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Abstract #3496

Estimating Inclusion Stiffness with Artificial Neural Networks in Magnetic Resonance Elastography

Jonathan M. Scott1, Matthew C. Murphy1, Arvin Arani1, Christopher G. Schwarz1, Armando Manduca1, John Huston III1, and Richard L. Ehman1

1Radiology, Mayo Clinic, Rochester, MN, United States

Magnetic Resonance Elastography stiffness estimates in intracranial tumors correlate with intraoperative assessment of tumor consistency, but the spatial kernel-based stiffness calculation of Direct Inversion (DI) creates challenges for small or heterogeneous tumors. The objective of this study is to evaluate an artificial neural network based inversion technique (NNI) in the assessment of small stiff inclusions in a brain phantom. This study shows that NNI can resolve inclusions as small as 1.75cm in diameter with a contrast to noise ratio higher than that of DI. Furthermore, preliminary clinical results show agreement with intraoperative findings.

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