Deterministic Group Tractography with Local Uncertainty Quantification

Andreas Nugaard Holm; Aasa Feragen; Tom Dela Haije; Sune Darkner

doi:10.1007/978-3-030-05831-9_30

Deterministic Group Tractography with Local Uncertainty Quantification

Andreas Nugaard Holm^*, Aasa Feragen, Tom Dela Haije, Sune Darkner

^*Corresponding author for this work

Department of Computer Science

Abstract

While tractography is routinely used to trace the white-matter connectivity in individual subjects, the population analysis of tractography output is hampered by the difficulty of comparing populations of curves. As a result, analysis is often reduced to population summaries such as TBSS, or made pointwise with similar interaction of remote and nearby tracts. As an easy-to-use alternative, we propose population-wide tractography in MNI space, by simultaneously considering diffusion data from the entire population, registered to MNI. We include voxel-wise quantification of population variability as a measure of uncertainty. The group tractography algorithm is illustrated on a population of subjects from the Human Connectome Project, obtaining robust population estimates of the white matter tracts.

Original language	English
Title of host publication	Computational Diffusion : International MICCAI Workshop, Granada,
Editors	Elisenda Bonet-Carne, Francesco Grussu, Lipeng Ning, Farshid Sepehrband, Chantal M. W. Tax
Publisher	Springer
Publication date	2019
Edition	226249
Pages	377-386
ISBN (Print)	978-3-030-05830-2
ISBN (Electronic)	978-3-030-05831-9
DOIs	https://doi.org/10.1007/978-3-030-05831-9_30
Publication status	Published - 2019
Event	International Workshop on Computational Diffusion MRI, CDMRI 2018 held with International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 - Granada, Spain Duration: 20 Sept 2018 → 20 Sept 2018

Conference

Conference	International Workshop on Computational Diffusion MRI, CDMRI 2018 held with International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
Country/Territory	Spain
City	Granada
Period	20/09/2018 → 20/09/2018

Series	Mathematics and Visualization
ISSN	1612-3786

Keywords

Population analysis
Tractography
Uncertainty quantification

Access to Document

10.1007/978-3-030-05831-9_30

Cite this

Deterministic Group Tractography with Local Uncertainty Quantification. / Holm, Andreas Nugaard; Feragen, Aasa; Dela Haije, Tom et al.
Computational Diffusion: International MICCAI Workshop, Granada,. ed. / Elisenda Bonet-Carne; Francesco Grussu; Lipeng Ning; Farshid Sepehrband; Chantal M. W. Tax. 226249. ed. Springer, 2019. p. 377-386 (Mathematics and Visualization).

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

Holm, AN, Feragen, A, Dela Haije, T & Darkner, S 2019, Deterministic Group Tractography with Local Uncertainty Quantification. in E Bonet-Carne, F Grussu, L Ning, F Sepehrband & CMW Tax (eds), Computational Diffusion: International MICCAI Workshop, Granada,. 226249 edn, Springer, Mathematics and Visualization, pp. 377-386, International Workshop on Computational Diffusion MRI, CDMRI 2018 held with International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018, Granada, Spain, 20/09/2018. https://doi.org/10.1007/978-3-030-05831-9_30

Holm, Andreas Nugaard ; Feragen, Aasa ; Dela Haije, Tom et al. / Deterministic Group Tractography with Local Uncertainty Quantification. Computational Diffusion: International MICCAI Workshop, Granada,. editor / Elisenda Bonet-Carne ; Francesco Grussu ; Lipeng Ning ; Farshid Sepehrband ; Chantal M. W. Tax. 226249. ed. Springer, 2019. pp. 377-386 (Mathematics and Visualization).

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N2 - While tractography is routinely used to trace the white-matter connectivity in individual subjects, the population analysis of tractography output is hampered by the difficulty of comparing populations of curves. As a result, analysis is often reduced to population summaries such as TBSS, or made pointwise with similar interaction of remote and nearby tracts. As an easy-to-use alternative, we propose population-wide tractography in MNI space, by simultaneously considering diffusion data from the entire population, registered to MNI. We include voxel-wise quantification of population variability as a measure of uncertainty. The group tractography algorithm is illustrated on a population of subjects from the Human Connectome Project, obtaining robust population estimates of the white matter tracts.

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ER -

Deterministic Group Tractography with Local Uncertainty Quantification

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Keywords

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