Modules:RicianLMMSEImageFilter-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. Images corresponding to each gradient direction, including baseline, are processed individually. The noise parameter is automatically estimated (noise estimation improved but slower).

Note that this is a general purpose filter for MRi images. The module jointLMMSE has been specifically designed for DWI volumes and shows a better performance, so its use is recommended instead.

A complete description of the algorithm in this module can be found in:

S. Aja-Fernández, M. Niethammer, M. Kubicki, M. Shenton, and C.-F. Westin. "Restoration of DWI data using a Rician LMMSE estimator". IEEE Transactions on Medical Imaging, 27(10): pp. 1389-1403, Oct. 2008.

Usage

Examples, Use Cases & Tutorials

A brief description of the most important parameters is given below.

Before filtering:

Original unfiltered image

After filtering:

Original unfiltered image

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

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 funded by grant number TEC2007-67073/TCM from the Ministerio de Ciencia e Innovación (Spain) and "FEDER" European Regional Development Fund.

References