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Functional MRI (fMRI) is an important tool for non-invasive studies of brainfunction. Over the past decade, multi-echo fMRI methods that sample multipleecho times has become popular with potential to improve quantification. Whilethese acquisitions are typically performed with Cartesian trajectories,non-Cartesian trajectories, in particular spiral acquisitions, hold promise fordenser sampling of echo times. However, such acquisitions require very highacceleration rates for sufficient spatiotemporal resolutions. In this work, wepropose to use a physics-driven deep learning (PD-DL) reconstruction toaccelerate multi-echo spiral fMRI by 10-fold. We modify a self-supervisedlearning algorithm for optimized training with non-Cartesian trajectories anduse it to train the PD-DL network. Results show that the proposedself-supervised PD-DL reconstruction achieves high spatio-temporal resolutionwith meaningful BOLD analysis.

Non-Cartesian Self-Supervised Physics-Driven Deep Learning Reconstruction for Highly-Accelerated Multi-Echo Spiral fMRI