Sinogram domain metal artifact correction of CT via deep learning.

Journal: Computers in biology and medicine
Published Date: Feb 20, 2023

Abstract

PURPOSE: Metal artifacts can significantly decrease the quality of computed tomography (CT) images. This occurs as X-rays penetrate implanted metals, causing severe attenuation and resulting in metal artifacts in the CT images. This degradation in image quality can hinder subsequent clinical diagnosis and treatment planning. Beam hardening artifacts are often manifested as severe strip artifacts in the image domain, affecting the overall quality of the reconstructed CT image. In the sinogram domain, metal is typically located in specific areas, and image processing in these regions can preserve image information in other areas, making the model more robust. To address this issue, we propose a region-based correction of beam hardening artifacts in the sinogram domain using deep learning.

Authors

  • Yulin Zhu
    School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, Guangdong 518055, China.
  • Hanqing Zhao
    Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
  • Tangsheng Wang
    Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
  • Lei Deng
    1] Center for Brain Inspired Computing Research (CBICR), Department of Precision Instrument, Tsinghua University, Beijing 100084, China [2] Optical Memory National Engineering Research Center, Department of Precision Instrument, Tsinghua University, Beijing 100084, China.
  • Yupeng Yang
    Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
  • Yuming Jiang
    Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China.
  • Na Li
    School of Nursing, Fujian University of Traditional Chinese Medicine, Fuzhou, China.
  • Yinping Chan
    Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
  • Jingjing Dai
    Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, China.
  • Chulong Zhang
    Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, China.
  • Yunhui Li
    Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
  • Yaoqin Xie
    Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China.
  • Xiaokun Liang

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