Fully Automatic Quantitative Measurement of Equilibrium Radionuclide Angiocardiography Using a Convolutional Neural Network.

Journal: Clinical nuclear medicine
PMID: 38967505

Abstract

PURPOSE: The aim of this study was to generate deep learning-based regions of interest (ROIs) from equilibrium radionuclide angiography datasets for left ventricular ejection fraction (LVEF) measurement.

Authors

  • Sejin Ha
    From the Department of Nuclear Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea.
  • Seung Yeon Seo
    Department of Nuclear Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, South Korea.
  • Byung Soo Park
    Department of Nuclear Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-Ro 43-Gil, Songpa-Gu, Seoul, 05505, Korea.
  • Sangwon Han
    Department of Nuclear Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Korea.
  • Jungsu S Oh
    Department of Nuclear Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea.
  • Sun Young Chae
    Department of Nuclear Medicine, Uijeongbu Eulji Medical Center, Eulji University School of Medicine, Uijeongbu, Republic of Korea.
  • Jae Seung Kim
    Department of Nuclear Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea. jaeskim@amc.seoul.kr.
  • Dae Hyuk Moon
    From the Department of Nuclear Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea.

Keywords

External Resources

Popular Topics
Recent Journals