# Difference between revisions of "Modules:JointRicianLMMSEImageFilter-Documentation-3.4"

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* '''Filtering radius:''' This is the 3D radius of the neighborhood used for filtering: local means and covariance matrices are estimated within this neighborhood | * '''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. | * '''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. | ||

+ | ** NOTE: If N=1 is used this filter is similar (but not equal) to RicianLMMSEImageFilter. Two main differences exist: 1) 4-th order moments have to be computed only for baseline(s) image(s), and 2) if more than one baseline is present all of them are filtered together even if N=1. | ||

== Development == | == Development == |

## Latest revision as of 20:06, 26 May 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.- NOTE: If N=1 is used this filter is similar (but not equal) to RicianLMMSEImageFilter. Two main differences exist: 1) 4-th order moments have to be computed only for baseline(s) image(s), and 2) if more than one baseline is present all of them are filtered together even if N=1.

## 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).