Tricuspid valve flow measurement using a deep learning framework for automated valve-tracking 2D phase contrast.

Journal: Magnetic resonance in medicine
PMID: 38817154

Abstract

PURPOSE: Tricuspid valve flow velocities are challenging to measure with cardiovascular MR, as the rapidly moving valvular plane prohibits direct flow evaluation, but they are vitally important to diastolic function evaluation. We developed an automated valve-tracking 2D method for measuring flow through the dynamic tricuspid valve.

Authors

  • Jérôme Lamy
    Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut, USA.
  • Ricardo A Gonzales
    Oxford University Centre for Clinical Magnetic Resonance Research (OCMR), Level 0, John Radcliffe Hospital, Headington, Oxford OX3 9DU, United Kingdom.
  • Jie Xiang
    College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China.
  • Felicia Seemann
    Cardiovascular Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA.
  • Steffen Huber
    Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA.
  • Jeremy Steele
    Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut, USA.
  • Oliver Wieben
    Departments of Medical Physics and Radiology, University of Wisconsin-Madison, Madison, WI, USA.
  • Einar Heiberg
    Clinical Physiology, Department of Clinical Sciences Lund, Lund University, Skane University Hospital, Lund, Sweden.
  • Dana C Peters
    Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut, USA.

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