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Abstract #1561

Sequence Optimisation for Multi-Component Analysis in Magnetic Resonance Fingerprinting

David Heesterbeek1,2, Frans Vos1,3, Martin van Gijzen2, and Martijn Nagtegaal1
1Department of Imaging Physics, Delft University of Technology, Delft, Netherlands, 2Department of Numerical Analysis, Delft University of Technology, Delft, Netherlands, 3Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, Netherlands

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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