• Laser & Optoelectronics Progress
  • Vol. 59, Issue 12, 1210014 (2022)
Dengqiang Zhang*, Xiaohan Liu, and Yanwei Pang
Author Affiliations
  • School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
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    DOI: 10.3788/LOP202259.1210014 Cite this Article Set citation alerts
    Dengqiang Zhang, Xiaohan Liu, Yanwei Pang. Reconstruction of Magnetic Resonance Images Based on Dual-Domain Crossed Codec Network[J]. Laser & Optoelectronics Progress, 2022, 59(12): 1210014 Copy Citation Text show less
    Cascaded dual-domain crossed encoding and decoding network structure
    Fig. 1. Cascaded dual-domain crossed encoding and decoding network structure
    Image-domain and K-domain convolution structures. (a) Image-domain convolution; (b) K-domain convolution
    Fig. 2. Image-domain and K-domain convolution structures. (a) Image-domain convolution; (b) K-domain convolution
    Dual domain cascaded modes. (a) Parallel cascade; (b) crossed cascade
    Fig. 3. Dual domain cascaded modes. (a) Parallel cascade; (b) crossed cascade
    Comparison of 4-fold under-sampled and full-sampled K-domain data visualization.(a) Under-sampled K-space; (b) full-sampled K-space
    Fig. 4. Comparison of 4-fold under-sampled and full-sampled K-domain data visualization.(a) Under-sampled K-space; (b) full-sampled K-space
    Reconstruction results of under-sampled MRI. (a) 4× under-sampled zero-filled magnetic resonance images; (b) magnetic resonance images reconstructed by U-Net; (c) magnetic resonance images reconstructed by DPC-Net; (d) magnetic resonance images reconstructed by KIKI-Net; (e) full-sampled true value
    Fig. 5. Reconstruction results of under-sampled MRI. (a) 4× under-sampled zero-filled magnetic resonance images; (b) magnetic resonance images reconstructed by U-Net; (c) magnetic resonance images reconstructed by DPC-Net; (d) magnetic resonance images reconstructed by KIKI-Net; (e) full-sampled true value
    Reconstruction error. (a) 4× under-sampled zero filled MRI error; (b) reconstruction error of U-Net; (c) reconstruction error of DPC-Net; (d) reconstruction error of KIKI-Net
    Fig. 6. Reconstruction error. (a) 4× under-sampled zero filled MRI error; (b) reconstruction error of U-Net; (c) reconstruction error of DPC-Net; (d) reconstruction error of KIKI-Net
    Objective indicatorDPC-Net(dual-domain parallel)DPC-Net(dual-domain crossover)
    NMSE0.03300.0325
    SSIM0.73630.7268
    PSNR /dB32.1631.37
    Table 1. Performance comparison of two cascaded methods
    ConditionNMSESSIMPSNR /dB
    α=0.1, β=0.90.03380.731031.83
    α=0.2, β=0.80.03300.736332.16
    α=0.3,β=0.70.03410.728732.02
    α=0.4,β=0.60.03480.721331.78
    α=0.5,β=0.50.03520.718931.01
    Table 2. Performance comparison of loss function weights in reconstruction
    Number of cascadesNMSESSIMPSNR /dB
    U-NetDPC-NetU-NetDPC-NetU-NetDPC-Net
    10.03570.03500.72710.736332.1532.16
    20.03510.03410.72980.741732.2332.33
    30.03480.03400/73540.744032.6732.36
    40.03420.03380.74350.746033.0332.44
    50.03470.03260.74260.749832.7832.98
    60.03490.03180.74170.753232.5333.16
    70.03520.03240.73870.752032.4733.02
    Table 3. Objective indicators of reconstruction results of cascaded dual-domain codec network
    ParameterNetwork2-fold4-fold8-fold
    NMSEZero filled0.03230.05220.1091
    U-Net0.02340.03490.0604
    KIKI-Net0.02310.03260.0602
    DPC-Net0.02210.03180.0601
    SSIMZero filled0.78200.65590.548
    U-Net0.8260.7410.623
    KIKI-Net0.8310.7480.634
    DPC-Net0.8450.7530.648
    PSNR /dBZero filled31.7629.5921.63
    U-Net34.6032.5327.5
    KIKI-Net34.9833.0127.9
    DPC-Net35.0433.1628.8
    Table 4. Reconstruction performance of different networks under different acceleration magnifications
    Dengqiang Zhang, Xiaohan Liu, Yanwei Pang. Reconstruction of Magnetic Resonance Images Based on Dual-Domain Crossed Codec Network[J]. Laser & Optoelectronics Progress, 2022, 59(12): 1210014
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