Image quality improvement in low-dose chest CT with deep learning image reconstruction.

Journal: Journal of applied clinical medical physics
Published Date: Oct 9, 2022

Abstract

OBJECTIVES: To investigate the clinical utility of deep learning image reconstruction (DLIR) for improving image quality in low-dose chest CT in comparison with 40% adaptive statistical iterative reconstruction-Veo (ASiR-V40%) algorithm.

Authors

  • Qian Tian
    Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, P. R. China.
  • Xinyu Li
    School of Pharmacy, Binzhou Medical University, Yantai, China.
  • Jianying Li
    CT Research Center, GE Healthcare China, Beijing 100176, China.
  • Yannan Cheng
    Department of Radiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi province, PR China.
  • Xinyi Niu
    Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, P. R. China.
  • Shumeng Zhu
    Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, P. R. China.
  • Wenting Xu
    Department of Radiation Convergence Engineering, College of Health Science, Yonsei University, 1 Yonseidae-gil, Wonju, Gangwon, 26493, South Korea.
  • Jianxin Guo
    Department of Radiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi province, PR China.

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