Difference between revisions of "Modules:JointRicianLMMSEImageFilter-Documentation-3.6"

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Type: CLI
 
Type: CLI
  
Category: Diffusion Applications
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Category: Diffusion MRI Applications
  
 
===Authors, Collaborators & Contact===
 
===Authors, Collaborators & Contact===

Revision as of 15:51, 8 May 2010

Home < Modules:JointRicianLMMSEImageFilter-Documentation-3.6

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Gallery of New Features

Module Name

jointLMMSE


General Information

Module Type & Category

Type: CLI

Category: Diffusion MRI Applications

Authors, Collaborators & Contact

  • Author: Antonio Tristán Vega and Santiago Aja Fernández
  • Contact: atriveg@bwh.harvard.edu

Module Description

This module reduces noise (or unwanted detail) on a set of diffusion weighted images. For this, it filters the image 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", Antonio Tristan Vega and Santiago Aja-Fernandez, Medical Image Analysis, Volume 14, Issue 2, Pages 205-218.

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

Source Code: Follow this link

Doxygen 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).

References