Deep learning trained algorithm maintains the quality of half-dose contrast-enhanced liver computed tomography images: Comparison with hybrid iterative reconstruction: Study for the application of deep learning noise reduction technology in low dose.

Journal: European journal of radiology
Published Date: Jan 5, 2021

Abstract

PURPOSE: This study compares the image and diagnostic qualities of a DEep Learning Trained Algorithm (DELTA) for half-dose contrast-enhanced liver computed tomography (CT) with those of a commercial hybrid iterative reconstruction (HIR) method used for standard-dose CT (SDCT).

Authors

  • Lingming Zeng
    Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.
  • Xu Xu
    College of Chemistry, Liaoning University, Shenyang, 110036, China.
  • Wen Zeng
    Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.
  • Wanlin Peng
    Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.
  • Jinge Zhang
    Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.
  • Hu Sixian
    Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.
  • Keling Liu
    Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.
  • Chunchao Xia
    Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.
  • Zhenlin Li
    Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, P.R.China.

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