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Abstract #2171

Myelin water fraction determination from relaxation times and proton density through deep learning neural network

Nikkita Khattar1, Zhaoyuan Gong1, Matthew Kiely1, Curtis Triebswetter1, Maryam H. Alsameen1, and Mustapha Bouhrara1
1Laboratory of Clinical Investigation, National Institute on Aging, Baltimore, MD, United States

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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