When System Model meets Image Prior: An Unsupervised Deep Learning Architecture for Accelerated Magnetic Resonance Imaging

Unsupervised deep learning framework that integrates system priors using unrolled optimization and general image priors can reconstruct high quality Magnetic Resonance images comparable to supervised methods from highly undersampled k-space data. We develop an unsupervised deep learning framework that integrates system priors in MR acquisition and image priors to reconstruct high quality MR images from highly undersampled k-space data without using ground truth images.