Low-dose CT urography using deep learning image reconstruction: a prospective study for comparison with conventional CT urography.

Journal: The British journal of radiology
PMID: 33571034

Abstract

OBJECTIVES: To compare the image quality of low-dose CT urography (LD-CTU) using deep learning image reconstruction (DLIR) with conventional CTU (C-CTU) using adaptive statistical iterative reconstruction (ASIR-V).

Authors

  • Yannan Cheng
    Department of Radiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi province, PR China.
  • Yangyang Han
    Department of Radiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi province, PR China.
  • Jianying Li
    CT Research Center, GE Healthcare China, Beijing 100176, China.
  • Ganglian Fan
    Department of Radiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi province, PR China.
  • Le Cao
    Department of Radiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi province, PR China.
  • Junjun Li
  • Xiaoqian Jia
    Department of Radiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi province, PR China.
  • Jian Yang
    Drug Discovery and Development Research Group, College of Pharmacy and Nutrition, University of Saskatchewan, Saskatoon, SK, Canada.
  • Jianxin Guo
    Department of Radiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi province, PR China.

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