Magnetic Resonance Image Reconstruction of Multi-scale Residual Unet Fused with Attention Mechanism

Abstract

This paper integrates the attention mechanism and multi-scale residual convolution to construct a Unet network, aiming at improving the quality of magnetic resonance image (MRI) reconstructed from under-sampled k-space data. To enhance the feature representation ability of the network and prevent gradient disappearance and degradation during network training, multi-scale residual convolution was embedded in the encoding path of the Unet network to extract different scale feature information of MRI. Moreover, to accurately recover the detailed texture features of MRI, the convolution attention module was embedded in the jump connection part between the encoding and decoding path of the Unet network to respond to the key information, such as details and textures in different degrees. Experiments showed that the proposed network could effectively reconstruct high-quality MRIs with clear texture and without overlapping artifacts from the under-sampled k-space data.

Keywords

• magnetic resonance imaging
• image reconstruction
• unet
• attention mechanism
• deep learning