Abstract #1369

A transfer learning approach to predict Axon Diameter and g-ratio distributions from MRI Data

Gustavo Chau Loo Kung*^1,2, Emmanuelle M. M. Weber*², Juliet Knowles³, Ankita Batra³, Lijun Ni³, and Jennifer McNab²

¹Bioengineering Department, Stanford University, Stanford, CA, United States, ²Radiology Department, Stanford University, Stanford, CA, United States, ³Neurology Department, Stanford University, Stanford, CA, United States

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Microstructure, Histology, Diffusion Imaging, g-ratio, axon diameterTo better establish the influence of histological features on the MRI signal, we present a multi-task neural network trained to predict parametrized microstructural distributions (axon diameters and g-ratios) from diffusion and magnetization transfer MRI data. To begin, we trained the model using histologically-derived synthetic MRI data before applying transfer learning by fine tuning on empirical data. Our initial results on both synthetic and empirical ex vivo mouse brain MRI data demonstrate the feasibility of this approach.

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