Abstract #1296

Accelerated 4D-flow MRI using Machine Learning (ML) Enabled Three Point Flow Encoding

Dahan Kim^1,2, Laura Eisenmenger³, and Kevin M. Johnson^3,4

¹Department of Medical Physics, University of Wisconsin, Madison, WI, United States, ²Department of Physics, University of Wisconsin, Madison, WI, United States, ³Department of Radiology, University of Wisconsin, Madison, WI, United States, ⁴Department of Medical Physics, University of Wisconsin, Middleton, WI, United States

4D-flow MRI suffers from long scan time due to a minimum of four velocity encodings necessary to solve for three velocity components and the reference background phase. We examine the feasibility of using machine learning (ML) to determine the background phase and hence three velocity components from only three flow encodings. The results show that ML is capable of estimating three-directional velocities from three flow encodings with high accuracy (1.5%-3.8% velocity underestimation) and high precision (R²=0.975). These findings indicate that 4D-flow MRI can be accelerated without requiring a dedicated reference scan, with a scan time reduction of 25%.

This abstract and the presentation materials are available to members only; a login is required.

Join Here