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Rigid motion artifact correction in multi-echo GRE using navigator detection and Parallel imaging reconstruction with Deep Learning
Seul Lee1, Jae-Hun Lee1, Soozy Jung1, and Dong-Hyun Kim1
1Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea, Republic of
We propose a rigid motion artifact correction framework, which eliminates the motion-corrupted phase encoding lines detected by navigator echoes and reconstructs motion-compensated images using parallel imaging with deep learning. 
Figure 1. Proposed rigid motion correction process.
Figure 4. The resultant mGRE images corrected from real motion-corrupted images.