Summary of AI Approaches for PET Image Enhancement
| Paper | Data details | Architecture | Loss function |
|---|---|---|---|
| Xiang et al. (21) | 16 brain 18F-FDG PET + T1 MRI | Autocontext CNN | MSE |
| Xu et al. (22) | 9 brain 18F-FDG PET + T1 MRI | U-Net | MAE |
| Wang et al. (23) | 16 brain 18F-FDG PET | Concatenated 3D cGAN with a 3D U-Netlike generator | MAE; cGAN |
| Ouyang et al. (24) | 39 brain 18F-florbetaben PET | cGAN with U-Net-like generator | MAE; cGAN; perceptual; content |
| Wang et al. (25) | 20 simulated + 16 clinical brain 18F-FDG PET, T1 MRI, diffusion tensor imaging | Locality adaptive multimodality GAN | MAE; adversarial |
| Schaefferkoetter et al. (26) | 31 lung 18F-FDG PET | U-Net | MSE |
| Spuhler et al. (27) | 35 brain 18F-FDG PET | Dilated U-Net | MAE |
| Xue et al. (28) | 10 whole-body 18F-FDG PET | 3D attention residual least-squares GAN | MSE; least-squares adversarial |
| Zhao et al. (29) | 109 brain 18F-FDG PET/CT | Supervised cycleGAN | Adversarial; cycle consistency; identity |
| Gong et al. (30) | 9 cardiac torso 18F-FDG PET | Parameter-transferred Wasserstein GAN | MSE; Adversarial |
| Gong et al. (31) | 120 brain 18F-FDG PET; 140 brain 18F-MK-6240 PET + T1 MRI | Denoising diffusion probabilistic model | MSE |
| Zhang et al. (32) | 70 brain 18F-FDG PET + T1 MRI | Spatially adaptive and transformer fusion network | MAE |
| Cui et al. (34) | 10 lung 68Ga-PRGD2 PET/CT; 30 lung 18F-FDG PET/T1 MRI | Modified 3D U-Net with DIP | MSE |
| Cui et al. (35) | 10 lung 68Ga-PRGD2 PET/CT; 30 lung 18F-FDG PET/T1 MRI | Modified 3D U-Net with DIP | MSE |
| Song et al. (36) | 20 simulated brain 18F-FDG PET; 17 brain 18F-FDG PET + T1 MRI | Noise2Void (U-Net with 3 resolution levels) | MSE |
| Liu et al. (37) | 195 cardiac torso 18F-FDG PET | 3D U-Net | MSE |
| Zhou et al. (38) | Heterogeneous multiinstitutional PET | Deep attention residual U-Net | MSE |
| Xue et al. (39) | 310 brain across 18F-FDG, 18F-FET, 18F-florbetapir PET | cGAN | Conventional content; voxelwise |
| Jang et al. (33) | 44 whole-body 18F-FDG, 40 whole-body 18F-ACBC, 10 whole-body 18F-DCFPyL, 18 whole-body 68Ga-DOTATATE | Spach transformer | Charbonnier |
| Song et al. (40) | 20 simulated brain 18F-FDG PET; 30 clinical brain 18F-FDG PET + T1 MRI | Very deep superresolution CNN | MAE |
| Song et al. (41) | 20 simulated brain 18F-FDG PET; 30 clinical brain 18F-FDG PET | Self-supervised superresolution (CycleGAN) | Adversarial; cycle consistency; total variation |
| Sanaat et al. (42) | 50 brain 18F-FDG, 50 brain 18F-flortaucipir, 36 brain 18F-flutemetamol, 76 brain 18F-fluoro-DOPA + T1 MRI | CycleGAN | Adversarial |
| Sanaat et al. (43) | 100 brain 18F-FDG, 100 18F-flortaucipir, 100 brain 18F-flutemetamol | CycleGAN | Adversarial |
| Azimi et al. (44) | 160 brain 18F-FDG PET/CT | Attention-based network (U-Net) | MSE |
| Mehranian et al. (45) | 273 whole-body 18F-FDG PET | 3D residual U-Net | MSE |
MSE = mean-squared error; MAE = mean absolute error; 18F-ACBC = 1-amino-3-18F-fluorocyclobutane-1-carboxylic acid.