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Abstract #2096

Deep 2D Residual Attention U-net for Accelerated 4D Flow MRI of Aortic Valvular Flows

Ruponti Nath1, Sean Callahan1, Marcus Stoddard2, and Amir Amini1
1ECE, University of Louisville, Louisville, KY, United States, 2Department of Medicine, University of Louisville, Louisville, KY, United States

We propose a novel deep learning-based approach for accelerated 4D Flow MRI by reducing artifact in complex image domain from undersampled k-space. A deep 2D residual attention network is proposed which is trained independently for three velocity-sensitive encoding directions to learn the mapping of complex image from zero-filled reconstruction to complex image from fully sampled k-space. Network is trained and tested on 4D flow MRI data of aortic valvular flow in 18 human subjects. Proposed method outperforms state of the art TV regularized reconstruction method and deep learning reconstruction approach by U-net.

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