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Deterministic Group Tractography with Local Uncertainty Quantification

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Part of the Mathematics and Visualization book series (MATHVISUAL)


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.


  • Tractography
  • Population analysis
  • Uncertainty quantification

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Data were provided [in part] by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University.

This research was supported by Center for Stochastic Geometry and Advanced Bioimaging, funded by a grant from the Villum Foundation, as well as a grant from the Lundbeck Foundation. TDH was covered by a block stipend from the Villum Foundation.

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Correspondence to Andreas Nugaard Holm .

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Holm, A.N., Feragen, A., Dela Haije, T., Darkner, S. (2019). Deterministic Group Tractography with Local Uncertainty Quantification. In: Bonet-Carne, E., Grussu, F., Ning, L., Sepehrband, F., Tax, C. (eds) Computational Diffusion MRI. MICCAI 2019. Mathematics and Visualization. Springer, Cham.

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