Meeting Banner
Abstract #0446

Highly Accelerated Magnetic Resonance Elastography via Bayesian Modeling

Christopher Ebersole1,2, Rizwan Ahmad1, Adam Rich1, Lee C. Potter1, and Arunark Kolipaka2

1Electrical and Computer Engineering, The Ohio State University, Columbus, OH, United States, 2Radiology, The Ohio State University Wexner Medical Center, Columbus, OH, United States

While magnetic resonance elastography (MRE) provides a non-invasive method of estimating tissue stiffness, which is indicative of a variety of diseases, MRE scans typically require lengthy breath-holds which are prohibitive for many patients. We have extended a recently proposed Bayesian imaging method, called ReVEAL, for MRE. This method is capable of reconstructing images from highly undersampled data by leveraging both sparsity and the near equal magnitude across multiple offsets, inherent to MRE acquisition, as reconstruction constraints. This reconstruction method is validated against SENSE using a retrospectively downsampled phantom dataset and three retrospectively downsampled in vivo liver datasets.

This abstract and the presentation materials are available to members only; a login is required.

Join Here