Abstract #1963

Deep Learning Image Reconstruction from Incomplete Fast Spin Echo MR Data

Linfang Xiao^1,2, Yilong Liu^1,2, Yujiao Zhao^1,2, Zheyuan Yi^1,2,3, Vick Lau^1,2, Alex T.L. Leong^1,2, and Ed X. Wu^1,2

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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