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 but is plagued by streak artifacts
that often arise from undersampling which can lead to poor image quality. We
demonstrate a combination of the spatial and temporal DL processing that enables
high quality high spatio-temporal imaging.
Figure 2: Demonstrating the impact of DL
processing. Four phases of a DCE scan are shown with arrows highlighting areas
where DL delivers improved performance. Best viewed on a high brightness
setting on the monitor
Figure 1: A) Offline trained spatial DL
processing in which streaks are suppressed in the spatial dimension, B) Online
trained temporal DL processing where a network is trained on the fly for
suppressing streaks in the time domain, C) Flow chart of the overall workflow
of the proposed method