AFNI DWI Uncertainty Estimation (3dDWUncert)¶
Library: AFNI | Docker Image: brainlife/afni
Function¶
Estimates uncertainty of diffusion tensor parameters via jackknife or bootstrap resampling of DWI data.
Modality: 4D diffusion-weighted dataset with gradient vector file.
Typical Use: Generating uncertainty estimates for probabilistic tractography in AFNI diffusion pipelines.
Key Parameters¶
-inset (input DWI), -prefix (output prefix), -grads (gradient file), -mask (brain mask), -iters (iterations, default 300)
Key Points¶
Provides confidence intervals for FA and eigenvector directions. Output used as input for probabilistic tractography with 3dTrackID. Computationally intensive.
Inputs¶
| Name | Type | Required | Label | Flag |
|---|---|---|---|---|
inset |
File |
Yes | Input DWI 4D dataset | -inset |
prefix |
string |
Yes | Output dataset prefix | -prefix |
grads |
File |
Yes | Gradient vector file | -grads |
mask |
File |
No | Brain mask dataset | -mask |
iters |
int |
No | Number of jackknife/bootstrap iterations (default 300) | -iters |
pt_choose_seed |
int |
No | Seed for random number generator | -pt_choose_seed |
Accepted Input Extensions¶
- inset:
.nii,.nii.gz,.HEAD - grads:
.1D,.txt - mask:
.nii,.nii.gz,.HEAD
Outputs¶
| Name | Type | Glob Pattern |
|---|---|---|
uncertainty |
File[] |
$(inputs.prefix)+orig.HEAD, $(inputs.prefix)+orig.BRIK, $(inputs.prefix)+tlrc.HEAD, $(inputs.prefix)+tlrc.BRIK |
log |
File |
$(inputs.prefix).log |
Output Extensions¶
- uncertainty:
.HEAD,.nii,.nii.gz
Docker Tags¶
Available versions: latest, 16.3.0
Categories¶
- Diffusion MRI > AFNI > Uncertainty