# Hybrid Multimodal Medical Image Fusion Method Based on LatLRR and ED-D\({}^{2}\)GAN
## 3 Experimental Section
### Ablation Experiments
#### 3.4.1 CT Lung Window Images and PET Images  
The fusion results of ablation experiments about CT lung window images and PET images are shown in Figure 7, and the evaluation metric results of the fused images are shown in Table 3. The best metrics are represented by red; the second-best metrics are represented by blue. In Figure 8, the evaluation metric values of the ablation experiment are visualized.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Images** & **Methods** & **AG** & **EI** & **IE** & **SF** & \(\mathbf{Q^{AB/F}}\) & **SD** \\ \hline \multirow{6}{*}{1} & LatLRR\_AE & 7.1936 & 70.4249 & 6.7235 & 22.6081 & 0.5044 & 6.6990 \\  & ED-D\({}^{2}\)GAN & 8.5586 & 82.8161 & 6.7909 & 30.9804 & **0.5293** & 7.1277 \\  & Single\_D\({}_{\text{CT}}\) & 10.5909 & 105.5618 & 7.2029 & 32.2329 & 0.4568 & 7.8626 \\  & Single\_D\({}_{\text{PET}}\) & 10.5980 & 105.1268 & 7.1799 & 32.5337 & 0.4580 & 7.7477 \\  & LatLRR-GAN & **11.0359** & **109.2525** & **7.2611** & **33.2306** & 0.4649 & 7.9309 \\ \hline \multirow{6}{*}{2} & LatLRR\_AE & 7.4516 & 72.8877 & 6.7520 & 23.2457 & **0.5010** & 6.6615 \\  & ED-D\({}^{2}\)GAN & 8.7314 & 84.2797 & 6.9045 & 31.6064 & **0.5160** & 7.1360 \\  & Single\_D\({}_{\text{CT}}\) & **11.1608** & **110.8545** & 7.2271 & 33.5400 & 0.4567 & 7.9171 \\  & Single\_D\({}_{\text{PET}}\) & 10.9083 & 108.0114 & 7.2447 & 33.1174 & 0.4668 & 7.7543 \\  & LatLRR-GAN & **11.4452** & **113.0691** & **7.2678** & **33.8591** & 0.4583 & **7.9639** \\ \hline \multirow{6}{*}{3} & LatLRR\_AE & 6.7730 & 66.6580 & 6.7490 & 22.2463 & **0.4994** & 6.3988 \\  & ED-D\({}^{2}\)GAN & 8.2582 & 80.0652 & 6.7024 & 32.2703 & 0.4976 & 7.1346 \\ \cline{1-1}  & Single\_D\({}_{\text{CT}}\) & 10.3707 & 103.2422 & 7.2092 & 32.6115 & 0.4502 & 7.7318 \\ \cline{1-1}  & Single\_D\({}_{\text{PET}}\) & 10.1196 & 100.4910 & 7.1579 & 31.9520 & 0.4533 & 7.5195 \\ \cline{1-1}  & LatLRR-GAN & **10.6438** & **105.3437** & **7.2439** & **33.2622** & 0.4583 & 7.7910 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The fused images’ evaluation metric results of CT lung window images and PET images about the ablation experiments. Method 1: LatLRR_AE; Method 2: ED-D\({}^{2}\)GAN; Method 3: Single_D\({}_{\text{CT}}\); Method 4: Single_D\({}_{\text{PET}}\); Method 5: LatLRR-GAN. The best evaluation metric values are represented in red; the second-best evaluation metric values are represented in blue.  
Figure 7: The fusion results of CT lung window images and PET images about the ablation experiments. Method 1: LatLRR_AE; Method 2: ED-D\({}^{2}\)GAN; Method 3: Single_D\({}_{\text{CT}}\); Method 4: Single_D\({}_{\text{PET}}\); Method 5: LatLRR-GAN.  
Figure 7 shows the fusion results about ablation studies of five methods on CT lung window images and PET images. It can be seen from the Figure that the visual effect of fused images of Method 1 is poor, and the contrast information of the lung bronchus is weak, which can reflect the obvious advantages of the low-rank region fusion rule based on ED-D\({}^{2}\)GAN in the LatLRR-GAN proposed in this paper. Although the fusion results of Method 2 improved compared with Method 1, it can be seen from Table 3 that the AG and EI of Method 2 are relatively low, which reflects the advantages of using LatLRR to decompose the source images. Compared with Methods 1 and 2, Methods 3 and 4 can better highlight the lesion area and better retain detailed information of the bronchial lung, but it can be seen from Table 3 and Figure 8 that using dual discriminators to distinguish the fused image from the two source images can make the fused images retain more detail in the CT source images and PET source images simultaneously. Moreover, Method 5 has advantages over the single discriminator in terms of AG, EI, SF and SD. Therefore, the proposed method can effectively improve the quality of the fused images.