GtractTensor

Tensor Estimation

Tensor Estimation

User Interface

Output

Caption

General Information

Module Type & Category

Type: CLI

Category: Diffusion.GTRACT

Authors, Collaborators & Contact

Author: This tool was developed by Vincent Magnotta and Greg Harris.

Contributors:

Contact: name, email

Module Description

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.
Program version	4.0.0
Program documentation-url	-

Usage

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:

Tutorials

• 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.

Input Image Files Input Image Volume [--inputVolume] : Required: input image 4D NRRD image. Must contain data based on at least 6 distinct diffusion directions. The inputVolume is allowed to have multiple b0 and gradient direction images. Averaging of the b0 image is done internally in this step. Prior averaging of the DWIs is not required. Output Image Files Output Tensor Image Volume [--outputVolume] : Required: name of output NRRD file containing the Tensor vector image Tensor Conversion Parameters Median Filter Size [--medianFilterSize] : Median filter radius in all 3 directions Default value: 0,0,0 B0 Image Threshold [--backgroundSuppressingThreshold] : Image threshold to suppress background. This sets a threshold used on the b0 image to remove background voxels from processing. Typically, values of 100 and 500 work well for Siemens and GE DTI data, respectively. Check your data particularly in the globus pallidus to make sure the brain tissue is not being eliminated with this threshold. Default value: 100 Resample To Isotropic Voxels [--resampleIsotropic] : Flag to resample to isotropic voxels. Enabling this feature is recommended if fiber tracking will be performed. Default value: 0 Isotropic Voxel Size [--size] : Isotropic voxel size to resample to Default value: 2.0 Vector Image B0 Index [--b0Index] : Index in input vector index to extract Default value: 0 Apply Measurement Frame [--applyMeasurementFrame] : Flag to apply the measurement frame to the gradient directions Default value: 0 Ignore Indices [--ignoreIndex] : Ignore diffusion gradient index. Used to remove specific gradient directions with artifacts.

User Interface

Development

Notes from the Developer(s)

Algorithms used, library classes depended upon, use cases, etc.

Dependencies

Other modules or packages that are required for this module's use.

Tests

On the Dashboard, these tests verify that the module is working on various platforms:

• MyModuleTest1 MyModuleTest1.cxx
• MyModuleTest2 MyModuleTest2.cxx

Known bugs

Links to known bugs in the Slicer3 bug tracker

• Bug 000: description

Usability issues

Follow this link to the Slicer3 bug tracker. Please select the usability issue category when browsing or contributing.

Source code & documentation

Links to the module's source code:

Source code:

• file.cxx
• file.h

Doxygen documentation:

• class1

More Information

Acknowledgment

Funding for this version of the GTRACT program was provided by NIH/NINDS R01NS050568-01A2S1

References

Publications related to this module go here. Links to pdfs would be useful.