Jong Chul Ye^{1}

Compressed sensing have been extensively investigated as a means to accelerate the MR acquisition by exploiting the redundancy in spatial domain. In this presentation, I will first review the basic principle of compressed sensing and recent development. Then, I will provide a unified view of the compressed sensing, parallel imaging and recent structured low-rank matrix approaches for accelerated MRI. More specifically, inspired by k-space interpolation methods, an annihilating filter based low-rank Hankel matrix approach (ALOHA) is proposed as a general framework for sparsity-driven k-space interpolation method which unifies pMRI and CS-MRI. This converts pMRI and CS-MRI to a k-space interpolation problem using a structured matrix completion. In addition, it provides an important link to the recent deep learning approaches for accelerated MRI.