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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