## 4 Discussion
Among these techniques, the filtering technique has the strengths of suppressing noise from low doses, preserving edge signals, and balancing spatial resolution with less data. In this study, the proposed algorithm showed high spatial resolution in the region of interest, effectively preserving the microstructure; however, there were limitations such as heterogeneous appearance in non-structural regions. Therefore, we plan to model animproved algorithm with a more sophisticated nonlocal patch-based approach to balance noise suppression and signal preservation in these non-structural regions in future studies. Recent advances in deep learning have led to several research efforts aimed at the application of the technology to the improvement of image quality in medical imaging [59; 60]. To train deep-learning models to denoise CT images, large datasets of low- and standard-dose acquisitions from the same patient are essential [61]. Especially in pediatric patients, repeat examinations are limited due to the risk of radiation exposure, and images are acquired with lower radiation doses than standard protocols [62]. Owing to these challenges, obtaining sufficient datasets to train deep-learning models is difficult, and the noise in low-dose images degrades the quality of the training data. The adaptive TV-NL algorithm proposed in this study effectively reduces noise in low-dose CT images while maintaining high spatial resolution to produce images that significantly resemble standard-dose images. Our approach allows one to generate high-quality data for training deep-learning models without additional radiation exposure to pediatric patients. Based on this approach, we expect to improve the accuracy of deep-learning-based analyses by minimizing radiation exposure. In clinical practice, PET/CT is commonly used to evaluate localized diseases, nodules, and the presence of distant metastases [63]. However, because of the inherent spatial resolution limitations of PET/CT, the acquired images suffer from the partial volume effect, which results in the underestimation of tumor size or tracer uptake [64]. In particular, the partial volume effect becomes more severe as the lesion size decreases and can influence the examination results of small lesions in PET/CT images [65]. Thus, correction for the partial volume effect is necessary for accurate quantification. We believe that the proposed algorithm can improve spatial resolution to correct for the partial volume effect, which can be useful for biomarker quantification in oncology applications [66] and can effectively improve the sensitivity and accuracy of PET/CT quantification [67]. In addition, artifacts such as blurring are common in PET/CT imaging because of shallow breathing [68]. The blurring caused by breathing can reduce average uptake measurements by up to 30% and significantly reduce detection sensitivity, especially for small lesions and lesions close to the diaphragm [69]. Therefore, to consider both the partial volume effect and breathing-induced artifacts in PET/CT images, we plan to develop a hybrid algorithm modeled by combining the algorithm proposed in our study with a deblurring algorithm in future studies. One of the limitations of this study is that we only applied our method to 2D images. We plan to extend our method to process 3D data in future studies. Achieving effective noise reduction and maintaining a high spatial resolution in 3D images can assist surgeons in precise surgical planning, especially in areas with small nerves or blood vessels. In addition, for surgeries (such as liver resection) that necessitate the identification of small blood vessels around a tumor, our approach can be useful in preventing side effects and enhancing the safety of the procedure. This study confirms the efficaciousness of the NL-TV filter for denoising 2D images; further, it is significant to quantitative evaluation based on real low-dose CT phantom images and for comparison with conventional algorithms.