Abstract #2626
Learning microstructure parameters from diffusion-weighted MRI using random forests
Gemma Nedjati-Gilani 1 , Matt G Hall 1 , Claudia Angela M Wheeler-Kingshott 2 , and Daniel C Alexander 1
1
Computer Science & Centre for Medical Image
Computing, University College London, London, London,
United Kingdom,
2
Department
of Neuroinflammation, Institute of Neurology, UCL,
London, United Kingdom
Deriving analytical models of the diffusion MR signal
which account for permeability is inherently difficult
and often requires strong assumptions to be made about
the compartmentation of water within the tissue. Given
these problems, in this study we construct a
computational model using Monte Carlo simulations and
machine learning. We use random forest regression to
learn a mapping between simulations and microstructure
parameters and obtain an efficient and accurate model
for microstructure imaging that accounts for
permeability. We show that unseen microstructure
parameters are well-predicted by the random forest
regressor for both noise-free and noisy data.
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