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Abstract #3559

Machine learning based estimation of axonal properties in the presence of beading

Kévin GINSBURGER1, Felix MATUSCHKE2, Fabrice POUPON3, Jean-François MANGIN3, Markus AXER2, and Cyril POUPON1

1UNIRS, CEA/Joliot/Neurospin, GIf-sur-Yvette, France, 2Research Centre Jülich, Institute of Neuroscience and Medicine, Jülich, Germany, Juelich, Germany, 3UNATI, CEA/Joliot/Neurospin, GIf-sur-Yvette, France

In this work, we investigate the potential of machine learning techniques to make one step forward by quantitatively estimating beading amplitude, a specific marker of pathological beading using frequency-dependent changes in diffusion measurements. Classification and regression are performed using Extremely Randomized Trees from OGSE signals corresponding to 6 distinct frequencies and synthesized from numerical simulations in realistic white matter phantoms depicting beaded axons.

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