CSA: A Channel-Separated Attention Module for Enhancing MRI Reconstruction.

Abstract

Channel attention mechanisms have been proven to effectively enhance network performance in various visual tasks, including the Magnetic Resonance Imaging (MRI) reconstruction task. Channel attention mechanisms typically involve channel dimensionality reduction and cross-channel interaction operations to achieve complexity reduction and generate more effective weights of channels. However, the operations may negatively impact MRI reconstruction performance since it was found that there is no discernible correlation between adjacent channels and the low information value in some feature maps. Therefore, we proposed the Channel-Separated Attention (CSA) module tailored for MRI reconstruction networks. Each layer of the CSA module avoids compressing channels, thereby allowing for lossless information transmission. Additionally, we employed the Hadamard product to realize that each channel's importance weight was generated solely based on itself, avoiding cross-channel interaction and reducing the computational complexity. We replaced the original channel attention module with the CSA module in an advanced MRI reconstruction network and noticed that CSA module achieved superior reconstruction performance with fewer parameters. Furthermore, we conducted comparative experiments with state-of-the-art channel attention modules on an identical network backbone, CSA module achieved competitive reconstruction outcomes with only approximately 1.036% parameters of the Squeeze-and-Excitation (SE) module. Overall, the CSA module makes an optimal trade-off between complexity and reconstruction quality to efficiently and effectively enhance MRI reconstruction. The code is available at https://github.com/smd1997/CSA-Net.