nipype.interfaces.slicer.legacy.diffusion.denoising module

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DWIUnbiasedNonLocalMeansFilter

Link to code

Bases: SEMLikeCommandLine

Wrapped executable: DWIUnbiasedNonLocalMeansFilter.

title: DWI Unbiased Non Local Means Filter

category: Legacy.Diffusion.Denoising

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.

version: 0.0.1.$Revision: 1 $(alpha)

documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.1/Modules/UnbiasedNonLocalMeansFilterForDWI

contributor: Antonio Tristan Vega (UVa), Santiago Aja Fernandez (UVa)

acknowledgements: Partially founded by grant number TEC2007-67073/TCM from the Comision Interministerial de Ciencia y Tecnologia (Spain).

Optional Inputs:

  • args (a string) – Additional parameters to the command. Maps to a command-line argument: %s.

  • environ (a dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’) – Environment variables. (Nipype default value: {})

  • hp (a float) – This parameter is related to noise; the larger the parameter, the more aggressive the filtering. Should be near 1, and only values between 0.8 and 1.2 are allowed. Maps to a command-line argument: --hp %f.

  • inputVolume (a pathlike object or string representing an existing file) – Input DWI volume. Maps to a command-line argument: %s (position: -2).

  • ng (an integer) – The number of the closest gradients that are used to jointly filter a given gradient direction (a maximum of 5 is allowed). Maps to a command-line argument: --ng %d.

  • outputVolume (a boolean or a pathlike object or string representing a file) – Output DWI volume. Maps to a command-line argument: %s (position: -1).

  • rc (a list of items which are an integer) – Similarity between blocks is measured using windows of this size. Maps to a command-line argument: --rc %s.

  • re (a list of items which are an integer) – A neighborhood of this size is used to compute the statistics for noise estimation. Maps to a command-line argument: --re %s.

  • rs (a list of items which are an integer) – The algorithm search for similar voxels in a neighborhood of this size (larger sizes than the default one are extremely slow). Maps to a command-line argument: --rs %s.

Outputs:

outputVolume (a pathlike object or string representing an existing file) – Output DWI volume.