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Abstract #1025

Effect of Spatial Inhomogeneity Models on Performance of Machine Learning Based Inversion Algorithms for Brain Magnetic Resonance Elastography

Jonathan M Scott1, Joshua D Trzasko1, Armando Manduca1, Matthew L Senjem1, Clifford R Jack1, John Huston III1, Richard L Ehman1, and Matthew C Murphy1
1Mayo Clinic, Rochester, MN, United States

Four machine learning inversion algorithms with different material spatial property assumptions (trained on simulated data with homogeneous, piecewise constant, smooth, or piecewise smoothly varying material properties) were evaluated in a brain simulating phantom with stiff inclusions, a test-retest repeatability dataset, and an Alzheimer’s disease dataset. The piecewise smooth inversion produced the highest contrast to noise ratio and allowed the best visualization of inclusions in the phantom study. All four inversions produced stiffness estimates that were repeatable and sensitive to stiffness changes in Alzheimer’s disease.

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