## 1 Introduction  
Computed tomography (CT) is a computerized imaging technique in which many X-ray measurements of a body are made, with an X-ray tube usually rotating around the patient during the CT scan [1; 2]. These signals are computationally processed and, consequently, cross-sectional images of the body are reconstructed. The utilization of CT for image-based diagnosis has increased steeply in the medical field [3; 4]. Additionally, in the past ten years, state of-the-art CT detectors and sources and several techniques, such as automatic exposure control and iterative reconstruction, have been launched; these can provide a higher image quality by using a lower radiation dose [5; 6; 7; 8; 9; 10]. However, despite the reduced radiation exposure per single CT examination, the total number of CT examinations performed has increased consistently [11; 12]. Therefore, it is imperative that CT scanners are properly managed and quality control (QC) is accurately performed [13; 14; 15; 16].  
QC related to radiation dose and image quality for CT is universally composed of quantitative assessments with a standard phantom [13; 14; 15; 16]. In particular, the decreases in image quality of CT scanners need to be managed quantitatively because a relatively high radiation dose may be used to overcome the image quality during CT examination. Although the standard phantom evaluation exists, this is often included in the quantitative evaluation,especially for contrast resolution (CR) and spatial resolution (SR) assessments [14; 15; 16]. For the CR and the SR evaluation, radiologists sometimes perform visual evaluations of these images (e.g., holes that are visible as distinct circles), instead of making use of quantitative cut-off values. However, quantitative tools are considered more effective for evaluating phantom images [17; 18; 19]. There are several types of standard phantoms with similarities in composition such as the American Association of Physicists in Medicine (AAPM) CT Performance Phantom, and the American College of Radiology (ACR) phantom [14; 17; 18]. As a result, measurement of the contrast-to-noise ratio (CNR) as an objective tool has been conducted in recent years to evaluate the CR with the ACR phantom in the United States, which overcomes the limitations of qualitative evaluation [17]. However, as the CNR is usually focused on specific regions of interest (ROIs) and does not encompass the entire CT image, there is a risk of sampling error [20]. In fact, a specific location with a small size can be problematic as it represents the quality of the overall image. Therefore, computer-aided analysis, such as texture analysis (TA), that covers the entire ROI was considered as a more appropriate tool than the value of the CNR, which varies by measured location, because it has a lower standard error.  
Such tools have already been investigated, making use of the TA, which may be able to distinguish between diseases with objective values [21; 22; 23; 24]. There are five types of TA methods: structural, statistical, local binary patterns, model-based, or transform-based [25; 26]. Among these, the statistical method has been utilized as the most common approach for CT TA or digital image-based diagnosis [25; 27]. Since the specific characteristics of the tumor can be distinguished through TA [21; 22; 23; 24], we hypothesized that statistical TA may also be useful for classification of standard phantom images during QC of CT scanners. CR and SR represent whether the pixel value in the ROI can be identified in a situation where the difference of signal between the ROI and the surrounding area is small or large. The rationale for this study was whether it would be possible to quantify the distinction for CT QC, similarly to the distinction of tumors through TA mentioned above. Therefore, the purpose of our study was to perform the TA based on the statistical method for CR and SR assessment and to evaluate its feasibility as a quantitative component of CT QC.