Artificial neural networks for magnetic resonance elastography stiffness estimation in inhomogeneous materials.

ABSTRACT:

Purpose

To test the hypothesis that removing the assumption of material homogeneity will improve the spatial accuracy of stiffness estimates made by Magnetic Resonance Elastography (MRE).

Methods

An artificial neural network was trained using synthetic wave data computed using a coupled harmonic oscillator model. Material properties were allowed to vary in a piecewise smooth pattern. This neural network inversion (Inhomogeneous Learned Inversion (ILI)) was compared against a previous homogeneous neural network inversion (Homogeneous Learned Inversion (HLI)) and conventional direct inversion (DI) in simulation, phantom, and in-vivo experiments.

Results

In simulation experiments, ILI was more accurate than HLI and DI in predicting the stiffness of an inclusion in noise-

SUBMITTER: Scott JM 

PROVIDER: S-EPMC7583310 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature