  Magnetic Resonance Elastography (MRE) can characterize biomechanical
properties of soft tissue for disease diagnosis and treatment planning.
However, complicated wavefields acquired from MRE coupled with noise pose
challenges for accurate displacement extraction and modulus estimation. Here we
propose a pipeline for processing MRE images using optimization-based
displacement extraction and Traveling Wave Expansion-based Neural Network
(TWENN) modulus estimation. Phase unwrapping and displacement extraction were
achieved by optimization of an objective function with Dual Data Consistency
(Dual-DC). A complex-valued neural network using displacement covariance as
input has been constructed for the estimation of complex wavenumbers. A model
of traveling wave expansion is used to generate training datasets with
different levels of noise for the network. The complex shear modulus map is
obtained by a fusion of multifrequency and multidirectional data. Validation
using images of brain and liver simulation demonstrates the practical value of
the proposed pipeline, which can estimate the biomechanical properties with
minimum root-mean-square-errors compared with state-of-the-art methods.
Applications of the proposed method for processing MRE images of phantom,
brain, and liver show clear anatomical features and that the pipeline is robust
to noise and has a good generalization capability.
