DE-Net: Detail-enhanced MR reconstruction network via global-local dependent attention

Deep learning (DL) is widely used for MRI reconstruction and leverages significant promotion. However, the existing DL -based methods still have some weaknesses. First, the information of the original input images and the extracted features of the shallow network are gradually weakened or even lost due to the deepening of the deep neural network, which is not thoroughly utilized during the reconstruction process. Second, due to the limitation of computation, most attention -based methods adopt self -attention layers on reduced -resolution images of Ushaped networks, which leads to the loss of information. Moreover, these approaches ignore the distinction between global dependencies and local region representations. In this paper, a novel detail -enhanced MR reconstruction network, termed DE -Net, is proposed to handle these limitations. Within, a proposed dense connection architecture is used as the backbone module for feature fusion and information reuse, to preserve the detailed information of initial images and extracted features. Meanwhile, a global -local dependent attention (GLDA) mechanism is designed to capture richer contextual associations on both global and local level while retaining the resolution, which further force the reconstruction on the relevant regions with complex structures according to contextual information. Comprehensive experiments on two benchmarks show that our proposed DE -Net outperforms all the baselines and achieves remarkable improvement in texture structure preservation and artifacts reduction.