Abstract #3161

Accelerated T1 weighted PROPELLER imaging of the brain with deep-learning based parallel imaging reconstruction

Motohide Kawamura¹, Daiki Tamada¹, Kazuyuki Sato², Masahiro Hamasaki², Satoshi Funayama¹, Tetsuya Wakayama³, Utaroh Motosugi⁴, 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, ⁴Department of Radiology, Kofu-Kyoritsu Hospital, Kofu, Japan

Synopsis

PROPELLER sequence is useful because of its robustness to patient motion. Longer acquisition than FSE is a major drawback limiting its wider application in clinical practice. Here, we propose an accelerated T1 weighted PROPELLER of the brain using deep learning based parallel imaging (PI) reconstruction. Our method can unfold highly undersampled aliased images (PI factor = 7), enabling 2.3 times faster acquisition than full-sampling. A preliminary reader study with prospectively undersampled data showed that the proposed method significantly outperformed a conventional SENSE reconstruction in terms of streak artifact.

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