nipype.interfaces.slicer.diffusion.diffusion module¶
Autogenerated file - DO NOT EDIT If you spot a bug, please report it on the mailing list and/or change the generator.
DTIexport¶
Bases: SEMLikeCommandLine
Wrapped executable:
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.
- 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:
{})inputTensor (a pathlike object or string representing an existing file) – Input DTI volume. Maps to a command-line argument:
%s(position: -2).outputFile (a boolean or a pathlike object or string representing a file) – Output DTI file. Maps to a command-line argument:
%s(position: -1).- Outputs:
outputFile (a pathlike object or string representing an existing file) – Output DTI file.
DTIimport¶
Bases: SEMLikeCommandLine
Wrapped executable:
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.
- 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:
{})inputFile (a pathlike object or string representing an existing file) – Input DTI file. Maps to a command-line argument:
%s(position: -2).outputTensor (a boolean or a pathlike object or string representing a file) – Output DTI volume. Maps to a command-line 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. Maps to a command-line argument:
--testingmode.- Outputs:
outputTensor (a pathlike object or string representing an existing file) – Output DTI volume.
DWIJointRicianLMMSEFilter¶
Bases: SEMLikeCommandLine
Wrapped executable:
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).
- Optional Inputs:
args (a string) – Additional parameters to the command. Maps to a command-line argument:
%s.compressOutput (a boolean) – Compress the data of the compressed file using gzip. Maps to a command-line argument:
--compressOutput.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:
{})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 (0 to use all). 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).re (a list of items which are an integer) – Estimation radius. Maps to a command-line argument:
--re %s.rf (a list of items which are an integer) – Filtering radius. Maps to a command-line argument:
--rf %s.- Outputs:
outputVolume (a pathlike object or string representing an existing file) – Output DWI volume.
DWIRicianLMMSEFilter¶
Bases: SEMLikeCommandLine
Wrapped executable:
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).
- Optional Inputs:
args (a string) – Additional parameters to the command. Maps to a command-line argument:
%s.compressOutput (a boolean) – Compress the data of the compressed file using gzip. Maps to a command-line argument:
--compressOutput.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:
{})hrf (a float) – How many histogram bins per unit interval. Maps to a command-line argument:
--hrf %f.inputVolume (a pathlike object or string representing an existing file) – Input DWI volume. Maps to a command-line argument:
%s(position: -2).iter (an integer) – Number of iterations for the noise removal filter. Maps to a command-line argument:
--iter %d.maxnstd (an integer) – Maximum allowed noise standard deviation. Maps to a command-line argument:
--maxnstd %d.minnstd (an integer) – Minimum allowed noise standard deviation. Maps to a command-line argument:
--minnstd %d.mnve (an integer) – Minimum number of voxels in kernel used for estimation. Maps to a command-line argument:
--mnve %d.mnvf (an integer) – Minimum number of voxels in kernel used for filtering. Maps to a command-line argument:
--mnvf %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).re (a list of items which are an integer) – Estimation radius. Maps to a command-line argument:
--re %s.rf (a list of items which are an integer) – Filtering radius. Maps to a command-line argument:
--rf %s.uav (a boolean) – Use absolute value in case of negative square. Maps to a command-line argument:
--uav.- Outputs:
outputVolume (a pathlike object or string representing an existing file) – Output DWI volume.
DWIToDTIEstimation¶
Bases: SEMLikeCommandLine
Wrapped executable:
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, weighted 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.
