Slice-based combination of rest and dobutamine-stress cardiac MRI using a statistical motion model to identify myocardial infarction: validation against contrast-enhanced MRI

  • Authors:

  • Avan Suinesiaputra;Alejandro F. Frangi;Theodorus A. M. Kaandorp;Hildo J. Lamb;Jeroen J. Bax;Johan H. C. Reiber;Boudewijn P. F. Lelieveldt

  • Affiliations:

  • Dept. of Radiology, Leiden University of Medical Center, Leiden, the Netherlands;Center of Computational Imaging and Simulation Technologies in Biomedicine, Universitat Pompeu Fabra, Barcelona, Spain;Dept. of Radiology, Leiden University of Medical Center, Leiden, the Netherlands;Dept. of Radiology, Leiden University of Medical Center, Leiden, the Netherlands;Dept. of Cardiology, Leiden University of Medical Center, Leiden, the Netherlands;Dept. of Radiology, Leiden University of Medical Center, Leiden, the Netherlands;Dept. of Radiology, Leiden University of Medical Center, Leiden, the Netherlands and Dept. of Mediamatics, Delft University of Technology, Delft, the Netherlands

  • Venue:
  • FIMH'11 Proceedings of the 6th international conference on Functional imaging and modeling of the heart
  • Year:
  • 2011

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Abstract

This paper presents an automated method for regional wall motion abnormality detection (RWMA) from rest and stress cardiac MRI. The automated RWMA detection is based on a statistical shape model of myocardial contraction trained on slice-based myocardial contours from in ED and ES. A combination of rigid and non-rigid registrations is introduced to align a patient shape to the normokinetic myocardium model, where pure contractility information is kept. The automated RWMA method is applied to identify potentially infarcted myocardial segments from rest-stress MRI alone. In this study, 41 cardiac MRI studies of healthy subjects were used to build the statistical normokinetic model, while 12 myocardial infarct patients were included for validation. The rest-stress data produced a better separation between scar and normal segments compared to the rest-only data. The sensitivity, specificity and accuracy were increased by 34%, 30%, and 32%, respectively. The area under the ROC curve for the rest-stress data was improved to 0.87 compared to 0.63 for the rest-only data.