Keywords: Machine Learning/Artificial Intelligence, HeartCurrent gold-standard method for obtaining myocardial strain is based on MRI tagged images, although this increases the MRI exam time and requires special analysis software. We propose to use cine MR images to train a deep neural-network to generate myocardial strain based on target strain maps generated from tagged images acquired at the same locations and timepoints as the cine images. The results showed high agreement between the output and target strain maps. Our method not only saves MRI scan time by acquiring only cine images but also pre and post image processing time by quantifying myocardial strains automatically.
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