Wraps command **/home/raid3/gorgolewski/software/slicer/Slicer –launch 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:
[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
inputTensor: (an existing file name)
Input DTI volume
outputFile: (a boolean or a file name)
Output DTI file
Outputs:
outputFile: (an existing file name)
Output DTI file
Wraps command **/home/raid3/gorgolewski/software/slicer/Slicer –launch 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:
[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
inputFile: (an existing file name)
Input DTI file
outputTensor: (a boolean or a file name)
Output DTI volume
testingmode: (a boolean)
Enable testing mode. Sample helix file (helix-DTI.nhdr) will be loaded into Slicer and
converted in Nifti.
Outputs:
outputTensor: (an existing file name)
Output DTI volume
Wraps command **/home/raid3/gorgolewski/software/slicer/Slicer –launch 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:
[Mandatory]
[Optional]
args: (a string)
Additional parameters to the command
compressOutput: (a boolean)
Compress the data of the compressed file using gzip
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.
Wraps command **/home/raid3/gorgolewski/software/slicer/Slicer –launch 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:
[Mandatory]
[Optional]
args: (a string)
Additional parameters to the command
compressOutput: (a boolean)
Compress the data of the compressed file using gzip
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.
Wraps command **/home/raid3/gorgolewski/software/slicer/Slicer –launch 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 $(alpha)
documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.1/Modules/DiffusionTensorEstimation
license: slicer3
contributor: Raul San Jose (SPL, BWH)
acknowledgements: This command module is based on the estimation functionality provided by the Teem library. This work is part of the National Alliance for Medical Image Computing (NAMIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149.
Inputs:
[Mandatory]
[Optional]
args: (a string)
Additional parameters to the command
enumeration: ('LS' or 'WLS')
LS: Least Squares, WLS: Weighted Least Squares
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
inputVolume: (an existing file name)
Input DWI volume
mask: (an existing file name)
Mask where the tensors will be computed
outputBaseline: (a boolean or a file name)
Estimated baseline volume
outputTensor: (a boolean or a file name)
Estimated DTI volume
shiftNeg: (a boolean)
Shift eigenvalues so all are positive (accounts for bad tensors related to noise or
acquisition error)
Outputs:
outputBaseline: (an existing file name)
Estimated baseline volume
outputTensor: (an existing file name)
Estimated DTI volume
Wraps command **/home/raid3/gorgolewski/software/slicer/Slicer –launch DiffusionTensorScalarMeasurements **
title: Diffusion Tensor Scalar Measurements
category: Diffusion.Diffusion Tensor Images
description: Compute a set of different scalar measurements from a tensor field, specially oriented for Diffusion Tensors where some rotationally invariant measurements, like Fractional Anisotropy, are highly used to describe the anistropic behaviour of the tensor.
version: 0.1.0.$Revision: 1892 $(alpha)
documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.1/Modules/DiffusionTensorMathematics
contributor: Raul San Jose (SPL, BWH)
acknowledgements: LMI
Inputs:
[Mandatory]
[Optional]
args: (a string)
Additional parameters to the command
enumeration: ('Trace' or 'Determinant' or 'RelativeAnisotropy' or 'FractionalAnisotropy'
or 'Mode' or 'LinearMeasure' or 'PlanarMeasure' or 'SphericalMeasure' or
'MinEigenvalue' or 'MidEigenvalue' or 'MaxEigenvalue' or 'MaxEigenvalueProjectionX' or
'MaxEigenvalueProjectionY' or 'MaxEigenvalueProjectionZ' or 'RAIMaxEigenvecX' or
'RAIMaxEigenvecY' or 'RAIMaxEigenvecZ' or 'MaxEigenvecX' or 'MaxEigenvecY' or
'MaxEigenvecZ' or 'D11' or 'D22' or 'D33' or 'ParallelDiffusivity' or
'PerpendicularDffusivity')
An enumeration of strings
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
inputVolume: (an existing file name)
Input DTI volume
outputScalar: (a boolean or a file name)
Scalar volume derived from tensor
Outputs:
outputScalar: (an existing file name)
Scalar volume derived from tensor
Wraps command **/home/raid3/gorgolewski/software/slicer/Slicer –launch DiffusionWeightedVolumeMasking **
title: Diffusion Weighted Volume Masking
category: Diffusion.Diffusion Weighted Images
description: <p>Performs a mask calculation from a diffusion weighted (DW) image.</p><p>Starting from a dw image, this module computes the baseline image averaging all the images without diffusion weighting and then applies the otsu segmentation algorithm in order to produce a mask. this mask can then be used when estimating the diffusion tensor (dt) image, not to estimate tensors all over the volume.</p>
version: 0.1.0.$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:
[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
otsuomegathreshold: (a float)
Control the sharpness of the threshold in the Otsu computation. 0: lower threshold, 1:
higher threhold
outputBaseline: (a boolean or a file name)
Estimated baseline volume
removeislands: (a boolean)
Remove Islands in Threshold Mask?
