Probabilistic shortest path tractography in DTI using Gaussian Process ODE solvers

Michael Schober; Niklas Kasenburg; Aasa  Feragen; Philipp Hennig; Søren Hauberg

doi:10.1007/978-3-319-10443-0_34

Probabilistic shortest path tractography in DTI using Gaussian Process ODE solvers

Michael Schober, Niklas Kasenburg, Aasa Feragen, Philipp Hennig, Søren Hauberg

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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Abstract

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.

Original language	English
Title of host publication	Proceedings of the 17th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2014), Part III
Editors	Polina Golland, Nobuhiko Hata, Christian Barillot, Joachim Hornegger, Robert Howe
Publisher	Springer
Publication date	2014
Pages	265-272
ISBN (Print)	978-3-319-10442-3
ISBN (Electronic)	978-3-319-10443-0
DOIs	https://doi.org/10.1007/978-3-319-10443-0_34
Publication status	Published - 2014
Event	17th International Conference on Medical Image Computing and Computer Assisted Intervention - Massachusetts Institute of Technology, Cambridge, MA, Boston, United States Duration: 14 Sep 2014 → 18 Sep 2014 Conference number: 17 http://miccai2014.org/ http://miccai2014.org/cfp.html

Conference

Conference	17th International Conference on Medical Image Computing and Computer Assisted Intervention
Number	17
Location	Massachusetts Institute of Technology, Cambridge, MA
Country	United States
City	Boston
Period	14/09/2014 → 18/09/2014
Internet address	http://miccai2014.org/ http://miccai2014.org/cfp.html

Series	Lecture Notes in Computer Science
Number	8675
ISSN	0302-9743

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Schober, M., Kasenburg, N., Feragen, A., Hennig, P., & Hauberg, S. (2014). Probabilistic shortest path tractography in DTI using Gaussian Process ODE solvers. In P. Golland, N. Hata, C. Barillot, J. Hornegger, & R. Howe (Eds.), Proceedings of the 17th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2014), Part III (pp. 265-272). Springer. Lecture Notes in Computer Science, No. 8675 https://doi.org/10.1007/978-3-319-10443-0_34

Schober, Michael ; Kasenburg, Niklas ; Feragen, Aasa ; Hennig, Philipp ; Hauberg, Søren. / Probabilistic shortest path tractography in DTI using Gaussian Process ODE solvers. Proceedings of the 17th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2014), Part III. editor / Polina Golland ; Nobuhiko Hata ; Christian Barillot ; Joachim Hornegger ; Robert Howe. Springer, 2014. pp. 265-272 (Lecture Notes in Computer Science; No. 8675).

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abstract = "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.",

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booktitle = "Proceedings of the 17th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2014), Part III",

note = "17th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2014 ; Conference date: 14-09-2014 Through 18-09-2014",

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Schober, M, Kasenburg, N, Feragen, A, Hennig, P & Hauberg, S 2014, Probabilistic shortest path tractography in DTI using Gaussian Process ODE solvers. in P Golland, N Hata, C Barillot, J Hornegger & R Howe (eds), Proceedings of the 17th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2014), Part III. Springer, Lecture Notes in Computer Science, no. 8675, pp. 265-272, 17th International Conference on Medical Image Computing and Computer Assisted Intervention, Boston, United States, 14/09/2014. https://doi.org/10.1007/978-3-319-10443-0_34

Probabilistic shortest path tractography in DTI using Gaussian Process ODE solvers. / Schober, Michael; Kasenburg, Niklas; Feragen, Aasa ; Hennig, Philipp; Hauberg, Søren.

Proceedings of the 17th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2014), Part III. ed. / Polina Golland; Nobuhiko Hata; Christian Barillot; Joachim Hornegger; Robert Howe. Springer, 2014. p. 265-272 (Lecture Notes in Computer Science; No. 8675).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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N2 - 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.

AB - 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.

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Schober M, Kasenburg N, Feragen A, Hennig P, Hauberg S. Probabilistic shortest path tractography in DTI using Gaussian Process ODE solvers. In Golland P, Hata N, Barillot C, Hornegger J, Howe R, editors, Proceedings of the 17th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2014), Part III. Springer. 2014. p. 265-272. (Lecture Notes in Computer Science; No. 8675). https://doi.org/10.1007/978-3-319-10443-0_34