Tobias Goodwin-Allcock1, Ting Gong1, Robert Gray2, Parashkev Nachev2, and Hui Zhang1
1Centre for Medical Image Computing (CMIC), UCL, London, United Kingdom, 2High-Dimensional Neurology, University College London Queen Square Institute of Neurology, London, United Kingdom
1Centre for Medical Image Computing (CMIC), UCL, London, United Kingdom, 2High-Dimensional Neurology, University College London Queen Square Institute of Neurology, London, United Kingdom
Proposed Patch-CNN-DTI as a method for estimating accurate
diffusion tensors from as few as 6 diffusion-weighted images (DWI) with only
one training subject, which outperforms conventional fitting with twice the
number of DWIs.
Figure 2) Top are the FA
weighted colour maps of the primary direction of diffusion. The motor tract,
highlighted in yellow, is enlarged at the bottom where the primary directions
of diffusion are illustrated as colour encoded sticks. The sticks are masked such
that only WM voxels remain, determined by FA>0.2. Estimations from
Patch-CNN are visually more similar to the GT for both RGB colourmap and sticks.
Figure 4) Boxplots
computed over the medians of errors for each of the 4 testing subjects. For
(a,c,d) the median error is computed across all of the brain voxels at each
subject. For (b) median error is computed for each subject across voxels for
which the primary direction of diffusion is well defined, where the linearity
coefficient9>0.6.
