MR artifacts degrade image quality and affect diagnosis, requiring thorough examination by the MR technician, and reacquisition in some cases. We employ a combination of segmented acquisitions and a deep learning tool (ArtifactID) to perform more frequent updates to image quality during acquisition. ArtifactID identified wrap-around, Gibbs ringing and motion artifacts, with a mean accuracy of 99.43%. The segmented acquisitions for rescans resulted in a 12.98% time gain over the full-FOV sequence. In addition, ArtifactID alleviates burden on the MR technician via automatic artifact identification, saving image quality evaluation time and augmenting expertise.
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