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GRAPH SPECTRAL ANALYSIS OF VOXEL-WISE BRAIN GRAPHS FROM DIFFUSION-WEIGHTED MRI
Ecole Polytech Fed Lausanne, Switzerland; Univ Geneva UNIGE, Switzerland.
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
Lund Univ, Sweden.
Ecole Polytech Fed Lausanne, Switzerland; Univ Geneva UNIGE, Switzerland.
2019 (English)In: 2019 IEEE 16TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2019), IEEE , 2019, p. 159-163Conference paper, Published paper (Refereed)
Abstract [en]

Non-invasive characterization of brain structure has been made possible by the introduction of magnetic resonance imaging (MRI). Graph modeling of structural connectivity has been useful, but is often limited to defining nodes as regions from a brain atlas. Here, we propose two methods for encoding structural connectivity in a huge brain graph at the voxel-level resolution (i.e., 850000 voxels) based on diffusion tensor imaging (DTI) and the orientation density functions (ODF), respectively. The eigendecomposition of the brain graphs Laplacian operator is then showing highly resolved eigenmodes that reflect distributed structural features which are in good correspondence with major white matter tracks. To investigate the intrinsic dimensionality of eigenspace across subjects, we used a Procrustes validation that characterizes inter-subject variability. We found that the ODF approach using 3-neighborhood captures the most information from the diffusion-weighted MRI. The proposed methods open a wide range of possibilities for new research avenues, especially in the field of graph signal processing applied to functional brain imaging.

Place, publisher, year, edition, pages
IEEE , 2019. p. 159-163
Series
IEEE International Symposium on Biomedical Imaging, ISSN 1945-7928
Keywords [en]
brain graph; eigenmodes; diffusion tensor imaging; orientation density functions
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:liu:diva-160632DOI: 10.1109/ISBI.2019.8759496ISI: 000485040000038ISBN: 978-1-5386-3641-1 (electronic)OAI: oai:DiVA.org:liu-160632DiVA, id: diva2:1360193
Conference
16th IEEE International Symposium on Biomedical Imaging (ISBI)
Available from: 2019-10-11 Created: 2019-10-11 Last updated: 2019-10-11

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CiteExportLink to record
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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • 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