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Graph spectral anal...
Graph spectral analysis of voxel-wise brain graphs from diffusion-weighted mri
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- Tarun, Anjali (författare)
- Swiss Federal Institute of Technology,University of Geneva,Ecole Polytech Fed Lausanne, Switzerland; Univ Geneva UNIGE, Switzerland
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- Abramian, David, 1992- (författare)
- Linköpings universitet,Linköping University,Avdelningen för medicinsk teknik,Tekniska fakulteten
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- Behjat, Hamid (författare)
- Lund University,Lunds universitet,Institutionen för biomedicinsk teknik,Institutioner vid LTH,Lunds Tekniska Högskola,Department of Biomedical Engineering,Departments at LTH,Faculty of Engineering, LTH,Lund Univ, Sweden
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- De Ville, Dimitri Van (författare)
- Swiss Federal Institute of Technology,University of Geneva,Ecole Polytech Fed Lausanne, Switzerland; Univ Geneva UNIGE, Switzerland
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(creator_code:org_t)
- IEEE, 2019
- 2019
- Engelska 5 s.
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Ingår i: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). - : IEEE. - 9781538636411 ; , s. 159-163
- Relaterad länk:
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http://dx.doi.org/10...
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https://lup.lub.lu.s...
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https://doi.org/10.1...
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https://urn.kb.se/re...
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Abstract
Ämnesord
Stäng
- 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., 850'000 voxels) based on diffusion tensor imaging (DTI) and the orientation density functions (ODF), respectively. The eigendecomposition of the brain graph's 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 in-formation 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.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering -- Medical Image Processing (hsv//eng)
Nyckelord
- Brain graph
- Diffusion tensor imaging
- Eigenmodes
- Orientation density functions
- brain graph; eigenmodes; diffusion tensor imaging; orientation density functions
Publikations- och innehållstyp
- kon (ämneskategori)
- ref (ämneskategori)
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