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
1Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles, CA, United States
Gibbs ringing artifact remains a challenge in SWI studies. Here we propose a deep learning approach, which removes Gibbs artifacts while still preserving anatomical features. Our model’s results outperform current artifact removal methods.
QSM Pipeline: Raw phase and magnitude images from SWI are combined (Bernstein method), the phase is unwrapped (fast phase unwrapping algorithm), background removed (Laplacian boundary value method), and the QSM reconstructed (MEDI).
Comparison of de-Gibbs-ed outputs from MRtrix3 and our UNet model removing the simulated Gibbs artifacts that were generated by truncating 70% of the high frequency k-space components of a full-sized mask from an image with minimal visually detectable Gibbs artifact. The second row is a zoomed in look into the black square on the first row.