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Abstract #1012

A Learned Phase Correction Algorithm Outperforms a Conventional Filter for 3D Vector MRE

Jonathan M Trevathan1, Jonathan M Scott1, Joshua D Trzasko1, Armando Manduca1, John Huston1, Richard L Ehman1, and Matthew C Murphy1
1Mayo Clinic, Rochester, MN, United States

Bulk motion during the motion encoding of multi-slice spin-echo EPI pulse sequences used to acquire 3D MRE data creates a slice-to-slice phase jitter. A 3D convolutional neural network was trained to estimate a phasor corresponding to noise-free displacements given noisy, complex-valued MRE data. The net outperformed its filtering counterpart in noisy and noise-free simulation data, and decreased test-retest repeatability error in vivo.

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