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

To 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.