Improved robustness for deep learning-based segmentation of multi-center myocardial perfusion cardiovascular MRI datasets using data-adaptive uncertainty-guided space-time analysis.

Journal: Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
PMID: 39142567

Abstract

BACKGROUND: Fully automatic analysis of myocardial perfusion cardiovascular magnetic resonance imaging datasets enables rapid and objective reporting of stress/rest studies in patients with suspected ischemic heart disease. Developing deep learning techniques that can analyze multi-center datasets despite limited training data and variations in software (pulse sequence) and hardware (scanner vendor) is an ongoing challenge.

Authors

  • Dilek M Yalcinkaya
    Laboratory for Translational Imaging of Microcirculation, Indiana University School of Medicine, Indianapolis, Indiana, USA; Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana, USA.
  • Khalid Youssef
  • Bobak Heydari
    Stephenson Cardiac Imaging Centre, Department of Cardiac Sciences, University of Calgary, Calgary, Alberta, Canada.
  • Janet Wei
    Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
  • C Noel Bairey Merz
  • Robert Judd
    Division of Cardiology, Department of Medicine, Duke University, Durham, North Carolina, USA.
  • Rohan Dharmakumar
  • Orlando P Simonetti
    Departments of Radiology and Medicine, Davis Heart and Lung Research Institute, The Ohio State University, Columbus, Ohio, USA.
  • Jonathan W Weinsaft
    Department of Radiology, Weill Cornell Medicine, 525 E 68th St, New York, NY, 10065, USA. jww2001@med.cornell.edu.
  • Subha V Raman
    Indiana University Cardiovascular Institute and Krannert Cardiovascular Research Center, Indianapolis, IN, USA.
  • Behzad Sharif

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