Meeting Banner
Abstract #0812

Unsupervised radial streak artifact reduction in time resolved MRI

Sagar Mandava1, Ty Cashen2, Daniel V Litwiller3, Tetsuya Wakayama4, and Ersin Bayram5
1Global MR Applications & Workflow, GE Healthcare, Tucson, AZ, United States, 2Global MR Applications & Workflow, GE Healthcare, Madison, WI, United States, 3Global MR Applications & Workflow, GE Healthcare, New York, NY, United States, 4Global MR Applications & Workflow, GE Healthcare, Hino, Japan, 5Global MR Applications & Workflow, GE Healthcare, Houston, TX, United States

Radial magnetic resonance imaging is attractive due to its inherently high motion robustness and its ability to support accelerated imaging but is plagued by streaking artifact. The problem is exacerbated in time resolved imaging, like DCE-MRI, which deal with higher levels of undersampling due to the need to jointly deliver high spatial and temporal resolution. While reconstructive methods typically based on sparse or low rank methods exist to minimize streak artifact, their use is currently limited due to their high computational complexity. As an alternative, we describe a temporal neural network to suppress streak artifact from a time-series of images.

This abstract and the presentation materials are available to 2020 meeting attendees and eLibrary customers only; a login is required.

Join Here