at 3T. ASSESSMENT: w images. Dixon data were used to generate FF and R2* maps. Both manually and automatically generated segmentations of the CA and vessel wall were used to extract compositional data. STATISTICAL TESTS: Two-tailed t-tests were used to examine differences between results generated using manual and automated analyses, and among different configurations of the automated method. Interobserver agreement was assessed with Fleiss' kappa. RESULTS: Automated segmentation of the CA using SVM had a Dice score of 0.89 ± 0.02 and true-positive ratio 0.93 ± 0.03 when compared against ground truth, and median qualitative score of 4/5 when assessed visually by multiple observers. Vessel wall regions of 0.5 and 1 mm yielded compositional information similar to that gained from manual analyses. Using the 0.5 mm vessel wall region, the mean difference was 0.1 ± 2.5% considering FF and 1.1 ± 5.7[1/s] for R2*. LEVEL OF EVIDENCE: 1. TECHNICAL EFFICACY STAGE: 1. J. Magn. Reson. Imaging 2020;52:710-719.