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Noise suppression–guided image filtering for low-SNR CT reconstruction

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Abstract

In practical computed tomography (CT) applications, projections with low signal-to-noise ratio (SNR) are often encountered due to the reduction of radiation dose or device limitations. In these situations, classical reconstruction algorithms, like simultaneous algebraic reconstruction technique (SART), cannot reconstruct high-quality CT images. Block-matching and 3D filtering (BM3D)-based iterative reconstruction algorithm (POCS-BM3D) has remarkable effect in dealing with CT reconstruction from noisy projections. However, BM3D may restrain noise with excessive loss of details in the case of low-SNR CT reconstruction. In order to achieve a preferable trade-off between noise suppression and edge preservation, we introduce guided image filtering (GIF) into low-SNR CT reconstruction, and propose noise suppression–guided image filtering reconstruction (NSGIFR) algorithm. In each iteration of NSGIFR, the output image of SART reserves more details and is used as input image of GIF, while the image denoised by BM3D serves as guidance image of GIF. Experimental results indicate that the proposed algorithm displays outstanding performance on preserving structures and suppressing noise for low-SNR CT reconstruction. NSGIFR can achieve more superior image quality than SART, POCS-TV and POCS-BM3D in terms of visual effect and quantitative analysis.

Block-matching and 3D filtering (BM3D)-based iterative reconstruction algorithm (POCS-BM3D) has remarkable effect in dealing with CT reconstruction from noisy projections. However, BM3D may restrain noise with excessive loss of details in the case of low-SNR CT reconstruction. In order to achieve a preferable trade-off between noise suppression and edge preservation, we introduce guided image filtering (GIF) into low-SNR CT reconstruction, and propose noise suppression–guided image filtering reconstruction (NSGIFR) algorithm.

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Funding

This work was supported in part by Graduate Scientific Research and Innovation Foundation of Chongqing, China, under Grant CYS19026; the National Natural Science Foundation of China under Grants 61771003 and 61701174; and Natural Science Foundation of Hubei Province under Grant 2017CFB168.

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Authors and Affiliations

  1. College of Mathematics and Statistics, Chongqing University, Chongqing, 401331, China

    Yuanwei He & Li Zeng

  2. Engineering Research Center of Industrial Computed Tomography Nondestructive Testing of the Education Ministry of China, Chongqing University, Chongqing, 400044, China

    Yuanwei He, Li Zeng & Changcheng Gong

  3. School of Biomedical Engineering, Hubei University of Science and Technology, Xianning, 437100, China

    Wei Yu

  4. Key Laboratory of Optoelectronic Technology and System of the Education Ministry of China, Chongqing University, Chongqing, 400044, China

    Changcheng Gong

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  1. Yuanwei He
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  2. Li Zeng
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  3. Wei Yu
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  4. Changcheng Gong
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Correspondence to Li Zeng.

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He, Y., Zeng, L., Yu, W. et al. Noise suppression–guided image filtering for low-SNR CT reconstruction. Med Biol Eng Comput 58, 2621–2629 (2020). https://doi.org/10.1007/s11517-020-02246-1

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  • DOI: https://doi.org/10.1007/s11517-020-02246-1

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