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A comprehensive survey on deep learning techniques in CT image quality improvement

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Abstract

High-quality computed tomography (CT) images are key to clinical diagnosis. However, the current quality of an image is limited by reconstruction algorithms and other factors and still needs to be improved. When using CT, a large quantity of imaging data, including intermediate data and final images, that can reflect important physical processes in a statistical sense are accumulated. However, traditional imaging techniques cannot make full use of them. Recently, deep learning, in which the large quantity of imaging data can be utilized and patterns can be learned by a hierarchical structure, has provided new ideas for CT image quality improvement. Many researchers have proposed a large number of deep learning algorithms to improve CT image quality, especially in the field of image postprocessing. This survey reviews these algorithms and identifies future directions.

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Funding

This study received funding from the Fundamental Research Funds for the Central University of China (N2024005-2).

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Authors and Affiliations

  1. College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110819, China

    Disen Li, Limin Ma, Jining Li, Shouliang Qi & Yueyang Teng

  2. Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, 07039, USA

    Yudong Yao

  3. Key Laboratory of Medical Image Intelligent Computing, Ministry of Education, Shenyang, 110169, China

    Yueyang Teng

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  1. Disen Li
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  2. Limin Ma
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Li, D., Ma, L., Li, J. et al. A comprehensive survey on deep learning techniques in CT image quality improvement. Med Biol Eng Comput 60, 2757–2770 (2022). https://doi.org/10.1007/s11517-022-02631-y

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