Abstract #1355

Fast abdominal T2 weighted PROPELLER using deep learning-based acceleration of parallel imaging

Motohide Kawamura¹, Daiki Tamada¹, Masahiro Hamasaki², Kazuyuki Sato², Tetsuya Wakayama³, Satoshi Funayama¹, Hiroyuki Morisaka¹, and Hiroshi Onishi¹

¹Department of Radiology, University of Yamanashi, Chuo, Japan, ²Division of Radiology, University of Yamanashi Hospital, Chuo, Japan, ³MR Collaboration and Development, GE Healthcare, Hino, Japan

Respiratory motion is a big problem in abdominal T2W imaging. PROPELLER sequence is an excellent solution for motion artifact. However, it requires longer acquisition than FSE, limiting its wider adoption in clinical situations. Here, we propose to use a deep learning-based parallel imaging reconstruction for accelerating PROPELLER. Our approach applies deep learning to the reconstruction of blade images. Thus, training is robust to respiratory motion because blade data can be obtained with single shot. Preliminary results showed that the proposed method significantly outperformed SENSE reconstruction.

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