Average Mean Diffusivity
Derive mean diffusivity from each diffusion tensor eigenvalue triplet and aggregate supplied ROI weights; eigenvalues share one caller-selected diffusivity unit.
Description
Tensor mean diffusivity is the average of three diffusion eigenvalues.
Tensor mean diffusivity is the average of three diffusion eigenvalues. A weighted regional summary then combines voxel values using supplied weights.
The tool returns voxelMeanDiffusivities (voxel mean diffusivities); weightedMeanDiffusivity (weighted mean diffusivity). Input measurements and model parameters are supplied by the caller; the tool computes the stated summary rather than acquiring those measurements.
When to use Average Mean Diffusivity
Use Average Mean Diffusivity when you already have eigenvalues, voxelWeights and want voxelMeanDiffusivities, weightedMeanDiffusivity. This is useful for checking a supplied calculation, documenting a measurement convention, or comparing observations prepared on the same basis.
How to use Average Mean Diffusivity and understand its results
- Enter eigenvalues, voxelWeights 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 voxelMeanDiffusivities, weightedMeanDiffusivity. 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 |
|---|---|---|---|
| eigenvalues | Eigenvalues | list of list of number | Required |
| voxelWeights | Voxel weights | list of number | Required |
Require three nonnegative eigenvalues per voxel, aligned nonnegative weights and a positive total weight. Zero eigenvalues are allowed.
MD[v] = (λ1+λ2+λ3)/3; ROI MD = Σweight×MD/Σweight The symbols refer to the supplied quantities or generated values described above; unit conversions and comparison boundaries follow the stated implementation.
{
"input": {
"eigenvalues": [
[
1,
2,
3
],
[
3,
3,
3
]
],
"voxelWeights": [
1,
1
]
},
"output": {
"voxelMeanDiffusivities": [
2,
3
],
"weightedMeanDiffusivity": 2.5
}
}Limitations and assumptions
This averages voxel MD, not raw images or tensors. Eigenvalue estimation, region definition and diffusivity units are external.
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. ADC Calculation: Fit apparent diffusion coefficient from all supplied b-value/log-signal observations; rising signals yield a preserved negative ADC estimate rather than a fabricated nonnegative diffusivity.
Methodological background: DTIFIT: diffusion tensor output measures.1
References
-
DTIFIT: diffusion tensor output measures — FMRIB Software Library developers
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.
- ADC Calculation
Fit apparent diffusion coefficient from all supplied b-value/log-signal observations; rising signals yield a preserved negative ADC estimate rather than a fabricated nonnegative diffusivity.