Motion artifacts in MRI can be confused with pathology, and render the scans not diagnosable. Ideally during examination, scan operators should identify these artifacts immediately, and reacquire the scan. But it can be challenging to do so in a clinical time-constraint workflow. Motion artifacts are especially difficult to identify accurately, as they come in a wide variety. Here, we propose an autonomous scan control framework, based on deep learning, to detect motion artifacts immediately after reconstruction. The deep learning model was integrated into a real-time imaging system, and enabled an interactive scanning pipeline.
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