{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T05:53:59Z","timestamp":1790315639756,"version":"4.1.0"},"reference-count":41,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2017,10,17]],"date-time":"2017-10-17T00:00:00Z","timestamp":1508198400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01\u2010NS083503\u201003, R01\u2010NS092207\u201001A1, R01\u2010HL103609\u201001A1 and 1R56NS092207"],"award-info":[{"award-number":["R01\u2010NS083503\u201003, R01\u2010NS092207\u201001A1, R01\u2010HL103609\u201001A1 and 1R56NS092207"]}],"id":[{"id":"10.13039\/100000002","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":[[2018,6]]},"abstract":"<jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To develop a quantitative intracranial artery measurement technique to extract comprehensive artery features from time\u2010of\u2010flight MR angiography (MRA).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>By semiautomatically tracing arteries based on an open\u2010curve active contour model in a graphical user interface, 12 basic morphometric features and 16 basic intensity features for each artery were identified. Arteries were then classified as one of 24 types using prediction from a probability model. Based on the anatomical structures, features were integrated within 34 vascular groups for regional features of vascular trees. Eight 3D MRA acquisitions with intracranial atherosclerosis were assessed to validate this technique.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Arterial tracings were validated by an experienced neuroradiologist who checked agreement at bifurcation and stenosis locations. This technique achieved 94% sensitivity and 85% positive predictive values (PPV) for bifurcations, and 85% sensitivity and PPV for stenosis. Up to 1,456 features, such as length, volume, and averaged signal intensity for each artery, as well as vascular group in each of the MRA images, could be extracted to comprehensively reflect characteristics, distribution, and connectivity of arteries. Length for the M1 segment of the middle cerebral artery extracted by this technique was compared with reviewer\u2010measured results, and the intraclass correlation coefficient was 0.97.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>A semiautomated quantitative method to trace, label, and measure intracranial arteries from 3D\u2010MRA was developed and validated. This technique can be used to facilitate quantitative intracranial vascular research, such as studying cerebrovascular adaptation to aging and disease conditions. Magn Reson Med 79:3229\u20133238, 2018. \u00a9 2017 International Society for Magnetic Resonance in Medicine.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mrm.26961","type":"journal-article","created":{"date-parts":[[2017,10,17]],"date-time":"2017-10-17T21:52:40Z","timestamp":1508277160000},"page":"3229-3238","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":79,"title":["Development of a quantitative intracranial vascular features extraction tool on 3\n                    <scp>D<\/scp>\n                    <scp>MRA<\/scp>\n                    using semiautomated open\u2010curve active contour vessel tracing"],"prefix":"10.1002","volume":"79","author":[{"given":"Li","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering University of Washington Seattle Washington 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