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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 3 Applications of deep learning in abdominal reconstruction
Ref. | Task | Method | Images | Metric |
Lv et al[55], 2018 | Respiratory motion correction for free-breathing 3D abdominal MRI | CNN | 3D golden angle-radial SOS abdominal images | SNR: 207.42 ± 96.73 |
Jiang et al[56], 2019 | Respiratory motion correction in abdominal MRI | U-NetGAN | T1-weighted abdominal images | FSE: 0.920; GRE: 0.910; Simulated motion: 0.928 |
Küstner et al[57], 2020 | Motion-corrected image reconstruction in 4D MRI | U-netCNN | T1-weighted in-vivo 4D MR images | EPE: 0.17 ± 0.26; EAE: 7.9 ± 9.9; SSIM: 0.94 ± 0.04; NRMSE: 0.5 ± 0.1 |
Akagi et al[58], 2019 | Improving image quality of abdominal U-HRCT using DLR method | DLR | U-HRCT abdominal CT images | P < 0.01 |
Nakamura et al[59], 2019 | To evaluate the effect of a DLR method | DLR | Abdominal CT images | P < 0.001 |
- 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