MRI mapping of myelin water fraction (MWF), a surrogate of myelin content, has provided important insights into brain maturation and neurodegeneration, with promising potential use to quantify disease progression or therapeutic effect. Besides the complex modeling, MWF imaging, using either conventional or advanced methods such as the BMC-mcDESPOT approach, requires prolonged acquisition times, hampering their integration in clinical investigations. In this proof-of-concept work, we propose an artificial neural network model to derive MWF maps from conventional relaxation times and proton density maps. This work opens a way to further developments for practical and fast MWF imaging.
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