Documentation/4.0/Modules/DWIRicianLMMSEFilter

From SlicerWiki

Jump to: navigation, search
Home < Documentation < 4.0 < Modules < DWIRicianLMMSEFilter


Introduction and Acknowledgements

This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on NA-MIC can be obtained from the NA-MIC website.
Author: FIRSTNAME LASTNAME, AFFILIATION
Contributor1: FIRSTNAME LASTNAME, AFFILIATION
Contributor2: FIRSTNAME LASTNAME, AFFILIATION
Contact: FIRSTNAME LASTNAME,

Isomics, Inc. <- Replace this logo with yours  
Surgical Planning Laboratory (SPL) <-Replace this logo with yours  

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-Fernandez, 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.


Use Cases

N/A

Tutorials

N/A

Panels and their use

Parameters:

  • DWI Noise Removal Parameters
    • Number of iterations: Number of iterations for the noise removal filter.
    • Estimation Radius: Estimation radius.
    • Filtering Radius: Filtering radius.
    • Minimum voxels # for filtering.: Minimum number of voxels in kernel used for filtering.
    • Minimum voxels # for estimation.: Minimum number of voxels in kernel used for estimation.
    • Minimum noise STD.: Minimum allowed noise standard deviation.
    • Maximum noise STD.: Maximum allowed noise standard deviation.
    • Histogram resolution factor.: How many histogram bins per unit interval.
    • Use absolute value.: Use absolute value in case of negative square.
  • IO
    • Input Volume: Input DWI volume.
    • Output Volume: Output DWI volume.


Similar Modules

N/A

References

N/A

Information for Developers

Personal tools