Opto-Electronic Engineering, Volume. 52, Issue 5, 250016(2025)
Integrating hierarchical semantic networks with physical models for MRI reconstruction
Figures & Tables(10)
Fig. 2. Network composition module. (a) Multi-scale aggregation module; (b) Context extraction module
Fig. 5. Reconstruction results using random masking for 8× acceleration on the IXI dataset
Fig. 6. Validation loss using random masking for 4-fold and 8-fold acceleration on IXI dataset
Table 1. Numerical results under three kinds of masks: radial, random and equidistant, and acceleration rates of 4× and 8×
View tableView in ArticleTable 1. Numerical results under three kinds of masks: radial, random and equidistant, and acceleration rates of 4× and 8×
Dataset Ratio Mask Metric U-Net MICCAN MDUNet GA-HQS H-DSLR PGIUN Ours IXI 4× Radial PSNR 34.04 41.62 45.87 45.28 45.30 47.08 48.15 SSIM 0.971 0.990 0.992 0.993 0.993 0.995 0.996 Random PSNR 31.18 35.42 37.45 37.07 35.98 37.97 39.12 SSIM 0.953 0.972 0.978 0.980 0.981 0.984 0.989 Equispaced PSNR 30.12 33.48 34.96 35.46 33.63 35.50 35.65 SSIM 0.944 0.964 0.968 0.977 0.965 0.977 0.978 8× Radial PSNR 29.74 32.82 35.48 34.13 34.50 36.26 36.42 SSIM 0.934 0.958 0.977 0.968 0.971 0.980 0.981 Random PSNR 29.05 31.68 34.02 33.58 31.96 34.07 34.11 SSIM 0.931 0.949 0.959 0.962 0.955 0.964 0.968 Equispaced PSNR 27.90 29.98 31.33 31.29 29.80 31.63 31.75 SSIM 0.921 0.939 0.951 0.940 0.938 0.943 0.953 fastMRI 4× Radial PSNR 28.59 30.11 30.18 30.82 30.22 30.96 31.09 SSIM 0.828 0.861 0.879 0.873 0.865 0.873 0.881 Random PSNR 27.86 29.03 30.09 39.98 29.01 30.01 30.15 SSIM 0.810 0.830 0.843 0.848 0.831 0.849 0.851 Equispaced PSNR 27.16 28.11 28.53 28.51 28.23 28.53 28.62 SSIM 0.779 0.795 0.809 0.807 0.786 0.803 0.826 8× Radial PSNR 26.42 27.41 28.01 27.91 27.28 28.01 28.16 SSIM 0.723 0.745 0.764 0.789 0.741 0.791 0.816 Random PSNR 26.38 27.26 27.65 27.79 27.04 27.75 27.81 SSIM 0.754 0.771 0.739 0.781 0.762 0.792 0.803 Equispaced PSNR 26.21 26.96 27.31 27.33 26.84 27.29 27.34 SSIM 0.743 0.745 0.747 0.767 0.752 0.768 0.783
Table 2. Computational complexity and runtime analysis results
View tableView in ArticleTable 2. Computational complexity and runtime analysis results
Method Params./M Flops/G Inference/ms U-Net 7.756 11.2 21 MICCAN 2.622 36.6 34 MDUNet 1.800 90.7 49 GA-HQS 6.100 120.2 63 H-DSLR 1.650 85.3 44 PGIUN 1.250 74.8 53 Ours 1.530 86.3 57
Table 3. Multi-coil reconstruction contrast results
View tableView in ArticleTable 3. Multi-coil reconstruction contrast results
Ratio Mask Method PSNR/dB SSIM NRMSE Equispaced U-Net 25.78 0.877 0.763 H-DSLR 31.54 0.942 0.212 MoDL 29.12 0.919 0.304 PGIUN 30.01 0.926 0.307 Ours 32.70 0.958 0.190 Random U-Net 32.35 0.946 0.222 H-DSLR 40.84 0.986 0.073 MoDL 39.63 0.977 0.081 PGIUN 38.50 0.976 0.095 Ours 41.35 0.990 0.062
Table 4. Module ablation experiment results
View tableView in ArticleTable 4. Module ablation experiment results
No. Contextual extraction Multiscale aggregation Semantic graph reasoning Dual-scale attention IXI Complexity PSNR/dB SSIM Param./M FLOPs/G 1 √ 30.01 0.901 4.20 55.30 2 √ 30.25 0.912 5.10 66.12 3 √ √ 31.10 0.921 6.50 72.45 4 √ √ 31.95 0.931 6.80 80.32 5 √ √ 32.50 0.940 7.15 85.20 6 √ √ √ 33.75 0.950 7.85 95.00 7 √ √ √ √ 35.65 0.976 8.17 102.12
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Xiaomin Zhang, Lingxin Bao. Integrating hierarchical semantic networks with physical models for MRI reconstruction[J]. Opto-Electronic Engineering, 2025, 52(5): 250016
Paper Information
Category: Article
Received: Jan. 20, 2025
Accepted: Mar. 12, 2025
Published Online: Jul. 18, 2025
The Author Email: Lingxin Bao (鲍玲鑫)
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