PET/CT for Brain Amyloid: A Feasibility Study for Scan Time Reduction by Deep Learning.

Journal: Clinical nuclear medicine
Published Date: Mar 1, 2021

Abstract

PURPOSE: This study was to develop a convolutional neural network (CNN) model with a residual learning framework to predict the full-time 18F-florbetaben (18F-FBB) PET/CT images from corresponding short-time scans.

Authors

  • Sangwon Lee
    Urban Robotics Laboratory (URL), Dept. Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 305-338, Korea. lsw618@gmail.com.
  • Jin Ho Jung
    Department of Electronic Engineering, Sogang University, Seoul, Korea.
  • Dongwoo Kim
    From the Department of Nuclear Medicine, Yonsei University College of Medicine.
  • Hyun Keong Lim
    Department of Electronic Engineering, Sogang University, Seoul, Korea.
  • Mi-Ae Park
    Department of Radiology, Brigham and Women's Hospital & Harvard Medical School, Boston, MA.
  • Garam Kim
    Department of Electronic Engineering, Sogang University, Seoul, Korea.
  • Minjae So
    Yonsei University College of Medicine.
  • Sun Kook Yoo
    Departments of Medical Engineering.
  • Byoung Seok Ye
    Department of Neurology, Severance hospital, Yonsei University School of Medicine, Seoul, Korea.
  • Yong Choi
    Department of Electronic Engineering, Sogang University, Seoul, Korea.
  • Mijin Yun
    Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea. YUNMIJIN@yuhs.ac.

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