Deterministic Group Tractography with Local Uncertainty Quantification
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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 |
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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 | |
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 Sep 2018 → 20 Sep 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 |
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Land | Spain |
By | Granada |
Periode | 20/09/2018 → 20/09/2018 |
Series | Mathematics and Visualization |
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ISSN | 1612-3786 |
- Population analysis, Tractography, Uncertainty quantification
Research areas
ID: 223569891