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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