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Abstract #0949

Impact of image resolution on brain stiffness estimation using neural network inversion

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

Synopsis

Keywords: Data Acquisition, Data Acquisition, MRE,ElastrographyTo improve the clinical value of brain stiffness measurements, increased resolution is desired to accurately map the mechanical signatures of disease processes. However, MR elastography-based stiffness maps must be estimated by inverting the measured displacement fields. It is not well-established that increased acquisition resolution will directly translate to improved accuracy in the final mechanical property maps. In this simulation study, using two neural network inversions, we show 2-mm data outperforms 3-mm data for stiffness accuracy and precision across a range of signal-to-noise ratios.

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