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Abstract #3619

Patch-CNN provides high-fidelity directional & scalar parameter estimation from 6-directional DWI robust to pathology unseen during training

Tobias Goodwin-Allcock1, Guglielmo Genovese2,3,4, Belen Zaid3,5, Stéphane Lehericy2,3, Charlotte Rosso3,5, Ting Gong1, Robert Gray6, Parashkev Nachev6, Marco Palombo7,8, and Hui Zhang1
1Department of Computer Science and Centre for Medical Image Computing, UCL, London, United Kingdom, 2Centre de NeuroImagerie de Recherche - CENIR, Paris Brain Institute - ICM, Paris, France, 3UMR S 1127, Inserm U 1127, CNRS UMR 7225, ICM, F-75013, Sorbonne Université, Paris, France, 4Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, United States, 5Paris Brain Institute - ICM, Centre de NeuroImagerie de Recherche - CENIR, Paris, France, 6University College London Queen Square Institute of Neurology, London, United Kingdom, 7Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom, 8School of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom

Synopsis

Keywords: Data Processing, Diffusion Tensor Imaging, Machine LearningThis work evaluates the clinical viability of Patch-CNN for estimating diffusion MRI (dMRI) parameters from only 6 diffusion-weighted images (DWIs). Machine learning (ML) has been proposed to improve fitting from 6-directional DWIs. However, directional measures, e.g. primary fibre orientation, have only been estimated using CNNs. CNNs have not yet been validated on pathology that is not contained within the training dataset. As pathological diversity is difficult to capture in typical applications, ML methods are clinically viable only if they can generalise to unseen pathology. We show that Patch-CNN may generalise to unseen pathology and estimate directional measures.

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