## 4 Discussion  
In this study, we proposed an adaptive search window-based NL-TV filter that addresses the limitations of conventional NL means and TV filters in reducing noise in low-dose CT images. To confirm the efficaciousness and feasibility of the adaptive search window-based NL-TV filter, we applied the proposed filter to the quantitative evaluation of phantom CT images and compared it with the conventional TV, NL, and NL-TV filters. The CNR and COV evaluations to assess the noise level showed that the adaptive NL-TV algorithm improved by approximately 2.60 and 2.36 times compared to the low-dose CT image. Visual evaluation showed that the noise was effectively reduced and the highest spatial resolution was achieved. The LSF evaluation showed that the proposed adaptive NL-TV filter had the highest sigma value following the low-dose CT image, and that the conventional TV and NL-TV filters had errors in the edge region. Based on these evaluation results, we confirmed the efficacy of the proposed adaptive NL-TV filter. According to the Global Cancer Report, lung cancer causes approximately 1.8 million deaths annually [47], and lung cancer survival rates increase dramatically with early detection; therefore, the ability to detect malignant lung nodules at an early stage is crucial [48]. Lung nodules present an important clinical challenge for both general X-ray imaging and CT. Comparative studies of general chest radiography and CT have indicated that CT is the most appropriate modality for detecting lung nodules in vivo [49]. In particular, lung nodules as small as 1 cm have shown increasing diagnostic significance as CT imaging technology has improved [50]. Studies have shown that CT scans for lung cancer detect small lung nodules in up to 60% of patients, indicating the advantage of CT over general X-ray imaging in detecting lung abnormalities. In addition, CT can significantly improve the detection of small nodules by providing higher spatial resolution [51]. However, CT scans involve relatively high radiation exposure compared to general X-ray examination methods that acquire images at one projection angle, which can increase the risk of cancer. According to the International Commission on Radiological Protection, each 1 mSv increase in the radiation dose is associated with a 0.005% increase in cancer risk [52]. The effective dose of a chest CT scan was reported to be approximately 7 mSv on average [53], which is much larger than the 0.05 mSv value from a general chest X-ray [54]. Especially for malignant cancer patients with lung nodules, follow-up CT after the initial treatment is necessary for the diagnosis and effective treatment of lung nodules. However, exposure to ionizing radiation from repeated CT scans can increase the risk of secondary cancers [52]. Low-dose CT protocols are important to reduce the risk of radiation exposure and cancer. However, low-dose CT generates noise because of low photon numbers, which interferes with the observation of micronodules [55]. Conventional NL means algorithms provide sharp and smooth images; however, as the noise level increases, the edges become blurred, which can lead to the obfuscation of fine details [56]. In our experimental results (Figure 2), the 6.4 mm and 3.2 mm holes that were not visible in the noisy images became clearly observable after applying the adaptive search window-based NL-TV filter proposed in this study. Based on these results, the proposed algorithm is expected to enhance lung nodule detection in low-dose CT images by effectively reducing noise and improving the detection ability. Moreover, this postprocessing technique has the potential to significantly improve the detection of lung nodules [57]. In particular, low-dose scans lead to the amplification of electronic noise in energy-integrated detectors [58]. To address these issues, effective noise reduction is essential in CT low-dose imaging, and various filtering techniques and deep-learning models have been applied.