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Abstract #2772

Quantitative Synthetic T1 Mapping of the Brain from Structural Imaging using Deep Learning

Samuel Anthony Hurley1,2, Jacob M Johnson1, Barbara B Bendlin3, and Alan B McMillan1

1Radiology, University of Wisconsin, Madison, WI, United States, 2Neuroscience, University of Wisconsin, Madison, WI, United States, 3Medicine, University of Wisconsin, Madison, WI, United States

We propose a method to generate synthetic T1 maps directly from conventional T1-weighted imaging. Rather than rely on fitting an explicit signal model or precomputing a dictionary from a closed form equation (e.g. Bloch equations or extended phase graph), we employ deep learning combined with training data from variable flip angle (VFA) T1 mapping experiments to generate an implicit machine learning model of T1 signal. The use of deep learning to enable quantitative imaging directly from an acquired T1-weighted image is a provocative approach with promising capability, as demonstrated herein with less than 3% error compared to a VFA approach.

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