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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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