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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