Orientation Dispersion Index
Convert nonnegative fitted Watson concentration κ to ODI=(2/π)atan(1/κ), with κ=0 mapped to one; fitting or validating a neurite model is not performed.
Description
The Watson concentration parameter describes directional concentration in an orientation model.
The Watson concentration parameter describes directional concentration in an orientation model. This conversion maps concentration to a dimensionless dispersion index.
The tool returns orientationDispersionIndexes (orientation dispersion indexes). Input measurements and model parameters are supplied by the caller; the tool computes the stated summary rather than acquiring those measurements.
When to use Orientation Dispersion Index
Use Orientation Dispersion Index when you already have watsonKappaValues and want orientationDispersionIndexes. This is useful for checking a supplied calculation, documenting a measurement convention, or comparing observations prepared on the same basis.
How to use Orientation Dispersion Index and understand its results
- Enter watsonKappaValues using the field table below. Match the units, ordering and data boundaries described in the calculation.
- Run the tool after checking the stated bounds and matching paired observations or categories. A malformed request gives a tool error rather than a usable measurement.
- Read orientationDispersionIndexes. Use the calculation and interpretation below to distinguish amounts, ratios, signs and decision flags where applicable.
- Review the supplied parameters and assumptions before using the result in a report or comparing it with another observation.
| Input field | Meaning | Value type and bounds | Requirement |
|---|---|---|---|
| watsonKappaValues | Watson kappa values | list of number | Required |
Convert each nonnegative fitted κ independently; κ=0 gives ODI=1 and κ=1 gives 0.5. Preserve list order.
ODI = (2/π) × atan2(1, κ) The symbols refer to the supplied quantities or generated values described above; unit conversions and comparison boundaries follow the stated implementation.
{
"input": {
"watsonKappaValues": [
0,
1
]
},
"output": {
"orientationDispersionIndexes": [
1,
0.5
]
}
}Limitations and assumptions
This converts existing fits; it neither fits the neurite model nor verifies its applicability.
Numerical inputs must be finite. The tool checks its declared input shape and calculation constraints; those checks do not verify the quality, calibration or representativeness of the supplied observations.
Alternative or Complementary methods
Average Fractional Anisotropy: Compute voxel FA from nonnegative tensor eigenvalues and an ROI-weighted average, rather than averaging tensor eigenvalues before deriving FA. Color Coded Fractional Anisotropy: Compute FA-weighted absolute principal-direction RGB and rounded 8-bit channels; coordinate-axis orientation must match the caller’s image convention.
Methodological background: NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain.1
References
-
NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain — Zhang et al.; NeuroImage
Similar or alternative tools
- Average Fractional Anisotropy
Compute voxel FA from nonnegative tensor eigenvalues and an ROI-weighted average, rather than averaging tensor eigenvalues before deriving FA.
- Color Coded Fractional Anisotropy
Compute FA-weighted absolute principal-direction RGB and rounded 8-bit channels; coordinate-axis orientation must match the caller’s image convention.