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Abstract #1033

Improved Depiction of Meningioma Boundaries in MR Elastography Using a Novel Inhomogeneous Learned Inversion

Jonathan M Scott1, Arvin Arani2, Armando Manduca2, Joshua D Trzasko2, John Huston III2, Richard L Ehman2, and Matthew C Murphy2
1Medical Scientist Training Program, Mayo Clinic, Rochester, MN, United States, 2Radiology, Mayo Clinic, Rochester, MN, United States

Magnetic Resonance Elastography stiffness estimates in small focal lesions are often inaccurate. The assumption of material homogeneity made by most inversion algorithms likely contributes to these errors. Here we describe a machine-learning based inversion algorithm trained on wave simulations of materials with piecewise smooth stiffness variations (Inhomogeneous Learned Inversion, ILI). We show that ILI offers improved delineation of tumor boundaries over two inversions assuming material homogeneity in a series of 17 patients with stiff meningiomas.

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