Sensitivity of Arterial Hyperelastic Models to Uncertainties in Stress-Free Measurements

[+] Author and Article Information
Nir Emuna

Faculty of Aerospace Engineering, TechnionȔIsrael Institute of Technology

David Durban

Faculty of Aerospace Engineering, TechnionȔIsrael Institute of Technology

Shmuel Osovski

Faculty of Mechanical Engineering, TechnionȔIsrael Institute of Technology

1Corresponding author.

ASME doi:10.1115/1.4040400 History: Received November 09, 2017; Revised May 04, 2018


Despite major advances made in modeling vascular tissue biomechanics, the predictive power of constitutive models is still limited by uncertainty of the input data. Specifically, key measurements, like the geometry of the stress-free (SF) state, involve a definite, sometimes non-negligible, degree of uncertainty. Here, we introduce a new approach for sensitivity analysis of vascular hyperelastic constitutive models to uncertainty in SF measurements. We have considered two vascular hyperelastic models: the phenomenological Fung model, and the structure-motivated Holzapfel-Gasser-Ogden model. Our results indicate up to 160% errors in the identified constitutive parameters for a 5% measurement uncertainty in the SF data. Relative margins of errors of up to 30% in the luminal pressure, 36% in the axial force, and over 200% in the stress predictions, were recorded for 10% uncertainties. These findings are relevant to the large body of studies involving experimentally based modeling and analysis of vascular tissues. The impact of uncertainties on calibrated constitutive parameters is significant in context of studies that use constitutive parameters to draw conclusions about the underlying microstructure of vascular tissues, their growth and remodeling processes, aging and disease states. The propagation of uncertainties into the predictions of biophysical parameters e.g. force, luminal pressure, and wall stresses, is of practical importance in the design and execution of clinical devices and interventions. Furthermore, insights provided by the present findings may lead to more robust parameters identification techniques, and serve as selection criteria in the trade-off between model complexity and sensitivity.

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