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Abstract #2605

Spherical-CNN based diffusion MRI parameter estimation is robust to gradient schemes and equivariant to rotation

Tobias Goodwin-Allcock1, Robert Gray2, Parashkev Nachev2, Jason McEwan3, and Hui Zhang1
1Department of Computer Science and Centre for Medical Image Computing, UCL, London, United Kingdom, 2Department of Brain Repair & Rehabilitation, Institute of Neurology, UCL, London, United Kingdom, 3Kagenova Limited, Guildford, United Kingdom


We demonstrate the advantages of spherical convolutional neural networks over conventional fully connected networks at estimating rotationally invariant microstructure indices. Fully-connected networks (FCN) have outperformed conventional model fitting for estimating microstructure indices, such as FA. However, these methods are not robust to changes diffusion weighted image sampling scheme nor are they rotationally equivariant. Recently spherical-CNN have been supposed as a solution to this problem. However, the advantages of spherical-CNNs have not been leveraged. We demonstrate both spherical-CNNs robust to new gradient schemes as well as the rotational equivariance. This has potential to decrease the number of training datapoints required.

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