Unlocking 20-fold acceleration towards 0.5-second whole-brain HCP-style fMRI

Functional MRI (fMRI) is acquired with simultaneous multi-slice (SMS) imaging and in-plane acceleration to provide sufficient coverage and spatio-temporal resolutions. However, further accelerations are desirable to achieve BRAIN initiative targets. In this work, we investigate self-supervised deep learning reconstruction at 20-fold (SMS×in-plane=5×4) retrospective and prospective accelerations. Results show DL at 20-fold retrospective acceleration is similar to split slice-GRAPPA at 10-fold acceleration. Furthermore, we show that DL method trained on retrospective 20-fold acceleration generalizes well and successfully reconstructs prospectively 20-fold accelerated fMRI data.