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dc.contributor.otherPeshock, Ronalden
dc.contributor.otherMcColl, Rodericken
dc.contributor.otherKing, Kevinen
dc.contributor.otherWhittemore, Anthonyen
dc.creatorGoel, Akshayen
dc.date.accessioned2015-06-29T12:49:46Z
dc.date.available2015-06-29T12:49:46Z
dc.date.issued2013-01-22
dc.identifier.citationGoel, A., Peshock, R., McColl, R., King, K., & Whittlemore, A. (2013, January 22). An automated tool for measuring aortic pulse wave velocity. Poster session presented at the 51st Annual Medical Student Research Forum, Dallas, TX. Retrieved from http://hdl.handle.net/2152.5/1625en
dc.identifier.urihttp://hdl.handle.net/2152.5/1625
dc.descriptionThe 51st Annual Medical Student Research Forum at UT Southwestern Medical Center (Tuesday, January 22, 2013, 3-6 p.m., D1.602)en
dc.description.abstractPURPOSE: Aortic Pulse wave Velocity (APV) has been shown to be associated with end organ damage independent of age, sex, and hypertension duration. The purpose of this study is to evaluate an automated approach for computing transit time (Δt) for the measurement of APV as a tool for future investigations and clinical application. METHODS AND MATERIALS: Phase contrast cardiac gated MRI of the aorta in the transverse plane at the level of the pulmonary artery was utilized from the Dallas Heart Study-2 (DHS2), a multiethnic, population-based study of cardiovascular health. A three-step algorithm was used to analyze all 1884 phase contrast MRI studies from the DHS2 central database. The algorithm functions in three key steps: 1) Isolating contours for the ascending aorta and descending aorta using a computer vision technique known as the Hough Transform. 2) Using isolated contours and phase contrast MRI to generate flow curves for the ascending and descending aorta. 3) Computing Δt defined as the time shift between the flow curves in the ascending aorta (AA) and descending aorta (DA), calculated using the half maximum of AA and DA. Fifty of these studies uniformly distributed across all Δt were then randomly selected and manually analyzed with the standard approach utilizing QFlow (v. 4.1.6, Medis) and the corresponding manually derived flow curves were used to compute Δt. The results from the manual analysis using QFlow were compared to results from the automated algorithm using linear regression Bland-Altman difference analysis. RESULTS: The mean Δt in the 1884 studies analyzed with our automated tool was 19.8+/-6.5 ms. In the validation set of 50 studies, linear regression analysis showed an excellent correlation between the automated (A) and manual (M) methods (r=0.97, A = 1.01M-0.885 ms). Bland-Altman difference analysis showed strong agreement with no significant bias (mean difference (A-M) = -0.386 ± 0.768 ms). CONCLUSION: Our automated approach for computing transit time (Δt) for the measurement of APV demonstrates excellent agreement with the standard manual method. These findings suggest this approach could serve as a useful tool for future investigations and clinical application.en
dc.description.sponsorshipSouthwestern Medical Foundationen
dc.language.isoenen
dc.relation.ispartofseries51st Annual Medical Student Research Forumen
dc.subjectClinical Research and Case Reportsen
dc.subject.meshAlgorithmsen
dc.subject.meshAortaen
dc.subject.meshAortographyen
dc.subject.meshBlood Volumeen
dc.subject.meshMagnetic Resonance Angiographyen
dc.subject.meshSoftwareen
dc.titleAn Automated Tool for Measuring Aortic Pulse Wave Velocityen
dc.typePresentationen


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