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Abstract #0448

Development, Validation, and Application of an Automated Deep Learning Workflow for Strain Analysis based on cine-MRI

Manuel A. Morales1,2, Maaike van den Boomen2,3,4, Christopher Nguyen2,4, Jayashree Kalpathy-Cramer2, Bruce R. Rosen1,2, Collin Stultz 1,5,6, David Izquierdo-Garcia1,2, and Ciprian Catana2
1Health Sciences & Technology, Massachusetts Institute of Technology, Cambridge, MA, United States, 2Radiology, Athinoula A. Martinos Center for Biomedical Imaging, MGH, HMS, Charlestown, MA, United States, 3Radiology, University Medical Center Groningen, Groningen, Netherlands, 4Cardiovascular Research Center, MGH, HMS, Charlestown, MA, United States, 5Cardiology, Massachusetts General Hospital, Boston, MA, United States, 6Electrical Engineering and Computer Science, MIT, Cambridge, MA, United States

Myocardial strain analysis from cinematic magnetic resonance imaging (cine-MRI) data is a promising technique for earlier detection of subclinical dysfunction prior to reduction in left-ventricular ejection fraction (LVEF), but sources of discrepancies including user-related variations have limited its wide clinical adoption. Using healthy and cardiovascular disease (CVD) subjects (n=150) we developed a fast, user-independent deep-learning-based workflow for strain analysis from cine-MRI data. Relative to a reference tagging-MRI method, there was no significant difference in end-systolic global strain based on subject-paired cine-MRI data from 15 heathy subjects. Applications in CVD subjects without reduced LVEF showed both global and asymmetric strain abnormalities.

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