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Purpose: To investigate the feasibility of myelin water contentquantification using fast dual-echo steady-state (DESS) scans and machinelearning with kernels.  Methods: We optimized combinations of steady-state (SS) scans for preciselyestimating the fast-relaxing signal fraction ff of a two-compartment signalmodel, subject to a scan time constraint. We estimated ff from the optimizedDESS acquisition using a recently developed method for rapid parameterestimation via regression with kernels (PERK). We compared DESS PERK ffestimates to conventional myelin water fraction (MWF) estimates from a longermulti-echo spin-echo (MESE) acquisition in simulation, in vivo, and ex vivostudies.  Results: Simulations demonstrate that DESS PERK ff estimators and MESE MWFestimators achieve comparable error levels. In vivo and ex vivo experimentsdemonstrate that MESE MWF and DESS PERK ff estimates are quantitativelycomparable measures of WM myelin water content. To our knowledge, theseexperiments are the first to demonstrate myelin water images from a SSacquisition that are quantitatively similar to conventional MESE MWF images.  Conclusion: Combinations of fast DESS scans can be designed to enable preciseff estimation. PERK is well-suited for ff estimation. DESS PERK ff and MESE MWFestimates are quantitatively similar measures of WM myelin water content.

Fast, Precise Myelin Water Quantification using DESS MRI and Kernel Learning