Low-rank and sparse matrix decomposition based on S1/2 and L1/2 regularizations in dynamic MRI

In recent years, compressed sensing (CS) has been proposed and successfully applied to speed up the acquisition in dynamic MRI. However, how to improve the quality of dynamic MRI is still a worthwhile question. Recently, a low-rank plus sparse (L+S) matrix decomposition model with S1 and L1 regularizations is proposed for reconstruction of under-sampled dynamic MRI with separation of background and dynamic components. It can effectively detect dynamic information in the process of imaging. In our work, we propose an improved L+S matrix decomposition model with S1/2 and L1/2 regularizations in order to improve the quality of original separation. To solve the model, we use an iterative half-thresholding decomposition algorithm. Finally, empirical results show that the new model can produce better performance and capture more completed dynamic information than the existing model.