## 4 Discussion
To compare the noise reduction ability of the FDK algorithm using various filters, we conducted comparative evaluations by analyzing the COV and SNR graphs. In addition, to evaluate the tendency of noise reduction for each structure, we set the ROIs in air, brain, and bone. We observed a significant increase in the slope of the COV graph at a projection angle of 2.8\({}^{\circ}\), with the brain region exhibiting the lowest average COV. Moreover, when comparing the COV graphs for various filters, we found that the Ram-Lak filter exhibited the lowest noise reduction ability in all three regions, whereas the Hann filter demonstrated the most effective noise reduction. When analyzing the SNR graphs, we observed a rapid decrease in the SNR of the brain and bone regions at an average projection angle of 2.8\({}^{\circ}\), with relatively small variation in the air region. Both the COV and SNR graphs showed similar tendencies for the Hamming and Hann filters. However, for all projection angles and tissues, the Hann filter proved to be the most effective in denoising. Based on these results, when the FDK algorithm was applied to reconstruct noisy projection data under a low-dose protocol, the image quality rapidly deteriorated for projection angles of 2.8\({}^{\circ}\) or more. Therefore, other reconstruction algorithms appropriate for the projection angle should be considered. Moreover, the Hann filter is the most effective in reducing noise. Considering visual evaluation, the application of the Ram-Lak filter appears to be effective when considering resolution and sharpness. Therefore, in future studies, we expect that the Hann filter will perform more effectively when applied in conjunction with improved resolution using a deblurring deep learning algorithm to compensate for the blurring phenomenon. In addition, as the values of COV and SNR rapidly increase and decrease, respectively, with an increased projection angle owing to streak artifacts, and if the streak artifacts are effectively reduced using deep learning, it is expected that improved reconstruction image quality can be achieved under a low-dose protocol using a larger projection angle. The similarity evaluation results show that the Hann filter has the most improved performance, and the Ram-Lak filter has the most inferior performance in terms of both RMSE and UQI; this is the same trend as the noise evaluation results. The RMSE measures the difference in pixel values between the target and reference images, and the UQI measures the structural similarity between the target and reference images, considering the brightness, contrast, and structural information between the two images [45, 46]. The noise, intensified by applying the Ram-Lak filter in the reconstruction process, increased the difference in pixel values between the target and reference images, which affected both the RMSE and UQI values, causing an increase in the RMSE value and a decrease in the UQIvalue. Therefore, for the Hann filter, which showed the most superior results in the noise evaluation, visual evaluation demonstrates that the blurring phenomenon is relatively intensified compared to the Ram-Lak filter. However, as the noise is effectively reduced, the similarity evaluation results show a superior performance corresponding to the noise evaluation results. To quantitatively assess image quality in the medical field, evaluation factors that utilize reference images to effectively analyze the overall difference between images have been used. However, in most medical images, it is very difficult to obtain a perfect image that can be used as a reference for image evaluation, thus recent research has focused on no-reference evaluation factors. Although no-reference evaluation factors were proposed to evaluate \"naturalness\" in natural images, they are beginning to be applied to medical images [47; 48]. However, the no-reference evaluation factors in medical images have limitations [49].