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Research Papers

Effect of Inlet Velocity Profiles on Patient-Specific Computational Fluid Dynamics Simulations of the Carotid Bifurcation

[+] Author and Article Information
Ian C. Campbell

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

Jared Ries

Wallace H. Coulter Department of Biomedical Engineering,  Georgia Institute of Technology and Emory University, Atlanta, GA 30332

Saurabh S. Dhawan, Arshed A. Quyyumi

Division of Cardiology, Department of Medicine,  Emory University, Atlanta, GA 30322

W. Robert Taylor

Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332; Division of Cardiology,Department of Medicine,  Emory University, Atlanta, GA 30322; Cardiology Division, Atlanta VA Medical Center, Decatur, GA 30032

John N. Oshinski

Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332; Radiology and Imaging Sciences,  Emory University, Atlanta, GA 30322

J Biomech Eng 134(5), 051001 (May 25, 2012) (8 pages) doi:10.1115/1.4006681 History: Received June 30, 2011; Revised March 28, 2012; Posted May 01, 2012; Published May 25, 2012; Online May 25, 2012

Patient-specific computational fluid dynamics (CFD) is a powerful tool for researching the role of blood flow in disease processes. Modern clinical imaging technology such as MRI and CT can provide high resolution information about vessel geometry, but in many situations, patient-specific inlet velocity information is not available. In these situations, a simplified velocity profile must be selected. We studied how idealized inlet velocity profiles (blunt, parabolic, and Womersley flow) affect patient-specific CFD results when compared to simulations employing a “reference standard” of the patient’s own measured velocity profile in the carotid bifurcation. To place the magnitude of these effects in context, we also investigated the effect of geometry and the use of subject-specific flow waveform on the CFD results. We quantified these differences by examining the pointwise percent error of the mean wall shear stress (WSS) and the oscillatory shear index (OSI) and by computing the intra-class correlation coefficient (ICC) between axial profiles of the mean WSS and OSI in the internal carotid artery bulb. The parabolic inlet velocity profile produced the most similar mean WSS and OSI to simulations employing the real patient-specific inlet velocity profile. However, anatomic variation in vessel geometry and the use of a nonpatient-specific flow waveform both affected the WSS and OSI results more than did the choice of inlet velocity profile. Although careful selection of boundary conditions is essential for all CFD analysis, accurate patient-specific geometry reconstruction and measurement of vessel flow rate waveform are more important than the choice of velocity profile. A parabolic velocity profile provided results most similar to the patient-specific velocity profile.

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Copyright © 2012 by American Society of Mechanical Engineers
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Figures

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Figure 1

Snapshot at peak systole of the four inlet velocity profiles prescribed at the common carotid artery (CCA). The real measured velocity profile (top left) was derived from direct phase-contrast magnetic resonance (PCMR) imaging of a patient over the cardiac cycle. The volume flow rate of this “reference standard” velocity profile was then used to compute the blunt (top right), Womersley (bottom left), and parabolic (bottom right) velocity profiles. Data is from subject #4.

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Figure 2

On the reconstructed 3D geometry, centerlines along the carotid bifurcation were detected using VMTK software (a), and split into branches (b). Angular metric (c), and distance along the centerline were used to map OSI and time-averaged WSS distributions (d) in the internal carotid artery onto a flat surface to create a “virtual enface” view of hemodynamics (see Fig. 4). Data is from subject #4.

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Figure 3

Oscillatory shear index (OSI) in the carotid bulb varies in magnitude and distribution among CFD simulations employing four different inlet velocity profiles. Data is from subject #4.

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Figure 4

Using VMTK software, we mapped hemodynamic parameters from a 3D model (see Fig. 3) into the 2D plane using the techniques in Fig. 2. We analyzed the internal carotid artery around the carotid bulb and investigated the magnitude and distribution of the mean WSS (left) and OSI (right) under varying inlet velocity profile conditions. Mean WSS magnitudes are more affected by inlet conditions than distribution, while OSI distributions are relatively more affected by inlet conditions than magnitude. Data is from subject #5.

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Figure 5

Extracting axial profiles of mean WSS results in the ICA allows for the visualization and quantification of spatial and magnitude differences in the mean WSS resulting from different inlet velocity profiles. Profiles aligned with the inner and outer ridge of the ICA (yellow axial line, in 3D at left, and brown [inner, at −π/2] and green [outer, at +π/2] lines in virtual en face at top right) can be compared visually (bottom right) and using the intra-class correlation coefficient (see Fig. 7). Patient-specific CFD allows researchers to study how local minima and maxima of the WSS vary spatially, but the inlet flow profile influences this distribution. Data is from subject #9.

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Figure 6

Pointwise percent difference of results from simulations using different inlet velocity profiles. The choice of inlet flow conditions affects the WSS and OSI calculations, but not as much as the natural anatomic variation among patients or the use of a non-subject-specific flow waveform to compute the velocity profiles. Error bars shown are standard deviation, and * indicates p < 0.002, relative to other inlet velocity profiles.

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Figure 7

Intraclass correlation coefficient (ICC) calculated between axial profiles of the mean WSS extracted from the inside and outside ridges of the internal carotid artery (ICA), as in Fig. 5. On average, results from the simulations using the parabolic velocity profile show the highest correlation to results from simulations using the real measured velocity profile, while the ICC among subjects and between simulations using velocity profiles derived from non-subject-specific flow waveforms have a lower correlation, demonstrating the strong importance of patient anatomy and individual flow waveforms when performing computational fluid dynamics.

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