Machine learning based quantification of ejection and filling parameters by fully automated dynamic measurement of left ventricular volumes from cardiac magnetic resonance images.

Journal: Magnetic resonance imaging
Published Date: Apr 1, 2020

Abstract

BACKGROUND: Although analysis of cardiac magnetic resonance (CMR) images provides accurate and reproducible measurements of left ventricular (LV) volumes, these measurements are usually not performed throughout the cardiac cycle because of lack of tools that would allow such analysis within a reasonable timeframe. A fully-automated machine-learning (ML) algorithm was recently developed to automatically generate LV volume-time curves. Our aim was to validate ejection and filling parameters calculated from these curves using conventional analysis as a reference.

Authors

  • Neha Goyal
    Department of Medicine, University of Chicago Medical Center, 5758 South Maryland Ave, MC 9067 Room 5513, Chicago, IL, USA.
  • Victor Mor-Avi
    Cardiac Imaging Center, University of Chicago Medical Center, Chicago, Illinois.
  • Valentina Volpato
    Centro Cardiologico Monzino Scientific Institute for Research, Hospitalisation and Health Care (IRCCS) Milan Italy.
  • Akhil Narang
    Cardiac Imaging Center, University of Chicago Medical Center, Chicago, Illinois.
  • Shuo Wang
    College of Tea & Food Science, Anhui Agricultural University, Hefei, China.
  • Michael Salerno
    Department of Medicine, University of Virginia, Charlottesville, VA, USA.
  • Roberto M Lang
    Cardiac Imaging Center, University of Chicago Medical Center, Chicago, Illinois.
  • Amit R Patel
    Cardiac Imaging Center, University of Chicago Medical Center, Chicago, Illinois.

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