Undersampled magnetic resonance image reconstruction employing sparsity regularization has fascinated many researchers in\nrecent years under the support of compressed sensing theory. Nevertheless, most existing sparsity-regularized reconstruction\nmethods either lack adaptability to capture the structure information or suffer from high computational load. With the aim of\nfurther improving image reconstruction accuracy without introducing too much computation, this paper proposes a data-driven\ntight frame magnetic image reconstruction (DDTF-MRI) method. By taking advantage of the efficiency and effectiveness of data driven\ntight frame, DDTF-MRI trains an adaptive tight frame to sparsify the to-be-reconstructed MR image. Furthermore, a two level\nBregman iteration algorithm has been developed to solve the proposed model.The proposed method has been compared to\ntwo state-of-the-art methods on four datasets and encouraging performances have been achieved by DDTF-MRI.
Loading....