A survey on Motion Artifact Correction in Magnetic Resonance Imaging for Improved Diagnostics

Motion artifacts occur in magnetic resonance imaging (MRI) due to the motion or movement of the object being scanned. Motion artifacts can have various origins such as voluntary or involuntary patient movement, faulty components, improper software configuration, etc. Blurry MRI scans are generated due to the presence of motion artifacts. In some cases motion artifact induced MRI scans can tamper with crucial information and consequently leads to a faulty diagnosis. We attempt to provide a review of current suggested technologies (such as, deep learning, and encoding) used to remove motion artifacts from the MRI scans. The different approaches are summarized in brief, with their advantages and disadvantages. We expect the readers to be better equipped with the knowledge and tools available for magnetic resonance image artifact removal. We have also proposed many simple cases and their corresponding solutions in the discussion section of this manuscript. It was found that deep learning with CNN can reach maximum accuracy reported in motion artifact correction is about 97%. With the advancements in the field of artificial intelligence, and more and more sample data available (due to non-linear expansion of big data and cloud storage capabilities), it is expected that motion artifacts could soon be eliminated quite accurately. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.