Automatic reorientation by deep learning to generate short-axis SPECT myocardial perfusion images.

Journal: Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
PMID: 36859594

Abstract

BACKGROUND: Single photon emission computed tomography (SPECT) myocardial perfusion images (MPI) can be displayed both in traditional short-axis (SA) cardiac planes and polar maps for interpretation and quantification. It is essential to reorient the reconstructed transaxial SPECT MPI into standard SA slices. This study is aimed to develop a deep-learning-based approach for automatic reorientation of MPI.

Authors

  • Fubao Zhu
    School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, Henan, China.
  • Guojie Wang
    School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450000, Henan, China.
  • Chen Zhao
    Department of Ophthalmology, Fudan Eye & ENT Hospital, Shanghai, China.
  • Saurabh Malhotra
    Division of Cardiology, Cook County Health and Hospitals System, Chicago, IL, USA.
  • Min Zhao
    Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Zhuo He
    College of Computing, Michigan Technological University, 1400 Townsend Drive, Houghton, MI, USA.
  • Jianzhou Shi
    Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Guangzhou Road 300, Nanjing, 210029, Jiangsu, China.
  • Zhixin Jiang
    Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Guangzhou Road 300, Nanjing, 210029, Jiangsu, China. zhixin_jiang@njmu.edu.cn.
  • Weihua Zhou
    School of Computing, University of Southern Mississippi, Hattiesburg, MS, United States of America.

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