%0 Generic
%A Hill, I
%A Palombo, M
%A Santin, MD
%A Branzoli, F
%A Philippe, A-C
%A Wassermann, D
%A Aigrot, M-S
%A Stankoff, B
%A Zhang, H
%A Lehericy, S
%A Petiet, A
%A Alexander, D
%A Ciccarelli, O
%A Drobnjak, I
%C Concord, CA, USA
%D 2018
%E Miller, KL
%E Port, JD
%F discovery:10074390
%I ISMRM (International Society for Magnetic Resonance in Medicine)
%T Deep neural network based framework for in-vivo axonal permeability estimation
%U https://discovery.ucl.ac.uk/id/eprint/10074390/
%V 2018
%X This study introduces a novel framework for estimating permeability from diffusion-weighted MRI data using deep learning. Recent work introduced a random forest (RF) regressor model that outperforms approximate mathematical models (Kärger model). Motivated by recent developments in machine learning, we propose a deep neural network (NN) approach to estimate the permeability associated with the water residence time. We show in simulations and in in-vivo mouse brain data that the NN outperforms the RF method. We further show that the performance of either ML method is unaffected by the choice of training data, i.e. raw diffusion signals or signal-derived features yield the same results.
%Z This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.