Introduction

The rise of compressed sensing in the 2000s [1,2] has generated great excitement, and its application to MRI (known as compressed sensing MRI or sparse MRI) has introduced a new and powerful approach to improve imaging speed by exploiting image sparsity/compressibility [3]. To date, the power of sparsity-enforcing reconstruction has been demonstrated in various applications with a substantial clinical impact. This talk will present an overview of compressed sensing and its application in rapid MRI. The compressed sensing theory will first be briefly reviewed. The conditions required for successful implementation of compressed sensing and how these requirements can be implemented in MRI will be discussed. The talk will also present combinations of compressed sensing with parallel imaging, which can form a synergistic reconstruction framework to exploit joint multi-coil sparsity for further improved image quality. The clinical applications of compressed sensing in MRI will then be summarized. Finally, existing challenges of compressed sensing MRI and potential solutions will be discussed.

Acknowledgements

No acknowledgement found.