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Abstract #0771

Cardiac Tag Tracking with Deep Learning Trained with Comprehensive Synthetic Data Generation

Michael Loecher1, Luigi E Perotti2, and Daniel B Ennis1,3,4,5
1Radiology, Stanford, Palo Alto, CA, United States, 2Mechanical Engineering, University of Central Florida, Orlando, FL, United States, 3Radiology, Veterans Administration Health Care System, Palo Alto, CA, United States, 4Cardiovascular Institute, Stanford, Palo Alto, CA, United States, 5Center for Artificial Intelligence in Medicine & Imaging, Stanford, Palo Alto, CA, United States

A convolutional neural network based tag tracking method for cardiac grid-tagged data was developed and validated. An extensive synthetic data simulator was created to generate large amounts of training data from natural images with analytically known ground-truth motion. The method was validated using both a digital cardiac deforming phantom and tested using in vivo data. Very good agreement was seen in tag locations (<1.0mm) and calculated strain measures (<0.02 midwall Ecc)

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