Abstract #0820

High Resolution T2W imaging using Deep Learning Reconstruction and Reduced Field-of-View PROPELLER

Xinzeng Wang¹, Daniel Litwiller², Marc Lebel³, Ali Ersoz⁴, Lloyd Estkowski⁴, Jason Stafford⁵, and Ersin Bayram⁶

¹GE Healthcare, Houston, TX, United States, ²Global MR Applications & Workflow, GE Healthcare, New York, NY, United States, ³Global MR Applications & Workflow, GE Healthcare, Calgary, AB, Canada, ⁴Global MR Applications & Workflow, GE Healthcare, Waukesha, WI, United States, ⁵Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX, United States, ⁶Global MR Applications & Workflow, GE Healthcare, Houston, TX, United States

T2W FSE-PROPELLER is robust to susceptibility artifacts and bulk motion, but requires longer acquisition times compared to conventional FSE methods. Recently, a reduced Field-Of-View PROPELLER sequence using rotating outer volume suppression method has been proposed and optimized to reduce the scan time for small FOV and high-resolution T2W imaging. However, image SNR is comparatively lower compared to the conventional PROPELLER with phase oversampling. In this work, a deep learning based PROPELLER reconstruction method was used to improve the SNR and image quality of the reduced Field-Of-View PROPELLER.

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