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Abstract #4780

Neural style transfer: Applications in cardiac MR image registration

Alper Ozan Turgut1, Matthew Van Houten2, Junyu Wang2, Xue Feng2, and Michael Salerno3
1School of Medicine, University of Virginia, Charlottesville, VA, United States, 2Biomedical Engineering, University of Virginia, Charlottesville, VA, United States, 3Department of Medicine and Radiology, Stanford University, Stanford, CA, United States


Contrast-enhanced cardiac magnetic resonance (CMR) stress perfusion imaging shows excellent utility in evaluating coronary artery disease1. Registering perfusion CMR image series is difficult due to the varying image contrast. Neural style transfer is a deep learning method used to transfer the “style” of one domain to another while preserving the content. Two neural style transfer networks were implemented in Python using TensorFlow and PyTorch. Training of each network was done using three, slice matched patient profiles and cine-like perfusion images were generated and registered. This method is compared to a KL-transform based registration approach.

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