Fiber Orientation Mapping
Fiber Orientation Mapping - Related Videos
12:09
Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
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Cited by 14 •
2013
A methodology to estimate ventricular fiber orientations from in vivo images of patient heart geometries for personalized modeling is described. Validation of the methodology performed using normal and failing canine hearts demonstrate that that there are no significant differences between estimated and acquired fiber orientations at a clinically observable level.
09:48
Fiber Optic Distributed Sensors for High-resolution Temperature Field Mapping
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2016
We demonstrate use of a fiber optic distributed sensor for mapping the temperature field of mixing air jets. The Rayleigh scattering-based sensor generates thousands of data points along a single fiber to provide exceptional spatial resolution that is unattainable with traditional sensors such as thermocouples.
08:28
Microfluidic Dry-spinning and Characterization of Regenerated Silk Fibroin Fibers
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A protocol for the microfluidic spinning and microstructure characterization of regenerated silk fibroin monofilament is presented.
05:36
Determining Spatial Orientation of Rock Layers with the Brunton Compass
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2023
Source: Laboratory of Alan Lester - University of Colorado Boulder Most rock units exhibit some form of planar surfaces or linear features. Examples include bedding-, fault-, fracture-, and joint-surfaces, and various forms of foliation and mineral alignment. The spatial orientation of these features form the critical raw data used to constrain models addressing the origin and subsequent deformation of rock units. Although now over 100 years since its invention and introduction, the Brunton...
16:23
Fiber Connections of the Supplementary Motor Area Revisited: Methodology of Fiber Dissection, DTI, and Three Dimensional Documentation
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Cited by 32 •
2017
The purpose of this study is to show each step of the fiber dissection technique on human cadaveric brains, the 3D documentation of these dissections, and the diffusion tensor imaging of the anatomically dissected fiber pathways.
FAQs
Fiber orientation mapping relies on the contrast between diffusion along an aligned bundle and diffusion across it. When cellular alignment constrains water differently by direction, the diffusion MRI signal contains directional information that models can translate into estimated fiber orientations. This relationship makes the maps sensitive to tissue organization, rather than simply showing anatomical brightness.
Direction-dependent diffusion signals are the key measurable input. Strong alignment produces preferential movement along fiber bundles, while the cross-fiber direction produces a different signal pattern. Models compare these directional patterns to estimate local orientations. The resulting representation can reveal how tissue is organized and can serve as the basis for following pathways in tractography.
Interpretation depends on image quality and the assumptions built into the model used to convert diffusion signals into orientations. If either source is limiting, an apparent pathway direction may not fully represent the underlying tissue arrangement. Therefore, orientation maps should be treated as estimates of organization, not direct photographs of individual fibers.
Tractography and fiber orientation mapping are related but not identical. The orientation map provides estimated local directions, whereas tractography can use those directions to represent longer pathways. This distinction matters when interpreting results: a visually continuous reconstructed tract depends on the quality of the underlying directional estimates and should not be confused with direct visualization of every fiber.
A basic workflow begins with diffusion magnetic resonance imaging, followed by modeling of the direction-dependent signals. The model produces an orientation map, and the map may then support tractography when pathway visualization is needed. Each stage contributes differently: imaging supplies the signal, modeling estimates local direction, and tractography uses those estimates to represent connectivity.
It can show the estimated arrangement of aligned pathways and help visualize connectivity within the brain or spinal cord. The output is useful for examining how structures relate spatially and for identifying changes in organization associated with neurological injury or disease. These results provide structural context when changes are being assessed.
By depicting the relationship between critical pathways and a lesion, the maps can help clinicians consider pathway location when planning an intervention. Their value is greatest when the directional estimates are interpretable, because image quality and modeling assumptions can influence whether a displayed pathway accurately reflects the underlying tissue organization.
The technique estimates orientation from diffusion behavior through a chosen model rather than directly displaying fiber direction. Consequently, image quality and modeling assumptions can alter the displayed organization. Careful interpretation is especially important when maps are used to assess neurological injury, disease, or connectivity, where misleading pathway estimates could affect the resulting scientific or clinical conclusions.