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Abstract #1963

Deep Learning Image Reconstruction from Incomplete Fast Spin Echo MR Data

Linfang Xiao1,2, Yilong Liu1,2, Yujiao Zhao1,2, Zheyuan Yi1,2,3, Vick Lau1,2, Alex T.L. Leong1,2, and Ed X. Wu1,2
1Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong, China, 2Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China, 3Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China

Fast spin echo (FSE) is the most commonly used multi-shot sequence in clinical MRI. In this study, we propose to acquire single-channel FSE data with incomplete number of shots (TRs), and reconstruct such periodically undersampled k-space data using a deep learning approach. The results demonstrate that the proposed method can effectively remove the aliasing artifacts and recover the high frequency information without noise amplification, enabling a FSE acceleration that can be readily implemented in practice.

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