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