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Artificial neural networks for stiffness estimation in magnetic resonance elastography
Author(s) -
Murphy Matthew C.,
Manduca Armando,
Trzasko Joshua D.,
Glaser Kevin J.,
Huston John,
Ehman Richard L.
Publication year - 2018
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.27019
Subject(s) - artificial neural network , magnetic resonance elastography , stiffness , computer science , artificial intelligence , smoothing , pattern recognition (psychology) , algorithm , elastography , physics , computer vision , acoustics , engineering , structural engineering , ultrasound
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 with DI results in the liver (R 2 = 0.974) and in the brain (R 2 = 0.915), and also correlated with established biological effects including fibrosis stage in the liver and age in the brain. Finally, repeatability error was lower in the brain using NNI compared to DI, and voxel‐wise modeling using NNI stiffness maps detected larger effects than using DI maps with similar levels of smoothing. Conclusion Artificial neural networks represent a new approach to inversion of MRE data. Summary results from NNI and DI are highly correlated and both are capable of detecting biologically relevant signals. Preliminary evidence suggests that NNI stiffness estimates may be more resistant to noise than an algebraic DI approach. Taken together, these results merit future investigation into NNIs to improve the estimation of stiffness in small regions. Magn Reson Med 80:351–360, 2018. © 2017 International Society for Magnetic Resonance in Medicine.