interfaces.slicer.diffusion.diffusion¶
DTIexport¶
Wraps the executable command ``DTIexport ``.
title: DTIexport
category: Diffusion.Diffusion Data Conversion
description: Export DTI data to various file formats
version: 1.0
documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.1/Modules/DTIExport
contributor: Sonia Pujol (SPL, BWH)
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.
Inputs:
[Optional]
inputTensor: (an existing file name)
Input DTI volume
argument: ``%s``, position: -2
outputFile: (a boolean or a file name)
Output DTI file
argument: ``%s``, position: -1
args: (a unicode string)
Additional parameters to the command
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', nipype default value: {})
Environment variables
Outputs:
outputFile: (an existing file name)
Output DTI file
DTIimport¶
Wraps the executable command ``DTIimport ``.
title: DTIimport
category: Diffusion.Diffusion Data Conversion
description: Import tensor datasets from various formats, including the NifTi file format
version: 1.0
documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.1/Modules/DTIImport
contributor: Sonia Pujol (SPL, BWH)
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.
Inputs:
[Optional]
inputFile: (an existing file name)
Input DTI file
argument: ``%s``, position: -2
outputTensor: (a boolean or a file name)
Output DTI volume
argument: ``%s``, position: -1
testingmode: (a boolean)
Enable testing mode. Sample helix file (helix-DTI.nhdr) will be
loaded into Slicer and converted in Nifti.
argument: ``--testingmode ``
args: (a unicode string)
Additional parameters to the command
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', nipype default value: {})
Environment variables
Outputs:
outputTensor: (an existing file name)
Output DTI volume
DWIJointRicianLMMSEFilter¶
Wraps the executable command ``DWIJointRicianLMMSEFilter ``.
title: DWI Joint Rician LMMSE Filter
category: Diffusion.Diffusion Weighted Images
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/4.1/Modules/JointRicianLMMSEImageFilter
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).
Inputs:
[Optional]
re: (a list of items which are an integer (int or long))
Estimation radius.
argument: ``--re %s``
rf: (a list of items which are an integer (int or long))
Filtering radius.
argument: ``--rf %s``
ng: (an integer (int or long))
The number of the closest gradients that are used to jointly filter
a given gradient direction (0 to use all).
argument: ``--ng %d``
inputVolume: (an existing file name)
Input DWI volume.
argument: ``%s``, position: -2
outputVolume: (a boolean or a file name)
Output DWI volume.
argument: ``%s``, position: -1
compressOutput: (a boolean)
Compress the data of the compressed file using gzip
argument: ``--compressOutput ``
args: (a unicode string)
Additional parameters to the command
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', nipype default value: {})
Environment variables
Outputs:
outputVolume: (an existing file name)
Output DWI volume.
DWIRicianLMMSEFilter¶
Wraps the executable command ``DWIRicianLMMSEFilter ``.
title: DWI Rician LMMSE Filter
category: Diffusion.Diffusion Weighted Images
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/4.1/Modules/RicianLMMSEImageFilter
contributor: Antonio Tristan Vega (UVa), Santiago Aja Fernandez (UVa), Marc Niethammer (UNC)
acknowledgements: Partially founded by grant number TEC2007-67073/TCM from the Comision Interministerial de Ciencia y Tecnologia (Spain).
Inputs:
[Optional]
iter: (an integer (int or long))
Number of iterations for the noise removal filter.
argument: ``--iter %d``
re: (a list of items which are an integer (int or long))
Estimation radius.
argument: ``--re %s``
rf: (a list of items which are an integer (int or long))
Filtering radius.
argument: ``--rf %s``
mnvf: (an integer (int or long))
Minimum number of voxels in kernel used for filtering.
argument: ``--mnvf %d``
mnve: (an integer (int or long))
Minimum number of voxels in kernel used for estimation.
argument: ``--mnve %d``
minnstd: (an integer (int or long))
Minimum allowed noise standard deviation.
argument: ``--minnstd %d``
maxnstd: (an integer (int or long))
Maximum allowed noise standard deviation.
argument: ``--maxnstd %d``
hrf: (a float)
How many histogram bins per unit interval.
argument: ``--hrf %f``
uav: (a boolean)
Use absolute value in case of negative square.
argument: ``--uav ``
inputVolume: (an existing file name)
Input DWI volume.
argument: ``%s``, position: -2
outputVolume: (a boolean or a file name)
Output DWI volume.
argument: ``%s``, position: -1
compressOutput: (a boolean)
Compress the data of the compressed file using gzip
argument: ``--compressOutput ``
args: (a unicode string)
Additional parameters to the command
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', nipype default value: {})
Environment variables
Outputs:
outputVolume: (an existing file name)
Output DWI volume.
