{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T17:23:34Z","timestamp":1769189014537,"version":"3.49.0"},"reference-count":28,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2020,3,10]],"date-time":"2020-03-10T00:00:00Z","timestamp":1583798400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance Imaging"],"published-print":{"date-parts":[[2020,9]]},"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>MRI can be used to generate fat fraction (FF) and R2* data, which have been previously shown to characterize the plaque compositional features lipid\u2010rich necrotic core (LRNC) and intraplaque hemorrhage (IPH) in the carotid arteries (CAs). Previously, these data were extracted from CA plaques using time\u2010consuming manual analyses.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>To design and demonstrate a method for segmenting the CA and extracting data describing the composition of the vessel wall.<\/jats:p><\/jats:sec><jats:sec><jats:title>Study Type<\/jats:title><jats:p>Prospective.<\/jats:p><\/jats:sec><jats:sec><jats:title>Subjects<\/jats:title><jats:p>31 subjects from the Swedish CArdioPulmonary bioImage Study (SCAPIS).<\/jats:p><\/jats:sec><jats:sec><jats:title>Field Strength\/Sequences<\/jats:title><jats:p>T<jats:sub>1<\/jats:sub>\u2010weighted (T<jats:sub>1<\/jats:sub>W) quadruple inversion recovery, contrast\u2010enhanced MR angiography (CE\u2010MRA), and 4\u2010point Dixon data were acquired at 3T.<\/jats:p><\/jats:sec><jats:sec><jats:title>Assessment<\/jats:title><jats:p>The vessel lumen of the CA was automatically segmented using support vector machines (SVM) with CE\u2010MRA 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 T<jats:sub>1<\/jats:sub>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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Statistical Tests<\/jats:title><jats:p>Two\u2010tailed <jats:italic>t<\/jats:italic>\u2010tests 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Automated segmentation of the CA using SVM had a Dice score of 0.89\u2009\u00b1\u20090.02 and true\u2010positive ratio 0.93\u2009\u00b1\u20090.03 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\u2009mm yielded compositional information similar to that gained from manual analyses. Using the 0.5\u2009mm vessel wall region, the mean difference was 0.1\u2009\u00b1\u20092.5% considering FF and 1.1\u2009\u00b1\u20095.7[1\/s] for R2*.<\/jats:p><\/jats:sec><jats:sec><jats:title>Level of Evidence<\/jats:title><jats:p>1.<\/jats:p><\/jats:sec><jats:sec><jats:title>Technical Efficacy Stage<\/jats:title><jats:p>1. J. Magn. Reson. Imaging 2020;52:710\u2013719.<\/jats:p><\/jats:sec>","DOI":"10.1002\/jmri.27116","type":"journal-article","created":{"date-parts":[[2020,3,10]],"date-time":"2020-03-10T13:22:17Z","timestamp":1583846537000},"page":"710-719","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Towards Automated Quantification of Vessel Wall Composition Using MRI"],"prefix":"10.1002","volume":"52","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9184-9234","authenticated-orcid":false,"given":"Magnus","family":"Ziegler","sequence":"first","affiliation":[{"name":"Cardiovascular Sciences, Department of Health, Medicine and Caring Sciences Link\u00f6ping University  Link\u00f6ping Sweden"},{"name":"Center for Medical Image Science and Visualization (CMIV) Link\u00f6ping University  Link\u00f6ping 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