Towards Automated Quantification of Vessel Wall Composition Using MRI

• Published in: Journal of magnetic resonance imaging Vol. 52; no. 3; pp. 710 - 719
• Main Authors: Ziegler, Magnus, Good, Elin, Engvall, Jan, Warntjes, Marcel, Muinck, Ebo, Dyverfeldt, Petter
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.09.2020; Wiley Subscription Services, Inc
• Subjects: Angiography; Arteries; Atherosclerosis; Automation; Blood vessels; Carotid arteries; Carotid Arteries - diagnostic imaging; Carotid artery; Composition; contrast‐enhanced; Data acquisition; Field strength; Ground truth; Hemorrhage; Humans; Image segmentation; Lipids; Magnetic Resonance Imaging; Observers; plaque composition; Plaque, Atherosclerotic - diagnostic imaging; Plaques; Prospective Studies; segmentation; Statistical analysis; Statistical tests; Support vector machines; atherosclerosis; contrast-enhanced; segmentation; magnetic resonance imaging; carotid arteries; plaque composition
• ISSN: 1053-1807; 1522-2586; 1522-2586
• DOI: 10.1002/jmri.27116

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 T1‐weighted (T1W) quadruple inversion recovery, contrast‐enhanced MR angiography (CE‐MRA), and 4‐point Dixon data were acquired at 3T. Assessment The vessel lumen of the CA was automatically segmented using support vector machines (SVM) with CE‐MRA data, and the vessel wall region was subsequently delineated. Automatically generated segmentations were quantitatively measured and three observers visually compared the segmentations to manual segmentations performed on T1w 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.