Improved overall image quality in low-dose dual-energy computed tomography enterography using deep-learning image reconstruction.

Journal: Abdominal radiology (New York)
Published Date: Mar 14, 2024

Abstract

OBJECTIVE: To demonstrate the clinical advantages of a deep-learning image reconstruction (DLIR) in low-dose dual-energy computed tomography enterography (DECTE) by comparing images with standard-dose adaptive iterative reconstruction-Veo (ASIR-V) images.

Authors

  • Xu Lin
    College of Chemistry, Sichuan University Chengdu 610064 People's Republic of China xmpuscu@scu.edu.cn +86 028 8541 2290.
  • Yankun Gao
    Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui, China.
  • Chao Zhu
    Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui, China.
  • Jian Song
    School of International Studies, Sun Yat-sen University, Guangzhou, China.
  • Ling Liu
    College of Mathematics and Statistics, Hubei Normal University, Huangshi 435002, China. Electronic address: lliu9308@sina.com.
  • Jianying Li
    CT Research Center, GE Healthcare China, Beijing 100176, China.
  • Xingwang Wu
    Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.

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