{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T05:35:11Z","timestamp":1769232911791,"version":"3.49.0"},"reference-count":18,"publisher":"ASME International","issue":"11","content-domain":{"domain":["asmedigitalcollection.asme.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2010,11,1]]},"abstract":"<jats:p>The detailed geometry of atherosclerosis-prone vascular segments may influence their susceptibility by mediating local hemodynamics. An appreciation of the role of specific geometric variables is complicated by the considerable correlation among the many parameters that can be used to describe arterial shape and size. Factor analysis is a useful tool for identifying the essential features of such an inter-related data set, as well as for predicting hemodynamic risk in terms of these features and for interpreting the role of specific geometric variables. Here, factor analysis is applied to a set of 14 geometric variables obtained from magnetic resonance images of 50 human carotid bifurcations. Two factors alone were capable of predicting 12 hemodynamic metrics related to shear and near-wall residence time with adjusted squared Pearson\u2019s correlation coefficient as high as 0.54 and P-values less than 0.0001. One factor measures cross-sectional expansion at the bifurcation; the other measures the colinearity of the common and internal carotid artery axes at the bifurcation. The factors explain the apparent lack of an effect of branch angle on hemodynamic risk. The relative risk among the 50 bifurcations, based on time-average wall shear stress, could be predicted with a sensitivity and specificity as high as 0.84. The predictability of the hemodynamic metrics and relative risk is only modestly sensitive to assumptions about flow rates and flow partitions in the bifurcation.<\/jats:p>","DOI":"10.1115\/1.4002538","type":"journal-article","created":{"date-parts":[[2010,9,10]],"date-time":"2010-09-10T22:02:22Z","timestamp":1284156142000},"update-policy":"https:\/\/doi.org\/10.1115\/crossmarkpolicy-asme","source":"Crossref","is-referenced-by-count":18,"title":["Use of Factor Analysis to Characterize Arterial Geometry and Predict Hemodynamic Risk: Application to the Human Carotid Bifurcation"],"prefix":"10.1115","volume":"132","author":[{"given":"Qi","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Electronic and Information Engineering, Shanghai University, 200072, Shanghai, China; Department of Biomedical Engineering, Duke University, Durham, NC 27708"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David A.","family":"Steinman","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, M5S 3G8 Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Morton H.","family":"Friedman","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Duke University, Durham, NC 27708; Department of Mechanical and Aerospace Engineering, George Washington University, Washington, DC 20052"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"33","published-online":{"date-parts":[[2010,10,27]]},"reference":[{"issue":"2","key":"2019100513312599500_c1","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1016\/0021-9150(83)90113-2","article-title":"Arterial Geometry Affects Hemodynamics: A Potential Risk Factor for Atherosclerosis","volume":"46","author":"Friedman","journal-title":"Atherosclerosis","ISSN":"https:\/\/id.crossref.org\/issn\/0021-9150","issn-type":"print"},{"issue":"12","key":"2019100513312599500_c2","doi-asserted-by":"publisher","first-page":"121005","DOI":"10.1115\/1.4000168","article-title":"Differences in Aortic Arch Geometry, Hemodynamics, and Plaque Patterns Between C57bl\/6 and 129\/Svev Mice","volume":"131","author":"Zhu","journal-title":"ASME J. 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