Quantitative MRI and especially MR Fingerprinting make use of complicated acquisition schemes and signal models to measure tissues parameters. The sequence choice is crucial for the noise robustness for parameter estimations of different tissues. Multi-component (MC) signal models for MRF are of importance to estimate partial volume effects or myelin water fractions for example. We propose to use the Cramér-Rao bound to assess and optimise the multi-component parameter estimations for MRF. The optimised flip angle and TR patterns for MC-MRF were highly structured which was also observed for the optimisation based on the single-component model, but structural differences were noticed.
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