Abstract #0448

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

Manuel A. Morales^1,2, Maaike van den Boomen^2,3,4, Christopher Nguyen^2,4, Jayashree Kalpathy-Cramer², Bruce R. Rosen^1,2, Collin Stultz ^1,5,6, David Izquierdo-Garcia^1,2, 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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