Head motion can severely degrade the quality of MR brain images. A deep convolutional neural network was implemented in this study to retrospectively compensate for motion in spiral imaging. The network was trained on images with simulated motion artifacts and tested on both simulated and in vivo data. The image quality was improved after the motion correction.
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