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Abstract #3450

High-Quality 0.5mm Isotropic Functional MRI Using a Synergistic Combination of NORDIC Denoising and Deep Learning Reconstruction

Omer Burak Demirel1,2, Steen Moeller2, Luca Vizioli2,3, Burhaneddin Yaman1,2, Logan Dowdle2,3, Essa Yacoub2, Kamil Ugurbil2, 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, 3Department of Neurosurgery, University of Minnesota, Minneapolis, MN, United States


Submillimeter fMRI allows studying brain function at the mesoscale level, but scans at such resolutions require trade-offs in SNR and coverage, necessitating better image reconstruction. In this work, we combine NOise Reduction with Distribution Corrected (NORDIC) denoising prior to image reconstruction with self-supervised physics-guided deep learning (PG-DL) for high-quality 0.5mm isotropic fMRI. The former removes components of image series that cannot be distinguished from thermal noise, while the latter enables higher acceleration rates. Results show that the proposed combination of NORDIC and PG-DL improves on NORDIC or PG-DL alone, both visually, and in terms of tSNR and GLM-derived t-maps.

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