  Purpose: The suppression of motion artefacts from MR images is a challenging
task. The purpose of this paper is to develop a standalone novel technique to
suppress motion artefacts from MR images using a data-driven deep learning
approach. Methods: A deep learning convolutional neural network (CNN) was
developed to remove motion artefacts in brain MR images. A CNN was trained on
simulated motion corrupted images to identify and suppress artefacts due to the
motion. The network was an encoder-decoder CNN architecture where the encoder
decomposed the motion corrupted images into a set of feature maps. The feature
maps were then combined by the decoder network to generate a motion-corrected
image. The network was tested on an unseen simulated dataset and an
experimental, motion corrupted in vivo brain dataset. Results: The trained
network was able to suppress the motion artefacts in the simulated motion
corrupted images, and the mean percentage error in the motion corrected images
was 2.69 % with a standard deviation of 0.95 %. The network was able to
effectively suppress the motion artefacts from the experimental dataset,
demonstrating the generalisation capability of the trained network. Conclusion:
A novel and generic motion correction technique has been developed that can
suppress motion artefacts from motion corrupted MR images. The proposed
technique is a standalone post-processing method that does not interfere with
data acquisition or reconstruction parameters, thus making it suitable for a
multitude of MR sequences.
