Polar map-free 3D deep learning algorithm to predict obstructive coronary artery disease with myocardial perfusion CZT-SPECT.

Journal: European journal of nuclear medicine and molecular imaging
PMID: 36102963

Abstract

PURPOSE: Deep learning (DL) models have been shown to outperform total perfusion deficit (TPD) quantification in predicting obstructive coronary artery disease (CAD) from myocardial perfusion imaging (MPI). However, previously published methods have depended on polar maps, required manual correction, and normal database. In this study, we propose a polar map-free 3D DL algorithm to predict obstructive disease.

Authors

  • Chi-Lun Ko
    Department of Biomedical Engineering, National Taiwan University, Taipei, Taiwan.
  • Shau-Syuan Lin
    Department of Biomedical Engineering, National Taiwan University, Taipei, Taiwan.
  • Cheng-Wen Huang
    Department of Biomedical Engineering, National Taiwan University, Taipei, Taiwan.
  • Yu-Hui Chang
    PharmD Program, Division of Clinical Pharmacy, College of Pharmacy, Taipei Medical University, Taipei, Taiwan.
  • Kuan-Yin Ko
    Department of Nuclear Medicine, National Taiwan University Cancer Center, Taipei, Taiwan.
  • Mei-Fang Cheng
    Department of Nuclear Medicine, National Taiwan University Hospital, Taipei, Taiwan.
  • Shan-Ying Wang
    Department of Nuclear Medicine, Far Eastern Memorial Hospital, New Taipei City, Taiwan.
  • Chung-Ming Chen
    Institute of Biomedical Engineering, National Taiwan University, Taipei 100, Taiwan.
  • Yen-Wen Wu
    Department of Nuclear Medicine, National Taiwan University Hospital, Taipei, Taiwan. wuyw0502@gmail.com.

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