Synopsis

Compressed sensing MRI reconstruction techniques have transformed the field and have been investigated in many clinical applications during the last decade to speed up MRI scans. This talk introduces the three key components of Compressed Sensing - sparsity, incoherence and non-linear reconstruction - and discusses how these key components are implemented in MRI. The combination of Compressed sensing with parallel imaging is briefly discussed. Current challenges of Compressed sensing are summarised and more recent developments in deep learning based reconstruction (proposed to overcome some of these challenges) are briefly introduced.