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

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Filters a set of diffusion weighted images in the mean squared error sense using a Rician noise model. The noise parameter is automatically estimated. New version using ITK pipelines and multi-threading. | Filters a set of diffusion weighted images in the mean squared error sense using a Rician noise model. The noise parameter is automatically estimated. New version using ITK pipelines and multi-threading. | ||

− | * NOTE: This filter is different from jointLMMSE. In this case each DWI volume (data corresponding to each gradient direction) is filtered separately and independently, so in fact it works by performing N independent | + | * NOTE: This filter is different from jointLMMSE. In this case each DWI volume (data corresponding to each gradient direction) is filtered separately and independently, so in fact it works by performing N independent filtering operations. We strongly recommend to use jointLMMSE to filter DWI volumes, since it is more accurate and only slightly slower. Even if N=1 is set for jointLMMSE, its behavior is different (see the documentation for this other method). |

== Usage == | == Usage == |

## Revision as of 10:10, 3 March 2009

Home < Modules:RicianLMMSEImageFilter-Documentation-3.4Return to Slicer 3.4 Documentation

### Module Name

RicianLMMSEImageFilter

## General Information

### Module Type & Category

Type: Interactive

Category: CLI/DiffusionApplications

### Authors, Collaborators & Contact

- Author: Antonio Tristán Vega , Santiago Aja Fernández and Marc Niethammer
- 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 noise parameter is automatically estimated. New version using ITK pipelines and multi-threading.

- NOTE: This filter is different from jointLMMSE. In this case each DWI volume (data corresponding to each gradient direction) is filtered separately and independently, so in fact it works by performing N independent filtering operations. We strongly recommend to use jointLMMSE to filter DWI volumes, since it is more accurate and only slightly slower. Even if N=1 is set for jointLMMSE, its behavior is different (see the documentation for this other method).

## 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**Minimum number of voxels for filtering:**The filter neglects background pixels, since they are often set to the artificial value 0. If less than this number of voxels are estimated to be out of the background, no filtering is performed.**Minimum number of voxels for estimation:**The filter neglects background pixels, since they are often set to the artificial value 0. If less than this number of voxels are estimated to be out of the background, this pixel is not included in noise estimation**Minimum noise std:**If the estimated noise is too low, it is likely to occur that the estimation is non well done, so this minimum value is used instead.**Maximum noise std:**If the estimated noise is too high, it is likely to occur that the estimation is non well done, so this maximum value is used instead.**Histogram resolution factor:**Noise power is estimated as the mode of the histogram of local variances. This parameter fix how accurately this value is searched for. Too fine resolution may produce weak estimates of the histogram bins.

## Development

### Dependencies

Volumes. Needed to load DWI volumes

### Known bugs

### Usability issues

### Source code & documentation

## More Information

### Acknowledgment

Antonio Tristan Vega, Santiago Aja Fernandez and Marc Niethammer. Partially founded by grant number TEC2007-67073/TCM from the Comision Interministerial de Ciencia y Tecnologia (Spain).