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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

pliODFs based on different super-voxel sizes. (A) The FOM of the simulated dataset (cf. Figure 2) was divided into super-voxels composed of (B) 10 × 10 × 1, (C) 20 × 20 × 1, and (D) 40 × 40 × 1 native voxels. pliODFs were generated by means of series expansions to the 6th band for the different super-voxels. The color sphere defines the relation between orientation and color-coding.
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Figure 4: pliODFs based on different super-voxel sizes. (A) The FOM of the simulated dataset (cf. Figure 2) was divided into super-voxels composed of (B) 10 × 10 × 1, (C) 20 × 20 × 1, and (D) 40 × 40 × 1 native voxels. pliODFs were generated by means of series expansions to the 6th band for the different super-voxels. The color sphere defines the relation between orientation and color-coding.

Mentions: The resampling results for different super-voxel dimensions are shown in Figures 4B–D. Compared to the original unit vector description of the fiber orientations, the peaks of the pliODFs obtained from the small super-voxels (Figures 4B,C) reflect the main underlying (microscopic) fiber orientations corroborated by the matching colors. In addition, the general (macroscopic) orientations of the letters agree with the orientations of the input structures. As expected, the complexity of the pliODF shapes increases in larger samples (Figure 4D), maintaining the major portions of fiber orientations.


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)

pliODFs based on different super-voxel sizes. (A) The FOM of the simulated dataset (cf. Figure 2) was divided into super-voxels composed of (B) 10 × 10 × 1, (C) 20 × 20 × 1, and (D) 40 × 40 × 1 native voxels. pliODFs were generated by means of series expansions to the 6th band for the different super-voxels. The color sphere defines the relation between orientation and color-coding.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4835454&req=5

Figure 4: pliODFs based on different super-voxel sizes. (A) The FOM of the simulated dataset (cf. Figure 2) was divided into super-voxels composed of (B) 10 × 10 × 1, (C) 20 × 20 × 1, and (D) 40 × 40 × 1 native voxels. pliODFs were generated by means of series expansions to the 6th band for the different super-voxels. The color sphere defines the relation between orientation and color-coding.
Mentions: The resampling results for different super-voxel dimensions are shown in Figures 4B–D. Compared to the original unit vector description of the fiber orientations, the peaks of the pliODFs obtained from the small super-voxels (Figures 4B,C) reflect the main underlying (microscopic) fiber orientations corroborated by the matching colors. In addition, the general (macroscopic) orientations of the letters agree with the orientations of the input structures. As expected, the complexity of the pliODF shapes increases in larger samples (Figure 4D), maintaining the major portions of fiber orientations.

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