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Abstract #1880

Representation Learning of Fiber Tracts and Direct Comparison of Tract Distributions

Kaikai Shen1,2,3, Lee Reid1, Samantha Burnham1, and Jurgen Fripp1
1Australian eHealth Research Centre, CSIRO, Herston, Australia, 2Department of Biomedical Sciences, Macquarie University, Sydney, Australia, 3Rapiscan Systems, Sydney, Australia

The distribution of fiber population in the whole brain can be inferred from the samples generated by tractography on diffusion MRI. In this paper, we modeled the distribution of fiber population globally based on representation learning method. Using deep neural networks, we performed dimension reduction on the fiber tracts, and modeled the fiber population by a probability distribution over a latent space in lower dimension. This method enabled us to identify tracts distributed with different densities when compared with another tractogram, and can thus be used to identify structural difference or to detect spurious tracts caused by probabilistic tractography.

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