IFR-Net: Iterative Feature Refinement Network for Compressed Sensing MRI

To improve the compressive sensing MRI (CS - MRI) approaches in terms of fine structure loss under high acceleration factors, we have propose d an iterative feature refinement model (IFR - CS), equipped with fixed transforms, to restore the meaningful structure s 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 procedure s in IFR - CS to a supervised model - driven network, dubbed IFR - Net. Equipped with training data pairs, both 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 v ivo 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. Index terms -- Compressed Sensing; Undersampled image reconstruction; IFR - CS; Deep learning; Model - driven network. Magnetic resonance imaging (MRI) is a non - invasive and widely used imaging technique that can provide both functional and anatomical information for clinic al diagnosis. However, the slow imaging speed may result in patient discomfort and motion artifacts. Therefore, increasing MR imaging speed is an important and worthwhile research goal. During the past decades, compressed sensing (CS) has become a popular and successful strategy for fast MR imaging reconstruction [1] - [6] . Zhang and Q. Yang are with the Department of Electronic Information Engineering, Nanchang Universi ty, Nanchang 330031, China. Liu did the work during her internship at Paul C. Lauterbur Research Center for Biomedical Imaging, Chinese Academy of Sciences, Shenzhen, China. S. Wang and D. Liang are with Paul C. Lauterbur Research Center for Biomedical Imaging and the Medical AI Research Center, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China ( sophiasswang@hotmail.com, dong.liang@siat.ac.cn).