NIPY logo
Home · Quickstart · Documentation · Citation · NiPy
Loading

Table Of Contents

Versions

ReleaseDevel
0.6.0pre-0.7
Download Github

Links

interfaces.slicer.diffusion.denoising

dwiNoiseFilter

Code: file:///build/buildd/nipype-0.6.0/nipype/interfaces/slicer/diffusion/denoising.py#L66

Wraps command ** dwiNoiseFilter **

title: Rician LMMSE Image Filter

category: Diffusion.Denoising

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.

version: 0.1.1.$Revision: 1 $(alpha)

documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.0/Modules/RicianLMMSEImageFilter

contributor: Antonio Tristan Vega, Santiago Aja Fernandez and Marc Niethammer. Partially founded by grant number TEC2007-67073/TCM from the Comision Interministerial de Ciencia y Tecnologia (Spain).

Inputs:

[Mandatory]

[Optional]
args: (a string)
        Additional parameters to the command
environ: (a dictionary with keys which are a value of type 'str' and with values which
         are a value of type 'str', nipype default value: {})
        Environment variables
hrf: (a float)
        How many histogram bins per unit interval.
ignore_exception: (a boolean, nipype default value: False)
        Print an error message instead of throwing an exception in case the interface fails to
        run
inputVolume: (an existing file name)
        Input DWI volume.
iter: (an integer)
        Number of iterations for the noise removal filter.
maxnstd: (an integer)
        Maximum allowed noise standard deviation.
minnstd: (an integer)
        Minimum allowed noise standard deviation.
mnve: (an integer)
        Minimum number of voxels in kernel used for estimation.
mnvf: (an integer)
        Minimum number of voxels in kernel used for filtering.
outputVolume: (a boolean or a file name)
        Output DWI volume.
re: (an integer)
        Estimation radius.
rf: (an integer)
        Filtering radius.
uav: (a boolean)
        Use absolute value in case of negative square.

Outputs:

outputVolume: (an existing file name)
        Output DWI volume.

jointLMMSE

Code: file:///build/buildd/nipype-0.6.0/nipype/interfaces/slicer/diffusion/denoising.py#L21

Wraps command ** jointLMMSE **

title: Joint Rician LMMSE Image Filter

category: Diffusion.Denoising

description: This module reduces Rician 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. The N closest gradient directions to the direction being processed are filtered together to improve the results: the noise-free signal is seen as an n-diemensional vector which has to be estimated with the LMMSE method from a set of corrupted measurements. To that end, the covariance matrix of the noise-free vector and the cross covariance between this signal and the noise have to be estimated, which is done taking into account the image formation process. The noise parameter is automatically estimated from a rough segmentation of the background of the image. In this area the signal is simply 0, so that Rician statistics reduce to Rayleigh and the noise power can be easily estimated from the mode of the histogram. 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.

version: 0.1.1.$Revision: 1 $(alpha)

documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.0/Modules/JointRicianLMMSEImageFilter

contributor: Antonio Tristan Vega, Santiago Aja Fernandez. University of Valladolid (SPAIN). Partially founded by grant number TEC2007-67073/TCM from the Comision Interministerial de Ciencia y Tecnologia (Spain).

Inputs:

[Mandatory]

[Optional]
args: (a string)
        Additional parameters to the command
environ: (a dictionary with keys which are a value of type 'str' and with values which
         are a value of type 'str', nipype default value: {})
        Environment variables
ignore_exception: (a boolean, nipype default value: False)
        Print an error message instead of throwing an exception in case the interface fails to
        run
inputVolume: (an existing file name)
        Input DWI volume.
ng: (an integer)
        The number of the closest gradients that are used to jointly filter a given gradient
        direction (0 to use all).
outputVolume: (a boolean or a file name)
        Output DWI volume.
re: (an integer)
        Estimation radius.
rf: (an integer)
        Filtering radius.

Outputs:

outputVolume: (an existing file name)
        Output DWI volume.