Impact of material homogeneity assumption on cortical stiffness estimates by MR elastography.

ABSTRACT:

Purpose

Inversion algorithms used to convert acquired MR elastography wave data into material property estimates often assume that the underlying materials are locally homogeneous. Here we evaluate the impact of that assumption on stiffness estimates in gray-matter regions of interest in brain MR elastography.

Methods

We describe an updated neural network inversion framework using finite-difference model-derived data to train convolutional neural network inversion algorithms. Neural network inversions trained on homogeneous simulations (homogeneous learned inversions [HLIs]) or inhomogeneous simulations (inhomogeneous learned inversions [ILIs]) are generated with a variety of kernel sizes. These inversions are evaluated in a brain MR elastography simulation experiment and in

SUBMITTER: Scott JM 

PROVIDER: S-EPMC9561798 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature