Keywords: Machine Learning/Artificial Intelligence, Motion Correction, Motion Detection, Self-Supervised LearningMotion is still one of the major extrinsic sources for imaging artifacts in MRI that can strongly deteriorate image quality. Any impairment by motion artifacts can reduce the reliability and precision of the diagnosis and a motion‐free reacquisition can become time‐ and cost‐intensive. Furthermore, in large-scale epidemiological cohorts, manual quality screening becomes impracticable. An automated quality assessment is thus of interest. Reliable motion estimation in varying domains (imaging sequences, multiple scanners, sites) is however challenging. In this work, we propose an attention-based transformer that can detect motion in various MR imaging scenarios.
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