Multi-modality deep learning-based [Ga]Ga-DOTA-FAPI-04 PET polar map generation: potential value in detecting reactive fibrosis after myocardial infarction.

Journal: European journal of nuclear medicine and molecular imaging
PMID: 39060373

Abstract

PURPOSE: Generating polar map (PM) from [Ga]Ga-DOTA-FAPI-04 PET images is challenging and inaccurate using existing automatic methods that rely on the myocardial anatomical integrity in PET images. This study aims to enhance the accuracy of PM generated from [Ga]Ga-DOTA-FAPI-04 PET images and explore the potential value of PM in detecting reactive fibrosis after myocardial infarction and assessing its relationship with cardiac function.

Authors

  • Xiaoya Qiao
  • Hanzhong Wang
    Department of Nuclear Medicine, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
  • Hongping Meng
    Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Yun Xi
    1 Department of Laboratory Medicine, The Third Affiliated Hospital of Sun Yat-sen University , Guangzhou, China .
  • David Dagan Feng
  • Biao Li
    Key Laboratory of Renewable Energy, Guangdong Key Laboratory of New and Renewable Energy Research and Development, Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou 510640, China.
  • Xiaoxiang Yan
    Department of Cardiology, Rui Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Min Zhang
    Department of Infectious Disease, The Second Xiangya Hospital of Central South University, Changsha, China.
  • Qiu Huang
    Department of Nuclear Medicine, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.

Keywords

External Resources

Popular Topics
Recent Journals