Impact of novel deep learning image reconstruction algorithm on diagnosis of contrast-enhanced liver computed tomography imaging: Comparing to adaptive statistical iterative reconstruction algorithm.

Journal: Journal of X-ray science and technology
Published Date: Jan 1, 2021

Abstract

OBJECTIVE: To assess clinical application of applying deep learning image reconstruction (DLIR) algorithm to contrast-enhanced portal venous phase liver computed tomography (CT) for improving image quality and lesions detection rate compared with using adaptive statistical iterative reconstruction (ASIR-V) algorithm under routine dose.

Authors

  • Shuo Yang
    Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China.
  • Yifan Bie
    Department of Radiology, the Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong Province, China.
  • Guodong Pang
    Department of Radiology, the Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong Province, China.
  • Xingchao Li
    Department of Radiology, the Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong Province, China.
  • Kun Zhao
    Frontier Science Center for Synthetic Biology, Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University Tianjin 300072 P. R. China kunzhao@tju.edu.cn.
  • Changlei Zhang
    Department of Radiology, the Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong Province, China.
  • Hai Zhong
    The Second Hospital of Shandong University, Cheeloo College of Medicine, Shandong University, Jinan 250000, China. Electronic address: 18753107255@163.com.

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