Difference between revisions of "Modules:DiffusionTensorEstimation-Documentation-3.4"
From Slicer Wiki
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===Examples, Use Cases & Tutorials=== | ===Examples, Use Cases & Tutorials=== | ||
| − | * Compute DTI volume from DWI. It is the first step of any pipeline that | + | * Compute DTI volume from DWI. It is the first step of any pipeline that employs DTI to assess white matter structure. |
===Quick Tour of Features and Use=== | ===Quick Tour of Features and Use=== | ||
The module takes one DWI volume and computes a DTI volume. The parameters are the following: | The module takes one DWI volume and computes a DTI volume. The parameters are the following: | ||
| − | + | * '''Input/Output:''' Defines input and output files. | |
| − | * '''Input | + | ** ''Input DWI Volume'' is the input DWI volume, |
| − | * '''Parameters | + | ** ''Output DTI Volume'' is the DTI volume that will be estimated. |
| − | * ''' | + | ** ''Output Baseline Volume'' is the average of the B0 images (non-diffusion weighted images) of the DWI sequence. This volume is useful to have a structural representation of the DTI volume . |
| − | * ''' | + | ** ''Otsu Threshold Mask'' is a approximated mask of the white matter that can be used to filter out the background. |
| + | * '''Estimation Parameters:''' | ||
| + | ** ''Least Squares'': Least Squares estimation method. This is the method by default and stable [Basser, 2002]. | ||
| + | ** ''Weighted Least Squares'': WLS estimation method based on Salvador. This method implementation is still experimental [Salvador, 2005]. | ||
| + | ** ''Non-linear'': direct non-least squares fitting of the tensor model to the data without log-transformation. This method is experimental. | ||
| + | ** ''Otsu Omega Threshold Parameter'': weight that controls the otsu threshold. | ||
| + | ** ''Remove Island in Tensor Mask'': if active, holes in the produced mask will be removed. | ||
| + | ** ''Apply Mask to Tensor Image'': Mask output DTI volume with the computed mask. Tensor outside the mask will be set to zero. | ||
== Development == | == Development == | ||
| Line 46: | Line 53: | ||
Follow this [http://na-mic.org/Mantis/main_page.php link] to the Slicer3 bug tracker. | Follow this [http://na-mic.org/Mantis/main_page.php link] to the Slicer3 bug tracker. | ||
| − | |||
| − | |||
===Usability issues=== | ===Usability issues=== | ||
| Line 63: | Line 68: | ||
===Acknowledgment=== | ===Acknowledgment=== | ||
| − | |||
===References=== | ===References=== | ||
| − | + | *P. J. Basser and D. K. Jones. Diffusion-tensor MRI: theory, experimental design and data analysis - a technical review. NMR Biomed, 15(7-8):456–67, 2002. | |
| + | *R. Salvador, A. Pena, D. K. Menon, T. A. Carpenter, J. D. Pickard, and E. T. Bullmore. Formal characterization and extension of the linearized diffusion tensor model. Hum Brain Mapp, 24(2):144–55, 2005. | ||
Revision as of 20:25, 14 April 2009
Home < Modules:DiffusionTensorEstimation-Documentation-3.4Return to Slicer 3.4 Documentation
Module Name
MyModule
General Information
Module Type & Category
Type: CLI
Category: Base or (Filtering, Registration, etc.)
Authors, Collaborators & Contact
- Author1: Raúl San José Estépar, BWH
- Contributor1: Gordon Kindlmann, University of Chicago
- Contact: [1]
Module Description
This module estimates the diffusion tensor model from a Diffusion Weighted Image (DWI) Volume. The results is a tensor volume that can be used to compute different anisotropy measurements, for example Fractional Anisotropy, and perform tractography.
Usage
Examples, Use Cases & Tutorials
- Compute DTI volume from DWI. It is the first step of any pipeline that employs DTI to assess white matter structure.
Quick Tour of Features and Use
The module takes one DWI volume and computes a DTI volume. The parameters are the following:
- Input/Output: Defines input and output files.
- Input DWI Volume is the input DWI volume,
- Output DTI Volume is the DTI volume that will be estimated.
- Output Baseline Volume is the average of the B0 images (non-diffusion weighted images) of the DWI sequence. This volume is useful to have a structural representation of the DTI volume .
- Otsu Threshold Mask is a approximated mask of the white matter that can be used to filter out the background.
- Estimation Parameters:
- Least Squares: Least Squares estimation method. This is the method by default and stable [Basser, 2002].
- Weighted Least Squares: WLS estimation method based on Salvador. This method implementation is still experimental [Salvador, 2005].
- Non-linear: direct non-least squares fitting of the tensor model to the data without log-transformation. This method is experimental.
- Otsu Omega Threshold Parameter: weight that controls the otsu threshold.
- Remove Island in Tensor Mask: if active, holes in the produced mask will be removed.
- Apply Mask to Tensor Image: Mask output DTI volume with the computed mask. Tensor outside the mask will be set to zero.
Development
Dependencies
None
Known bugs
Follow this link to the Slicer3 bug tracker.
Usability issues
Follow this link to the Slicer3 bug tracker. Please select the usability issue category when browsing or contributing.
Source code & documentation
Customize following links for your module.
Links to documentation generated by doxygen.
More Information
Acknowledgment
References
- P. J. Basser and D. K. Jones. Diffusion-tensor MRI: theory, experimental design and data analysis - a technical review. NMR Biomed, 15(7-8):456–67, 2002.
- R. Salvador, A. Pena, D. K. Menon, T. A. Carpenter, J. D. Pickard, and E. T. Bullmore. Formal characterization and extension of the linearized diffusion tensor model. Hum Brain Mapp, 24(2):144–55, 2005.
