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Introduction and Acknowledgements

Partially founded by grant number TEC2007-67073/TCM from the Comision Interministerial de Ciencia y Tecnologia (Spain). 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: Antonio Tristan Vega, Universidad de Valladolid, Laboratory of Mathematics in Imaging (LMI) Contributor1: Santiago Aja Fernandez, Universidad de Valladolid Contributor2: Marc Niethammer, University of North Carolina at Chapel Hill (UNC) Contact: Antonio Tristan Vega,

Laboratory of Mathematics in Imaging (LMI)   University of North Carolina at Chapel Hill (UNC)

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

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Tutorials

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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

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References

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.

Information for Developers

Section under construction.