Abstract #0774

Deep neural network based framework for in-vivo axonal permeability estimation

Ioana Diana Hill¹, Marco Palombo¹, Mathieu David Santin^2,3, Francesca Branzoli^2,3, Anne-Charlotte Philippe^2,3, Demian Wassermann^4,5, Marie-Stephane Aigrot², Bruno Stankoff^2,6, Hui Zhang¹, Stephane Lehericy^2,7,8, Alexandra Petiet^2,7, Daniel C. Alexander¹, Olga Ciccarelli⁹, and Ivana Drobnjak¹

¹Centre for Medical Image Computing and Dept of Computer Science, University College London, London, United Kingdom, ²CENIR, ICM, Paris, France, ³Inserm U 1127, CNRS UMR 7225, Sorbonne Universités, UPMC Univ Paris 06 UMR S 1127, Institut du Cerveau et de la Moelle épinière, ICM, Paris, France, ⁴INRIA, Université Côte d'Azur, Sophia-Antipolis, France, ⁵Parietal, CEA, INRIA, Saclay, Sophia-Antipolis, France, ⁶AP-HP, Hôpital Saint-Antoine, Paris, France, ⁷Hôpital de la Pitié Salpêtrière, Sorbonne Universités, UPMC Paris 06 UMR S 1127, Inserm UMR S 1127, CNRS UMR 7225, Institut du Cerveau et de la Moelle épinière, Paris, France, ⁸AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France, ⁹Department of Brain Repair and Rehabilitation, Institute of Neurology, University College London, London, United Kingdom

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.

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