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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 1 Abdominal image reconstruction algorithms based on a convolutional neural network
Ref. | Task | Method | Images | Metric |
Kang et al[30], 2017 | Low-dose CT reconstruction | CNN | Abdominal CT images | PSNR: 34.55 |
Chen et al[31], 2017 | Low-dose CT reconstruction | RED-CNN | Low-dose abdominal CT images | PSNR: 43.79 ± 2.01; SSIM: 0.98 ± 0.01; RMSE: 0.69 ± 0.07 |
Han et al[27], 2018 | Accelerated projection-reconstruction MRI | U-netCNN | Low-dose abdominal CT images; synthetic radial abdominal MR images | PSNR: 31.55 |
Lv et al[26], 2018 | Undersampled radial free-breathing 3D abdominal MRI | Auto-encoderCNN | 3D golden angle-radial SOS liver MR images | P < 0.001 |
Ge et al[32], 2020 | CT image reconstruction directly from a sinogram | Residual encoder-decoder + CNN | Low-dose abdominal CT images | PSNR: 43.15 ± 1.93; SSIM: 0.97 ± 0.01; NRMSE: 0.71 ± 0.16 |
MacDougall et al[33], 2019 | Improving low-dose pediatric abdominal CT | CNN | Liver CT images;Spleen CT images | P < 0.001 |
Tamada et al[29], 2020 | DCE MR imaging of the liver | CNN | T1-weighted liver MR images | SSIM: 0.91 |
Zhou et al[28], 2019 | Applications in low-latency accelerated real-time imaging | PICNN | bSSFP cardiac MR images; bSSFP abdominal MR images | Abdominal: NRMSE: 0.08 ± 0.02; SSIM: 0.90 ± 0.02 |
Zhang et al[34], 2020 | Reconstructing 3D liver vessel morphology from contrasted CT images | GNNCNN | Multi-phase contrasted liver CT images | F1 score: 0.8762 ± 0.0549 |
Zhou et al[35], 2020 | Limited view tomographic reconstruction | Residual dense spatial-channel attention + CNN | Whole body CT images | LAR: PSNR: 35.82; SSIM: 0.97 SVR: PSNR: 41.98; SSIM: 0.97 |
Kazuo et al[36], 2021 | Image reconstructionin low-dose and sparse-view CT | CS + CNN | Low-dose abdominal CT images; Sparse-view abdominal CT images | Low-Dose CT case: PSNR: 33.2; SSIM: 0.91 Sparse-View CT case: PSNR: 29.2; SSIM: 0.91 |
- 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