Domain‐adaptive denoising network for low‐dose CT via noise estimation and transfer learning. Issue 1 (2nd September 2022) - Wang, Jiping; Tang, Yufei; Wu, Zhongyi; Tsui, Benjamin M. W.; Chen, Wei; Yang, Xiaodong; Zheng, Jian; Li, Ming
Abstract: Background: In recent years, low‐dose computed tomography (LDCT) has played an important role in the diagnosis CT to reduce the potential adverse effects of X‐ray radiation on patients, while maintaining the same diagnostic image quality. Purpose: Deep learning (DL)‐based methods have played an increasingly important role in the field of LDCT imaging. However, its performance is highly dependent on the consistency of feature distributions between training data and test data. Due to patient's breathing movements during data acquisition, the paired LDCT and normal dose CT images are difficult to obtain from realistic imaging scenarios. Moreover, LDCT images from simulation or clinical CT examination often have different feature distributions due to the pollution by different amounts and types of image noises. If a network model trained with a simulated dataset is used to directly test clinical patients' LDCT data, its denoising performance may be degraded. Based on this, we propose a novel domain‐adaptive denoising network (DADN) via noise estimation and transfer learning to resolve the out‐of‐distribution problem in LDCT imaging. Methods: To overcome the previous adaptation issue, a novel network model consisting of a reconstruction network and a noise estimation network was designed. The noise estimation network based on a double branch structure is used for image noise extraction and adaptation. Meanwhile, the U‐Net‐based reconstruction network uses several spatially adaptive normalization modules to fuse multi‐scale noise input. Moreover, to facilitate the adaptation of the proposed DADN network to new imaging scenarios, we set a two‐stage network training plan. In the first stage, the public simulated dataset is used for training. In the second transfer training stage, we will continue to fine‐tune the network model with a torso phantom dataset, while some parameters are frozen. The main reason using the two‐stage training scheme is based on the fact that the feature distribution of image content from the public dataset is complex and diverse, whereas the feature distribution of noise pattern from the torso phantom dataset is closer to realistic imaging scenarios. Results: In an evaluation study, the trained DADN model is applied to both the public and clinical patient LDCT datasets. Through the comparison of visual inspection and quantitative results, it is shown that the proposed DADN network model can perform well in terms of noise and artifact suppression, while effectively preserving image contrast and details. Conclusions: In this paper, we have proposed a new DL network to overcome the domain adaptation problem in LDCT image denoising. Moreover, the results demonstrate the feasibility and effectiveness of the application of our proposed DADN network model as a new DL‐based LDCT image denoising method.