Abstract #0771

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

Michael Loecher¹, Luigi E Perotti², and Daniel B Ennis^1,3,4,5

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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