Abstract #4319

TractLearn: comparison with the General Linear Model for optic pathways exploration

Clement Jean¹, Arnaud Attyé^1,2, Alexandre Krainik¹, Sylvie Grand¹, Christophe Chiquet³, Olivier Casez⁴, Laurent Lamalle⁵, Felix Renard⁶, and Fernando Calamante^2,7

¹Neuroradiology, Grenoble University Hospital, Grenoble, France, ²School of Biomedical Engineering, The University of Sydney, Sydney, Australia, ³Ophthalmology, Grenoble University Hospital, Grenoble, France, ⁴Neurology, Grenoble University Hospital, Grenoble, France, ⁵IRMaGe, Inserm US 17, CNRS UMS 3552, Grenoble, France, ⁶Pixyl Medical, Grenoble, France, ⁷Sydney Imaging, Sydney, Australia

TractLearn was recently proposed for tract-based MRI quantitative analyses, based on Riemannian distances between anatomical structures. It allows to detect joint quantitative variations in a group of voxels, and in theory to decrease the number of false negatives compared with the General Linear Model (GLM). TractLearn also takes advantage of a manifold approach to capture controls variability as standard reference. Here we aim at comparing the performance of TractLearn with the GLM in detecting optic nerve voxel alteration, using the side of visual impairment as clinical reference.

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