Sequence Optimisation Mitigating Undersampling Errors in Magnetic Resonance Fingerprinting

In MR Fingerprinting acquisitions, the choice of flip angle sequence has a strong effect on the accuracy of the estimated parameter maps. Undersampling artefacts in time-series images are a dominant source of error for standard MRF estimations. We propose to use an undersampling-error model leveraging on perturbation theory, to optimise flip angle patterns taking into account the k-space readout, a realistic ground truth and signal phase. In vivo scans show strong visible reduction in error. Root-mean-square errors in $$$T_1$$$ and $$$T_2$$$ were reduced with at least 6.4%-points and 9%-points respectively, compared to a Cramér-Rao bound optimised and a conventional pattern.