融合注意力机制的多尺度残差Unet的磁共振图像重建

李奕洁,杨馨雨,杨晓梅

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

Li Yijie,YANG Xinyu,YANG Xiaomei

表1 5种网络模型重建图像的平均性能

Table 1 Average performance of reconstructed images by five network models

网络模型 PSNR SSIM ×2 ×3 ×4 ×2 ×3 ×4 PD-net 32.5438±1.0981 30.9821±1.1230 30.6958±1.0923 0.7736±0.0812 0.6802±0.0800 0.6345±0.0813 Cascade-net 31.5583±1.1120 30.1253±1.096 29.8148±1.0220 0.7651±0.0795 0.6752±0.0820 0.6340±0.0760 KIKI-net 30.7728±1.4592 28.6321±1.3201 29.7129±1.5329 0.7532±0.0821 0.6457±0.0832 0.6274±0.0846 DC-WCNN 32.8576±1.0786 30.9972±1.2356 30.7214±1.0643 0.7821±0.0831 0.6853±0.0801 0.6399±0.0842 AttMRes-Unet 33.2185±1.2759 31.7255±1.1794 30.9175±1.2376 0.7862±0.0862 0.6972±0.0859 0.6497±0.0865