Abstract #0705

Machine Learned Wave Encoded Neurovascular 4D Flow

Chenwei Tang¹, Leonardo A Rivera-Rivera^1,2, Laura B Eisenmenger³, and Kevin M Johnson^1,3

¹Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, United States, ²Department of Medicine, University of Wisconsin-Madison, Madison, WI, United States, ³Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States

Synopsis

Keywords: Blood vessels, Velocity & Flow

Keywords: Wave Encoding, Trajectory Optimization, 4D Flow

Non-Cartesian sampling is often required for 4D Flow imaging because of more efficient sampling. Due to the heuristic nature of the optimization of such trajectories, we propose to parameterize and optimize wave encoded 3D Cartesian sampling using a gradient descent algorithm in a data-driven way. We demonstrate the feasibility of our framework in learning the sampling patterns and the wave parameters and providing high image quality for highly accelerated scans in digital phantoms, phantoms and in vivo with phase contrast.

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