## 3 Results and Discussion
Therefore, in this study, we propose a method of additionally applying the NLM noise removal algorithm from the proposed ADMIRE and Kernel techniques to improve the noise characteristics that occur during low-dose CT examinations using annotation filters to reduce the patient's radiation dose. As a result of the quantitative evaluation, the highest CNR was obtained in Group 4, with an average of approximately 43.51. In addition, it was measured to be approximately 4.66 times higher in Group 4 than in Group 3, based on a relatively high ADMIRE intensity and low kernel. The ERD evaluation resulted in the best value for Group 3, with an average of approximately 0.94. Group 4, which applied our proposed NLM noise removal algorithm to low-dose CT images, obtained results similar to those of Group 3, with an average of approximately 1.19. Additionally, as compared with Groups 1 and 2, Group 4 was approximately 1.69 and 1.63 times higher, respectively. This proved that the proposed NLM noise removal algorithm can effectively preserve image-edge information when reconstructing CT images. The BRISQUE evaluation yielded similar values in Groups 3 and 4, with averages of approximately 32.66 and 35.23, respectively. The noise level was reduced in the low-dose CT images obtained by increasing the intensity of ADMIRE using the same kernel (Group 1 vs. Group 2). In addition, the overall image quality was improved by reducing information loss at the border in low-dose CT images obtained using a high kernel based on the same ADMIRE intensity (Group 1 vs. Group 3). When comparing Group 3 with Groups 1 and 2, it was confirmed that Group 3 improved the image quality and maintained the edge signal of the image well; however, the noise removal efficiency was low. When comparing Group 4 with the NLM filter applied in Group 3 and in Groups 1 and 2, it was confirmed that in Group 4, as in Group 3, the signal at the edge of the image was well preserved; noise was removed more effectively. Therefore, by applying the NLM filter to Group 3, which had a low noise removal efficiency, it was confirmed that superior image quality was obtained as compared to that of Groups 1 and 2, and that edge information could be well preserved. This proves that the proposed NLM noise removal algorithm can effectively preserve the edge information of the resulting image while improving the temporal resolution measured during CT image reconstruction. Overall, the CNR evaluation results show that the application of the NLM algorithm to low-dose CT images with a low ADMIRE and high kernel settings can solve the noise problem while maintaining an excellent temporal resolution. Additionally, ERD and BRISQUE have demonstrated that they can reduce the loss of edge information that occursduring the NLM algorithm-based smoothing process. The proposed method can improve the quality of CT images under low-dose conditions with excellent performance because it can produce excellent image quality with excellent temporal resolution. In particular, if applied in conjunction with a tin filter that can effectively remove the radiation dose, it is expected to effectively reduce radiation exposure to patients and radiologists. A limitation of this study is that the usefulness of the proposed noise reduction algorithm was evaluated using only one CT image. Based on the research results of this pilot concept, we plan to improve reliability by conducting statistical analysis and calculating absolute error values using CT image data by applying various ADMIRE reconstruction methods and the NLM algorithm in the future. Based on the CT image reconstruction parameters used by [PERSON] et al., we will further analyze the efficiency of the NLM algorithm according to each condition in the future [19]. In addition, we plan to conduct additional research on the correlation between quantitative CT image quality evaluation results and qualitative evaluation indicators using a receiver operating characteristic curve or Likert scale.