  To improve the compressive sensing MRI (CS-MRI) approaches in terms of fine
structure loss under high acceleration factors, we have proposed an iterative
feature refinement model (IFR-CS), equipped with fixed transforms, to restore
the meaningful structures and details. Nevertheless, the proposed IFR-CS still
has some limitations, such as the selection of hyper-parameters, a lengthy
reconstruction time, and the fixed sparsifying transform. To alleviate these
issues, we unroll the iterative feature refinement procedures in IFR-CS to a
supervised model-driven network, dubbed IFR-Net. Equipped with training data
pairs, both regularization parameter and the utmost feature refinement operator
in IFR-CS become trainable. Additionally, inspired by the powerful
representation capability of convolutional neural network (CNN), CNN-based
inversion blocks are explored in the sparsity-promoting denoising module to
generalize the sparsity-enforcing operator. Extensive experiments on both
simulated and in vivo MR datasets have shown that the proposed network
possesses a strong capability to capture image details and preserve well the
structural information with fast reconstruction speed.
