Deep learning model for low-dose CT late iodine enhancement imaging and extracellular volume quantification.

Journal: European radiology
Published Date:

Abstract

OBJECTIVES: To develop and validate deep learning (DL)-models that denoise late iodine enhancement (LIE) images and enable accurate extracellular volume (ECV) quantification.

Authors

  • Yarong Yu
    Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, #85 Wujin Rd, Shanghai, 200080, China.
  • Dijia Wu
  • Ziting Lan
    Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, #85 Wujin Rd, Shanghai, China 200080.
  • Xiaoting Dai
    Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Wenli Yang
    Discipline of Information & Communication Technology, School of Technology, Environments & Design, University of Tasmania Sandy Bay Campus, Launceston, Australia. yang.wenli@utas.edu.au.
  • Jiajun Yuan
    Shanghai Engineering Research Center of Intelligence Pediatrics (SERCIP), Shanghai, 200127, People's Republic of China.
  • Zhihan Xu
    Siemens Healthineers China, Shanghai, China.
  • Jiayu Wang
    Department of Cardiology, the Second Hospital of Shandong University, 250033 Jinan, Shandong, China.
  • Ze Tao
    Shanghai United Imaging Intelligence, Shanghai, China.
  • Runjianya Ling
    From the Departments of Radiology (M.L., LY., J.Z.) and Cardiology (W.Y.), Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, 85 Wujin Rd, Shanghai 200080, China; Institute of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China (R.L.); and Shanghai United Imaging Intelligence, Shanghai, China (Z.C., D.W.).
  • Su Zhang
  • Jiayin Zhang
    Department of Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.