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
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
We developed a framework for fast PROPELLER of abdominal T2W imaging. Deep learning enabled a higher parallel imaging factor than SENSE reconstruction, suggesting the reduction of scan times of PROPELLER.
Reconstructed images with CG-SENSE and the proposed method, and ground truth images. Compared to CG-SENSE, noises are reduced by the proposed method. (A) CG-SENSE, (B) the proposed method, (C) ground truth. (D)-(F) Magnified images of the solid boxes in (A)-(C), respectively.
Schemas of parallel imaging (PI) reconstruction of blade images for our accelerated PROPELLER. The neural network achieves a higher PI factor than the standard SENSE reconstruction, enabling faster acquisition.