# 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 == | ||

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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=== | ||

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===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.