Keywords: Machine Learning/Artificial Intelligence, Artifacts, deep learning, AI-guided radiology, neuroimaging, computer visionSubject motion remains the major source of artifacts in magnetic resonance imaging (MRI). Motion correction approaches have been successfully applied in research, but clinical MRI typically involves repeating corrupted acquisitions. To alleviate this inefficiency, we propose a deep-learning strategy for training networks that predict a quality rating from the first few shots of accelerated multi-shot multi-slice acquisitions, scans frequently used for neuroradiological screening. We demonstrate accurate prediction of the scan outcome from partial acquisitions, assuming no further motion. This technology has the potential to inform the operator's decision on aborting corrupted scans early instead of waiting until the acquisition completes.
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