DWIToDTIEstimation¶
Wraps the executable command ``DWIToDTIEstimation ``.
title: DWI to DTI Estimation
category: Diffusion.Diffusion Weighted Images
description: Performs a tensor model estimation from diffusion weighted images.
There are three estimation methods available: least squares, weigthed least squares and non-linear estimation. The first method is the traditional method for tensor estimation and the fastest one. Weighted least squares takes into account the noise characteristics of the MRI images to weight the DWI samples used in the estimation based on its intensity magnitude. The last method is the more complex.
version: 0.1.0.$Revision: 1892 $(alpha)
documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.1/Modules/DiffusionTensorEstimation
license: slicer3
contributor: Raul San Jose (SPL, BWH)
acknowledgements: This command module is based on the estimation functionality provided by the Teem library. This work is part of the National Alliance for Medical Image Computing (NAMIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149.
Inputs:
[Optional]
inputVolume: (an existing file name)
Input DWI volume
argument: ``%s``, position: -3
mask: (an existing file name)
Mask where the tensors will be computed
argument: ``--mask %s``
outputTensor: (a boolean or a file name)
Estimated DTI volume
argument: ``%s``, position: -2
outputBaseline: (a boolean or a file name)
Estimated baseline volume
argument: ``%s``, position: -1
enumeration: ('LS' or 'WLS')
LS: Least Squares, WLS: Weighted Least Squares
argument: ``--enumeration %s``
shiftNeg: (a boolean)
Shift eigenvalues so all are positive (accounts for bad tensors
related to noise or acquisition error)
argument: ``--shiftNeg ``
args: (a unicode string)
Additional parameters to the command
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', nipype default value: {})
Environment variables
Outputs:
outputTensor: (an existing file name)
Estimated DTI volume
outputBaseline: (an existing file name)
Estimated baseline volume
DiffusionTensorScalarMeasurements¶
Wraps the executable command ``DiffusionTensorScalarMeasurements ``.
title: Diffusion Tensor Scalar Measurements
category: Diffusion.Diffusion Tensor Images
description: Compute a set of different scalar measurements from a tensor field, specially oriented for Diffusion Tensors where some rotationally invariant measurements, like Fractional Anisotropy, are highly used to describe the anistropic behaviour of the tensor.
version: 0.1.0.$Revision: 1892 $(alpha)
documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.1/Modules/DiffusionTensorMathematics
contributor: Raul San Jose (SPL, BWH)
acknowledgements: LMI
Inputs:
[Optional]
inputVolume: (an existing file name)
Input DTI volume
argument: ``%s``, position: -3
outputScalar: (a boolean or a file name)
Scalar volume derived from tensor
argument: ``%s``, position: -1
enumeration: ('Trace' or 'Determinant' or 'RelativeAnisotropy' or
'FractionalAnisotropy' or 'Mode' or 'LinearMeasure' or
'PlanarMeasure' or 'SphericalMeasure' or 'MinEigenvalue' or
'MidEigenvalue' or 'MaxEigenvalue' or 'MaxEigenvalueProjectionX'
or 'MaxEigenvalueProjectionY' or 'MaxEigenvalueProjectionZ' or
'RAIMaxEigenvecX' or 'RAIMaxEigenvecY' or 'RAIMaxEigenvecZ' or
'MaxEigenvecX' or 'MaxEigenvecY' or 'MaxEigenvecZ' or 'D11' or
'D22' or 'D33' or 'ParallelDiffusivity' or
'PerpendicularDffusivity')
An enumeration of strings
argument: ``--enumeration %s``
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
of class 'str' and with values which are a bytes or None or a
value of class 'str', nipype default value: {})
Environment variables
Outputs:
outputScalar: (an existing file name)
Scalar volume derived from tensor
DiffusionWeightedVolumeMasking¶
Wraps the executable command ``DiffusionWeightedVolumeMasking ``.
