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Abstract #0457

Improved Accelerated fMRI Reconstruction using Self-supervised Deep Learning

Omer Burak Demirel1,2, Burhaneddin Yaman1,2, Steen Moeller2, Logan Dowdle2, Luca Vizioli2, Kendrick Kay2, Essa Yacoub2, John Strupp2, Cheryl Olman2, Kâmil Uğurbil2, and Mehmet Akçakaya1,2
1Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, United States, 2Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States

There are significant benefits to HCP-style fMRI acquisitions, which acquires high spatial and temporal resolution across the whole brain in an effort to better understand the human brain. This can be achieved through simultaneous multi-slice (SMS)/Multiband (MB) imaging, which provides rapid whole-brain coverage using high acceleration rates albeit with increased noise amplification. Deep learning reconstruction techniques have recently gained substantial interest in improving accelerated MRI. Here we utilize a physics-guided self-supervised deep learning reconstruction on a 5-fold SMS and 2-fold in-plane accelerated whole brain 7T fMRI acquisition to reduce the reconstruction noise without altering the subsequent fMRI result.

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