SUMMARY

With the advancement of clinical cardiac imaging modalities and computational tools, patient-specific biomechanical evaluation of aortic disease conditions, such as aortic aneurysm dissection and rupture, is getting closer to reality. Among the three key components necessary for an engineering biomechanics analysis, i.e., geometries, material properties, and loading/boundary conditions, the in vivo material properties is clearly the biggest unknown. Indeed, accurate estimation of in vivo mechanical properties of the aortic wall, which is nonlinear and anisotropic, has been a challenging problem in the field of cardiovascular biomechanics for several decades.
In this study, in vivo nonlinear anisotropic mechanical properties of the aortic wall will be estimated from clinical in vivo 3D CT image data of the human thoracic aorta. Inverse method will be developed for an accurate and efficient estimation. Numerically-generated data will be used for initial verification. Next, the developed inverse approach will be validated using 3D CT images and surgically-resected aortic wall tissue samples of two thoracic aortic aneurysm patients. A machine learning approach will be developed as a surrogate of the inverse method to rapidly (i.e., within seconds) estimate thoracic aorta material properties. Finally, a novel probabilistic rupture risk index will be developed using uniaxial testing data of thoracic aortas. Using the two patients’ data, finite element simulation will be performed to estimate the rupture risk index under supra-physiological pressure levels.