Magnetic resonance image quality is susceptible to several artifacts including Gibbs-ringing. Although there have been deep learning approaches to address these artifacts on T1-weighted scans, Quantitative susceptibility maps (QSMs), derived from susceptibility-weighted imaging, are often more prone to Gibbs artifacts than T1w images, and require their own model. Removing such artifacts from QSM will improve the ability to non-invasively map iron deposits, calcification, inflammation, and vasculature in the brain. In this work, we develop a 3D U-Net based approach to remove Gibbs-ringing from QSM maps.
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