# Development of Artificial Intelligence-Based Dual-Energy Subtraction for Chest Radiography
## 4 Discussion
The ribs and clavicles demonstrated significantly superior sharpness and noise characteristics compared to the images obtained by the Discovery system, as shown in Figures 8b and 9b. However, the visibility of the spines was much lower than that of the clinically applied system, primarily due to the presence of blacked-out artifacts. Considering the importance of selectively enhanced bone images for the detection of bone fractures or tumors, addressing this issue is crucial. In addition, some of the soft-tissue-enhanced images contained faint bone shadows, as previously mentioned. We attribute these issues to the following three factors. First, the training dataset was limited to only 240 cases. Training the AI network on a larger image dataset may address these issues, although our AI network has already produced virtual low-energy images that closely resemble real images. In future studies, we intend to investigate whether the training dataset contains misalignments between high- and low-energy images owing to patient motion. Removing misaligned data may further improve the performance of the AI network. Secondly, we wonder whether the weight factors used in the subtraction process were not optimized. We determined the individual values of \(\omega\) for each patient to generate more selectively enhanced soft tissue and bone images. However, these values may not have been optimal. Moreover, bone thickness and density vary across different body locations, even within the same patient. Accordingly, it is possibly challenging to keep all bone shadows but completely eliminate soft tissues in the entire image with a single weight factor. Even so, we believe that selectively enhanced tissue images aid in lesion detection if target tissues were effectively enhanced. Therefore, we will attempt to selectively emphasize target tissues by optimizing the weight factor in future studies. Thirdly, the effect of quantization error was perhaps more prominently visualized through log amplification in the subtraction process. Alternatively, the cause of the artifacts may be attributed to the discrepancy between the virtual and real low-energy images, despite their high similarity indices. We noticed that the location of black artifacts around lower thoracic and lumbar spines in bone-enhanced images corresponded to areas with excessively high X-ray absorption, where the pixel values ranged from 0 to 2 in real high-energy and virtual low-energy images. We speculate that numerical errors of such small values are further enhanced by logarithmic conversion, resulting in noticeable artifacts. We performed the subtraction process using 12 bit image data, but in future studies, we plan to use images with higher contrast resolution to eliminate artifacts. Here, we discuss the computational complexity of our AI-DES. We adopted the widely used pix2 pix with a few changes, so its implementation was not a challenging task. Despite using only 240 cases for training, the similarity between the produced and target images was high enough to support stable performance. Although the network requires paired data of high- and low-energy images, the preprocessing is far from a complex computation. However, due to the high resolution of the generated images (1024 \(\times\) 1024), the batch size had to be limited to a maximum of two to prevent an excessive GPU load. Next, the AI-DES requires the application of a weighted image subtraction process to the images generated by the AI network. In our current method, the weight factors are determined manually through visual inspection in order to emphasize the desired tissues to the greatest extent possible. Taken together, we consider that the construction of our AI network is no more complicated than those proposed in existing studies to directly generate bone-suppressed images [23; 24; 25; 26; 27; 28; 29; 30; 31]. However, the image subtraction process, which is the latter part of AI-DES, requires human intervention and time rather than computational complexity.