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Voxel-Wise Clustering of Tractography Data for Building Atlases of Local Fiber Geometry
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.ORCID iD: 0000-0002-6827-9162
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.ORCID iD: 0000-0002-7750-1917
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.ORCID iD: 0000-0001-5765-2964
2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

This paper aims at proposing a method to generate atlases of white matter fibers’ geometry that consider local orientation and curvature of fibers extracted from tractography data. Tractography was performed on diffusion magnetic resonance images from a set of healthy subjects and each tract was characterized voxel-wise by its curvature and Frenet–Serret frame, based on which similar tracts could be clustered separately for each voxel and each subject. Finally, the centroids of the clusters identified in all subjects were clustered to create the final atlas. The proposed clustering technique showed promising results in identifying voxel-wise distributions of curvature and orientation. Two tractography algorithms (one deterministic and one probabilistic) were tested for the present work, obtaining two different atlases. A high agreement between the two atlases was found in several brain regions. This suggests that more advanced tractography methods might only be required for some specific regions in the brain. In addition, the probabilistic approach resulted in the identification of a higher number of fiber orientations in various white matter areas, suggesting it to be more adequate for investigating complex fiber configurations in the proposed framework as compared to deterministic tractography.

Place, publisher, year, edition, pages
2019. p. 345-357
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:kth:diva-259768DOI: 10.1007/978-3-030-05831-9_27Scopus ID: 2-s2.0-85066883835OAI: oai:DiVA.org:kth-259768DiVA, id: diva2:1353460
Conference
International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018; Granada; Spain; 20 September 2018 through 20 September 2018
Note

QC 20190923

Available from: 2019-09-23 Created: 2019-09-23 Last updated: 2019-09-23Bibliographically approved

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Brusini, IreneJörgens, DanielSmedby, ÖrjanMoreno, Rodrigo

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