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Abstract #0960

Inline artifact identification using segmented acquisitions and deep learning

Keerthi Sravan Ravi1,2, Marina Manso Jimeno1,2, John Thomas Vaughan Jr.2, and Sairam Geethanath2
1Department of Biomedical Engineering, Columbia University, New York, NY, United States, 2Columbia Magnetic Resonance Research Center, New York, NY, United States


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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