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Abstract #2657

Fully Automated Myocardium Strain Analysis using Deep Learning

Xiao Chen1, Masoud Edalati2, Qi Liu2, Xingxian Shou2, Abhishek Sharma1, Mary P. Watkins3, Daniel J. Lenihan3, Linzhi Hu2, Gregory M. Lanza3, Terrence Chen1, and Shanhui Sun1
1United Imaging Intelligence, Cambridge, MA, United States, 2UIH America, Inc., Houston, TX, United States, 3Cardiology, Washington University School of Medicine, St. Louis, MO, United States

Myocardium strain measures myocardial deformation and has been demonstrated a comprehensive, sensitive and early indicator of cardiac dysfunction. Feature tracking can assess myocardium strain from cine CMR images with no special acquisitions but requires observer intervention and expertise. We propose a deep-learning-based fully-automated myocardium strain assessment system that requires zero human intervention for accurate strain assessment.

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