## 1 Introduction  
In clinical medicine, X-ray imaging technologies are essential for diagnosing diseases [1, 2, 3]. Among these technologies, computed tomography (CT) has the advantage of being able to acquire three-dimensional images based on various projection data with a fast scan time [4]. CT can detect anatomical and pathological abnormalities, such as tumors [5], nodules [6], skeletal muscles [7], and vascular diseases [8], in various areas by extracting cross-sectional images of the desired area using X-rays. Owing to the recent spread of COVID-19, the importance of CT examinations for accurate detection of the virus in the diagnostic field is increasing, and the role of CT images in patient selection is especially emphasized in the Fleishner Society recommendations [9, 10, 11]. CT examinations require multiple projections and therefore pose a risk of increased radiation exposure. A comprehensive approach is essential to effectively manage this risk. It is important to properly record and manage the patient's radiation dose, especially for CT examinations and interventional procedures with high radiation exposure [12]. In addition, large doses of X-ray radiation can cause critical tissue damage and increase the risk of cancer. [PERSON] et al. reported that the overall cancer incidence rate was 24% higher in exposed individuals [13]. In particular, [PERSON] et al. stated that approximately 2% of cancers occurring in the United States are due to radiation exposure from CT, increasing interest in the risk of radiation exposure [14]. Thus, when diagnosing a disease, the tradeoff between the benefits of CT examination and the radiation effects must be appropriately maintained and considered [15]. CT examinations strive to balance diagnostic image quality with radiation dose. Standard-dose CT provides excellent image quality, but it poses a cumulative radiation risk, particularly for vulnerable populations such as children or patients undergoing repeated scans. Low-dose CT protocols significantly reduce radiation exposure but introduce increased noise that compromises image quality. This noise primarily includes quantum noise, caused by the statistical variability in the number of detected X-ray photons, which becomes more pronounced at lower radiation doses, and electronic noise, which originates from the CT detector and signal amplification system and is amplified in low-photon-count conditions [16; 17]. Research is being conducted to develop specialized CT imaging system hardware to address this issue. Recently, a photon-counting detector that has excellent X-ray detection efficiency and can separate energy bins to obtain cross-sectional CT images has been developed and used clinically [18; 19; 20]. However, building a photon-counting CT imaging system involves substantial expenses, limiting the application of this system in clinical practice at present [21]. To circumvent the issues inherent to addressing the noise in low-dose CT images through hardware-based enhancements, software-based approaches have been explored. Although methods employing iterative reconstruction have been demonstrated to mitigate the problem effectively, they may interfere with disease diagnosis by generating unnecessarily worsening spatial resolution [22; 23]. Deep-learning-based reconstruction (DLR) has presented outperforming results in low-dose exposure conditions [24; 25; 26]. DLR employs convolutional neural networks (CNNs) trained on extensive datasets to differentiate between noise and true anatomical structures, enabling effective noise reduction and preservation of image details. However, implementing DLR requires significant computing resources and access to large, high-quality training datasets. DLR algorithms need to be standardized across different CT systems and manufacturers to ensure consistent performance. It is necessary to validate the consistency and reliability of DLR-processed images across different patient populations and clinical settings.