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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

3D-PLI in a nutshell. The fiber orientation vector  located at the native voxel location i is defined by the direction angle φi and the inclination angle αi. The brain section is aligned to the x-y-plane (at z = 0). In general, a unit vector can be described as a distinct point on a unit sphere. For 3D-PLI, the determined unit vector represents an axis in space (in the mathematical and the anatomical sense), since the initial and the terminal points cannot be assigned uniquely. Therefore, the point reflection across the center of the sphere has to be assigned to the same vector. For visualization purposes, the vectors were encoded in RGB color space: the x-component of  is represented by the red color channel (R), the y-component by the green color channel (G), and the z-component by the blue color channel (B).
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Figure 1: 3D-PLI in a nutshell. The fiber orientation vector located at the native voxel location i is defined by the direction angle φi and the inclination angle αi. The brain section is aligned to the x-y-plane (at z = 0). In general, a unit vector can be described as a distinct point on a unit sphere. For 3D-PLI, the determined unit vector represents an axis in space (in the mathematical and the anatomical sense), since the initial and the terminal points cannot be assigned uniquely. Therefore, the point reflection across the center of the sphere has to be assigned to the same vector. For visualization purposes, the vectors were encoded in RGB color space: the x-component of is represented by the red color channel (R), the y-component by the green color channel (G), and the z-component by the blue color channel (B).

Mentions: 3D-PLI (Axer et al., 2011a,b; Zeineh et al., 2016) has demonstrated its unique capabilities (i) to reveal fiber structures at multiple scales, such as long-range connections and even single fibers and crossings within unstained histological brain sections, and (ii) to determine spatial fiber orientations (i.e., 3D unit vectors down to the scales of fiber diameters (0.4–15 μm). 3D-PLI is applicable to unstained microtome sections of post mortem brains and utilizes the optical birefringence of nerve fibers, which basically arises from the highly ordered arrangement of lipid molecules in the myelin sheath surrounding most of the axons in the brain. Polarimetric setups (e.g., a polarizing microscope) are employed to carry out birefringence measurements and to give contrast to individual nerve fibers and their tracts. Supported by fundamental principles of optics and dedicated simulation approaches (Dohmen et al., 2015; Menzel et al., 2015), the measured signals are additionally interpreted in terms of spatial fiber orientations by means of unit orientation vector descriptions (Figure 1). The algorithms used for the fiber orientation interpretation have been implemented as an automated 3D-PLI analysis workflow suitable for distributed supercomputing, as described by Axer et al. (2011b); Amunts et al. (2014).


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)

3D-PLI in a nutshell. The fiber orientation vector  located at the native voxel location i is defined by the direction angle φi and the inclination angle αi. The brain section is aligned to the x-y-plane (at z = 0). In general, a unit vector can be described as a distinct point on a unit sphere. For 3D-PLI, the determined unit vector represents an axis in space (in the mathematical and the anatomical sense), since the initial and the terminal points cannot be assigned uniquely. Therefore, the point reflection across the center of the sphere has to be assigned to the same vector. For visualization purposes, the vectors were encoded in RGB color space: the x-component of  is represented by the red color channel (R), the y-component by the green color channel (G), and the z-component by the blue color channel (B).
© Copyright Policy
Related In: Results  -  Collection

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

Figure 1: 3D-PLI in a nutshell. The fiber orientation vector located at the native voxel location i is defined by the direction angle φi and the inclination angle αi. The brain section is aligned to the x-y-plane (at z = 0). In general, a unit vector can be described as a distinct point on a unit sphere. For 3D-PLI, the determined unit vector represents an axis in space (in the mathematical and the anatomical sense), since the initial and the terminal points cannot be assigned uniquely. Therefore, the point reflection across the center of the sphere has to be assigned to the same vector. For visualization purposes, the vectors were encoded in RGB color space: the x-component of is represented by the red color channel (R), the y-component by the green color channel (G), and the z-component by the blue color channel (B).
Mentions: 3D-PLI (Axer et al., 2011a,b; Zeineh et al., 2016) has demonstrated its unique capabilities (i) to reveal fiber structures at multiple scales, such as long-range connections and even single fibers and crossings within unstained histological brain sections, and (ii) to determine spatial fiber orientations (i.e., 3D unit vectors down to the scales of fiber diameters (0.4–15 μm). 3D-PLI is applicable to unstained microtome sections of post mortem brains and utilizes the optical birefringence of nerve fibers, which basically arises from the highly ordered arrangement of lipid molecules in the myelin sheath surrounding most of the axons in the brain. Polarimetric setups (e.g., a polarizing microscope) are employed to carry out birefringence measurements and to give contrast to individual nerve fibers and their tracts. Supported by fundamental principles of optics and dedicated simulation approaches (Dohmen et al., 2015; Menzel et al., 2015), the measured signals are additionally interpreted in terms of spatial fiber orientations by means of unit orientation vector descriptions (Figure 1). The algorithms used for the fiber orientation interpretation have been implemented as an automated 3D-PLI analysis workflow suitable for distributed supercomputing, as described by Axer et al. (2011b); Amunts et al. (2014).

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