Background: For mechanical modeling, in vivo data are relatively incomplete in comparison to in vitro results. However, identification of mechanical properties from human clinical data to compute wall stress fields can play an important role in understanding better pathological evolutions.

Aim: Demonstrate the feasibility of material identification and stress computation from clinical data.

Methods: In vivo human common carotid arteries (CCAs) were explored non-invasively. During several cardiac cycles, medial diameter, intimal-medial thickness and blood pressure were measured by a high-resolution echotracking (Art. Lab®) and applanation tonometry (SphygmoCor®), respectively. To study the wall mechanical behavior, the CCA was assumed to be a thick-walled, three-dimensional cylinder subjected to dynamical intraluminal pressure and perivascular constraints. We also assumed a nonlinear, hyperelastic, fiber-reinforced, incompressible material with smooth muscle activity and residual stresses. We included wall mechanical contributions by microconstituents: an elastin-dominated matrix, collagen fibers, and vascular smooth muscle. We solved the in vivo boundary value problem semi-analytically to compute the intraluminal pressure during a cardiac cycle. Minimizing the difference between computed and measured inner pressures over the cardiac cycle provided the identification of optimal model parameters employing a nonlinear regression. Illustrative data were from two healthy subjects.

Results: The fit-to-data gave very good results. The predicted radial, circumferential, and axial stretches and stresses within the wall during the cardiac cycle were sensible.

Conclusion: We were able to identify experimentally unknown geometric and material parameters directly from in vivo human data. We can extend the proposed approach to pathological cases such as hypertension.