Activating high-frequency information nodes for super-resolution magnetic resonance imaging

• Published in: Biomedical signal processing and control 2024-07, Vol.93, p.106154, Article 106154
• Main Authors: Li, Lei, Liu, Yu, Meng, Xiangshui, Zhao, Yiming, Wei, Shufeng, Wang, Huixian, Wang, Zheng, Wei, Zhao, Yang, Wenhui
• Format: Article
• Language: English
• Subjects: Deep learning; Dual-domain; K-space; MRI; Super-resolution
• ISSN: 1746-8094; 1746-8108
• DOI: 10.1016/j.bspc.2024.106154

•Magnetic resonance images are not ordinary natural images.•The K-space is rich in information features between nodes.•Graph neural networks can extract the information features between nodes.•Combination of dual-domain features can improve the quality of reconstructed images. To recover the missing high-frequency components in low-resolution images by activating nodes in the high-frequency region of k-space. In addition, combining the information features in the k-space domain with the structural features in the image domain to generate high-quality super-resolution images. We propose a novel Dual-Domain cascaded Super-Resolution Network (DDSRNet), which shifts the starting point of the network to k-space. DDSRNet combines the ideas of GraphSAGE, Swin-Unet, and Hybrid Attention Transformer, to achieve complementary advantages of the k-space domain and the image domain. The k-space domain network activates zero-filled nodes in high-frequency regions and generates multi-order information features. The image domain network performs shallow, deep and gradient feature extraction to obtain the high-level representation of dual-domain hybrid features. In addition, to better utilize the features of both domains, we construct a domain interaction pool to facilitate cross-domain feature transfer and improve the efficiency of feature fusion. Exhaustive experiments are conducted on both public dataset and real scanning dataset. Compared with state-of-the-art algorithms, DDSRNet has the best numerical evaluation results (the average PSNR and SSIM improvements are more than 0.1 dB and 0.0078) and visual perception. In addition, DDSRNet enables high-quality 2x SR MRI with a reduced number of excitations, leading to increased low-field imaging speed. DDSRNet shows excellent performance in high- and low-field SR tasks and has the potential to be a powerful tool for clinical applications. The proposed method is of great practical significance for achieving fast and high-quality magnetic resonance imaging.