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©The Author(s) 2021.
Artif Intell Med Imaging. Aug 28, 2021; 2(4): 86-94
Published online Aug 28, 2021. doi: 10.35711/aimi.v2.i4.86
Published online Aug 28, 2021. doi: 10.35711/aimi.v2.i4.86
Table 2 Abdominal image reconstruction based on generative adversarial network and recurrent neural network
Ref. | Task | Method | Images | Metric |
Mardani et al[41], 2017 | Compressed sensing automates MRI reconstruction | GANCS | Abdominal MR images | SNR: 20.48; SSIM: 0.87 |
Yang et al[50], 2018 | Low dose CT image denoising | WGAN | Abdominal CT images | PSNR: 23.39; SSIM: 0.79 |
Kuanar et al[52], 2019 | Low-dose abdominal CT image reconstruction | Auto-encoderWGAN | Abdominal CT images | PSNR: 37.76; SSIM: 0.94; RMSE: 0.92 |
Lv et al[45], 2021 | A comparative study of GAN-based fast MRI reconstruction | DAGANKIGANReconGANRefineGAN | T2-weighted liver images; 3D FSE CUBE knee images; T1-weighted brain images | Liver: PSNR: 36.25 ± 3.39; SSIM: 0.95 ± 0.02; RMSE: 2.12 ± 1.54; VIF: 0.93 ± 0.05; FID: 31.94 |
Zhang et al[53], 2020 | 3D reconstruction for super-resolution CT images | Conditional GAN | 3D-IRCADb-01database liver CT images | Male: PSNR: 34.51; SSIM: 0.90Female: PSNR: 34.75; SSIM: 0.90 |
Cole et al[49], 2020 | Unsupervised MRI reconstruction | UnsupervisedGAN | 3D FSE CUBE knee images; DCE abdominal MR images | PSNR: 31.55; NRMSE: 0.23; SSIM: 0.83 |
Lv et al[48], 2021 | Accelerated multichannel MRI reconstruction | PIGAN | 3D FSE CUBE knee MR images; abdominal MR images | Abdominal: PSNR: 31.76 ± 3.04; SSIM: 0.86 ± 0.02; NMSE: 1.22 ± 0.97 |
Zhang et al[54], 2019 | 4D abdominal and in utero MR imaging | Self-supervised RNN | bSSFP uterus MR images; bSSFP kidney MR images | PSNR: 36.08 ± 1.13; SSIM: 0.96 ± 0.01 |
- Citation: Li GY, Wang CY, Lv J. Current status of deep learning in abdominal image reconstruction. Artif Intell Med Imaging 2021; 2(4): 86-94
- URL: https://www.wjgnet.com/2644-3260/full/v2/i4/86.htm
- DOI: https://dx.doi.org/10.35711/aimi.v2.i4.86