- Optional Inputs:
args (a string) – Additional parameters to the command. Maps to a command-line argument:
%s.enumeration (‘LS’ or ‘WLS’) – LS: Least Squares, WLS: Weighted Least Squares. Maps to a command-line argument:
--enumeration %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:
{})inputVolume (a pathlike object or string representing an existing file) – Input DWI volume. Maps to a command-line argument:
%s(position: -3).mask (a pathlike object or string representing an existing file) – Mask where the tensors will be computed. Maps to a command-line argument:
--mask %s.outputBaseline (a boolean or a pathlike object or string representing a file) – Estimated baseline volume. Maps to a command-line argument:
%s(position: -1).outputTensor (a boolean or a pathlike object or string representing a file) – Estimated DTI volume. Maps to a command-line argument:
%s(position: -2).shiftNeg (a boolean) – Shift eigenvalues so all are positive (accounts for bad tensors related to noise or acquisition error). Maps to a command-line argument:
--shiftNeg.- Outputs:
outputBaseline (a pathlike object or string representing an existing file) – Estimated baseline volume.
outputTensor (a pathlike object or string representing an existing file) – Estimated DTI volume.
DiffusionTensorScalarMeasurements¶
Bases: SEMLikeCommandLine
Wrapped executable:
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
- Optional Inputs:
args (a string) – Additional parameters to the command. Maps to a command-line argument:
%s.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. Maps to a command-line argument:
--enumeration %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:
{})inputVolume (a pathlike object or string representing an existing file) – Input DTI volume. Maps to a command-line argument:
%s(position: -3).outputScalar (a boolean or a pathlike object or string representing a file) – Scalar volume derived from tensor. Maps to a command-line argument:
%s(position: -1).- Outputs:
outputScalar (a pathlike object or string representing an existing file) – Scalar volume derived from tensor.
DiffusionWeightedVolumeMasking¶
Bases: SEMLikeCommandLine
Wrapped executable:
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)
- 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:
{})inputVolume (a pathlike object or string representing an existing file) – Input DWI volume. Maps to a command-line argument:
%s(position: -4).otsuomegathreshold (a float) – Control the sharpness of the threshold in the Otsu computation. 0: lower threshold, 1: higher threshold. Maps to a command-line argument:
--otsuomegathreshold %f.outputBaseline (a boolean or a pathlike object or string representing a file) – Estimated baseline volume. Maps to a command-line argument:
%s(position: -2).removeislands (a boolean) – Remove Islands in Threshold Mask?. Maps to a command-line argument:
--removeislands.thresholdMask (a boolean or a pathlike object or string representing a file) – Otsu Threshold Mask. Maps to a command-line argument:
%s(position: -1).- Outputs:
outputBaseline (a pathlike object or string representing an existing file) – Estimated baseline volume.
thresholdMask (a pathlike object or string representing an existing file) – Otsu Threshold Mask.
ResampleDTIVolume¶
Bases: SEMLikeCommandLine
Wrapped executable:
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
- Optional Inputs:
Inverse_ITK_Transformation (a boolean) – Inverse the transformation before applying it from output image to input image (only for rigid and affine transforms). Maps to a command-line argument:
--Inverse_ITK_Transformation.Reference (a pathlike object or string representing an existing file) – Reference Volume (spacing,size,orientation,origin). Maps to a command-line argument:
--Reference %s.args (a string) – Additional parameters to the command. Maps to a command-line argument:
%s.centered_transform (a boolean) – Set the center of the transformation to the center of the input image (only for rigid and affine transforms). Maps to a command-line argument:
--centered_transform.correction (‘zero’ or ‘none’ or ‘abs’ or ‘nearest’) – Correct the tensors if computed tensor is not semi-definite positive. Maps to a command-line argument:
--correction %s.defField (a pathlike object or string representing an existing file) – File containing the deformation field (3D vector image containing vectors with 3 components). Maps to a command-line argument:
--defField %s.default_pixel_value (a float) – Default pixel value for samples falling outside of the input region. Maps to a command-line argument:
--default_pixel_value %f.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). Maps to a command-line argument:
--direction_matrix %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:
