Background Computational fluid dynamics (CFD) has become a popular tool for studying ‘patient-specific’ blood flow dynamics in cerebral aneurysms; however, rarely are the inflow boundary conditions patient-specific. We aimed to test the impact of widespread reliance on generalized inflow rates.
Methods Internal carotid artery (ICA) flow rates were measured via 2D cine phase-contrast MRI for 24 patients scheduled for endovascular therapy of an ICA aneurysm. CFD models were constructed from 3D rotational angiography, and pulsatile inflow rates imposed as measured by MRI or estimated using an average older-adult ICA flow waveform shape scaled by a cycle-average flow rate (Qavg) derived from the patient’s ICA cross-sectional area via an assumed inlet velocity.
Results There was good overall qualitative agreement in the magnitudes and spatial distributions of time-averaged wall shear stress (TAWSS), oscillatory shear index (OSI), and spectral power index (SPI) using generalized versus patient-specific inflows. Sac-averaged quantities showed moderate to good correlations: R2=0.54 (TAWSS), 0.80 (OSI), and 0.68 (SPI). Using patient-specific Qavg to scale the generalized waveform shape resulted in near-perfect agreement for TAWSS, and reduced bias, but not scatter, for SPI. Patient-specific waveform had an impact only on OSI correlations, which improved to R2=0.93.
Conclusions Aneurysm CFD demonstrates the ability to stratify cases by nominal hemodynamic ‘risk’ factors when employing an age- and vascular-territory-specific recipe for generalized inflow rates. Qavg has a greater influence than waveform shape, suggesting some improvement could be achieved by including measurement of patient-specific Qavg into aneurysm imaging protocols.
- blood flow
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Contributors MN, NMC, OB, VMP, and DAS contributed to the conception and design of the study. All authors contributed to the acquisition, analysis, or interpretation of the data, and the drafting or critical revision of the manuscript. All authors approved the final manuscript.
Funding This study was supported by grant G-16-00012564 from the Heart and Stroke Foundation of Canada, and the Swiss National Science Foundation (grants SNF 32003B 160222 and SNF 320030 156813). Computations were performed on the Niagara supercomputer at the SciNet HPC Consortium. SciNet is funded by: the Canada Foundation for Innovation; the Government of Ontario; Ontario Research Fund – Research Excellence; and the University of Toronto.
Competing interests None declared.
Patient consent for publication Not required.
Provenance and peer review Not commissioned; externally peer reviewed.
Data availability statement Individual data informing Figure 3 are available upon request.
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