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Abstract #2618

CU-Net: A Completely Complex U-Net for MR k-space Signal Processing

Dipika Sikka1,2, Noah Igra3,4, Sabrina Gjerswold-Sellec1, Cynthia Gao5, Ed Wu6, and Jia Guo7
1Department of Biomedical Engineering, Columbia University, New York, NY, United States, 2VantAI, New York, NY, United States, 3Department of Applied Mathematics, Columbia University, New York, NY, United States, 4Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel, 5Department of Computer Science, Columbia University, New York, NY, United States, 6Department of Electrical and Electronic Engineering, The University of Hong Kong, Pok Fu Lam, Hong Kong, China, 7Department of Psychiatry, Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, United States

While the application of deep learning in MR image analysis has gained significant popularity, using raw MR k-space data as part of deep learning analysis is an underexplored area. Here we develop a completely complex U-Net deep learning architecture, CU-Net, where we apply deep learning components and operations in the complex space. CU-Net leverages k-space MR signals while training a U-Net with Attention and Residual components, as opposed to using processed spatial (real) data, typically seen with MRI deep learning applications. As part of a proof-of-concept study, the complex networks demonstrated their utility and potential superiority over their spatial counterparts.

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