### 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.

Original language | English |
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Title of host publication | Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 : Part 1 |

Editors | Nassir Navab, Joachim Hornegger, William M. Wells, Alejandro F. Frangi |

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 | 18th International Conference on Medical Image Computing and Computer-Assisted Intervention - Munich, Germany Duration: 5 Oct 2015 → 9 Oct 2015 Conference number: 18 http://www.miccai2015.org/ |

### Conference

Conference | 18th International Conference on Medical Image Computing and Computer-Assisted Intervention |
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Number | 18 |

Country | Germany |

City | Munich |

Period | 05/10/2015 → 09/10/2015 |

Internet address |

Series | Lecture Notes in Computer Science |
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Volume | 9349 |

ISSN | 0302-9743 |

## Cite this

Hauberg, S., Schober, M., Liptrot, M. G., Hennig, P., & Feragen, A. (2015). A Random Riemannian Metric for Probabilistic Shortest-Path Tractography. In N. Navab, J. Hornegger, W. M. Wells, & A. F. Frangi (Eds.),

*Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015: Part 1*(pp. 597-604). Springer. Lecture Notes in Computer Science, Vol.. 9349 https://doi.org/10.1007/978-3-319-24553-9_73