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Degradation Adaption Local-to-Global Transformer for Low-Dose CT Image Denoising

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Abstract

Computer tomography (CT) has played an essential role in the field of medical diagnosis. Considering the potential risk of exposing patients to X-ray radiations, low-dose CT (LDCT) images have been widely applied in the medical imaging field. Since reducing the radiation dose may result in increased noise and artifacts, methods that can eliminate the noise and artifacts in the LDCT image have drawn increasing attentions and produced impressive results over the past decades. However, recent proposed methods mostly suffer from noise remaining, over-smoothing structures, or false lesions derived from noise. To tackle these issues, we propose a novel degradation adaption local-to-global transformer (DALG-Transformer) for restoring the LDCT image. Specifically, the DALG-Transformer is built on self-attention modules which excel at modeling long-range information between image patch sequences. Meanwhile, an unsupervised degradation representation learning scheme is first developed in medical image processing to learn abstract degradation representations of the LDCT images, which can distinguish various degradations in the representation space rather than the pixel space. Then, we introduce a degradation-aware modulated convolution and gated mechanism into the building modules (i.e., multi-head attention and feed-forward network) of each Transformer block, which can bring in the complementary strength of convolution operation to emphasize on the spatially local context. The experimental results show that the DALG-Transformer can provide superior performance in noise removal, structure preservation, and false lesions elimination compared with five existing representative deep networks. The proposed networks may be readily applied to other image processing tasks including image reconstruction, image deblurring, and image super-resolution.

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Funding

This work was supported the National Natural Science Foundation of China under grant nos. U20A20197, 61973063, 61901098, and 61971118.

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Authors and Affiliations

  1. Northeastern University, NO. 195, Chuangxin Road, Shenyang, China

    Huan Wang, Jianning Chi, Chengdong Wu & Xiaosheng Yu

  2. University of Sydney, Sydney, NSW, 2006, Australia

    Hao Wu

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  1. Huan Wang
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Material preparation, data collection, and analysis were performed by Huan Wang, Jianning Chi, and Chengdong Wu. The first draft of the manuscript was written by Huan Wang and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Chengdong Wu.

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Wang, H., Chi, J., Wu, C. et al. Degradation Adaption Local-to-Global Transformer for Low-Dose CT Image Denoising. J Digit Imaging 36, 1894–1909 (2023). https://doi.org/10.1007/s10278-023-00831-y

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