Table 5. Comparison of multi-group model results for PET quantitative parameters of tumor lesions based on 8%PET images.
| Group | PET quantitative parameters of tumor lesions | |||
|---|---|---|---|---|
| SNR | CNR | SUVmean | SUVmax | |
| 3D Unet-15s | 33.81 (24.79, 45.52) | 9.94 (5.63, 13.72) | 3.78 (3.06, 4.86) | 6.57 (4.54, 9.61)* |
| P2P-15s | 20.45 (15.08, 29.24)* | 3.97 (1.06, 6.91)* | 3.03 (2.62, 3.99)* | 4.72 (3.70, 6.27)* |
| 8%PET | 16.38 (10.78, 19.90) | 3.19 (2.18, 4.82) | 4.25 (3.62, 5.34) | 10.81 (7.44, 16.41) |
| s-PET | 34.82 (24.92, 51.82) | 9.68 (5.90, 13.95) | 3.93 (3.23, 4.86) | 7.29( 5.78, 10.59) |
| H | 54.90 | 27.25 | 16.81 | 37.28 |
| P | <0.0001 | <0.0001 | 0.001 | <0.0001 |
The italic font represents a statistical difference between this value and that of the 8%PET group. And * indicates that the value is statistically significant compared with the s-PET group. SNR, signal-to-noise ratio; CNR, contrast-noise ratio; SUVmean, mean standardized uptake values for all voxels in the ROI; SUVmax, maximum standardized uptake values for all voxels in the ROI; PET, positron emission tomography; s-PET, standard positron emission tomography (per bed time: 90 s); 3D Unet, a deep network model based on CNN; P2P, Pixel2Pixel deep network model based on GAN; H, H value for the Kruskal-Wallis method; ROI, region of interest; CNN, convolutional neural network; GAN, generative adversarial network.