Abstract #3616

Gauge equivariant convolutional neural networks for diffusion MRI

Uzair Hussain¹ and Ali Khan^1,2,3

¹Robarts Research Institute, Centre for Functional and Metabolic Mapping, London, ON, Canada, ²Department of Medical Biophysics, Western University, London, ON, Canada, ³Western Institute for Neuroscience, Western University, London, ON, Canada

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Diffusion Tensor ImagingOne shortcoming of diffusion MRI (dMRI) is long scan times as numerous images have to be acquired to achieve a reliable angular resolution of diffusion gradient directions. In this work we introduce gauge equivariant convolutional neural network (gCNN) layers that overcome the challenges associated with the dMRI signal being acquired on a sphere instead of a rectangular grid. We apply this method to upsample angular resolution to predict diffusion tensor imaging (DTI) parameters from just six diffusion gradient directions. Additionally, gCNNs are able to train with fewer subjects and are general enough to be applied to other dMRI related problems.

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