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Abstract #2237

Accelerated Phase Contrast MRI Reconstruction with Deep U-NET Convolutional Neural Networks

Ruponti Nath1, Sean Callahan1, Narayana Singam2, Marcus Stoddard2, and Amir Amini1
1ECE, University of Louisville, Louisville, KY, United States, 2Cardiovascular Medicine, University of Louisville, Louisville, KY, United States

We propose a framework for accelerated reconstruction of 2D phase contrast MRI from undersampled K space by using deep convolutional neural networks. The reconstruction problem is considered as a de-aliasing problem in complex spatial domain. A U-net architecture was trained and tested on 4D flow MRI data in 10 patients with aortic stenosis and 4 healthy volunteers. The reconstructed complex two channel image showed that the U-net is able to unaliase the undersampled flow images with resulting magnitude and phase difference images showing good agreement with the fully sampled magnitude and phase images.

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