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Updated: Aug 9, 2025

Pulse Wave Velocity Testing in the Baltimore Longitudinal Study of Aging
Published on: February 7, 2014
Pulse wave velocity: A clinical measure to aid material parameter estimation in computational arterial biomechanics
Lise Gheysen1, Lauranne Maes2, Nele Famaey2
1Institute for Biomedical Engineering and Technology, Department of Electronics and Information Systems, Ghent University, Belgium.
Abstract:
Determining proper material parameters from clinical data remains a large, though unavoidable, challenge in patient-specific computational cardiovascular modeling. In an attempt to couple the clinical and modelling practice, this study investigated whether pulse wave velocity (PWV), a clinical arterial stiffness measure, can guide in determining appropriate parameter values for the Gasser-Ogden-Holzapfel (GOH) constitutive model. The reduction and uncertainty analysis was demonstrated on a cylindrical descending thoracic aorta model. Starting from discretized ranges of GOH parameters and using a full factorial design, the parameter sets yielding a physiological PWV (3.5-12.5 m/s) at diastolic pressure (80 mmHg; PWV80) were selected and their PWV at dicrotic notch pressure (110 mmHg; PWV110) was determined. These PWV measures were applied to determine the reduction of the 7D GOH parameter space, the 2D subspaces and the remaining uncertainty in case only PWV80 or both measurements are available. The resulting 12,032 parameter sets lead to a 7D parameter space reduction of ≥ 82.5 % using PWV80, which increased to 96.0 % when including PWV110, in particular at 3.5-8.5 m/s. A similar trend was observed for the remaining uncertainty and the 2D subspaces comprised of medial collagen fiber parameters, while scarce reductions were found for the adventitial and elastin parameters. In conclusion, PWV80 and PWV110 are complementary measures with the potential to reduce the GOH parameter space in arterial models, in particular for media- and collagen-related parameters. Moreover, this approach has the advantage that it allows the estimation of the remaining uncertainty after parameter space reduction.
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