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Research Papers

Choice of In Vivo Versus Idealized Velocity Boundary Conditions Influences Physiologically Relevant Flow Patterns in a Subject-Specific Simulation of Flow in the Human Carotid Bifurcation

[+] Author and Article Information
Amanda K. Wake

Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University; and Vanderbilt University Institute of Imaging Science, Vanderbilt University, 1161 21st Avenue South, Medical Center North, AA-1105, Nashville, TN 37232-2310amanda.wake@vanderbilt.edu

John N. Oshinski

Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University and Department of Radiology, Emory University, Atlanta, GA 30332jnoshin@emory.edu

Allen R. Tannenbaum

Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University; and School of Electrical and Computer Engineering, Georgia Institute of Technology, Room 4102, 777 Atlantic Drive, Atlanta, GA 30332-0250tannenba@ece.gatech.edu

Don P. Giddens

Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Administrative Building, Third Floor, 225 North Avenue, Atlanta, GA 30332-0360don.giddens@coe.gatech.edu

J Biomech Eng 131(2), 021013 (Dec 10, 2008) (8 pages) doi:10.1115/1.3005157 History: Received October 23, 2007; Revised September 17, 2008; Published December 10, 2008

Accurate fluid mechanics models are important tools for predicting the flow field in the carotid artery bifurcation and for understanding the relationship between hemodynamics and the initiation and progression of atherosclerosis. Clinical imaging modalities can be used to obtain geometry and blood flow data for developing subject-specific human carotid artery bifurcation models. We developed subject-specific computational fluid dynamics models of the human carotid bifurcation from magnetic resonance (MR) geometry data and phase contrast MR velocity data measured in vivo. Two simulations were conducted with identical geometry, flow rates, and fluid parameters: (1) Simulation 1 used in vivo measured velocity distributions as time-varying boundary conditions and (2) Simulation 2 used idealized fully-developed velocity profiles as boundary conditions. The position and extent of negative axial velocity regions (NAVRs) vary between the two simulations at any given point in time, and these regions vary temporally within each simulation. The combination of inlet velocity boundary conditions, geometry, and flow waveforms influences NAVRs. In particular, the combination of flow division and the location of the velocity peak with respect to individual carotid geometry landmarks (bifurcation apex position and the departure angle of the internal carotid) influences the size and location of these reversed flow zones. Average axial wall shear stress (WSS) distributions are qualitatively similar for the two simulations; however, instantaneous WSS values vary with the choice of velocity boundary conditions. By developing subject-specific simulations from in vivo measured geometry and flow data and varying the velocity boundary conditions in otherwise identical models, we isolated the effects of measured versus idealized velocity distributions on blood flow patterns. Choice of velocity distributions at boundary conditions is shown to influence pathophysiologically relevant flow patterns in the human carotid bifurcation. Although mean WSS distributions are qualitatively similar for measured and idealized inlet boundary conditions, instantaneous NAVRs differ and warrant imposing in vivo velocity boundary conditions in computational simulations. A simulation based on in vivo measured velocity distributions is preferred for modeling hemodynamics in subject-specific carotid artery bifurcation models when studying atherosclerosis initiation and development.

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Copyright © 2009 by American Society of Mechanical Engineers
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Figures

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Figure 7

Four different views (at 90deg increments) of time-averaged axial WSS (N∕m2) for (a) Simulation 1 and (b) Simulation 2

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Figure 1

(a) MR-based subject-specific geometry with PCMR data slice locations and (b) axial view of Slice 1 and Slice 2 locations relative to IC and EC bifurcation directions

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Figure 2

Flow rate (m3∕s) over time at the CC inlet, IC outlet, and EC outlet

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Figure 3

Axial velocity (UZ) (m/s) contours for Slice 1 at the model inlet over several time points: (a) initial systolic acceleration (t1), (b) immediately prior to peak systole (t2), (c) initial systolic deceleration (t3), (d) midsystolic deceleration (t4), (e) minimum flow rate (t5), and (f) mid-diastole (t6). Within each panel the left figure is from Simulation 1, and the right is from Simulation 2.

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Figure 5

NAVRs are shown in blue at several time points: (a) initial systolic acceleration (t1), (b) immediately prior to peak systole (t2), (c) initial systolic deceleration (t3), (d) midsystolic deceleration (t4), (e) minimum flow rate (t5), and (f) mid-diastole (t6). Within each panel the left figure is from Simulation 1, and the right is from Simulation 2

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Figure 6

NAVRs and axial velocity (UZ) (m/s) contours at several time points: (a) initial systolic acceleration (t1), (b) initial systolic deceleration (t3), (c) midsystolic deceleration (t4), and (d) minimum flow rate (t5). Axial velocity contours (viewed from proximal direction) at Slice 2 (top) and Slice 1 (bottom) are shown to the right of NAVR at each time point. Within each panel, the left set of figures is from Simulation 1, and the right is from Simulation 2

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Figure 4

Maximum axial velocities (Vmax) (m/s) at (a) Slice 1 and (b) Slice 2 locations

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