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Using Digital Image Correlation to Characterize Local Strains on Vascular Tissue Specimens
Published on: January 24, 2016
Progress in constitutive modeling of arterial wall tissue mechanics: from theoretical frameworks to clinical
Qian Fan1, Dezhong Qi2, Qiang Xiao1
1Department of Cardiology, The Second Affiliated Hospital of Shandong First Medical University, Taian, 27100, China.
Abstract:
Cardiovascular disease (CVD) is a leading cause of mortality worldwide, and the mechanical behavior of arterial wall tissue (AWT) is central to its initiation and progression. This review surveys advances in constitutive models of AWT over the past two decades, with the aim of improving understanding of vascular mechanics and informing clinical practice. Five major computational frameworks are evaluated-elastic, viscoelastic, hyperelastic, structural solid models, and growth and remodeling (G&R) models-which collectively provide insights into stress-strain relationships and mechanobiological interactions under physiological and pathological conditions. Simple elastic formulations cannot capture the intrinsic nonlinearity of AWT. Nonlinear elastic and pseudo-elastic models are better suited for large deformations and anisotropy, especially under cyclic loading. Viscoelastic models effectively represent time-dependent responses to pulsatile blood flow. Structural solid models, including layered anisotropic, equivalent homogeneous, and generalized structure tensor formulations, predict the mechanical behavior of individual wall layers with high fidelity. Extending beyond instantaneous mechanics, G&R models embed these constitutive relations within higher-level frameworks to simulate long-term adaptations to altered hemodynamics, such as hypertension, aneurysm progression, or vascular graft remodeling. Future research should focus on developing dynamic models that more accurately simulate pulsatile loading, refining the characterization of AWT heterogeneity and anisotropy, and establishing multiscale and multi-physics frameworks to connect cellular processes with tissue-level behavior. Integrating big data and machine learning offers additional potential for robust parameter identification and predictive modeling. In conclusion, this review provides a comprehensive evaluation of AWT constitutive modeling, from fundamental elasticity-based approaches to advanced G&R frameworks. By identifying limitations and outlining future directions, it highlights the role of biomechanics in advancing personalized medicine, improving CVD diagnosis and treatment, and promoting deeper understanding of disease mechanisms.
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