  Functional MRI (fMRI) is an important tool for non-invasive studies of brain
function. Over the past decade, multi-echo fMRI methods that sample multiple
echo times has become popular with potential to improve quantification. While
these acquisitions are typically performed with Cartesian trajectories,
non-Cartesian trajectories, in particular spiral acquisitions, hold promise for
denser sampling of echo times. However, such acquisitions require very high
acceleration rates for sufficient spatiotemporal resolutions. In this work, we
propose to use a physics-driven deep learning (PD-DL) reconstruction to
accelerate multi-echo spiral fMRI by 10-fold. We modify a self-supervised
learning algorithm for optimized training with non-Cartesian trajectories and
use it to train the PD-DL network. Results show that the proposed
self-supervised PD-DL reconstruction achieves high spatio-temporal resolution
with meaningful BOLD analysis.
