Module Type & Category
Authors, Collaborators & Contact
Author: This tool was developed by Vincent Magnotta and Greg Harris.
Contact: name, email
|Program title||Tensor Estimation|
|Program description||This step will convert a b-value averaged diffusion tensor image to a 3x3 tensor voxel image. This step takes the diffusion tensor image data and generates a tensor representation of the data based on the signal intensity decay, b values applied, and the diffusion difrections. The apparent diffusion coefficient for a given orientation is computed on a pixel-by-pixel basis by fitting the image data (voxel intensities) to the Stejskal-Tanner equation. If at least 6 diffusion directions are used, then the diffusion tensor can be computed. This program uses itk::DiffusionTensor3DReconstructionImageFilter. The user can adjust background threshold, median filter, and isotropic resampling.|
Use Cases, Examples
This module is especially appropriate for these use cases:
- Use Case 1:
- Use Case 2:
Examples of the module in use:
- Example 1:
- Example 2:
- Tutorial 1
- Data Set 1
Quick Tour of Features and Use
A list panels in the interface, their features, what they mean, and how to use them.
Notes from the Developer(s)
Algorithms used, library classes depended upon, use cases, etc.
Other modules or packages that are required for this module's use.
On the Dashboard, these tests verify that the module is working on various platforms:
Links to known bugs in the Slicer3 bug tracker
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Source code & documentation
Links to the module's source code:
Funding for this version of the GTRACT program was provided by NIH/NINDS R01NS050568-01A2S1
Publications related to this module go here. Links to pdfs would be useful.