Artificial neural networks for stiffness estimation in magnetic resonance elastography.

ABSTRACT:

Purpose

To investigate the feasibility of using artificial neural networks to estimate stiffness from MR elastography (MRE) data.

Methods

Artificial neural networks were fit using model-based training patterns to estimate stiffness from images of displacement using a patch size of ∼1 cm in each dimension. These neural network inversions (NNIs) were then evaluated in a set of simulation experiments designed to investigate the effects of wave interference and noise on NNI accuracy. NNI was also tested in vivo, comparing NNI results against currently used methods.

Results

In 4 simulation experiments, NNI performed as well or better than direct inversion (DI) for predicting the known stiffness of the data. Summary NNI results were also shown to be significantly correlated

SUBMITTER: Murphy MC 

PROVIDER: S-EPMC5876084 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature