C. Lenglet

D. Essen van

D. Wassermann

G. Parker

L. O’Donnell

M. Catani

M. Glasser

P.J. Basser

R.S. Desikan

S.N. Sotiropoulos

X. Hao

Y. Zhang

Tractography in diffusion tensor imaging estimates connectivity in the brain through observations of local diffusivity. These observations are noisy and of low resolution and, as a consequence, connections cannot be found with high precision. We use probabilistic numerics to estimate connectivity between regions of interest and contribute a Gaussian Process tractography algorithm which allows for both quantification and visualization of its posterior uncertainty. We use the uncertainty both in visualization of individual tracts as well as in heat maps of tract locations. Finally, we provide a quantitative evaluation of different metrics and algorithms showing that the adjoint metric [8] combined with our algorithm produces paths which agree most often with experts

Schober, Michael

Kasenburg, Niklas

Feragen, Aasa

Hennig, Philipp

Hauberg, Søren

Online Research Database In Technology

Probabilistic shortest path tractography in DTI using Gaussian Process ODE solvers

Copenhagen University Research Information System

Probabilistic shortest path tractography in DTI using gaussian process ODE solvers

Schober, M.

Kasenburg, N.

Feragen, A.

Hennig, P.

Hauberg, S.

MPG.PuRe

Probabilistic Shortest Path Tractography in DTI Using Gaussian Process ODE Solvers

Michael Schober

Niklas Kasenburg

Aasa Feragen

Philipp Hennig

Søren Hauberg

Crossref

Probabilistic Shortest Path Tractography in DTI Using Gaussian Process ODE Solvers

http://orbit.dtu.dk/ws/files/123923168/MICCAI2014.pdf

Probabilistic shortest path tractography in DTI using Gaussian Process ODE solvers

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Online Research Database In Technology

Copenhagen University Research Information System

MPG.PuRe

Crossref

MPG.PuRe