Motion artifacts negatively impact diagnosis and the radiology workflow, especially in cases where a patient recall is required. Detecting motion artifacts while the patient is still in the scanner could potentially improve workflow and reduce costs by enabling efficient corrective action. We introduce an algorithm that detects motion artifacts directly from raw k-space in a supervised learning manner in clinically important 2D FSE multi-slice scans, using cross-correlation between adjacent phase-encoding lines as features. This study employs a training approach that uses a motion simulator to generate k-space data with varying levels of motion artifact.
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