[Noise Reduction Effect of Deep-learning-based Image Reconstruction Algorithms in Thin-section Chest CT].

Journal: Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition
Published Date: Mar 1, 2021

Abstract

OBJECTIVE: To evaluate the noise reduction effect of deep learning-based reconstruction algorithms in thin-section chest CT images by analyzing images reconstructed with filtered back projection (FBP), adaptive statistical iterative reconstruction (ASIR), and deep learning image reconstruction (DLIR) algorithms.

Authors

  • Wen Zeng
    Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.
  • Ling-Ming Zeng
    Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China.
  • Xu Xu
    College of Chemistry, Liaoning University, Shenyang, 110036, China.
  • Si-Xian Hu
    Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China.
  • Ke-Ling Liu
    Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China.
  • Jin-Ge Zhang
    Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China.
  • Wan-Lin Peng
    Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China.
  • Chun-Chao Xia
    Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China.
  • Zhen-Lin Li
    Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China.

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