期刊论文

Xiaozhi Zhang

英文

Biomedical Signal Processing and Control

1746-8094

2023

83

104632

10.1016/j.bspc.2023.104632

The overview of Residual Non-Local Fourier Network (RNLFNet) and its data flow are illustrated in Fig. 1. Given the input degraded MR image Iu, the reconstructed MR image IR can be obtained as IR=HRNLFNetIu, where HRNLFNet denotes the model of the proposed RNLFNet. The proposed model maims to learn the degradation components of the degraded MR images. In this work, our RNLFNet is the data-driven model, which learns an end-to-end mapping between zero-filled and fully sampled MR images. Here, we

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Magnetic Resonance Imaging (MRI) has been widely applied in medical clinical diagnosis. Generally, obtaining a high spatial resolution MR image takes up to tens of minutes long. Reconstructing MR images from the undersampled k-space data has been playing a crucial role to accelerate MRI. Especially, the deep Convolutional Neural Networks (CNNs) have shown potential to significantly accelerate MRI. However, the receptive field size of CNNs is relatively small and it fails to capture the long-range dependencies. Nowadays, the non-local attention ...