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Abstract #1767

A 3D-UNET for Gibbs artifact removal from quantitative susceptibility maps

Iyad Ba Gari1, Shruti P. Gadewar1, Xingyu Wei1, Piyush Maiti1, Joshua Boyd1, and Neda Jahanshad1
1Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles, CA, United States

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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