Factorized Spatiotemporal Convolutions for Simultaneous Multislice Magnetic Resonance Fingerprinting

Motivation: Quantitative MR imaging with volumetric coverage is relatively slow, which hinders its clinical evaluation. Goal(s): To leverage simultaneous multislice MR Fingerprinting (SMSMRF) and deep learning to achieve high multi-band factors for rapid quantitative imaging. Approach: We introduced a Spatio-Temporal UNet (STUN) method to exploit both spatial and temporal correlations of signals in SMSMRF to largely accelerate data acquisition. Results: High multi-band factors (3 and 4) with an additional 4x acceleration along the temporal dimension were achieved, providing rapid data sampling of 1.5~2 sec per slice for quantitative brain imaging. Impact: The developed SMSMRF method holds great potential to accelerate quantitative imaging for challenging subjects, f.g. pediatric patients.