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Motion artifacts are a common occurrence in the Magnetic Resonance Imaging(MRI) exam. Motion during acquisition has a profound impact on workflowefficiency, often requiring a repeat of sequences. Furthermore, motionartifacts may escape notice by technologists, only to be revealed at the timeof reading by the radiologists, affecting their diagnostic quality. Designing acomputer-aided tool for automatic motion detection and elimination can improvethe diagnosis, however, it needs a deep understanding of motioncharacteristics. Motion artifacts in MRI have a complex nature and it isdirectly related to the k-space sampling scheme. In this study we investigatethe effect of three conventional k-space samplers, including Cartesian, UniformSpiral and Radial on motion induced image distortion. In this regard, varioussynthetic motions with different trajectories of displacement and rotation areapplied to T1 and T2-weighted MRI images, and a convolutional neural network istrained to show the difficulty of motion classification. The results show thatthe spiral k-space sampling method get less effect of motion artifact in imagespace as compared to radial k-space sampled images, and radial k-space sampledimages are more robust than Cartesian ones. Cartesian samplers, on the otherhand, are the best in terms of deep learning motion detection because they canbetter reflect motion.

Which K-Space Sampling Schemes is good for Motion Artifact Detection in Magnetic Resonance Imaging?