An Image-Based Computational Framework to Evaluate the Material Stiffness of Arterial Tissue With High-Resolution Magnetic Resonance Imaging

Atherosclerotic plaque rupture is the precipitating event in most acute coronary syndromes. As rupture results from the material failure of arterial tissue under mechanical loading, in vivo image-based techniques that can accurately characterize arterial material stiffness offer potential in risk-stratifying lesions. This study developed and validated a novel magnetic resonance (MR) image-based computational framework to evaluate the material stiffness of vascular tissue. Porcine carotid arteries (n = 4) were subjected to biaxial mechanical testing, followed by MR image acquisition under controlled loading. Best-fit material parameters for an anisotropic material model were estimated via regression analysis on the biaxial data. A deformable image registration technique, termed hyperelastic warping, was utilized to derive strain fields from the MR images and integrated with an inverse parameter estimation algorithm to identify the parameters for the same constitutive model. Experimentally and warping-estimated material stiffness values (tangent moduli) were not significantly different at physiologic lumen pressures of 80 (0.36 ± 0.15 and 0.48 ± 0.20 MPa; p = 0.14) and 120 mmHg (0.64 ± 0.27 and 0.73 ± 0.36 MPa; p = 0.60). The warping-directed inverse modeling framework identified subtle, but observable variations in material stiffness within a sample and accurately illustrated the physical influence of loading conditions on those properties. Collectively, these results demonstrated the robustness of an innovative approach to characterize nonlinear, hyperelastic behaviors of arterial tissue and quantify material stiffness directly from image data.