Fully-automated global and segmental strain analysis of DENSE cardiovascular magnetic resonance using deep learning for segmentation and phase unwrapping.

Journal: Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
PMID: 33691739

Abstract

BACKGROUND: Cardiovascular magnetic resonance (CMR) cine displacement encoding with stimulated echoes (DENSE) measures heart motion by encoding myocardial displacement into the signal phase, facilitating high accuracy and reproducibility of global and segmental myocardial strain and providing benefits in clinical performance. While conventional methods for strain analysis of DENSE images are faster than those for myocardial tagging, they still require manual user assistance. The present study developed and evaluated deep learning methods for fully-automatic DENSE strain analysis.

Authors

  • Sona Ghadimi
    Department of Biomedical Engineering, University of Virginia, Health System, Box 800759, Charlottesville, VA, 22908, USA.
  • Daniel A Auger
    Department of Biomedical Engineering, University of Virginia, Health System, Box 800759, Charlottesville, VA, 22908, USA.
  • Xue Feng
    Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia.
  • Changyu Sun
    Department of Biomedical Engineering, University of Virginia, Health System, Box 800759, Charlottesville, VA, 22908, USA.
  • Craig H Meyer
    Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, 22903, USA.
  • Kenneth C Bilchick
    Department of Medicine, University of Virginia Health System, Charlottesville, VA, USA.
  • Jie Jane Cao
    Department of Cardiology, St. Francis Hospital, New York, NY, USA.
  • Andrew D Scott
    Cardiovascular Magnetic Resonance Unit, Royal Brompton Hospital, London, United Kingdom.
  • John N Oshinski
    Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, USA.
  • Daniel B Ennis
    Department of Radiology, Stanford University, Stanford, CA, 94305, USA.
  • Frederick H Epstein
    Department of Biomedical Engineering, University of Virginia, Health System, Box 800759, Charlottesville, VA, 22908, USA. fhe6b@virginia.edu.

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