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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

Simulated 3D-PLI reference dataset. (A) The standard 3D-PLI measurement yields 18 images corresponding to equidistant rotation angles ρ between 0° and 170°. Here, a selection of nine generated images of simulated birefringent structures (the letter R and the ± sign) is shown. Each image has a size of 200 × 200 pixels. The varying pixel intensities are comparable to observed signals in measurements of brain sections. The red squares indicate a native voxel of interest that is displayed (B) in terms of the observed light intensity as a function of the rotation angle. The physical model that underlies 3D-PLI provides a sinusoidal description of the simulation (continuous black line), and relates (C) its amplitude to the inclination angle α via the retardation value sinδand (D) its phase to the direction angle φ. The introduced effects of blurring and noise are evident: the minus sign in the direction map (D), for example, shows direction angles that are spread around the initial direction φ = 0° by±2.5°. In a π-periodic system, this is equivalent to an angle range between 177.5° and 2.5°. (E) Visualization of the FOM with the determined vectors  encoded in RGB color space (see color sphere for the relation between orientation and color-coding).
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Figure 2: Simulated 3D-PLI reference dataset. (A) The standard 3D-PLI measurement yields 18 images corresponding to equidistant rotation angles ρ between 0° and 170°. Here, a selection of nine generated images of simulated birefringent structures (the letter R and the ± sign) is shown. Each image has a size of 200 × 200 pixels. The varying pixel intensities are comparable to observed signals in measurements of brain sections. The red squares indicate a native voxel of interest that is displayed (B) in terms of the observed light intensity as a function of the rotation angle. The physical model that underlies 3D-PLI provides a sinusoidal description of the simulation (continuous black line), and relates (C) its amplitude to the inclination angle α via the retardation value sinδand (D) its phase to the direction angle φ. The introduced effects of blurring and noise are evident: the minus sign in the direction map (D), for example, shows direction angles that are spread around the initial direction φ = 0° by±2.5°. In a π-periodic system, this is equivalent to an angle range between 177.5° and 2.5°. (E) Visualization of the FOM with the determined vectors encoded in RGB color space (see color sphere for the relation between orientation and color-coding).

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)

Simulated 3D-PLI reference dataset. (A) The standard 3D-PLI measurement yields 18 images corresponding to equidistant rotation angles ρ between 0° and 170°. Here, a selection of nine generated images of simulated birefringent structures (the letter R and the ± sign) is shown. Each image has a size of 200 × 200 pixels. The varying pixel intensities are comparable to observed signals in measurements of brain sections. The red squares indicate a native voxel of interest that is displayed (B) in terms of the observed light intensity as a function of the rotation angle. The physical model that underlies 3D-PLI provides a sinusoidal description of the simulation (continuous black line), and relates (C) its amplitude to the inclination angle α via the retardation value sinδand (D) its phase to the direction angle φ. The introduced effects of blurring and noise are evident: the minus sign in the direction map (D), for example, shows direction angles that are spread around the initial direction φ = 0° by±2.5°. In a π-periodic system, this is equivalent to an angle range between 177.5° and 2.5°. (E) Visualization of the FOM with the determined vectors  encoded in RGB color space (see color sphere for the relation between orientation and color-coding).
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Figure 2: Simulated 3D-PLI reference dataset. (A) The standard 3D-PLI measurement yields 18 images corresponding to equidistant rotation angles ρ between 0° and 170°. Here, a selection of nine generated images of simulated birefringent structures (the letter R and the ± sign) is shown. Each image has a size of 200 × 200 pixels. The varying pixel intensities are comparable to observed signals in measurements of brain sections. The red squares indicate a native voxel of interest that is displayed (B) in terms of the observed light intensity as a function of the rotation angle. The physical model that underlies 3D-PLI provides a sinusoidal description of the simulation (continuous black line), and relates (C) its amplitude to the inclination angle α via the retardation value sinδand (D) its phase to the direction angle φ. The introduced effects of blurring and noise are evident: the minus sign in the direction map (D), for example, shows direction angles that are spread around the initial direction φ = 0° by±2.5°. In a π-periodic system, this is equivalent to an angle range between 177.5° and 2.5°. (E) Visualization of the FOM with the determined vectors encoded in RGB color space (see color sphere for the relation between orientation and color-coding).
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