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Clustering of tensor votes for inference of fibre orientations in DTI data
KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization.ORCID iD: 0000-0002-6827-9162
KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization.ORCID iD: 0000-0002-7750-1917
KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization.ORCID iD: 0000-0001-5765-2964
2016 (English)Conference paper, Oral presentation only (Other academic)
Abstract [en]

mong the various diffusion MRI techniques, diffusion ten-sor imaging (DTI) is still most commonly used in clinicalpractice in order to investigate connectivity and fibre anatomyin the human brain. Besides its apparent advantages of a shortacquisition time and noise robustness compared to other tech-niques, it suffers from its major weakness of assuming a sin-gle fibre model in each voxel. This constitutes a problem forDTI fibre tracking algorithms in regions with crossing fibres.Methods approaching this problem in a postprocessing stepemploy diffusion-like techniques to correct the directional in-formation. We propose an extension of tensor voting in whichinformation from voxels with a single fibre is used to inferorientation distributions in multi fibre voxels. The method isable to resolve multiple fibre orientations by clustering tensorvotes instead of adding them up. Moreover, a new vote cast-ing procedure is proposed which is appropriate even for smallneighbourhoods. To account for the locality of DTI data, weuse a small neighbourhood for distributing information at atime, but apply the algorithm iteratively to close larger gaps.The method shows promising results in both synthetic casesand for processing DTI-data of the human brain.

Place, publisher, year, edition, pages
2016.
Keywords [en]
DTI, tensor voting, spherical clustering
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:kth:diva-205978OAI: oai:DiVA.org:kth-205978DiVA, id: diva2:1090917
Conference
Proc. Swedish Society of Image Analysis (SSBA)
Funder
Swedish Research Council, 2012-3512
Note

QCR 20170426

Available from: 2017-04-25 Created: 2017-04-25 Last updated: 2017-04-26Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf