This title appears in the Scientific Report :
2016
Please use the identifier:
http://hdl.handle.net/2128/10431 in citations.
Please use the identifier: http://dx.doi.org/10.3389/fnana.2016.00040 in citations.
Estimating Fiber Orientation Distribution Functions in 3D-Polarized Light Imaging
Estimating Fiber Orientation Distribution Functions in 3D-Polarized Light Imaging
Research of the human brain connectome requires multiscale approaches derived from independent imaging methods ideally applied to the same object. Hence, comprehensible strategies for data integration across modalities and across scales are essential. We have successfully established a concept to br...
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Personal Name(s): | Axer, Markus (Corresponding author) |
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Strohmer, Sven / Gräßel, David / Bücker, Oliver / Dohmen, Melanie / Reckfort, Julia / Zilles, Karl / Amunts, Katrin | |
Contributing Institute: |
Strukturelle und funktionelle Organisation des Gehirns; INM-1 Jülich Supercomputing Center; JSC |
Published in: | Frontiers in neuroanatomy, 10 (2016) S. 40 |
Imprint: |
Lausanne
Frontiers Research Foundation
2016
|
DOI: |
10.3389/fnana.2016.00040 |
PubMed ID: |
27147981 |
Document Type: |
Journal Article |
Research Program: |
Postnatal Development of Cortical Receptors and White Matter Tracts in the Vervet The Human Brain Project Supercomputing and Modelling for the Human Brain Computational Science and Mathematical Methods Theory, modelling and simulation SimLab Neuroscience |
Link: |
OpenAccess OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://dx.doi.org/10.3389/fnana.2016.00040 in citations.
Research of the human brain connectome requires multiscale approaches derived from independent imaging methods ideally applied to the same object. Hence, comprehensible strategies for data integration across modalities and across scales are essential. We have successfully established a concept to bridge the spatial scales from microscopic fiber orientation measurements based on 3D-Polarized Light Imaging (3D-PLI) to meso- or macroscopic dimensions. By creating orientation distribution functions (pliODFs) from high-resolution vector data via series expansion with spherical harmonics utilizing high performance computing and supercomputing technologies, data fusion with Diffusion Magnetic Resonance Imaging has become feasible, even for a large-scale dataset such as the human brain. Validation of our approach was done effectively by means of two types of datasets that were transferred from fiber orientation maps into pliODFs: simulated 3D-PLI data showing artificial, but clearly defined fiber patterns and real 3D-PLI data derived from sections through the human brain and the brain of a hooded seal. |