The Myelin water fraction (MWF) measure provides a direct assessment of myelin content. The widely utilized method is the multicomponent analysis of T2 relaxation time and MWF is determined by the fraction of the fast-relaxing component. However, using either conventional or advanced methods, such as the BMC-mcDESPOT, requires prolonged acquisition and computation times, hampering their integration in clinical investigations. In this proof-of-concept work, we propose artificial neural network models to derive MWF maps from under sampled mcDESPOT data through two distinct approaches. This work opens the way to further developments for practical and rapid MWF imaging.
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