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Abstract #0774

In-vivo application of a trained neural network using a fusion of computational fluid dynamic and 4D flow MRI data

David R Rutkowski1,2, Alejandro Roldán-Alzate1,2,3, and Kevin M Johnson1,4
1Radiology, University of Wisconsin, Madison, WI, United States, 2Mechanical Engineering, University of Wisconsin, Madison, WI, United States, 3Biomedical Engineering, University of Wisconsin, Madison, WI, United States, 4Medical Physics, University of Wisconsin, Madison, WI, United States

Augmentation of 4D flow MRI data with computational fluid dynamics (CFD) -informed training networks may provide a method to produce highly accurate physiological flow fields. In this preliminary work, the potential utility of such a method was demonstrated by using high resolution patient-specific CFD data to train a neural network, and then using the trained network to enhance MRI-derived velocity fields.

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