CRLB-Based Optimization for Combined FISP and PSIF MR Fingerprinting

Magnetic Resonance Fingerprinting (MRF) is a novel quantitative technique that enables simultaneous acquisition of quantitative maps for multiple parameters. Various sequences have been employed in MRF acquisitions. In this work, we propose the utilization of Cramér-Rao Lower bound (CRLB) optimization for the generalized combined FISP and PSIF sequences to enhance the quantitative accuracy in MRF. We derived the signal model under this sequence and performed optimization design on its sequence parameters. The performance of the proposed method was evaluated through both simulation experiments and in vivo experiments. During experiments, highly undersampled trajectory was used to acquired MRF data, and a comparison was made among conventional FISP approach, CRLB-FISP, FISP&PSIF, and our proposed approach. Experimental results demonstrate that the CRLB-optimized generalized combined FISP and PSIF sequence achieve higher accuracy in T
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quantification compared to traditional sequences.