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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