  Purpose: To develop a pipeline for motion artifact correction in mGRE and
quantitative susceptibility mapping (QSM). Methods: Deep learning is integrated
with autofocus to improve motion artifact suppression, which is applied QSM of
patients with Parkinson's disease (PD). The estimation of affine motion
parameters in the autofocus method depends on signal-to-noise ratio and lacks
accuracy when data sampling occurs outside the k-space center. A deep learning
strategy is employed to remove the residual motion artifacts in autofocus.
Results: Results obtained in simulated brain data (n =15) with reference truth
show that the proposed autofocus deep learning method significantly improves
the image quality of mGRE and QSM (p = 0.001 for SSIM, p < 0.0001 for PSNR and
RMSE). Results from 10 PD patients with real motion artifacts in QSM have also
been corrected using the proposed method and sent to an experienced radiologist
for image quality evaluation, and the average image quality score has increased
(p=0.0039). Conclusions: The proposed method enables substantial suppression of
motion artifacts in mGRE and QSM.
