  Motion is one of the main sources for artifacts in magnetic resonance (MR)
images. It can have significant consequences on the diagnostic quality of the
resultant scans. Previously, supervised adversarial approaches have been
suggested for the correction of MR motion artifacts. However, these approaches
suffer from the limitation of required paired co-registered datasets for
training which are often hard or impossible to acquire. Building upon our
previous work, we introduce a new adversarial framework with a new generator
architecture and loss function for the unsupervised correction of severe rigid
motion artifacts in the brain region. Quantitative and qualitative comparisons
with other supervised and unsupervised translation approaches showcase the
enhanced performance of the introduced framework.
