  Motion artifacts are a common occurrence in the Magnetic Resonance Imaging
(MRI) exam. Motion during acquisition has a profound impact on workflow
efficiency, often requiring a repeat of sequences. Furthermore, motion
artifacts may escape notice by technologists, only to be revealed at the time
of reading by the radiologists, affecting their diagnostic quality. Designing a
computer-aided tool for automatic motion detection and elimination can improve
the diagnosis, however, it needs a deep understanding of motion
characteristics. Motion artifacts in MRI have a complex nature and it is
directly related to the k-space sampling scheme. In this study we investigate
the effect of three conventional k-space samplers, including Cartesian, Uniform
Spiral and Radial on motion induced image distortion. In this regard, various
synthetic motions with different trajectories of displacement and rotation are
applied to T1 and T2-weighted MRI images, and a convolutional neural network is
trained to show the difficulty of motion classification. The results show that
the spiral k-space sampling method get less effect of motion artifact in image
space as compared to radial k-space sampled images, and radial k-space sampled
images are more robust than Cartesian ones. Cartesian samplers, on the other
hand, are the best in terms of deep learning motion detection because they can
better reflect motion.
