Distributed compressed sensing to accelerate cine cardiac MRI

Background Compressed sensing (CS) is an efficient tool that accelerates the data acquisition in MRI through the significant reduction of required measurements for image reconstruction. In recent years, there have been significant interests in the use of Compressed Sensing (CS) in Dynamic applications [1]. Since Cine cardiac images, as a dynamic data, has both spatial and temporal sparsity, it is a good candidate for CS with high acceleration factor [2]. Methods In this study we obtain Temporal Estimation (TE) from Temporal Information (TI) using forward-backward motion estimation (F-B ME) to highly accelerate dynamic MRI acquisition.We implement CS on TE in the first step and then use these reconstructed frames with under-sampling K-space data in aModified CS (MCS) scheme. Figure 1 illustrates our proposed method in details. In MCS we add another fidelity term to conventional CS to penalize the error between TE and the reconstructed frame at each iteration. The