# Development of Artificial Intelligence-Based Dual-Energy Subtraction for Chest Radiography
## 4 Discussion  
The main purpose of this study was to produce soft-tissue- and bone-enhanced images from only high-energy images by developing an AI-DES system. The performance of the AI model was assessed by image similarity between the generated and real low-energy images. The overall performance of the AI-DES system was evaluated by comparing the image quality of the soft-tissue- and bone-enhanced images with those produced by a clinically applied DES system. As shown in Figure 4, our AI-DES successfully generated virtual low-energy images that closely resembled the real low-energy images. The image similarity indicated a high level of the indices (PSNR = 33.8 dB, SSIM = 0.984 and MS-SSIM = 0.957). Our system achieved this result without the need for a specific imaging detector with a metal plate or multiple X-ray exposures. Consequently, AI-DES has the potential to reduce the image noise and X-ray dose compared to existing DES systems. Additionally, its motion-artifact-free nature resulting from avoidance of multiple exposures makes AI-DES particularly valuable for elderly or critically ill patients who may have difficulty in holding their breath. Furthermore, in the soft-tissue-enhanced images obtained via the weighted subtraction process, bone tissues within the lung fields were effectively suppressed, although faint residual bone edges or shadows were observed. We also confirmed that the contrast of pulmonary lesions was clearly enhanced in some cases, as indicated by red arrows in Figures 6 and 7. Nevertheless, the soft tissue contrast was slightly inferior to that of the clinical system, as shown in Figures 8b and 9b. Alternatively, AI-DES demonstrated advantages in terms of sharpness and noise characteristics. Overall, we subjectively verified that the image quality in the lung fields is almost comparable to that in the clinically applied system. However, these limited cases provide only weak evidence regarding the usefulness of AI-DES for improving lesion detectability. Therefore, further investigation is needed to determine whether AI-DES can truly improve the detectability for a larger number of cases. The similarity indices (PSNR = 21.1 dB, SSIM = 0.711, and MS-SSIM = 0.794) for our soft-tissue-enhanced images were considerably lower than the results of existing AI models for bone suppression in chest radiography [23; 25; 28; 29; 30; 31]. [PERSON] et al., generated bone-suppressed chest radiographs with a resolution of 256 \(\times\) 256 using a cGAN-based model citeref-DCGANand reported an average PSNR of 35.5 dB and an SSIM of 0.975 for the similarity between the generated images and the ground truth. [PERSON] et al., also developed a CNN model called DeBoNet, which suppresses bones in chest radiographs with a resolution of 256 \(\times\) 256 [30]. They achieved image similarity to the ground truth produced with commercial software, with an average PSNR of 36.8 dB, an SSIM of 0.947, and an MS-SSIM of 0.985. The low similarity indices of our soft-tissue-enhanced images can be attributed to bone edge artifacts and excessive contrast outside the lung fields, as well as the presence of black or white areas beyond the body contours where pixel values are nearly 0 or extremely high. Undoubtedly, our generated images need improvements by artifact reduction. However, a more substantially meaningful comparative analysis would have been provided by excluding the outer areas of the body contours for similarity index calculation. Additionally, it should be noted that our image generation was accomplished with a resolution of 1024 \(\times\) 1024, which is certainly a greater challenge compared to 256 \(\times\) 256 image synthesis. On the other hand, the generated bone-enhanced images contained noticeable blacked-out artifacts on and around the spines, despite the successful suppression of soft tissues and the selective enhancement of ribs.