Abstract #0774

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

David R Rutkowski^1,2, Alejandro Roldán-Alzate^1,2,3, and Kevin M Johnson^1,4

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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