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CN 11-3017/P
Volume 32 Issue 2
Mar.  2023
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ZHANG W J, HUANG G, DING H N, et al. Research Progress of Scattering Artifact Correction in Medical Cone-beam Computed Tomography Imaging Based on Deep Learning[J]. CT Theory and Applications, 2023, 32(2): 285-296. DOI: 10.15953/j.ctta.2022.131. (in Chinese).
Citation: ZHANG W J, HUANG G, DING H N, et al. Research Progress of Scattering Artifact Correction in Medical Cone-beam Computed Tomography Imaging Based on Deep Learning[J]. CT Theory and Applications, 2023, 32(2): 285-296. DOI: 10.15953/j.ctta.2022.131. (in Chinese).
shu

Research Progress of Scattering Artifact Correction in Medical Cone-beam Computed Tomography Imaging Based on Deep Learning

  • 1.

    School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China

  • 2.

    Shanghai Key Laboratory of Molecular Imaging, Jiading District Central Hospital, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China

  • 3.

    Nano Vision (Shanghai) Medical Technology Company Limited, Shanghai 200120, China

More Information
  • Received Date: June 28, 2022
  • Revised Date: October 10, 2022
  • Accepted Date: October 11, 2022
  • Available Online: October 23, 2022
  • Published Date: March 30, 2023
    Graphical Abstract
    • Abstract
  • In medical computed tomography imaging systems, Compton scattered photons generated by the interaction between X-rays and objects have a serious impact on image quality, especially in cone-beam computed tomography and multi-layer detector systems. Currently, there are many scattering artifact correction methods, which can be classified into three categories: hardware, software, and hybrid software and hardware correction methods. However, with the advances in computing power and development of deep learning in medical image processing, new methods of scattering artifact correction have appeared in recent years. This study first introduces traditional correction methods. Then, a method of scattering artifact correction based on deep learning is described in detail, which is divided into the correction method based on image domain and the correction method based on projection domain. Various deep-learning neural networks for this method are also introduced in detail. Finally, the application prospects of the deep learning method in multi-source computed tomography imaging scattering artifacts were probed .
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