Dual Objective Approach Using A Convolutional Neural Network for Magnetic Resonance Elastography
Year: 2018
Type: preprint
Abstract: Traditionally, nonlinear inversion, direct inversion, or wave estimation methods have been used for reconstructing images from MRE displacement data. In this work, we propose a convolutional neural network architecture that can map MRE displacement data directly into elastograms, circumventing the costly and computationally intensive classical approaches. In addition to the mean squared error reconstruction objective, we also introduce a secondary loss inspired by the MRE mechanical models for training the neural network. Our network is demonstrated to be effective for generating MRE images that compare well with equivalents from the nonlinear inversion method.
Source: arXiv (Cornell University)
Authors: Solamen, Ligin, Shi, Yipeng, Amoh, Justice
Institution: Dartmouth College
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
Funder: National Institutes of Health
OpenAlex ID: W2902969327