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Research Papers

PIV-Measured Versus CFD-Predicted Flow Dynamics in Anatomically Realistic Cerebral Aneurysm Models

[+] Author and Article Information
Matthew D. Ford

Imaging Research Laboratories, Robarts Research Institute, London, Canada N6A 5K8; Department of Medical Biophysics, The University of Western Ontario, London, Canada N6A 5C1; Biomedical Simulation Laboratory, University of Toronto, Toronto, Canada M5S 3G8

Hristo N. Nikolov, Jaques S. Milner

Imaging Research Laboratories, Robarts Research Institute, London, Canada N6A 5K8

Stephen P. Lownie

Imaging Research Laboratories, Robarts Research Institute, London, Canada N6A 5K8; Department of Clinical Neurological Sciences, The University of Western Ontario, London, Canada N6A 5A5

Edwin M. DeMont

Department of Biology, Saint Francis Xavier University, Antigonish, Canada B2G 2W5

Wojciech Kalata, Francis Loth

Department of Mechanical Engineering, University of Illinois at Chicago, Chicago, IL 60607

David W. Holdsworth

Imaging Research Laboratories, Robarts Research Institute, London, Canada N6A 5K8; Department of Medical Biophysics, The University of Western Ontario, London, Canada N6A 5C1

David A. Steinman1

Imaging Research Laboratories, Robarts Research Institute, London, Canada N6A 5K8; Department of Medical Biophysics, The University of Western Ontario, London, Canada N6A 5C1; Biomedical Simulation Laboratory, University of Toronto, Toronto, Canada M5S 3G8steinman@mie.utoronto.ca

PIV data from all planes, as well as volumetric CFD data, are available at http://www.mie.utoronto.ca/labs/bsl/data.html.

1

Author to whom correspondence should be addressed.

J Biomech Eng 130(2), 021015 (Apr 03, 2008) (9 pages) doi:10.1115/1.2900724 History: Received November 13, 2006; Revised June 28, 2007; Published April 03, 2008

Computational fluid dynamics (CFD) modeling of nominally patient-specific cerebral aneurysms is increasingly being used as a research tool to further understand the development, prognosis, and treatment of brain aneurysms. We have previously developed virtual angiography to indirectly validate CFD-predicted gross flow dynamics against the routinely acquired digital subtraction angiograms. Toward a more direct validation, here we compare detailed, CFD-predicted velocity fields against those measured using particle imaging velocimetry (PIV). Two anatomically realistic flow-through phantoms, one a giant internal carotid artery (ICA) aneurysm and the other a basilar artery (BA) tip aneurysm, were constructed of a clear silicone elastomer. The phantoms were placed within a computer-controlled flow loop, programed with representative flow rate waveforms. PIV images were collected on several anterior-posterior (AP) and lateral (LAT) planes. CFD simulations were then carried out using a well-validated, in-house solver, based on micro-CT reconstructions of the geometries of the flow-through phantoms and inlet/outlet boundary conditions derived from flow rates measured during the PIV experiments. PIV and CFD results from the central AP plane of the ICA aneurysm showed a large stable vortex throughout the cardiac cycle. Complex vortex dynamics, captured by PIV and CFD, persisted throughout the cardiac cycle on the central LAT plane. Velocity vector fields showed good overall agreement. For the BA, aneurysm agreement was more compelling, with both PIV and CFD similarly resolving the dynamics of counter-rotating vortices on both AP and LAT planes. Despite the imposition of periodic flow boundary conditions for the CFD simulations, cycle-to-cycle fluctuations were evident in the BA aneurysm simulations, which agreed well, in terms of both amplitudes and spatial distributions, with cycle-to-cycle fluctuations measured by PIV in the same geometry. The overall good agreement between PIV and CFD suggests that CFD can reliably predict the details of the intra-aneurysmal flow dynamics observed in anatomically realistic in vitro models. Nevertheless, given the various modeling assumptions, this does not prove that they are mimicking the actual in vivo hemodynamics, and so validations against in vivo data are encouraged whenever possible.

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Copyright © 2008 by American Society of Mechanical Engineers
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Figures

Grahic Jump Location
Figure 1

Anatomically realistic flow-through phantoms of a giant ICA aneurysm and a BA tip aneurysm, each shown in AP and LAT views. Both phantoms are shown to the same scale, per the indicated scaling bar. Numerals identify the outlet numbers referred to in Fig. 3. For the ICA model, Outlets 1 and 2 arise from the middle cerebral artery, and Outlet 3 arises from the anterior cerebral artery. For the BA model, the two outlets arise from the two posterior cerebral arteries.

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Figure 2

CFD meshes derived from micro-CT imaging of the flow-through phantoms, showing the positions of the five AP and LAT planes for the ICA model and BA models. Note that in all cases Plane 1 is the nominal central Plane, and the thickness of the lines corresponds to the nominal thickness of the PIV laser light sheet. The darker planes are those for which data are shown in the Results section.

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Figure 3

Inlet and outlet flow rate waveforms, measured by the EM flow meter, during acquisitions of PIV data for the ICA and BA cases. The vertical bars indicate the standard deviations of the phase-averaged flow rate measurements. Note the different time (x) axes, owing to the different periods required for the two models.

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Figure 4

PIV versus CFD velocities on AP planes of the ICA model: (a) AP Plane 1, peak systole; (b) AP Plane 1, late diastole; (c) AP Plane 4, peak systole; and (d) AP Plane 4, late diastole. For this and subsequent figures, PIV data are on the left side of each panel, and CFD data are on the right. The translucent circles correspond to vortex cores and the transparent arrows to areas of discrepancy between PIV and CFD, as described in the results.

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Figure 5

PIV versus CFD velocities on LAT planes of the ICA model: (a) LAT Plane 1, peak systole; (b) LAT Plane 1, late diastole; (c) LAT Plane 2, peak systole; (d) LAT Plane 2, late diastole. Refer to the caption of Fig. 4 for further details.

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Figure 6

PIV versus CFD velocities on LAT planes of the BA model: (a) LAT Plane 1, peak systole; (b) LAT Plane 1, late diastole; (c) LAT Plane 4, peak systole; (d) LAT Plane 4, late diastole. Refer to the caption of Fig. 4 for further details.

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Figure 7

PIV versus CFD velocities on AP planes of the BA model: (a) AP Plane 1, peak systole; (b) AP Plane 1, late diastole; (c) AP Plane 4, peak systole; (d) AP Plane 4, late diastole. Refer to the caption of Fig. 4 for further details.

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Figure 8

Cycle-to-cycle fluctuations for the BA model. (a) CFD convergence history, showing the maximum (L∞) and rms (L2) differences in the velocity fields between successive cycles (as defined in the Results section), each normalized with respect to the characteristic (mean inlet) velocity. Also shown is the inlet flow rate waveform shape for reference. (b) CFD and (c) PIV velocity vector fields from three consecutive cycles on the central AP plane, at a phase midway through the cycle where the greatest fluctuations are evident in Panel (a).

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Figure 9

Standard deviations of the velocity magnitude from PIV and CFD data on the central AP and LAT planes, at the same phase shown in Fig. 8.

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