Deep learning-accelerated T2WI: image quality, efficiency, and staging performance against BLADE T2WI for gastric cancer.

Abstract

Authors

• Qiong Li
  Department of Burns & Wound Care Centre, 2nd Affiliated Hospital of Zhejiang University, College of Medicine, Hangzhou, 310000, Zhejiang Province, China. 2504131@zju.edu.cn.
• Wei-Yue Xu
  Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
• Na-Na Sun
  Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
• Qiu-Xia Feng
  Department of Radiology, The First Affiliated Hospital with Nanjing Medical University, Nanjing, China.
• Ya-Jun Hou
  Guangdong Provincial Key Laboratory of Chemical Measurement and Emergency Test Technology, Guangdong Provincial Engineering Research Center for Ambient Mass Spectrometry, Institute of Analysis, Guangdong Academy of Sciences (China National Analytical Center, Guangzhou), Guangzhou, 510070, China.
• Zi-Tong Sang
  Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
• Zhen-Ning Zhu
  Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
• Yi-Cheng Hsu
  MR Collaboration, Siemens Healthineers Ltd, Shanghai, China.
• Dominik Nickel
  MR Applications Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany.
• Hao Xu
  Department of Nuclear Medicine, the First Affiliated Hospital, Jinan University, Guangzhou 510632, P.R.China.gdhyx2012@126.com.
• Yu-Dong Zhang
  University of Leicester, Leicester, United Kingdom.
• Xi-Sheng Liu
  Department of Radiology, The First Affiliated Hospital with Nanjing Medical University, Nanjing, China.

Keywords

External Resources