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Abstract #4076

Initial Evaluation of a Transverse Isotropic Finite Difference Model for Training Learned Inversion

Jonathan Trevathan1, Jonathan Scott1, Joshua Trzasko1, Armando Manduca1, John Huston1, Richard Ehman1, and Matthew Murphy1
1Mayo Clinic, Rochester, MN, United States


While most existing inversion algorithms used in MR elastography assume that the mechanical properties of tissue are isotropic, many tissues exhibit spatial anisotropy in structure that is not accommodated by these algorithms.1,2 In this work we present a framework for developing a learned inversion to address transverse isotropy, the simplest anisotropic case. A transversely isotropic stiffness matrix was used in a feed forward finite difference model to generate simulated displacements. The squared wave speeds anisotropic inclusions were calculated using direct inversion to validate the model against the theoretical wave speeds.3

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