thresholdMask: (a boolean or a file name)
Otsu Threshold Mask
Outputs:
outputBaseline: (an existing file name)
Estimated baseline volume
thresholdMask: (an existing file name)
Otsu Threshold Mask
Wraps command **/home/raid3/gorgolewski/software/slicer/Slicer –launch 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:
[Mandatory]
[Optional]
Inverse_ITK_Transformation: (a boolean)
Inverse the transformation before applying it from output image to input image (only for
rigid and affine transforms)
Reference: (an existing file name)
Reference Volume (spacing,size,orientation,origin)
args: (a string)
Additional parameters to the command
centered_transform: (a boolean)
Set the center of the transformation to the center of the input image (only for rigid
and affine transforms)
correction: ('zero' or 'none' or 'abs' or 'nearest')
Correct the tensors if computed tensor is not semi-definite positive
defField: (an existing file name)
File containing the deformation field (3D vector image containing vectors with 3
components)
default_pixel_value: (a float)
Default pixel value for samples falling outside of the input region
direction_matrix: (a float)
9 parameters of the direction matrix by rows (ijk to LPS if LPS transform, ijk to RAS if
RAS transform)
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
hfieldtype: ('displacement' or 'h-Field')
Set if the deformation field is an -Field
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
image_center: ('input' or 'output')
Image to use to center the transform (used only if 'Centered Transform' is selected)
inputVolume: (an existing file name)
Input volume to be resampled
interpolation: ('linear' or 'nn' or 'ws' or 'bs')
Sampling algorithm (linear , nn (nearest neighborhoor), ws (WindowedSinc), bs (BSpline)
~
notbulk: (a boolean)
The transform following the BSpline transform is not set as a bulk transform for the
BSpline transform
number_of_thread: (an integer)
Number of thread used to compute the output image
origin: (a list of items which are any value)
Origin of the output Image
outputVolume: (a boolean or a file name)
Resampled Volume
rotation_point: (a list of items which are any value)
Center of rotation (only for rigid and affine transforms)
size: (a float)
Size along each dimension (0 means use input size)
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)
spacing: (a float)
Spacing along each dimension (0 means use input spacing)
spline_order: (an integer)
Spline Order (Spline order may be from 0 to 5)
transform: ('rt' or 'a')
Transform algorithm, rt = Rigid Transform, a = Affine Transform
transform_matrix: (a float)
12 parameters of the transform matrix by rows ( --last 3 being translation-- )
transform_order: ('input-to-output' or 'output-to-input')
Select in what order the transforms are read
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)
transformationFile: (an existing file name)
window_function: ('h' or 'c' or 'w' or 'l' or 'b')
Window Function , h = Hamming , c = Cosine , w = Welch , l = Lanczos , b = Blackman
Outputs:
outputVolume: (an existing file name)
Resampled Volume
Wraps command **/home/raid3/gorgolewski/software/slicer/Slicer –launch 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:
[Mandatory]
[Optional]
InputVolume: (an existing file name)
Input DTI volume
OutputFibers: (a boolean or a file name)
Tractography result
args: (a string)
Additional parameters to the command
clthreshold: (a float)
Minimum Linear Measure for the seeding to start.
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
inputroi: (an existing file name)
Label map with seeding ROIs
integrationsteplength: (a float)
Distance between points on the same fiber in mm
label: (an integer)
Label value that defines seeding region.
maximumlength: (a float)
Maximum length of fibers (in mm)
minimumlength: (a float)
Minimum length of the fibers (in mm)
name: (a string)
Name to use for fiber files
outputdirectory: (a boolean or a directory name)
Directory in which to save fiber(s)
randomgrid: (a boolean)
Enable random placing of seeds
seedspacing: (a float)
Spacing (in mm) between seed points, only matters if use Use Index Space is off
stoppingcurvature: (a float)
Tractography will stop if radius of curvature becomes smaller than this number units are
degrees per mm
stoppingmode: ('LinearMeasure' or 'FractionalAnisotropy')
Tensor measurement used to stop the tractography
stoppingvalue: (a float)
Tractography will stop when the stopping measurement drops below this value
useindexspace: (a boolean)
Seed at IJK voxel grid
writetofile: (a boolean)
Write fibers to disk or create in the scene?
Outputs:
OutputFibers: (an existing file name)
Tractography result
outputdirectory: (an existing directory name)
Directory in which to save fiber(s)