To introduce a dual-domain reconstruction network with V-Net and K-Net for accurate MR image reconstruction from undersampled k-space data.Methods: Most state-of-the-art reconstruction methods apply U-Net or cascaded U-Nets in the image domain and/or k-space domain. Nevertheless, these methods have the following problems: (1) directly applying U-Net in the k-space domain is not optimal for extracting features; (2) classical image-domain-oriented U-Net is heavyweighted and hence inefficient when cascaded many times to yield good reconstruction accuracy; (3) classical image-domain-oriented U-Net does not make full use of information of the encoder network for extracting features in the decoder network; and (4) existing methods are ineffective in simultaneously extracting and fusing features in the image domain and its dual k-space domain. To tackle these problems, we present 3 different methods: (1) V-Net, an image-domain encoder-decoder subnetwork that is more lightweight for cascading and effective in fully utilizing features in the encoder for decoding; (2) K-Net, a k-space domain subnetwork that is more suitable for extracting hierarchical features in the k-space domain, and (3) KV-Net, a dual-domain reconstruction network in which V-Nets and K-Nets are effectively combined and cascaded. Results: Extensive experimental results on the fastMRI dataset demonstrate that the proposed KV-Net can reconstruct high-quality images and outperform state-of-the-art approaches with fewer parameters. Conclusions: To reconstruct images effectively and efficiently from incomplete k-space data, we have presented a dual-domain KV-Net to combine K-Nets and V-Nets. The KV-Net achieves better results with 9% and 5% parameters than comparable methods (XPD-Net and i-RIM).
The task of fast magnetic resonance (MR) image reconstruction is to reconstruct high-quality MR images from undersampled images. Most of the existing methods are based on U-Net, and these methods mainly adopt several simple connections within the network, which we call microscopic design ideas. However, these considerations cannot make full use of the feature information inside the network, which leads to low reconstruction quality. To solve this problem, we rethought the feature utilization method of the encoder and decoder network from a macroscopic point of view and propose a densely macroscopic feature fusion network for fast magnetic resonance image reconstruction. Our network uses three stages to reconstruct high-quality MR images from undersampled images from coarse to fine. We propose an inter-stage feature compensation structure (IFCS) which makes full use of the feature information of different stages and fuses the features of different encoders and decoders. This structure uses a connection method between sub-networks similar to dense form to fuse encoding and decoding features, which is called densely macroscopic feature fusion. A cross network attention block (CNAB) is also proposed to further improve the reconstruction performance. Experiments show that the quality of undersampled MR images is greatly improved, and the detailed information of MR images is enriched to a large extent. Our reconstruction network is lighter than many previous methods, but it achieves better performance. The performance of our method is about 10% higher than that of the original method, and about 3% higher than that of most existing methods. Compared with the nearest optimal algorithms, the performance of our method is improved by about 0.01–0.45%, and our computational complexity is only 1/14 of these algorithms.
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