Difference between revisions of "Modules:JointRicianLMMSEImageFilter-Documentation-3.4"
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* '''Input DWI Volume:''' set the DWI volume | * '''Input DWI Volume:''' set the DWI volume | ||
| + | * '''Output DWI Volume:''' the filtered DWI volume | ||
| + | * '''Estimation radius:''' This is the 3D radius of the neighborhood used for noise estimation. Noise power is estimated as the mode of the histogram of local variances | ||
| + | * '''Filtering radius:''' This is the 3D radius of the neighborhood used for filtering: local means and covariance matrices are estimated within this neighborhood | ||
| + | * '''Number of neighborhood gradients:''' This filter works gathering joint information from the N closest gradient directions to the one under study. This parameter is N. If N=0 is fixed, then all gradient directions are filtered together. | ||
== Development == | == Development == | ||
Revision as of 09:26, 3 March 2009
Home < Modules:JointRicianLMMSEImageFilter-Documentation-3.4Return to Slicer 3.4 Documentation
Module Name
jointLMMSE
General Information
Module Type & Category
Type: Interactive
Category: CLI/DiffusionApplications
Authors, Collaborators & Contact
- Author: Antonio Tristán Vega and Santiago Aja Fernández
- Contact: atriveg@bwh.harvard.edu
Module Description
Filters a set of diffusion weighted images in the mean squared error sense using a Rician noise model. The N closest gradient directions to the direction being processed are filtered together to improve the results. The noise parameter is automatically estimated (noise estimation improved but slower). A complete description of the algorithm may be found in "DWI filtering using joint information for DTI and HARDI", by Antonio Tristan Vega and Santiago Aja-Fernandez (under review).
Usage
Examples, Use Cases & Tutorials
Quick Tour of Features and Use
It is very easy to use it. Just select a DWI, set the parameters (if you really need it), and you're ready to go.
- Input DWI Volume: set the DWI volume
- Output DWI Volume: the filtered DWI volume
- Estimation radius: This is the 3D radius of the neighborhood used for noise estimation. Noise power is estimated as the mode of the histogram of local variances
- Filtering radius: This is the 3D radius of the neighborhood used for filtering: local means and covariance matrices are estimated within this neighborhood
- Number of neighborhood gradients: This filter works gathering joint information from the N closest gradient directions to the one under study. This parameter is N. If N=0 is fixed, then all gradient directions are filtered together.
Development
Dependencies
Volumes. Needed to load DWI volumes
Known bugs
Usability issues
Source code & documentation
More Information
Acknowledgment
Antonio Tristan Vega, Santiago Aja Fernandez. University of Valladolid (SPAIN). Partially founded by grant number TEC2007-67073/TCM from the Comision Interministerial de Ciencia y Tecnologia (Spain).
