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Research Papers

Outflow Conditions for Image-Based Hemodynamic Models of the Carotid Bifurcation: Implications for Indicators of Abnormal Flow

[+] Author and Article Information
Umberto Morbiducci1

Department of Mechanics, Politecnico di Torino, Turin 10129, Italyumberto.morbiducci@polito.it

Diego Gallo, Diana Massai, Filippo Consolo, Cristina Bignardi, Marco A. Deriu

Department of Mechanics, Politecnico di Torino, Turin 10129, Italy

Raffaele Ponzini

 CILEA Interuniversity Consortium, Milan 20090, Italy

Luca Antiga

 Mario Negri Institute, Bergamo 24125, Italy

Alberto Redaelli

Department of Bioengineering, Politecnico di Milano, Milan 20133, Italy

1

Corresponding author.

J Biomech Eng 132(9), 091005 (Aug 17, 2010) (11 pages) doi:10.1115/1.4001886 History: Received January 19, 2010; Revised May 10, 2010; Posted May 27, 2010; Published August 17, 2010; Online August 17, 2010

Computational fluid dynamics (CFD) models have become very effective tools for predicting the flow field within the carotid bifurcation, and for understanding the relationship between local hemodynamics, and the initiation and progression of vascular wall pathologies. As prescribing proper boundary conditions can affect the solutions of the equations governing blood flow, in this study, we investigated the influence to assumptions regarding the outflow boundary conditions in an image-based CFD model of human carotid bifurcation. Four simulations were conducted with identical geometry, inlet flow rate, and fluid parameters. In the first case, a physiological time-varying flow rate partition at branches along the cardiac cycle was obtained by coupling the 3D model of the carotid bifurcation at outlets with a lumped-parameter model of the downstream vascular network. Results from the coupled model were compared with those obtained by imposing three fixed flow rate divisions (50/50, 60/40, and 70/30) between the two branches of the isolated 3D model of the carotid bifurcation. Three hemodynamic wall parameters were considered as indicators of vascular wall dysfunction. Our findings underscore that the overall effect of the assumptions done in order to simulate blood flow within the carotid bifurcation is mainly in the hot-spot modulation of the hemodynamic descriptors of atherosusceptible areas, rather than in their distribution. In particular, the more physiological, time-varying flow rate division deriving from the coupled simulation has the effect of damping wall shear stress (WSS) oscillations (differences among the coupled and the three fixed flow partition models are up to 37.3% for the oscillating shear index). In conclusion, we recommend to adopt more realistic constraints, for example, by coupling models at different scales, as in this study, when the objective is the outcome prediction of alternate therapeutic interventions for individual patients, or to test hypotheses related to the role of local fluid dynamics and other biomechanical factors in vascular diseases.

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Copyright © 2010 by American Society of Mechanical Engineers
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Figures

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Figure 2

Abnormal wall surface fractions defined pooling data from all simulations (by using threshold values, Th, of the WSS-based metrics). The effect of the outflow boundary conditions on computed hemodynamics was quantified by calculating the rms of the patchwise difference in TAWSS, OSI, and RRT relative to simulations carried out in the MS model.

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Figure 3

Time course of the flow rate ratio waveforms computed at the interface sections of the MS and of the isolated models (upper panel), and flow rate time course (35) prescribed at the inlet section of the CCA (lower panel). Boundary conditions imposed at the outlet sections of the isolated 3D model, i.e., 50/50 and 60/40 flow ratios between ICA and ECA, are representative of the minimum and maximum flow rate splits between the two branches during the simulated cardiac cycle within the coupled model. The 70/30 flow rate partition represents the upper physiological flow ratio between ICA and ECA (35).

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Figure 4

WSS-based metrics distributions. As for OSI and RRT, contour levels depicted in each frame’s legend correspond to the 80th, 85th, 90th, and 95th percentile values. As for TAWSS, the contour levels correspond to the 5th, 10th, 15th, and 20th percentile values. Two different view angles, orientated to show the regions of greater interest, are displayed.

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Figure 5

Comparison of the TAWSS fields (Pa) for the simulated outflow boundary conditions. The first row shows the reference standard, that is the contour plot of the TAWSS in the case of the MS approach, and the contour plots in the case of fixed flow rate partitions imposed at the isolated 3D model. The next two rows show the contour maps of the local absolute and percentage difference with respect to the reference standard, respectively. Two different view angles, orientated to show the regions of greater interest, are displayed.

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Figure 6

Comparison of the OSI fields for the simulated outflow boundary conditions. The first row shows the reference standard, that is the contour plot of the OSI in the case of the MS approach, and the contour plots in the case of fixed flow rate partitions imposed at the isolated 3D model. The next two rows show the contour maps of the local absolute and percentage difference with respect to the reference standard, respectively. Two different view angles, orientated to show the regions of greater interest, are displayed.

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Figure 7

Comparison of the RRT fields (m2/N) for the simulated outflow boundary conditions. The first row shows the reference standard, that is the contour plot of the RRT in the case of the MS approach, and the contour plots in the case of fixed flow rate partitions imposed at the isolated 3D model. The next two rows show the contour maps of the local absolute (m2/N−1) and percentage difference with respect to the reference standard, respectively. Two different view angles, orientated to show the regions of greater interest, are displayed.

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Figure 8

Rms values of the three WSS-based metrics over patches (lower panel). For the sake of clarity, patches are also shown (upper panel).

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Figure 1

Image-based model of the investigated healthy carotid bifurcation (mid panel). The left panel shows the flow rate time course prescribed at the inlet section of the CCA (35). The downstream vascular bed was modeled with RCR networks (right panel), and the 3D computational model and 0D network were coupled in a multiscale model of the carotid bifurcation district.

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