Modules:RicianLMMSEImageFilter-Documentation-3.6

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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

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. Each gradient direction is processed individually. 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

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

Development

Dependencies

Volumes. Needed to load DWI volumes

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).