Cardiac motion correction with a deep learning network for perfusion defect assessment in single-photon emission computed tomography myocardial perfusion imaging.

Journal: Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
PMID: 39491716

Abstract

BACKGROUND: In myocardial perfusion imaging (MPI) with single-photon emission computed tomography (SPECT), ungated studies are used for evaluation of perfusion defects despite motion blur. We investigate the potential benefit of motion correction using a deep-learning (DL) network for evaluating perfusion defects.

Authors

  • Xirang Zhang
    Medical Imaging Research Center, Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL, USA.
  • Yongyi Yang
    Medical Imaging Research Center, Illinois Institute of Technology, 3440 S. Dearborn St., Suite 100, Chicago, IL, 60616, USA.
  • P Hendrik Pretorius
  • Piotr J Slomka
    Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California Piotr.Slomka@cshs.org.
  • Michael A King

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