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A random Riemannian metric for probabilistic shortest-path tractography. / Hauberg, Søren; Schober, Michael; Liptrot, Matthew George; Hennig, Philipp; Feragen, Aasa.
Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part I. Springer, 2015. s. 597-604 (Lecture notes in computer science, Bind 9349).
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Harvard
Hauberg, S, Schober, M, Liptrot, MG, Hennig, P & Feragen, A 2015,
A random Riemannian metric for probabilistic shortest-path tractography. i
Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part I. Springer, Lecture notes in computer science, bind 9349, s. 597-604, International Conference on Medical Image Computing and Computer Assisted Intervention 2015, Munich, Tyskland,
05/10/2015.
https://doi.org/10.1007/978-3-319-24553-9_73APA
Hauberg, S., Schober, M., Liptrot, M. G., Hennig, P., & Feragen, A. (2015).
A random Riemannian metric for probabilistic shortest-path tractography. I
Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part I (s. 597-604). Springer. Lecture notes in computer science Bind 9349
https://doi.org/10.1007/978-3-319-24553-9_73Vancouver
Hauberg S, Schober M, Liptrot MG, Hennig P, Feragen A.
A random Riemannian metric for probabilistic shortest-path tractography. I Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part I. Springer. 2015. s. 597-604. (Lecture notes in computer science, Bind 9349).
https://doi.org/10.1007/978-3-319-24553-9_73Author
Hauberg, Søren ; Schober, Michael ; Liptrot, Matthew George ; Hennig, Philipp ; Feragen, Aasa. / A random Riemannian metric for probabilistic shortest-path tractography. Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part I. Springer, 2015. s. 597-604 (Lecture notes in computer science, Bind 9349).
Bibtex
@inproceedings{294fba42531c4f6fb1598d93a22b6b6f,
title = "A random Riemannian metric for probabilistic shortest-path tractography",
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.",
author = "S{\o}ren Hauberg and Michael Schober and Liptrot, {Matthew George} and Philipp Hennig and Aasa Feragen",
year = "2015",
doi = "10.1007/978-3-319-24553-9_73",
language = "English",
isbn = "978-3-319-24552-2",
series = "Lecture notes in computer science",
publisher = "Springer",
pages = "597--604",
booktitle = "Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015",
address = "Switzerland",
note = "null ; Conference date: 05-10-2015 Through 09-10-2015",
}
RIS
TY - GEN
T1 - A random Riemannian metric for probabilistic shortest-path tractography
AU - Hauberg, Søren
AU - Schober, Michael
AU - Liptrot, Matthew George
AU - Hennig, Philipp
AU - Feragen, Aasa
N1 - Conference code: 18
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
U2 - 10.1007/978-3-319-24553-9_73
DO - 10.1007/978-3-319-24553-9_73
M3 - Article in proceedings
AN - SCOPUS:84947584544
SN - 978-3-319-24552-2
T3 - Lecture notes in computer science
SP - 597
EP - 604
BT - Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015
PB - Springer
Y2 - 5 October 2015 through 9 October 2015
ER -
