Ten-fold accelerated multi-echo spiral fMRI using self-supervised physics-driven DL reconstruction

Motivation: Multi-echo fMRI holds the promise of more potential applications, however it suffers from long readout lengths. Goal(s): Explore the possibility of using deep learning(DL) reconstruction for highly under-sampled spiral multi-echo fMRI acquisition. Approach: Multi-echo data from four subjects were collected for DL training. Multi-echo fMRI data from another subject was used for testing the DL model. The DL model has been designed and modified to enable the reconstruction of ten-fold under-sampled fMRI images for BOLD analysis. Results: The DL model has not only reconstructed the multi-echo spiral fMRI with good image quality, but also preserved its BOLD sensitivity with the highly under-sampled data. Impact: With the help of DL, multi-echo fMRI may become more versatile for clinical use and future studies.