ArtifactID: Identifying artifacts in low-field MRI of the brain using deep learning
Marina Manso Jimeno1, Keerthi Sravan Ravi1, Zhezhen Jin2
1Department of Biomedical Engineering, Columbia University in the City of New York, New York, NY 10027, USA; Columbia University Magnetic Resonance Research Center, Columbia University in the City of New York, New York, NY 10027, USA.
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Low-field MR scanners are more accessible in resource-constrained settings where skilled personnel are scarce. Images acquired in such scenarios are prone to artifacts such as wrap-around and Gibbs ringing. Such artifacts negatively affect the diagnostic quality and may be confused with pathology or reduce the region of interest visibility. As a first step solution, ArtifactID identifies wrap-around and Gibbs ringing in low-field brain MRI. We utilized two datasets: 179 T1-weighted pathological brain images from a 0.36 T scanner and 581 publicly available T1-weighted brain images. Individual binary classification models were trained to identify through-plane wrap-around, in-plane wrap-around, and Gibbs ringing. Visual explanations obtained via the GradCAM method helped develop trust in the wrap-around model. The mean precision and recall metrics across the four implemented models were 97.6% and 92.83% respectively. Agreement analysis of the models and the radiologists' labels returned Cohen's kappa values of 0.768 ± 0.062, 1.00 ± 0.000, 0.89 ± 0.085, and 0.878 ± 0.103 for the through-plane wrap-around, in-plane wrap-around, and Gibbs ringing models, respectively.