title: Diffusion Weighted Volume Masking
category: Diffusion.Diffusion Weighted Images
description: <p>Performs a mask calculation from a diffusion weighted (DW) image.</p><p>Starting from a dw image, this module computes the baseline image averaging all the images without diffusion weighting and then applies the otsu segmentation algorithm in order to produce a mask. this mask can then be used when estimating the diffusion tensor (dt) image, not to estimate tensors all over the volume.</p>
version: 0.1.0.$Revision: 1892 $(alpha)
documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.1/Modules/DiffusionWeightedMasking
license: slicer3
contributor: Demian Wassermann (SPL, BWH)
Inputs:
[Optional]
inputVolume: (an existing file name)
Input DWI volume
argument: ``%s``, position: -4
outputBaseline: (a boolean or a file name)
Estimated baseline volume
argument: ``%s``, position: -2
thresholdMask: (a boolean or a file name)
Otsu Threshold Mask
argument: ``%s``, position: -1
otsuomegathreshold: (a float)
Control the sharpness of the threshold in the Otsu computation. 0:
lower threshold, 1: higher threhold
argument: ``--otsuomegathreshold %f``
removeislands: (a boolean)
Remove Islands in Threshold Mask?
argument: ``--removeislands ``
args: (a unicode string)
Additional parameters to the command
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', nipype default value: {})
Environment variables
Outputs:
outputBaseline: (an existing file name)
Estimated baseline volume
thresholdMask: (an existing file name)
Otsu Threshold Mask
ResampleDTIVolume¶
Wraps the executable command ``ResampleDTIVolume ``.
title: Resample DTI Volume
category: Diffusion.Diffusion Tensor Images
description: Resampling an image is a very important task in image analysis. It is especially important in the frame of image registration. This module implements DT image resampling through the use of itk Transforms. The resampling is controlled by the Output Spacing. “Resampling” is performed in space coordinates, not pixel/grid coordinates. It is quite important to ensure that image spacing is properly set on the images involved. The interpolator is required since the mapping from one space to the other will often require evaluation of the intensity of the image at non-grid positions.
version: 0.1
documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.1/Modules/ResampleDTI
contributor: Francois Budin (UNC)
acknowledgements: This work is part of the National Alliance for Medical Image Computing (NAMIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on the National Centers for Biomedical Computing can be obtained from http://nihroadmap.nih.gov/bioinformatics
Inputs:
[Optional]
inputVolume: (an existing file name)
Input volume to be resampled
argument: ``%s``, position: -2
outputVolume: (a boolean or a file name)
Resampled Volume
argument: ``%s``, position: -1
Reference: (an existing file name)
Reference Volume (spacing,size,orientation,origin)
argument: ``--Reference %s``
transformationFile: (an existing file name)
argument: ``--transformationFile %s``
defField: (an existing file name)
File containing the deformation field (3D vector image containing
vectors with 3 components)
argument: ``--defField %s``
hfieldtype: ('displacement' or 'h-Field')
Set if the deformation field is an -Field
argument: ``--hfieldtype %s``
interpolation: ('linear' or 'nn' or 'ws' or 'bs')
Sampling algorithm (linear , nn (nearest neighborhoor), ws
(WindowedSinc), bs (BSpline) )
argument: ``--interpolation %s``
correction: ('zero' or 'none' or 'abs' or 'nearest')
Correct the tensors if computed tensor is not semi-definite positive
argument: ``--correction %s``
transform_tensor_method: ('PPD' or 'FS')
Chooses between 2 methods to transform the tensors: Finite Strain
(FS), faster but less accurate, or Preservation of the Principal
Direction (PPD)
argument: ``--transform_tensor_method %s``
transform_order: ('input-to-output' or 'output-to-input')
Select in what order the transforms are read
argument: ``--transform_order %s``
notbulk: (a boolean)
The transform following the BSpline transform is not set as a bulk
transform for the BSpline transform
argument: ``--notbulk ``
spaceChange: (a boolean)
Space Orientation between transform and image is different (RAS/LPS)
(warning: if the transform is a Transform Node in Slicer3, do not
select)
argument: ``--spaceChange ``
rotation_point: (a list of items which are any value)
Center of rotation (only for rigid and affine transforms)
argument: ``--rotation_point %s``
centered_transform: (a boolean)
Set the center of the transformation to the center of the input
image (only for rigid and affine transforms)
argument: ``--centered_transform ``
image_center: ('input' or 'output')
