We introduce a novel off-the-grid sparse approximation algorithm to separate multiple tissue compartments in image voxels and to estimate quantitatively their NMR properties and mixture fractions, given the MR fingerprinting (MRF) measurements. The proposed algorithm is an accurate and importantly a scalable alternative to the multicompartment MRF baselines because it does not rely on fine-gridded multiparametric MRF dictionaries. The method is theoretically described, and its feasibility is demonstrated and compared to other baselines on in-vivo healthy brain measurements.
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