A Robust and Subject-Specific Hemodynamic Model of the Lower Limb Based on Noninvasive Arterial Measurements

Abstract : Cardiovascular diseases are currently the leading cause of mortality in the population of developed countries, due to the constant increase in cardiovascular risk factors, such as high blood pressure, cholesterol, overweight, tobacco use, lack of physical activity, etc. Numerous prospective and retrospective studies have shown that arterial stiffening is a relevant predictor of these diseases. Unfortunately, the arterial stiffness distribution across the human body is difficult to measure experimentally. We propose a numerical approach to determine the arterial stiffness distribution of an arterial network using a subject-specific one-dimensional model. The proposed approach calibrates the optimal parameters of the reduced-order model, including the arterial stiffness, by solving an inverse problem associated with the noninvasive in vivo measurements. An uncertainty quantification analysis has also been carried out to measure the contribution of the model input parameters variability, alone or by interaction with other inputs, to the variation of clinically relevant hemodynamic indices, here the arterial pulse pressure. The results obtained for a lower limb model, demonstrate that the numerical approach presented here can provide a robust and subject-specific tool to the practitioner, allowing an early and reliable diagnosis of cardiovascular diseases based on a noninvasive clinical examination.
Document type :
Journal articles
Complete list of metadatas

https://hal.uvsq.fr/hal-02166372
Contributor : Équipe Hal Uvsq <>
Submitted on : Wednesday, June 26, 2019 - 5:15:44 PM
Last modification on : Saturday, July 27, 2019 - 11:52:01 AM

Identifiers

Citation

Laurent Dumas, Tamara El Bouti, Didier Lucor. A Robust and Subject-Specific Hemodynamic Model of the Lower Limb Based on Noninvasive Arterial Measurements. Journal of Biomechanical Engineering, American Society of Mechanical Engineers, 2017, 139 (1), ⟨10.1115/1.4034833⟩. ⟨hal-02166372⟩

Share

Metrics

Record views

22