Abstract
PURPOSE: The aim of this study was to develop an optimization framework to shorten GRE-based MRF sequences while keeping similar parameter estimation quality. METHODS: An optimization framework taking into account steady-state initial longitudinal magnetization, undersampling artifacts, and mitigating overfitting by drawing from a realistic numerical thighs phantom database was developed and validated on numerical simulations and 10 healthy volunteers. RESULTS: The sequences optimized with the proposed framework decreased the original sequence duration by 30% (8 s per repetition instead of 11.2 s) while showing improved accuracy (SSIM going up from 96% to 99% for FF , from 93% to 96% for T1H2O on numerical simulations) and precision, especially when compared with sequences optimized through other means. CONCLUSIONS: The proposed framework paves the way for fast 3D quantification of FF and T1H2O in the skeletal muscle.