Automated deep learning segmentation of cardiac inflammatory FDG PET.

Journal: Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
PMID: 39368659

Abstract

BACKGROUND: Fluorodeoxyglucose positron emission tomography (FDG PET) with suppression of myocardial glucose utilization plays a pivotal role in diagnosing cardiac sarcoidosis. Reorientation of images to match perfusion datasets and myocardial segmentation enables consistent image scaling and quantification. However, such manual tasks are cumbersome. We developed a 3D U-Net deep-learning (DL) algorithm for automated myocardial segmentation in cardiac sarcoidosis FDG PET.

Authors

  • Alexis Poitrasson-Rivière
    INVIA Medical Imaging Solutions, 3025 Boardwalk St, Suite 200, Ann Arbor, MI, 48108, USA.
  • Michael D Vanderver
    INVIA, LLC, Ann Arbor, MI, USA.
  • Tomoe Hagio
    INVIA Medical Imaging Solutions, 3025 Boardwalk St, Suite 200, Ann Arbor, MI, 48108, USA. thagio@inviasolutions.com.
  • Liliana Arida-Moody
    Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA.
  • Jonathan B Moody
  • Jennifer M Renaud
    INVIA Medical Imaging Solutions, 3025 Boardwalk St, Suite 200, Ann Arbor, MI, 48108, USA.
  • Edward P Ficaro
    INVIA Medical Imaging Solutions, 3025 Boardwalk St, Suite 200, Ann Arbor, MI, 48108, USA.
  • Venkatesh L Murthy
    Department of Cardiology, University of Michigan, Ann Arbor.

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