Joint Reconstruction Framework of Compressed Sensing and Nonlinear Parallel Imaging for Dynamic Cardiac Magnetic Resonance Imaging

Compressed Sensing (CS) and parallel imaging are two promising techniques that accelerate the MRI acquisition process. Combining these two techniques is of great interest due to the complementary information used in each. In this study, we propose a new reconstruction framework for dynamic cardiac imaging that takes advantage of both CS-based dynamic imaging and one nonlinear parallel imaging technique. The method decouples the reconstruction process into two sequential steps: use CS to reconstruct a series of aliased dynamic images from the highly undersampled k-space data; use nonlinear GRAPPA method, one nonlinear technique of parallel imaging, to reconstruct the original dynamic images from the k-space data that has been reconstructed by CS. The sampling scheme of the proposed method is designed to simultaneously satisfy the incoherent undersampling requirement for CS and the structured undersampling requirement for nonlinear parallel imaging. Four in vivo experiments of dynamic cardiac cine MRI were carried out with retrospective undersampling to evaluate the performance of the proposed method. Experiments show the proposed method of dynamic cardiac cine MRI is superior at reducing aliasing artifacts and preserving the spatial details and temporal variations, when compared with k-t FOCUSS and k-t FOCUSS with sensitivity encoding, using the same numbers of measurements. The proposed joint reconstruction framework effectively combines the CS method and one nonlinear technique of parallel imaging, and improves the image quality of dynamic cardiac cine MRI reconstruction when comparing to the state-of-the-art methods.