Summary of Deep-Learning Techniques for SPECT Image Enhancement
| Paper | Data details | Architecture | Loss function |
|---|---|---|---|
| Ramon et al. (46) | 930 cardiac torso 99mTc-sestamibi | 3D convolutional autoencoder | MSE |
| Ramon et al. (47) | 1,052 cardiac torso 99mTc-sestamibi | Convolutional autoencoder, CNN | MSE |
| Sun et al. (48) | 100 simulated; 20 cardiac torso clinical 99mTc-sestamibi | Pix2Pix GAN | MAE; adversarial |
| Sohlberg et al. (49) | 93 cardiac torso 99mTc-tetrososmin | CNN, residual network, U-Net, cGAN | MSE |
| Yu et al. (50) | 4,800 simulated | CNN | MSE |
| Shiri et al. (51) | 363 cardiac torso 99mTc-sestamibi | Deep residual neural network | MSE |
| Lin et al. (52) | 112 cardiac torso 99mTc-DMSA | 3D residual U-Net | MSE |
| Pan et al. (53) | 20 cardiac torso 99mTc-MDP SPECT/CT | Lesion-attention weighted U2Net | MAE; structural similarity index |
| Liu et al. (54) | 895 cardiac torso 99mTc-sestamibi | Noise2Noise (U-Net) | MSE |
| Liu et al. (55) | 1,050 cardiac torso 99mTc-sestamibi | 3D-coupled UNet | MSE |
| Xie et al. (56) | 28 cardiac 99mTc-RBC | Densely connected multidimensional dynamic U-Net | MAE; structural similarity index; Sobel operator; intramyocardial blood volume |
MSE = mean-squared error; MAE = mean absolute error; DMSA = pentavalent dimercaptosuccinic acid; MDP = methyl diphosphonate; RBC = red blood cell.