Meeting Banner
Abstract #0529

Deep learning based radial de-streaking for free breathing time resolved volumetric DCE MRI

Sagar Mandava1, Xinzeng Wang2, Ty Cashen3, Tetsuya Wakayama4, and Ersin Bayram2
1GE Healthcare, Atlanta, GA, United States, 2GE Healthcare, Houston, TX, United States, 3GE Healthcare, Madison, WI, United States, 4GE Healthcare, Hino, Japan

Radial imaging is becoming increasingly popular due to its ability to support highly accelerated imaging. However, it is plagued by streak artifacts that often arise from undersampling which can lead to poor image quality. The problem is particularly acute in time resolved imaging where the need for high spatio-temporal sampling usually leads to large amount of streaks. In this work, we propose a method for separate spatial and temporal deep learning for streak artifact reduction. The utility of the method is demonstrated on free breathing time resolved volumetric DCE MRI acquired using the stack-of-stars trajectory.

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

Join Here