Table 6. Comparison of multi-group model results for PET quantitative parameters of tumor lesions based on 17%PET images.
| Groups | PET quantitative parameters of tumor lesions | |||
|---|---|---|---|---|
| SNR | CNR | SUVmean | SUVmax | |
| 3D Unet-30s | 39.29 (29.10, 50.56) | 9.40 (4.66, 12.77) | 3.55 (3.27, 4.60)* | 7.45 (5.42, 9.73) |
| P2P-30s | 25.31 (19.83, 39.49)* | 4.88 (1.18, 8.96)* | 3.78 (3.32, 5.09) | 5.47 (4.47, 7.51)* |
| 17%PET | 17.61 (13.26, 24.68) | 4.39 (3.12, 7.10) | 4.17 (3.47, 5.30) | 8.37 (6.38, 11.29) |
| 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 | 38.19 | 17.67 | 2.63 | 12.20 |
| P | <0.0001 | 0.001 | 0.452 | 0.007 |
The italic font represents a statistical difference between this value and that of the 17%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.