Developed deep learning solutions using AFT-Net to correct motion artifacts in MRI scans. Created simulated motion-corrupted brain MRI data and trained models to reconstruct high quality, motion-free images directly from raw k-space data. Promising results improving structural similarity over 0.9 against ground truth. Deep learning on raw k-space shows potential for efficient, accurate MRI motion correction.
