ArtifactID: Identifying artifacts in low-field MRI of the brain using deep learning

• DOI: 10.1016/j.mri.2022.02.002
• Publication Date: 2022-02-15T03:16:51Z
• Authors: Marina Manso Jimeno; Keerthi Sravan Ravi; Zhezhen Jin; Dotun Oyekunle; Godwin Ogbole; Sairam Geethanath
• Keywords: 03 medical and health sciences; Deep Learning; 0302 clinical medicine; Brain; Neuroimaging; Artifacts; Magnetic Resonance Imaging

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.