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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,

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Module Description

This module reduces noise (or unwanted detail) on a set of diffusion weighted images. For this, it filters the images using a Unbiased Non Local Means for Rician noise algorithm. It exploits not only the spatial redundancy, but the redundancy in similar gradient directions as well; it takes into account the N closest gradient directions to the direction being processed (a maximum of 5 gradient directions is allowed to keep a reasonable computational load, since we do not use neither similarity maps nor block-wise implementation). The noise parameter is automatically estimated in the same way as in the jointLMMSE module. A complete description of the algorithm may be found in: Antonio Tristan-Vega and Santiago Aja-Fernandez, DWI filtering using joint information for DTI and HARDI, Medical Image Analysis, Volume 14, Issue 2, Pages 205-218. 2010. Please, note that the execution of this filter is extremely slow, son only very conservative parameters (block size and search size as small as possible) should be used. Even so, its execution may take several hours. The advantage of this filter over joint LMMSE is its better preservation of edges and fine structures.

Use Cases

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Tutorials

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Panels and their use

Parameters:

• UNLM for DWI Noise Removal Parameters
  • Search radius: The algorithm search for similar voxels in a neighborhood of this size (larger sizes than the default one are extremely slow).
  • Comparison radius: Similarity between blocks is measured using windows of this size.
  • h parameter: This parameter is related to noise; the larger the parameter, the more agressive the filtering. Should be near 1, and only values between 0.8 and 1.2 are allowed
  • Number of neighboring gradients: The number of the closest gradients that are used to jointly filter a given gradient direction (a maximum of 5 is allowed).
  • Noise Estimation Radius: A neighborhood of this size is used to compute the statistics for noise estimation.
• IO
  • Input Volume: Input DWI volume.
  • Output Volume: Output DWI volume.

Similar Modules

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References

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Information for Developers

Section under construction.