# Development of Artificial Intelligence-Based Dual-Energy Subtraction for Chest Radiography
## 3 Results
### Soft Tissue and Bone Images  
Figure 5 shows soft-tissue- and bone-enhanced images generated using Equation (10) for four test cases. The weight factors (\(\omega\)) used in the weighted subtraction are presented in the lower-right corner of each image. As shown in the left column in Figure 5, the soft-tissue-enhanced images demonstrate that the soft tissues were well-retained, and bone tissues were effectively suppressed, although the edges of the clavicles, ribs, and spine are faintly presented. On the other hand, as seen in the right column in Figure 5, the bone-enhanced images exhibited relatively suppressed soft tissues. However, the enhanced lower thoracic and lumbar spine are poorly visualized, as they appear to be blacked-out. Figure 4: Examples of low-energy images virtually generated by our trained AI network. Four test cases (**a–d**) are presented here. The similarity indices between real and virtual images are presented at the bottom of each figure. Figure 5: Examples of soft-tissue- and bone-enhanced images generated by AI-DES for four test cases (**a**–**d**). The weight factors used in the subtraction process are presented in the lower-right corner of each image. Table 3 presents the average and standard deviation of PSNR, SSIM, and MS-SSIM values across all test cases, evaluating the similarity between soft-tissue-enhanced images obtained using AI-DES and Discovery XR656, as well as the similarity between bone-enhanced images. Additionally, Table 3 includes weight factors used in weighted image subtraction of AI-DES, as well as the similarity indices between the real and virtual 60 kV images, as explained in Section 3.1. The PSNR, SSIM, and MS-SSIM values for soft-tissue- and bone-enhanced images are significantly lower than the indices for 60 kV images. Table 3 also demonstrates that the values of \(\omega\) varied slightly among patients, but \(\omega\) values for soft tissue enhancements were consistently higher than those for bone enhancements. Figures 6 and 7 compare our soft-tissue- and bone-enhanced images, which were generated from the real 130 kV and virtual 60 kV images, to those obtained using Discovery XR656 for the respective cases. The weight factors and calculated PSNR, SSIM, and MS-SSIM values are also shown for the soft-tissue- and bone-enhanced images. While the Discovery system uses real 60 kV images in the subtraction process, our AI-DES utilizes virtually generated 60 kV images. Not only in these two cases but most test cases, the soft-tissue-enhanced images demonstrated that the bone shadows within the lung fields were successfully suppressed in both systems. However, our soft-tissue-enhanced images contained artifacts, implying the presence of thoracic and lumbar spines. Furthermore, in some cases, the mediastum or liver area appeared overly bright in our soft-tissue-enhanced images when adjusting the contrast within the lung fields to match that in the images produced by the Discovery system, as particularly seen in the case of Figure 7.  
Subsequently, as compared in Figures 6 and 7, we confirmed that the bone images produced by the Discovery system exhibited remarkably more selective enhancement for bone tissues across the entire image. In contrast, our bone-enhanced images presented comparably enhanced ribs but contained artifacts in and around the region where the lower thoracic and lumbar spines should be depicted. Figures 8 and 9 also compare the soft-tissue- and bone-enhanced images generated by AI-DES to those generated by the Discovery system for other test cases, as well as enlarged views of specific areas. The enlarged views revealed that the images generated by AI-DES (the upper row in Figures 8b and 9b) exhibited superior sharpness and noise characteristics, particularly in the bone-enhanced images, compared to those generated by the Discovery system (the lower row in Figures 8b and 9b).