A two-stage deep-learning framework for CT denoising based on a clinically structure-unaligned paired data set.

"Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-23-403/coif). The authors have no conflicts of interest to declare."

"Funding: This work was supported by National Key Research and Development Program of China (2022YFC2406900), the Shenzhen Clinical Research Center for Cancer ( No. [2021] 287 ), the Shenzhen Science and Technology Program ( grant/award number: KCXFZ20201221173008022 ), the National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital and Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen ( No. SZ2020QN001 ), the Shenzhen Municipal Scheme for Basic Research of China ( No. JCYJ20210324100208022 ), the National Natural Science Foundation of China ( U22A20344 ), the Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province ( 2023B1212060052 )."