Book chapter · Conference paper
A Random Riemannian Metric for Probabilistic Shortest-Path Tractography
In
Lecture Notes in Computer Science
—
2015, Volume 9349, pp. 597-604
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.
| Language: | English |
|---|---|
| Publisher: | Springer |
| Year: | 2015 |
| Pages: | 597-604 |
| Proceedings: | 18th International Conference on Medical Image Computing and Computer-Assisted InterventionInternational Conference on Medical Image Computing and Computer Assisted Intervention |
| Series: | Lecture Notes in Computer Science |
| Journal subtitle: | Part 1 |
| ISBN: | 331924552X , 331924552x , 3319245538 , 9783319245522 and 9783319245539 |
| ISSN: | 16113349 and 03029743 |
| Types: | Book chapter and Conference paper |
| DOI: | 10.1007/978-3-319-24553-9_73 |
| ORCIDs: | Liptrot, Matthew George , 0000-0002-9945-981X and Hauberg, Søren |
