A Random Riemannian Metric for Probabilistic Shortest-Path Tractography
Lecture notes in computer sciencePublished 1 January 2015Open access
Søren Hauberg, Michael Schober, Matthew G. Liptrot, Philipp Hennig, Aasa Feragen
Citations19
SJR quartileQ2
SJR score0.35
SNIP0.55
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TL;DR
This work extends Riemannian SPT by modeling the stochasticity of the diffusion tensor as a "random RiemANNian metric", where a geodesic is a distribution over tracts and approximate this distribution with a Gaussian process and present a probabilistic numerics algorithm for computing the geodesIC distribution.
Abstract
Shortest-path tractography (SPT) algorithms solve global optimization problems defined from local distance functions. As diffusion MRI data is inherently noisy, so are the voxelwise tensors from which local distances are derived. We extend Riemannian SPT by modeling the stochasticity of the diffusion tensor as a "random Riemannian metric", where a geodesic is a distribution over tracts. We approximate this distribution with a Gaussian process and present a probabilistic numerics algorithm for computing the geodesic distribution. We demonstrate SPT improvements on data from the Human Connectome Project.
Keywords
MedicinePhysics and Astronomy
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