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A Hybrid Framework of Dual-Domain Signal Restoration and Multi-depth Feature Reinforcement for Low-Dose Lung CT Denoising

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Abstract

Low-dose computer tomography (LDCT) has been widely used in medical diagnosis. Various denoising methods have been presented to remove noise in LDCT scans. However, existing methods cannot achieve satisfactory results due to the difficulties in (1) distinguishing the characteristics of structures, textures, and noise confused in the image domain, and (2) representing local details and global semantics in the hierarchical features. In this paper, we propose a novel denoising method consisting of (1) a 2D dual-domain restoration framework to reconstruct noise-free structure and texture signals separately, and (2) a 3D multi-depth reinforcement U-Net model to further recover image details with enhanced hierarchical features. In the 2D dual-domain restoration framework, the convolutional neural networks are adopted in both the image domain where the image structures are well preserved through the spatial continuity, and the sinogram domain where the textures and noise are separately represented by different wavelet coefficients and processed adaptively. In the 3D multi-depth reinforcement U-Net model, the hierarchical features from the 3D U-Net are enhanced by the cross-resolution attention module (CRAM) and dual-branch graph convolution module (DBGCM). The CRAM preserves local details by integrating adjacent low-level features with different resolutions, while the DBGCM enhances global semantics by building graphs for high-level features in intra-feature and inter-feature dimensions. Experimental results on the LUNA16 dataset and 2016 NIH-AAPM-Mayo Clinic LDCT Grand Challenge dataset illustrate the proposed method outperforms the state-of-the-art methods on removing noise from LDCT images with clear structures and textures, proving its potential in clinical practice.

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Data Availability

The datasets we used in experiments are two public datasets and we have cited at the beginning of "Simulated Dataset" and "Real-world Dataset".

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Funding

This work was supported by the National Natural Science Foundation of China under Grants 61901098 and 61971118, and Science and Technology Plan of Liaoning Province 2021JH1/10400051.

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Authors and Affiliations

  1. Faculty of Robot Science and Engineering, Northeastern University, Zhihui Street, Shenyang, 110169, Liaoning, China

    Jianning Chi, Zhiyi Sun, Huan Wang & Siqi Wang

  2. Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education, Northeastern University, Zhihui Street, Shenyang, 110169, Liaoning, China

    Jianning Chi

  3. Graduate School, Dalian Medical University, Lyushunnan, Dalian, 116000, Liaoning, China

    Shuyu Tian

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  1. Jianning Chi
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Contributions

All authors contributed to the study conception and design. Methodology was presented by Jianning Chi and Zhiyi Sun. Material preparation, data collection and analysis were performed by Jianning Chi, Zhiyi Sun, Shuyu Tian, and Huan Wang. The first draft of the manuscript was written by Zhiyi Sun and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Jianning Chi.

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Chi, J., Sun, Z., Tian, S. et al. A Hybrid Framework of Dual-Domain Signal Restoration and Multi-depth Feature Reinforcement for Low-Dose Lung CT Denoising. J Digit Imaging. Inform. med. (2024). https://doi.org/10.1007/s10278-023-00934-6

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