Date of Award
Doctor of Philosophy (PhD)
The mechanical properties of brain tissue may reflect or influence growth, remodeling, development, aging, and disease. However, brain tissue, especially white matter, is structurally anisotropic and has nonlinear strain-stress relationships, so common methods are not very useful to describe the mechanical properties of brain tissue. Magnetic resonance elastography (MRE), based on the MRI imaging technique, can be used for measuring tissue mechanical properties non-invasively. Therefore, the mathematical model that describes the relation between wave speed and tissue mechanical properties will essentially influence the accuracy of the prediction. The objective of this dissertation is to investigate the relationship between shear wave speeds and mechanical properties of soft fibrous anisotropic material. This was achieved through pursuit of the following aims: (1) to predict shear wave speeds from given material parameters and, conversely, to estimate parameters from the shear wave speed values in the nearly-incompressible transversely isotropic (NITI) HGO model; (2) to extend the HGO one-fiber family model to a locally orthotropic model and a HGO two-fiber family model; (3) to develop an artificial neural network-based approach for estimating material properties of transversely isotropic materials.