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An approach to reduce motion artifacts in Quantitative Susceptibility Mappingusing deep learning is proposed. We use an affine motion model with randomlycreated motion profiles to simulate motion-corrupted QSM images. The simulatedQSM image is paired with its motion-free reference to train a neural networkusing supervised learning. The trained network is tested on unseen simulatedmotion-corrupted QSM images, in healthy volunteers and in Parkinson's diseasepatients. The results show that motion artifacts, such as ringing and ghosting,were successfully suppressed.

Motion Artifact Reduction in Quantitative Susceptibility Mapping using Deep Neural Network