## 1 Introduction  
Computed tomography (CT), which was developed by [PERSON] in 1972, uses projection data obtained by rotating opposite X-ray tubes and detectors to express the anatomical structure of the internal cross-section of the human body as an image. CT, which has the advantages of obtaining high-resolution images, is steadily increasing in the field of diagnostic medical imaging [1; 2; 3]. However, studies have reported that CT scans have the disadvantage of having a higher exposure dose for patients than other diagnostic techniques, thereby contributing to an increase in the incidence of cancer [4; 5]. As the number of CT scans per year increases, the incidence rate ratio increases proportionally [6]; studies have shown that 1.5-2.0% of all cancer occurrences are estimated to be due to CT use [7]. Therefore, there is an increasing need for low-dose CT to reduce radiation exposure in patients. However, during low-dose CT tests, noise in the image inevitably increases, thus resulting in a decrease in the overall image quality and diagnostic accuracy [8]. The methods for solving these problems can be broadly classified into three types: The first method uses an additional filter that is capable of obtaining improved image quality from the hardware of the CT system. Additional filters are also used in X-ray-based imaging systems. Although the additional filter plays a role in reducing the patient's exposure dose, the disadvantage of inevitably increasing the noise of the image due to the decrease in the number of photons detected occurs simultaneously. To overcome these shortcomings, many researchers have proposed and developed additional filters using tin instead of conventional aluminum or copper materials [9]. The use of tin filters removes low-energy regions from the X-ray spectrum, thereby reducing the radiation dose by 81% during CT scans and minimizing the noise generated as compared to conventional filters [10]. The second noise removal method involves the use of a CT image postprocessing technique. The most representative techniques are the median filter [11], which utilizes the median value of pixels, and the Wiener filter [12], which is based on the principle of minimizing the average square error between images. However, when these filters are applied to X-ray images, the disadvantages include a decrease in noise removal efficiency and in the sharpness of the border area [13]. To compensate for these shortcomings, a noise cancellation algorithm using a nonlocal means (NLM) algorithm was developed [13; 14]. Algorithms using the NLM approach have been applied in various ways to improve the quality of diagnostic medical X-ray images [14; 15]. [PERSON] et al. proposed an NLM approach that balanced noise suppression and edge preservation level in CT images [14], and [PERSON] et al. confirmed the applicability of NLM priorly in the iterative reconstruction process based on low-dose CT images [15]. Finally, a reconstruction technique suitable for low-dose CT imaging systems was developed. Currently, the most commonly used reconstruction technique in clinical practice is the filtered back projection (FBP). FBP is a method of converting a two-dimensional planar image of a subject acquired from various angles through filter correction, and then reversely projecting it in the acquired direction. However, this reconstruction technique has the disadvantage of significantly increasing the image noise at low doses [16]. To overcome these shortcomings, repetitive reconstruction (IR) techniques have been developed and applied in clinical trials. Unlike FBP, which processes data only once, IR techniques repeatedly process raw or reconstructed data to reduce the image noise. As a result, IR can reduce the exposure dose to patients by improving the noise level compared to FBP [16]. Among them, the Advanced Model-Based Interactive Reconstruction (ADMIRE) technique used in the Siemens CT imaging system can obtain an appropriate image quality using various parameters [17; 18].