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

Palombo, Marco ORCID: https://orcid.org/0000-0003-4892-7967, Hill, Ioana, Santin, Mathieu, Branzoli, Francesca, Philippe, Anne-Charlotte, Wassermann, Demian, Aigrot, Marie-Stephanie, Stankoff, Bruno, Zhang, Hui, Lehericy, Stephan, Petiet, Alexandra, Alexander, Daniel C. and Drobnjak, Ivana 2018. Machine learning based estimation of axonal permeability: validation on cuprizone treated in-vivo mouse model of axonal demyelination. Presented at: Joint Annual Meeting ISMRM-ESMRMB 2018, 16-21 June 2018. Published in: Miller, K. L. and Port, J. D. eds.

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Abstract

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.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Completion
Status:	Published
Schools:	Psychology
Date of First Compliant Deposit:	20 May 2022
Last Modified:	10 Nov 2022 10:42
URI:	https://orca.cardiff.ac.uk/id/eprint/147881

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