A random Riemannian metric for probabilistic shortest-path tractography
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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.
Original language | English |
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Title of host publication | Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015 : 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part I |
Number of pages | 8 |
Publisher | Springer |
Publication date | 2015 |
Pages | 597-604 |
ISBN (Print) | 978-3-319-24552-2 |
ISBN (Electronic) | 978-3-319-24553-9 |
DOIs | |
Publication status | Published - 2015 |
Event | International Conference on Medical Image Computing and Computer Assisted Intervention 2015 - Munich, Germany Duration: 5 Oct 2015 → 9 Oct 2015 Conference number: 18 |
Conference
Conference | International Conference on Medical Image Computing and Computer Assisted Intervention 2015 |
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Nummer | 18 |
Land | Germany |
By | Munich |
Periode | 05/10/2015 → 09/10/2015 |
Series | Lecture notes in computer science |
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Volume | 9349 |
ISSN | 0302-9743 |
ID: 154364349