Image to use to center the transform (used only if 'Centered
Transform' is selected)
argument: ``--image_center %s``
Inverse_ITK_Transformation: (a boolean)
Inverse the transformation before applying it from output image to
input image (only for rigid and affine transforms)
argument: ``--Inverse_ITK_Transformation ``
spacing: (a list of items which are a float)
Spacing along each dimension (0 means use input spacing)
argument: ``--spacing %s``
size: (a list of items which are a float)
Size along each dimension (0 means use input size)
argument: ``--size %s``
origin: (a list of items which are any value)
Origin of the output Image
argument: ``--origin %s``
direction_matrix: (a list of items which are a float)
9 parameters of the direction matrix by rows (ijk to LPS if LPS
transform, ijk to RAS if RAS transform)
argument: ``--direction_matrix %s``
number_of_thread: (an integer (int or long))
Number of thread used to compute the output image
argument: ``--number_of_thread %d``
default_pixel_value: (a float)
Default pixel value for samples falling outside of the input region
argument: ``--default_pixel_value %f``
window_function: ('h' or 'c' or 'w' or 'l' or 'b')
Window Function , h = Hamming , c = Cosine , w = Welch , l = Lanczos
, b = Blackman
argument: ``--window_function %s``
spline_order: (an integer (int or long))
Spline Order (Spline order may be from 0 to 5)
argument: ``--spline_order %d``
transform_matrix: (a list of items which are a float)
12 parameters of the transform matrix by rows ( --last 3 being
translation-- )
argument: ``--transform_matrix %s``
transform: ('rt' or 'a')
Transform algorithm, rt = Rigid Transform, a = Affine Transform
argument: ``--transform %s``
args: (a unicode string)
Additional parameters to the command
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', nipype default value: {})
Environment variables
Outputs:
outputVolume: (an existing file name)
Resampled Volume
TractographyLabelMapSeeding¶
Wraps the executable command ``TractographyLabelMapSeeding ``.
title: Tractography Label Map Seeding
category: Diffusion.Diffusion Tensor Images
description: Seed tracts on a Diffusion Tensor Image (DT) from a label map
version: 0.1.0.$Revision: 1892 $(alpha)
documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.1/Modules/Seeding
license: slicer3
contributor: Raul San Jose (SPL, BWH), Demian Wassermann (SPL, BWH)
acknowledgements: Laboratory of Mathematics in Imaging. This work is part of the National Alliance for Medical Image Computing (NAMIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149.
Inputs:
[Optional]
InputVolume: (an existing file name)
Input DTI volume
argument: ``%s``, position: -2
inputroi: (an existing file name)
Label map with seeding ROIs
argument: ``--inputroi %s``
OutputFibers: (a boolean or a file name)
Tractography result
argument: ``%s``, position: -1
useindexspace: (a boolean)
Seed at IJK voxel grid
argument: ``--useindexspace ``
seedspacing: (a float)
Spacing (in mm) between seed points, only matters if use Use Index
Space is off
argument: ``--seedspacing %f``
randomgrid: (a boolean)
Enable random placing of seeds
argument: ``--randomgrid ``
clthreshold: (a float)
Minimum Linear Measure for the seeding to start.
argument: ``--clthreshold %f``
minimumlength: (a float)
Minimum length of the fibers (in mm)
argument: ``--minimumlength %f``
maximumlength: (a float)
Maximum length of fibers (in mm)
argument: ``--maximumlength %f``
stoppingmode: ('LinearMeasure' or 'FractionalAnisotropy')
Tensor measurement used to stop the tractography
argument: ``--stoppingmode %s``
stoppingvalue: (a float)
Tractography will stop when the stopping measurement drops below
this value
argument: ``--stoppingvalue %f``
stoppingcurvature: (a float)
Tractography will stop if radius of curvature becomes smaller than
this number units are degrees per mm
argument: ``--stoppingcurvature %f``
integrationsteplength: (a float)
Distance between points on the same fiber in mm
argument: ``--integrationsteplength %f``
label: (an integer (int or long))
Label value that defines seeding region.
argument: ``--label %d``
writetofile: (a boolean)
Write fibers to disk or create in the scene?
argument: ``--writetofile ``
outputdirectory: (a boolean or a directory name)
Directory in which to save fiber(s)
argument: ``--outputdirectory %s``
name: (a unicode string)
Name to use for fiber files
argument: ``--name %s``
args: (a unicode string)
Additional parameters to the command
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', nipype default value: {})
Environment variables
Outputs:
OutputFibers: (an existing file name)
Tractography result
outputdirectory: (an existing directory name)
Directory in which to save fiber(s)