Temporal Low-Rank based k-space Sampling Pattern Optimization for MR Fingerprinting

Motivation: MRF can estimate tissue parameters with high efficiency, requiring optimization of sequence parameters alongside k-space sampling patterns. A comprehensive optimization framework was not established yet. Goal(s): Develop a framework for optimizing k-space sampling and understanding reconstruction errors for MRF using temporal low-rank reconstruction. Approach: We quantify MRF performance with the condition number of temporal low-rank system matrices and show suitability in simulation and phantom experiments. Results: We derive optimality-criteria for schedule and sampling, and provide an algorithm for sampling optimization. We demonstrate that systematic deviations from the signal model are a major source of errors in MRF, and address these with center-weighted sampling. Impact: Our results are relevant for researchers interested in the fundamental understanding of MR Fingerprinting. Our theory helps designing MRF sequences, guiding future aspirations to jointly optimize sampling and flip-angle schedule, and identifying significant sources of errors in existing implementations.