Accuracy and Repeatability of Joint Sparsity Multi-Component Estimations in MR Fingerprinting

Multi-component MR Fingerprinting estimations with Sparsity Promoting Iterative Joint Non-negative least squares (SPIJN-MRF) provide the possibility to obtain partial volume tissue segmentations and myelin water maps. We evaluated the accuracy and repeatability of SPIJN-MRF estimations in simulations and 5 subjects, scanned 8 times. The obtained segmentations were compared to segmentations from SPM12 and FSL. In simulations, SPIJN-MRF showed the highest accuracy in total volume and voxel-wise measures. In vivo, higher variation was observed with SPIJN-MRF than with SPM12 and FSL, especially in WM and GM. In conclusion, SPIJN-MRF provides accurate and precise tissue relaxation parameter estimations with partial volume estimations.