Motion artifacts which are occurred in subject motion during MR data acquisition can cause significant image degradation. In this study, we propose a rigid motion artifact correction method, which eliminates the motion-corrupted phase encoding lines detected by navigator echoes and reconstructs motion-compensated images using parallel imaging with deep learning. According to evaluation of simulated motion data and real motion-corrupted data, the proposed method achieved competent compensation for motion artifacts.
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