# A Deep-Learning-Based Quality Control Evaluation Method for CT Phantom Images
## 1 Introduction  
CT is an important diagnostic modality that utilizes radiation to capture medical images. While CT is highly beneficial for disease diagnosis, it exposes patients to significant levels of radiation [1, 2]. Therefore, ensuring the QC of CT is crucial, and regular evaluations are essential for CT users. There are various approaches available for conducting CT QC. Commonly employed methods include phantom image evaluations, radiation dose monitoring, image quality assurance based on actual clinical images, regular equipment calibration to maintain equipment, and radiologist training and certification. These methods work together to ensure the quality and safety of CT imaging. Specifically, phantom image evaluations employ specially designed phantoms for CT QC management and should be the most recommended objectively through quantitative measures [3]. However, some of the phantom evaluations rely on qualitative methods, where radiology experts subjectively assess detectability through visual inspection. This subjective evaluation (Spatial and Contrast resolution) can lead to inconsistencies among evaluators, resulting in low reproducibility [4]. To address this issue, quantitative evaluation methods based on traditional image-processing techniques have been developed [5; 6; 7]. These methods utilized formulaic outcomes to facilitate more accurate and rapid decision-making compared to qualitative methods. However, they made them vulnerable to unique pitfalls when optimized for specific conditions. Moreover, comprehensive system modifications were required in case of formula errors. In early studies, researchers discerned differences in pixel values between the target and surrounding pixels in CT phantom images [5; 6; 8]. These offered the advantage of being expressible through straightforward formulas and calculations. However, they faced the problem in fully meeting the criteria of visual discernibility to the human eye. For example, some evaluators might consider a circle visually discernible. Subsequent research had aimed to address this issue by utilizing contour extraction algorithms, followed by assessing visual discernibility based on the circularity index and eccentricity of the circle [7; 8]. But these methods have struggled to account for variations due to slight changes in shooting conditions or inherent errors in different equipment. To overcome this challenge, researchers attempted to quantify the intuitive quality of an image's region by incorporating texture analysis [3]. However, these methods also had a problem that was struggled to account for variations due to slight changes in shooting conditions or inherent errors in different equipment. Ultimately, these approaches had proven difficult to replace qualitative assessment, as human judgment considers a variety of factors. Alternatively, AI has been explored for application in diagnosing diseases based on medical images [9; 10; 11; 12]. Traditionally, the diagnosing diseases was primarily reliant on the medical images and the patient's condition, relying on the individual medical knowledge and judgment of the medical doctor. The medical doctor's judgment was hindered by the limitations of the one's knowledge level and the potential for errors. AI addresses the shortcomings of human judgment errors by providing relatively consistent assessments during diagnosis. Because it learns from vast amounts of data to make diagnoses based on extensive knowledge. As data from medical imaging modalities like CT, magnetic resonance imaging (MRI), and conventional X-ray images continue to accumulate, the value of AI in the medical field continues to rise, with specialized AI systems developed for medical diagnosis. However, unlike medical diagnosis, research on utilizing artificial intelligence for medical equipment management was currently very limited. The utilization of AI in CT QC can mitigate the variability of subjective assessments among individuals, leading the consistent responses.