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Home < Modules:JointRicianLMMSEImageFilter-Documentation-3.4

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Module Name


General Information

Module Type & Category

Type: Interactive

Category: CLI/DiffusionApplications

Authors, Collaborators & Contact

  • Author: Antonio Tristán Vega and Santiago Aja Fernández
  • Contact:

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).


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.



Volumes. Needed to load DWI volumes

Known bugs

Usability issues

Source code & documentation

More Information


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).