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Estimating Fiber Orientation Distribution Functions in 3D-Polarized Light Imaging.

Axer M, Strohmer S, Gräßel D, Bücker O, Dohmen M, Reckfort J, Zilles K, Amunts K - Front Neuroanat (2016)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Research Centre Jülich, Institute of Neuroscience and Medicine Jülich, Germany.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus

Coronal section from the central human brain region. (A) Segmented blockface image acquired from the surface of the frozen human brain. (B) Fiber orientation map comprising arcuate fascicle (AF), cingulum bundle (CB), corpus callosum (CC), caudate nucleus (Cd), corona radiata (CR), internal capsule (IC). Fiber orientations are RGB color-coded (see sphere). The white rectangles indicate FOMs (C(1) and D(1)) that were transferred into pliODFs. (C) pliODF representations in the region of CR/IC with super-voxel dimensions of (2) 50 × 50 × 1 and (3) 200 × 200 × 1 native voxels (from different views). Patches of crossing fiber bundles are clearly visible in the FOM and the pliODF maps. (D) pliODF representations in the region of CC with super-voxel dimensions of (2) 50 × 50 × 1 and (3) 200 × 200 × 1 native voxels (from different views). Although a predominant fiber direction is observable, small wriggling fiber bundles cause local inhomogeneities along the CC.
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Figure 7: Coronal section from the central human brain region. (A) Segmented blockface image acquired from the surface of the frozen human brain. (B) Fiber orientation map comprising arcuate fascicle (AF), cingulum bundle (CB), corpus callosum (CC), caudate nucleus (Cd), corona radiata (CR), internal capsule (IC). Fiber orientations are RGB color-coded (see sphere). The white rectangles indicate FOMs (C(1) and D(1)) that were transferred into pliODFs. (C) pliODF representations in the region of CR/IC with super-voxel dimensions of (2) 50 × 50 × 1 and (3) 200 × 200 × 1 native voxels (from different views). Patches of crossing fiber bundles are clearly visible in the FOM and the pliODF maps. (D) pliODF representations in the region of CC with super-voxel dimensions of (2) 50 × 50 × 1 and (3) 200 × 200 × 1 native voxels (from different views). Although a predominant fiber direction is observable, small wriggling fiber bundles cause local inhomogeneities along the CC.

Mentions: In addition, two high-resolution FOMs of selected regions of interest from a coronal section through the human brain (Figure 7) at the level of the central region were resampled at different super-voxel dimensions (Figures 7C,D), but with fixed histogram binning (50 latitudes × 100 longitudes + 2 polar caps = 5002 bins). The targeted super-voxel sizes of 65 × 65 × 70 μm3, and 260 × 260 × 70 μm3 correspond to 50 × 50 × 1, and 200 × 200 × 1 native voxels, respectively. The series expansion was confined to the 6th band.


Estimating Fiber Orientation Distribution Functions in 3D-Polarized Light Imaging.

Axer M, Strohmer S, Gräßel D, Bücker O, Dohmen M, Reckfort J, Zilles K, Amunts K - Front Neuroanat (2016)

Coronal section from the central human brain region. (A) Segmented blockface image acquired from the surface of the frozen human brain. (B) Fiber orientation map comprising arcuate fascicle (AF), cingulum bundle (CB), corpus callosum (CC), caudate nucleus (Cd), corona radiata (CR), internal capsule (IC). Fiber orientations are RGB color-coded (see sphere). The white rectangles indicate FOMs (C(1) and D(1)) that were transferred into pliODFs. (C) pliODF representations in the region of CR/IC with super-voxel dimensions of (2) 50 × 50 × 1 and (3) 200 × 200 × 1 native voxels (from different views). Patches of crossing fiber bundles are clearly visible in the FOM and the pliODF maps. (D) pliODF representations in the region of CC with super-voxel dimensions of (2) 50 × 50 × 1 and (3) 200 × 200 × 1 native voxels (from different views). Although a predominant fiber direction is observable, small wriggling fiber bundles cause local inhomogeneities along the CC.
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Figure 7: Coronal section from the central human brain region. (A) Segmented blockface image acquired from the surface of the frozen human brain. (B) Fiber orientation map comprising arcuate fascicle (AF), cingulum bundle (CB), corpus callosum (CC), caudate nucleus (Cd), corona radiata (CR), internal capsule (IC). Fiber orientations are RGB color-coded (see sphere). The white rectangles indicate FOMs (C(1) and D(1)) that were transferred into pliODFs. (C) pliODF representations in the region of CR/IC with super-voxel dimensions of (2) 50 × 50 × 1 and (3) 200 × 200 × 1 native voxels (from different views). Patches of crossing fiber bundles are clearly visible in the FOM and the pliODF maps. (D) pliODF representations in the region of CC with super-voxel dimensions of (2) 50 × 50 × 1 and (3) 200 × 200 × 1 native voxels (from different views). Although a predominant fiber direction is observable, small wriggling fiber bundles cause local inhomogeneities along the CC.
Mentions: In addition, two high-resolution FOMs of selected regions of interest from a coronal section through the human brain (Figure 7) at the level of the central region were resampled at different super-voxel dimensions (Figures 7C,D), but with fixed histogram binning (50 latitudes × 100 longitudes + 2 polar caps = 5002 bins). The targeted super-voxel sizes of 65 × 65 × 70 μm3, and 260 × 260 × 70 μm3 correspond to 50 × 50 × 1, and 200 × 200 × 1 native voxels, respectively. The series expansion was confined to the 6th band.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Research Centre Jülich, Institute of Neuroscience and Medicine Jülich, Germany.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus