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]
environ: (a dictionary with keys which are a newbytes or None or a
newstr or None and with values which are a newbytes or None or a
newstr or None, nipype default value: {})
Environment variables
outputFile: (a boolean or a file name)
Output DTI file
argument: ``%s``, position: -1
inputTensor: (an existing file name)
Input DTI volume
argument: ``%s``, position: -2
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
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]
outputTensor: (a boolean or a file name)
Output DTI volume
argument: ``%s``, position: -1
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
environ: (a dictionary with keys which are a newbytes or None or a
newstr or None and with values which are a newbytes or None or a
newstr or None, nipype default value: {})
Environment variables
testingmode: (a boolean)
Enable testing mode. Sample helix file (helix-DTI.nhdr) will be
loaded into Slicer and converted in Nifti.
argument: ``--testingmode ``
inputFile: (an existing file name)
Input DTI file
argument: ``%s``, position: -2
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]
outputVolume: (a boolean or a file name)
Output DWI volume.
argument: ``%s``, position: -1
args: (a unicode string)
Additional parameters to the command
argument: ``%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``
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``
environ: (a dictionary with keys which are a newbytes or None or a
newstr or None and with values which are a newbytes or None or a
newstr or None, nipype default value: {})
Environment variables
compressOutput: (a boolean)
Compress the data of the compressed file using gzip
argument: ``--compressOutput ``
inputVolume: (an existing file name)
Input DWI volume.
argument: ``%s``, position: -2
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]
outputVolume: (a boolean or a file name)
Output DWI volume.
argument: ``%s``, position: -1
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``
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``
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
iter: (an integer (int or long))
Number of iterations for the noise removal filter.
argument: ``--iter %d``
uav: (a boolean)
Use absolute value in case of negative square.
argument: ``--uav ``
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``
environ: (a dictionary with keys which are a newbytes or None or a
newstr or None and with values which are a newbytes or None or a
newstr or None, nipype default value: {})
Environment variables
compressOutput: (a boolean)
Compress the data of the compressed file using gzip
argument: ``--compressOutput ``
inputVolume: (an existing file name)
Input DWI volume.
argument: ``%s``, position: -2
hrf: (a float)
How many histogram bins per unit interval.
argument: ``--hrf %f``
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]
outputBaseline: (a boolean or a file name)
Estimated baseline volume
argument: ``%s``, position: -1
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
mask: (an existing file name)
Mask where the tensors will be computed
argument: ``--mask %s``
enumeration: ('LS' or 'WLS')
LS: Least Squares, WLS: Weighted Least Squares
argument: ``--enumeration %s``
environ: (a dictionary with keys which are a newbytes or None or a
newstr or None and with values which are a newbytes or None or a
newstr or None, nipype default value: {})
Environment variables
outputTensor: (a boolean or a file name)
Estimated DTI volume
argument: ``%s``, position: -2
inputVolume: (an existing file name)
Input DWI volume
argument: ``%s``, position: -3
shiftNeg: (a boolean)
Shift eigenvalues so all are positive (accounts for bad tensors
related to noise or acquisition error)
argument: ``--shiftNeg ``
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 $(alpha)
documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.1/Modules/DiffusionTensorMathematics
contributor: Raul San Jose (SPL, BWH)
acknowledgements: LMI
Inputs:
[Optional]
inputVolume: (an existing file name)
Input DTI volume
argument: ``%s``, position: -3
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
environ: (a dictionary with keys which are a newbytes or None or a
newstr or None and with values which are a newbytes or None or a
newstr or None, nipype default value: {})
Environment variables
enumeration: ('Trace' or 'Determinant' or 'RelativeAnisotropy' or
'FractionalAnisotropy' or 'Mode' or 'LinearMeasure' or
'PlanarMeasure' or 'SphericalMeasure' or 'MinEigenvalue' or
'MidEigenvalue' or 'MaxEigenvalue' or 'MaxEigenvalueProjectionX'
or 'MaxEigenvalueProjectionY' or 'MaxEigenvalueProjectionZ' or
'RAIMaxEigenvecX' or 'RAIMaxEigenvecY' or 'RAIMaxEigenvecZ' or
'MaxEigenvecX' or 'MaxEigenvecY' or 'MaxEigenvecZ' or 'D11' or
'D22' or 'D33' or 'ParallelDiffusivity' or
'PerpendicularDffusivity')
An enumeration of strings
argument: ``--enumeration %s``
outputScalar: (a boolean or a file name)
Scalar volume derived from tensor
argument: ``%s``, position: -1
Outputs:
outputScalar: (an existing file name)
Scalar volume derived from tensor
DiffusionWeightedVolumeMasking¶
Wraps the executable command ``DiffusionWeightedVolumeMasking ``.
title: Diffusion Weighted Volume Masking
category: Diffusion.Diffusion Weighted Images
description: <p>Performs a mask calculation from a diffusion weighted (DW) image.</p><p>Starting from a dw image, this module computes the baseline image averaging all the images without diffusion weighting and then applies the otsu segmentation algorithm in order to produce a mask. this mask can then be used when estimating the diffusion tensor (dt) image, not to estimate tensors all over the volume.</p>
version: 0.1.0.$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]
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
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
removeislands: (a boolean)
Remove Islands in Threshold Mask?
