Keywords: Diagnosis/Prediction, Elastography, Anisotropy, Stiffness, Shear, Tensile, Inversion
Motivation: Most Magnetic Resonance Elastography (MRE) inversion algorithms assume isotropic materials. However, in tissues with a preferred fiber direction, the effective mechanical properties computed under this assumption will reflect a mixture of the true underlying elastic moduli.
Goal(s): In this study, we extend neural network inversion (NNI) to include transversely isotropic (TI) materials. Assumptions are progressively relaxed and the TI inversion in each case is compared against isotropic inversion.
Approach: Data was generated to train and test multiple TI inversions.
Results: An NNI trained to handle TI material can more accurately estimate shear moduli in anisotropic materials and can predict the amount of anisotropy.
Impact: This research expands on currently used MRE to allow for more accurate property estimation in highly organized tissues such as brain and muscle. Moreover, it opens new paths of investigation into pathological changes of the highly organized tissues.
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