{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T04:45:14Z","timestamp":1790397914285,"version":"4.1.0"},"reference-count":45,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2020,11,23]],"date-time":"2020-11-23T00:00:00Z","timestamp":1606089600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100000024","name":"Canadian Institutes of Health Research","doi-asserted-by":"publisher","award":["93531"],"award-info":[{"award-number":["93531"]}],"id":[{"id":"10.13039\/501100000024","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance in Med"],"published-print":{"date-parts":[[2021,5]]},"abstract":"<jats:sec><jats:title>Purpose<\/jats:title><jats:p>To develop an approach for automated quantification of myocardial infarct heterogeneity in late gadolinium enhancement (LGE) cardiac MRI.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We acquired 2D short\u2010axis cine and 3D LGE in 10 pigs with myocardial infarct. The 2D cine myocardium was segmented and registered to the LGE images. LGE image signal intensities within the warped cine myocardium masks were analyzed to determine the thresholds of infarct core (IC) and gray zone (GZ) for the standard\u2010deviation (SD) and full\u2010width\u2010at\u2010halfmaximum (FWHM) methods. The initial IC, GZ, and IC + GZ segmentations were postprocessed using a normalized cut approach. Cine segmentation and cine\u2010LGE registration accuracies were evaluated using dice similarity coefficient and average symmetric surface distance. Automated IC, GZ, and IC + GZ volumes were compared with manual results using Pearson correlation coefficient (<jats:italic>r<\/jats:italic>), Bland\u2010Altman analyses, and intraclass correlation coefficient.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>For <jats:italic>n<\/jats:italic> = 87 slices containing scar, we achieved cine segmentation dice similarity coefficient = 0.87 \u00b1 0.12, average symmetric surface distance = 0.94 \u00b1 0.74 mm (epicardium), and 1.03 \u00b1 0.82 mm (endocardium) in the scar region. For cine\u2010LGE registration, dice similarity coefficient was 0.90 \u00b1 0.06 and average symmetric surface distance was 0.72 \u00b1 0.39 mm (epicardium) and 0.86 \u00b1 0.53 mm (endocardium) in the scar region. For both SD and FWHM methods, automated IC, GZ, and IC + GZ volumes were strongly (<jats:italic>r<\/jats:italic> &gt; 0.70) correlated with manual measurements, and the correlations were not significantly different from interobserver correlations (<jats:italic>P<\/jats:italic> &gt; .05). The agreement between automated and manual scar volumes (intraclass correlation coefficient = 0.85\u20100.96) was similar to that between two observers (intraclass correlation coefficient = 0.81\u20100.99); automated scar segmentation errors were not significantly different from interobserver segmentation differences (<jats:italic>P<\/jats:italic> &gt; .05).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Our approach provides fully automated cine\u2010LGE MRI registration and LGE myocardial infarct heterogeneity quantification in preclinical studies.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mrm.28596","type":"journal-article","created":{"date-parts":[[2020,11,24]],"date-time":"2020-11-24T04:00:32Z","timestamp":1606190432000},"page":"2842-2855","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Cine and late gadolinium enhancement MRI registration and automated myocardial infarct heterogeneity quantification"],"prefix":"10.1002","volume":"85","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6220-1455","authenticated-orcid":false,"given":"Fumin","family":"Guo","sequence":"first","affiliation":[{"name":"Sunnybrook Research Institute University of Toronto Toronto Canada"},{"name":"Department of Medical Biophysics University of Toronto Toronto Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5178-4189","authenticated-orcid":false,"given":"Philippa R. 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