## 4 Discussion
Noise distributions in natural images are generally represented by white Gaussian noise, whereas medical images contain various types of noise distributions. Moreover, natural images are generally in color, while medical images are grayscale. To address these limitations, research on no-reference evaluation factors for medical images should be conducted, however, there is still a relative lack of research [50]. In this study, we adopted a no-reference method by employing the BRISQUE factor to evaluate CT images. The BRISQUE evaluation factor is a recent no-reference metric that operates in the spatial domain and can characterize natural scene statistics in medical images based on Gaussian curve shape distortion analysis [51]. In particular, BRISQUE can be used to comprehensively analyze the decrease in spatial resolution or sharpness of medical images, which inevitably occurs depending on the denoising method [52]. No-reference evaluation has shown comparable performance to the peak signal to noise ratio across various distortions. In addition, the BRISQUE evaluation factor has the advantage of quantitatively analyzing overall image quality [53; 54]. According to the results of the no-reference evaluation in our simulation study, the Hann filter, which has the best performance in noise reduction and similarity evaluation, has the highest value, and the Hamming filter, which has a similar tendency to the Hann filter on average, has the second-highest value following the Hann filter. Additionally, the Ram-Lak and Shepp-Logan filters, which performed poorly in both evaluations, performed better in the no-reference evaluation. Based on these results, no-reference evaluation has a greater effect on the spatial resolution or sharpness of medical images than denoising. Therefore, we expect that this study will provide a preliminary study for the application of no-reference evaluation factors in medical imaging. In future work, we plan to add different forms of noise to various medical images and evaluate their tendency, and model a generalized no-reference evaluation factor that can incorporate various image characteristics. Recently, various algorithms based on the traditional FDK algorithm have been actively studied to improve the quality of CBCT reconstructed images. In one study, a combination-weighted Feldkamp algorithm (CW-FDK) was developed to improve the quality of CBCT images, and its usefulness was confirmed by applying it to phantom and clinical studies [55]. Based on these prior studies, we believe that the potential of FDK algorithms to improve the quality of CBCT reconstructed images is enormous, and that integrating FDK algorithms with other approaches will allow effective application to a variety of anatomical regions. Moreover, the flexibility of FDK algorithms allows them to address a wide range of clinical needs, making them a crucial tool in the field of medical imaging. In conclusion, with continuous research and improvement of FDK-based algorithms, the results of this study suggest that these approaches have significant potential for improving CBCT image quality in a variety of clinical settings. Furthermore, we expect that when FDK algorithms are combined with other advanced techniques, their applicability and effectiveness will be further expanded to achieve high-quality image acquisition. Recently, numerous deep learning-based research efforts have been conducted to address artifacts in conventional reconstruction algorithms. One study proposed an architecture that combines the conventional FDK algorithm with an image domain U-Netto address artifacts in the FDK algorithm [56]. The results of the study showed that the proposed algorithm achieved significant improvements over the conventional FDK reconstruction algorithm. Based on these preliminary studies, it seems that combining deep learning techniques with conventional reconstruction algorithms will lead to significant advances in reconstructed image quality for CBCT.