A random Riemannian metric for probabilistic shortest-path tractography

Søren Hauberg, Michael Schober, Matthew George Liptrot, Philipp Hennig, Aasa Feragen

10 Citationer (Scopus)

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.

OriginalsprogEngelsk
TitelMedical Image Computing and Computer-Assisted Intervention -- MICCAI 2015 : 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part I
Antal sider8
ForlagSpringer
Publikationsdato2015
Sider597-604
ISBN (Trykt)978-3-319-24552-2
ISBN (Elektronisk)978-3-319-24553-9
DOI
StatusUdgivet - 2015
BegivenhedInternational Conference on Medical Image Computing and Computer Assisted Intervention 2015 - Munich, Tyskland
Varighed: 5 okt. 20159 okt. 2015
Konferencens nummer: 18

Konference

KonferenceInternational Conference on Medical Image Computing and Computer Assisted Intervention 2015
Nummer18
Land/OmrådeTyskland
ByMunich
Periode05/10/201509/10/2015
NavnLecture notes in computer science
Vol/bind9349
ISSN0302-9743

Fingeraftryk

Dyk ned i forskningsemnerne om 'A random Riemannian metric for probabilistic shortest-path tractography'. Sammen danner de et unikt fingeraftryk.

Citationsformater