Deterministic Group Tractography with Local Uncertainty Quantification
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Deterministic Group Tractography with Local Uncertainty Quantification. / Holm, Andreas Nugaard; Feragen, Aasa; Dela Haije, Tom; Darkner, Sune.
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
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TY - GEN
T1 - Deterministic Group Tractography with Local Uncertainty Quantification
AU - Holm, Andreas Nugaard
AU - Feragen, Aasa
AU - Dela Haije, Tom
AU - Darkner, Sune
PY - 2019
Y1 - 2019
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.
AB - 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.
KW - Population analysis
KW - Tractography
KW - Uncertainty quantification
UR - http://www.scopus.com/inward/record.url?scp=85066914798&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-05831-9_30
DO - 10.1007/978-3-030-05831-9_30
M3 - Article in proceedings
AN - SCOPUS:85066914798
SN - 978-3-030-05830-2
T3 - Mathematics and Visualization
SP - 377
EP - 386
BT - Computational Diffusion
A2 - Bonet-Carne, Elisenda
A2 - Grussu, Francesco
A2 - Ning, Lipeng
A2 - Sepehrband, Farshid
A2 - Tax, Chantal M. W.
PB - Springer
T2 - International Workshop on Computational Diffusion MRI, CDMRI 2018 held with International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
Y2 - 20 September 2018 through 20 September 2018
ER -
ID: 223569891