# Development of Artificial Intelligence-Based Dual-Energy Subtraction for Chest Radiography
## 1 Introduction  
Lung cancer is the disease with the highest mortality and the second-highest incidence of cancer worldwide [1; 2]. Since early-stage lung cancer may have a better prognosis with appropriate treatment, early diagnosis and accurate staging are critical [2; 3]. Randomized controlled trials, including the National Lung Screening Trial (NLST), have demonstrated that the use of low-dose computed tomography (CT) for lung cancer screening reduces the mortality by 20% compared to chest radiography [4; 5]. Chest radiography lacks effectiveness [4], and the American Cancer Society Lung Cancer Screening Guidelines recommend low-dose CT rather than chest radiography [6]. Nevertheless, due to the low cost, low radiation dose, and high adoption rate of the equipment, chest radiography is widely performed for lung cancer screening, in addition to low-dose CT [7; 8; 9]. Additionally, some facilities utilize the dual-energy subtraction (DES) technique for chest radiography [10]. This technique can produce images that emphasize tissues with particular linear attenuation coefficients [11] and typically produces soft-tissue-enhanced and bone-enhanced images [12]. It has been reported that soft-tissue-enhanced images canimprove the ability to detect pulmonary lesions [13; 14; 15; 16]. [PERSON] et al., compared the performance of radiologists in detecting pulmonary lesions by clinical chest radiography with and without the DES technique, and their receiver operating characteristic (ROC) analysis demonstrated the statistically significant superiority of using DES images [15]. [PERSON] et al., reported that soft-tissue-enhanced images obtained through the DES technique statistically significantly reduced the reading time of radiologists and slightly improved the diagnostic accuracy of pulmonary lesions [16]. The superiority of soft-tissue-enhanced images obtained via the DES technique in the diagnosis of COVID-19 has been also confirmed [17]. Furthermore, research on advanced DES techniques, such as the optimization of exposure conditions [18] and the automatic determination of weight factors for bone and soft tissue enhancements in the subtraction process [19], has been actively reported. However, such a DES technique has some problems. First, only limited numbers of facilities own the necessary systems. Second, there are several problems associated with the imaging techniques of the one-shot and two-shot methods. In the one-shot method, two images are simultaneously obtained at different energies by placing a thin copper plate between two imaging detectors [20]. Because of the copper plate, noise characteristics can deteriorate the quality of high-energy images [21]. In the two-shot method, on the other hand, X-ray exposure is carried out twice at different energies [22]. As a result, motion artifacts and dose increments are unavoidable. Therefore, we aim to address these issues by developing a technique to virtually generate low-energy images from high-energy images using artificial intelligence (AI). This development of AI-based DES (AI-DES) does not require a specific imaging detector with a metal plate or multiple exposures. Moreover, the AI-DES can arbitrarily select the enhanced tissue by adjusting the weight of the image subtraction process. Consequently, our AI-DES has the potential to provide more enriched information than existing methods, where bone-suppressed images are directly produced [23; 24; 25; 26; 27; 28; 29; 30; 31]. For instance, [PERSON] et al., developed an AI model to generate DES-like soft tissue images, but their approach primarily focused on suppressing bone tissues, making it difficult to selectively enhance specific tissues [25]. Similarly, [PERSON] et al., developed a generative adversarial network (GAN)-based bone suppression model for chest radiography, and demonstrated that its ability to detect pulmonary lesions is comparable to that of a DES technique [26].