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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