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Abstract #1764

ArtifactID: identifying artifacts in low field MRI using deep learning

Marina Manso Jimeno1,2, Keerthi Sravan Ravi2,3, John Thomas Vaughan Jr.2,3, Dotun Oyekunle4, Godwin Ogbole4, and Sairam Geethanath2
1Biomedical Engineering, Columbia University, New York, NY, United States, 2Columbia Magnetic Resonance Research Center (CMRRC), New York, NY, United States, 3Columbia University, New York, NY, United States, 4Radiology, University College Hospital, Ibadan, Nigeria

Most MR images contain artifacts such as wrap-around and Gibbs ringing, which negatively affect the diagnostic quality and, in some cases, may be confused with pathology. This work presents ArtifactID, a deep learning based tool to help MR technicians to identify and classify artifacts in datasets acquired with low-field systems. We trained binary classification models to accuracies greater than 88% to identify wrap-around and Gibbs ringing artifacts in T1 brain images. ArtifactID can help novice MR technicians in low resource settings to identify and mitigate these artifacts.

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