An unsupervised deep learning-based method for in vivo high resolution Kidney MRI motion correction

A primary challenge for in vivo kidney MRI is the presence of different types of involuntary physiological motion, affecting the diagnostic utility of acquired images due to severe motion artifacts. Existing prospective and retrospective motion correction methods remain ineffective when dealing with complex large amplitude nonrigid motion artifacts. We introduce an unsupervised deep learning-based method for in vivo kidney MRI motion correction. We demonstrate that our deep learning model achieved the average structural similarity index measure (SSIM) of 0.76±0.06 between the reconstructed motion-corrected and ground truth motion-free images, showing an improvement of about 0.33 compared to the corresponding motion-corrupted images.