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Abstract #3402

Learned Gibbs Removal in Partial Fourier Acquisitions for Diffusion MRI

Matthew J. Muckley1, Antonios Papaioannou1, Benjamin Ades-Aron1, Daniel K. Sodickson1, Yvonne W. Lui1, Els Fieremans1, Dmitry S. Novikov1, and Florian Knoll1

1Radiology, NYU School of Medicine, New York, NY, United States

Despite significant advances in both denoising and Gibbs artifact removal, in acquisitions such as partial Fourier encoding, noise and Gibbs ringing continue to be an issue. Here we demonstrate that a machine learning approach can extend Gibbs ringing and noise removal to partial Fourier image acquisitions and show results on estimates of diffusion parameters on phantom and brain imaging data.

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