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Abstract #2599

Accuracy and repeatability of joint sparsity multi-component estimations in MR Fingerprinting

Martijn Nagtegaal1, Laura Nunez-Gonzalez2, Dirk Poot2, Jeroen de Bresser3, Thijs van Osch4, Juan Hernandez Tamames2, and Frans Vos1,2
1Imaging Physics, Delft University of Technology, Delft, Netherlands, 2Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, Netherlands, 3Department of Radiology, Leiden University Medical Centre, Leiden, Netherlands, 4C.J. Gorter Center for High Field MRI, Radiology Department, Leiden University Medical Centre, Leiden, Netherlands


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.

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