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Abstract #0434

Machine learning based estimation of axonal permeability: validation on cuprizone treated in-vivo mouse model of axonal demyelination

Marco Palombo1, Ioana Hill1, Mathieu David Santin2,3, Francesca Branzoli2,3, Anne-Charlotte Philippe2,3, Demian Wassermann4,5, Marie-Stephane Aigrot2, Bruno Stankoff2,6, Hui Zhang1, Stephane Lehericy2,7,8, Alexandra Petiet2,7, Daniel C. Alexander1, and Ivana Drobnjak1

1Computer Science Department and Centre for Medical Imaging Computing, 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, 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

Estimating axonal permeability reliably is extremely important, however not yet achieved because mathematical models that express its relationship to the MR signal accurately are intractable. Recently introduced machine learning based computational model showed to outperforms previous approximate mathematical models. Here we apply and validate this novel method experimentally on a highly controlled in-vivo mouse model of axonal demyelination, and demonstrate for the first time in practice the power of machine learning as a mechanism to construct complex biophysical models for quantitative MRI.

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