Patch-CNN: Training data-efficient deep learning for high-fidelity
  diffusion tensor estimation from minimal diffusion protocols
Tobias Goodwin-Allcock Ting Gong Robert Gray Parashkev Nachev Hui Zhang
We propose a new method, Patch-CNN, for diffusion tensor (DT) estimation from
only six-direction diffusion weighted images (DWI). Deep learning-based methods
have been recently proposed for dMRI parameter estimation, using either
voxel-wise fully-connected neural networks (FCN) or image-wise convolutional
neural networks (CNN). In the acute clinical context -- where pressure of time
limits the number of imaged directions to a minimum -- existing approaches
either require an infeasible number of training images volumes (image-wise
CNNs), or do not estimate the fibre orientations (voxel-wise FCNs) required for
tractogram estimation. To overcome these limitations, we propose Patch-CNN, a
neural network with a minimal (non-voxel-wise) convolutional kernel
(3$\times$3$\times$3). Compared with voxel-wise FCNs, this has the advantage of
allowing the network to leverage local anatomical information. Compared with
image-wise CNNs, the minimal kernel vastly reduces training data demand.
Evaluated against both conventional model fitting and a voxel-wise FCN,
Patch-CNN, trained with a single subject is shown to improve the estimation of
both scalar dMRI parameters and fibre orientation from six-direction DWIs. The
improved fibre orientation estimation is shown to produce improved tractogram.