argument: ``--removeislands ``
otsuomegathreshold: (a float)
Control the sharpness of the threshold in the Otsu computation. 0:
lower threshold, 1: higher threhold
argument: ``--otsuomegathreshold %f``
environ: (a dictionary with keys which are a newbytes or None or a
newstr or None and with values which are a newbytes or None or a
newstr or None, nipype default value: {})
Environment variables
inputVolume: (an existing file name)
Input DWI volume
argument: ``%s``, position: -4
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]
origin: (a list of items which are any value)
Origin of the output Image
argument: ``--origin %s``
correction: ('zero' or 'none' or 'abs' or 'nearest')
Correct the tensors if computed tensor is not semi-definite positive
argument: ``--correction %s``
defField: (an existing file name)
File containing the deformation field (3D vector image containing
vectors with 3 components)
argument: ``--defField %s``
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``
image_center: ('input' or 'output')
Image to use to center the transform (used only if 'Centered
Transform' is selected)
argument: ``--image_center %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``
size: (a list of items which are a float)
Size along each dimension (0 means use input size)
argument: ``--size %s``
spline_order: (an integer (int or long))
Spline Order (Spline order may be from 0 to 5)
argument: ``--spline_order %d``
transform: ('rt' or 'a')
Transform algorithm, rt = Rigid Transform, a = Affine Transform
argument: ``--transform %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 ``
transform_order: ('input-to-output' or 'output-to-input')
Select in what order the transforms are read
argument: ``--transform_order %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 ``
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``
interpolation: ('linear' or 'nn' or 'ws' or 'bs')
Sampling algorithm (linear , nn (nearest neighborhoor), ws
(WindowedSinc), bs (BSpline) )
argument: ``--interpolation %s``
outputVolume: (a boolean or a file name)
Resampled Volume
argument: ``%s``, position: -1
number_of_thread: (an integer (int or long))
Number of thread used to compute the output image
argument: ``--number_of_thread %d``
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
spacing: (a list of items which are a float)
Spacing along each dimension (0 means use input spacing)
argument: ``--spacing %s``
hfieldtype: ('displacement' or 'h-Field')
Set if the deformation field is an -Field
argument: ``--hfieldtype %s``
rotation_point: (a list of items which are any value)
Center of rotation (only for rigid and affine transforms)
argument: ``--rotation_point %s``
notbulk: (a boolean)
The transform following the BSpline transform is not set as a bulk
transform for the BSpline transform
argument: ``--notbulk ``
default_pixel_value: (a float)
Default pixel value for samples falling outside of the input region
argument: ``--default_pixel_value %f``
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``
Reference: (an existing file name)
Reference Volume (spacing,size,orientation,origin)
argument: ``--Reference %s``
transformationFile: (an existing file name)
argument: ``--transformationFile %s``
environ: (a dictionary with keys which are a newbytes or None or a
newstr or None and with values which are a newbytes or None or a
newstr or None, nipype default value: {})
Environment variables
inputVolume: (an existing file name)
Input volume to be resampled
argument: ``%s``, position: -2
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 ``
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]
minimumlength: (a float)
Minimum length of the fibers (in mm)
argument: ``--minimumlength %f``
inputroi: (an existing file name)
Label map with seeding ROIs
argument: ``--inputroi %s``
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``
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
stoppingmode: ('LinearMeasure' or 'FractionalAnisotropy')
Tensor measurement used to stop the tractography
argument: ``--stoppingmode %s``
useindexspace: (a boolean)
Seed at IJK voxel grid
argument: ``--useindexspace ``
outputdirectory: (a boolean or a directory name)
Directory in which to save fiber(s)
argument: ``--outputdirectory %s``
seedspacing: (a float)
Spacing (in mm) between seed points, only matters if use Use Index
Space is off
argument: ``--seedspacing %f``
stoppingvalue: (a float)
Tractography will stop when the stopping measurement drops below
this value
argument: ``--stoppingvalue %f``
randomgrid: (a boolean)
Enable random placing of seeds
argument: ``--randomgrid ``
environ: (a dictionary with keys which are a newbytes or None or a
newstr or None and with values which are a newbytes or None or a
newstr or None, nipype default value: {})
Environment variables
clthreshold: (a float)
Minimum Linear Measure for the seeding to start.
argument: ``--clthreshold %f``
OutputFibers: (a boolean or a file name)
Tractography result
argument: ``%s``, position: -1
InputVolume: (an existing file name)
Input DTI volume
argument: ``%s``, position: -2
label: (an integer (int or long))
Label value that defines seeding region.
argument: ``--label %d``
maximumlength: (a float)
Maximum length of fibers (in mm)
argument: ``--maximumlength %f``
writetofile: (a boolean)
Write fibers to disk or create in the scene?
argument: ``--writetofile ``
name: (a unicode string)
Name to use for fiber files
argument: ``--name %s``
Outputs:
OutputFibers: (an existing file name)
Tractography result
outputdirectory: (an existing directory name)
Directory in which to save fiber(s)