Abstract #1764

ArtifactID: identifying artifacts in low field MRI using deep learning

Marina Manso Jimeno^1,2, Keerthi Sravan Ravi^2,3, 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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