Reconstruction of Magnetic Resonance Imaging Based on Dual-Domain Densely-Connected Residual Convolutional Networks
Magnetic resonance imaging(MRI)is an important clinical tool in medical imaging.However,generating high-quality MRI images typically requires a long scanning time.To increase the speed of MRI and reconstruct high-quality images,this study proposes a magnetic-resonance reconstruction network that combines dense connections with residual modules in frequency and image domains.The proposed model comprises a frequency-domain reconstruction network and an image-domain reconstruction network.Each network is based on a U-shaped encoder-decoder architecture and transformed between the two domains using inverse Fourier transformation.The encoder utilizes densely-connected residual blocks,which enhances feature reuse and alleviates the issue of vanishing gradients.Coordinate attention is introduced at skip connections to extract global features and enhance the recovery of texture details.The performance of the proposed model is evaluated on the publicly-available CC-359 dataset.The experimental results show that the proposed method outperforms the existing methods by effectively removing artifacts and preserving more texture details at different sampling rates and masks,resulting in high-quality reconstructed MRI images.
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