Meeting Banner
Abstract #2449

Patch-CNN-DTI: Data-efficient high-fidelity tensor recovery from 6 direction diffusion weighted imaging.

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

We present Patch-CNN-DTI, a deep-learning method to estimate diffusion tensors (DT) accurately from only 6 diffusion-weighted images. Early voxel-wise deep-learning methods can only estimate scalar measures of DT. Later work shows DT can be estimated using image-wise methods based on convolutional neural networks (CNN), but they require large training cohort. Patch-CNN-DTI can estimate DT with only one training subject, by pooling information from local neighbourhood of a voxel similar to the CNN but at a much smaller scale to minimise training data requirements. Results show it outperforms conventional model fitting with twice the number of diffusion directions.

This abstract and the presentation materials are available to members only; a login is required.

Join Here