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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

Three steps toward pliODF generation. (A) First, a FOM is divided into regular domains or super-voxels. The exemplarily enlarged super-voxel contains 40 × 40 × 1 native voxels representing three predominant fiber orientations, which show a relative frequency of occurrence of ~¼(blue color), ~¼(magenta color) and ~½(cyan color), respectively. The color sphere defines the relation between orientation and color-coding. (B) Second, a normalized directional histogram with a discretized binning on a unit sphere is created for each super-voxel. The relative fraction of fiber orientations assigned to a particular bin is reflected by the length of the colored solid angle originating from the middle of the sphere. The symmetry of the histogram with respect to point reflection across the center of the sphere is evident. Here, the total number of bins distributed over the sphere was set to 164 and the three predominant input fiber orientations are still preserved. (C) Third, a spherical harmonics expansion is used to approximate each directional histogram. Depending on the selected depth of expansion (e.g., to the 4th or the 6th band), orientation distribution features might become occluded by interpolation.
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Figure 3: Three steps toward pliODF generation. (A) First, a FOM is divided into regular domains or super-voxels. The exemplarily enlarged super-voxel contains 40 × 40 × 1 native voxels representing three predominant fiber orientations, which show a relative frequency of occurrence of ~¼(blue color), ~¼(magenta color) and ~½(cyan color), respectively. The color sphere defines the relation between orientation and color-coding. (B) Second, a normalized directional histogram with a discretized binning on a unit sphere is created for each super-voxel. The relative fraction of fiber orientations assigned to a particular bin is reflected by the length of the colored solid angle originating from the middle of the sphere. The symmetry of the histogram with respect to point reflection across the center of the sphere is evident. Here, the total number of bins distributed over the sphere was set to 164 and the three predominant input fiber orientations are still preserved. (C) Third, a spherical harmonics expansion is used to approximate each directional histogram. Depending on the selected depth of expansion (e.g., to the 4th or the 6th band), orientation distribution features might become occluded by interpolation.

Mentions: In order to validate the different methodological steps employed to transfer a FOM into a set of orientation distribution functions, a well-defined template providing unambiguous structural macroscopic and microscopic features in terms of left/right, top/down and in-plane/out-of-plane orientations, was required. This dataset generated by means of SimPLI is shown in Figures 2A–D. It is composed of a stack of 18 images and comprises birefringent “fibers” forming human readable structures (“fiber bundles”), such as the capital letter “R” and the “±” sign. The line thickness of the letters (or the thickness of the “fiber bundles”) was chosen to be 20 pixels on average. The fiber inclination angles in “R” were all set to α = 0°, while the inclination angles were set to α = +45° and α = −45° for the “+” and “−” sign, respectively. The direction angles φ were aligned with the local structures using a right-handed coordinate system, i.e., the horizontal components (e.g., the “−“ sign) are identified by φ = 0° while the vertical components are represented by φ = 90°. The diagonal element of the “R” has a direction of φ = 135°. The background is composed of 90° inclined fibers corresponding to light intensity variations equal to zero. This dataset was subjected to the 3D-PLI analysis workflow to extract the corresponding FOM (Figures 2E, 3A).


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)

Three steps toward pliODF generation. (A) First, a FOM is divided into regular domains or super-voxels. The exemplarily enlarged super-voxel contains 40 × 40 × 1 native voxels representing three predominant fiber orientations, which show a relative frequency of occurrence of ~¼(blue color), ~¼(magenta color) and ~½(cyan color), respectively. The color sphere defines the relation between orientation and color-coding. (B) Second, a normalized directional histogram with a discretized binning on a unit sphere is created for each super-voxel. The relative fraction of fiber orientations assigned to a particular bin is reflected by the length of the colored solid angle originating from the middle of the sphere. The symmetry of the histogram with respect to point reflection across the center of the sphere is evident. Here, the total number of bins distributed over the sphere was set to 164 and the three predominant input fiber orientations are still preserved. (C) Third, a spherical harmonics expansion is used to approximate each directional histogram. Depending on the selected depth of expansion (e.g., to the 4th or the 6th band), orientation distribution features might become occluded by interpolation.
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Figure 3: Three steps toward pliODF generation. (A) First, a FOM is divided into regular domains or super-voxels. The exemplarily enlarged super-voxel contains 40 × 40 × 1 native voxels representing three predominant fiber orientations, which show a relative frequency of occurrence of ~¼(blue color), ~¼(magenta color) and ~½(cyan color), respectively. The color sphere defines the relation between orientation and color-coding. (B) Second, a normalized directional histogram with a discretized binning on a unit sphere is created for each super-voxel. The relative fraction of fiber orientations assigned to a particular bin is reflected by the length of the colored solid angle originating from the middle of the sphere. The symmetry of the histogram with respect to point reflection across the center of the sphere is evident. Here, the total number of bins distributed over the sphere was set to 164 and the three predominant input fiber orientations are still preserved. (C) Third, a spherical harmonics expansion is used to approximate each directional histogram. Depending on the selected depth of expansion (e.g., to the 4th or the 6th band), orientation distribution features might become occluded by interpolation.
Mentions: In order to validate the different methodological steps employed to transfer a FOM into a set of orientation distribution functions, a well-defined template providing unambiguous structural macroscopic and microscopic features in terms of left/right, top/down and in-plane/out-of-plane orientations, was required. This dataset generated by means of SimPLI is shown in Figures 2A–D. It is composed of a stack of 18 images and comprises birefringent “fibers” forming human readable structures (“fiber bundles”), such as the capital letter “R” and the “±” sign. The line thickness of the letters (or the thickness of the “fiber bundles”) was chosen to be 20 pixels on average. The fiber inclination angles in “R” were all set to α = 0°, while the inclination angles were set to α = +45° and α = −45° for the “+” and “−” sign, respectively. The direction angles φ were aligned with the local structures using a right-handed coordinate system, i.e., the horizontal components (e.g., the “−“ sign) are identified by φ = 0° while the vertical components are represented by φ = 90°. The diagonal element of the “R” has a direction of φ = 135°. The background is composed of 90° inclined fibers corresponding to light intensity variations equal to zero. This dataset was subjected to the 3D-PLI analysis workflow to extract the corresponding FOM (Figures 2E, 3A).

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