{})hfieldtype (‘displacement’ or ‘h-Field’) – Set if the deformation field is an -Field. Maps to a command-line argument:
--hfieldtype %s.image_center (‘input’ or ‘output’) – Image to use to center the transform (used only if ‘Centered Transform’ is selected). Maps to a command-line argument:
--image_center %s.inputVolume (a pathlike object or string representing an existing file) – Input volume to be resampled. Maps to a command-line argument:
%s(position: -2).interpolation (‘linear’ or ‘nn’ or ‘ws’ or ‘bs’) – Sampling algorithm (linear , nn (nearest neighbor), ws (WindowedSinc), bs (BSpline) ). Maps to a command-line argument:
--interpolation %s.notbulk (a boolean) – The transform following the BSpline transform is not set as a bulk transform for the BSpline transform. Maps to a command-line argument:
--notbulk.number_of_thread (an integer) – Number of thread used to compute the output image. Maps to a command-line argument:
--number_of_thread %d.origin (a list of items which are any value) – Origin of the output Image. Maps to a command-line argument:
--origin %s.outputVolume (a boolean or a pathlike object or string representing a file) – Resampled Volume. Maps to a command-line argument:
%s(position: -1).rotation_point (a list of items which are any value) – Center of rotation (only for rigid and affine transforms). Maps to a command-line argument:
--rotation_point %s.size (a list of items which are a float) – Size along each dimension (0 means use input size). Maps to a command-line argument:
--size %s.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). Maps to a command-line argument:
--spaceChange.spacing (a list of items which are a float) – Spacing along each dimension (0 means use input spacing). Maps to a command-line argument:
--spacing %s.spline_order (an integer) – Spline Order (Spline order may be from 0 to 5). Maps to a command-line argument:
--spline_order %d.transform (‘rt’ or ‘a’) – Transform algorithm, rt = Rigid Transform, a = Affine Transform. Maps to a command-line argument:
--transform %s.transform_matrix (a list of items which are a float) – 12 parameters of the transform matrix by rows ( –last 3 being translation– ). Maps to a command-line argument:
--transform_matrix %s.transform_order (‘input-to-output’ or ‘output-to-input’) – Select in what order the transforms are read. Maps to a command-line argument:
--transform_order %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). Maps to a command-line argument:
--transform_tensor_method %s.transformationFile (a pathlike object or string representing an existing file) – Maps to a command-line argument:
--transformationFile %s.window_function (‘h’ or ‘c’ or ‘w’ or ‘l’ or ‘b’) – Window Function , h = Hamming , c = Cosine , w = Welch , l = Lanczos , b = Blackman. Maps to a command-line argument:
--window_function %s.- Outputs:
outputVolume (a pathlike object or string representing an existing file) – Resampled Volume.
TractographyLabelMapSeeding¶
Bases: SEMLikeCommandLine
Wrapped executable:
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.
- Optional Inputs:
InputVolume (a pathlike object or string representing an existing file) – Input DTI volume. Maps to a command-line argument:
%s(position: -2).OutputFibers (a boolean or a pathlike object or string representing a file) – Tractography result. Maps to a command-line argument:
%s(position: -1).args (a string) – Additional parameters to the command. Maps to a command-line argument:
%s.clthreshold (a float) – Minimum Linear Measure for the seeding to start. Maps to a command-line argument:
--clthreshold %f.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:
{})inputroi (a pathlike object or string representing an existing file) – Label map with seeding ROIs. Maps to a command-line argument:
--inputroi %s.integrationsteplength (a float) – Distance between points on the same fiber in mm. Maps to a command-line argument:
--integrationsteplength %f.label (an integer) – Label value that defines seeding region. Maps to a command-line argument:
--label %d.maximumlength (a float) – Maximum length of fibers (in mm). Maps to a command-line argument:
--maximumlength %f.minimumlength (a float) – Minimum length of the fibers (in mm). Maps to a command-line argument:
--minimumlength %f.name (a string) – Name to use for fiber files. Maps to a command-line argument:
--name %s.outputdirectory (a boolean or a pathlike object or string representing a directory) – Directory in which to save fiber(s). Maps to a command-line argument:
--outputdirectory %s.randomgrid (a boolean) – Enable random placing of seeds. Maps to a command-line argument:
--randomgrid.seedspacing (a float) – Spacing (in mm) between seed points, only matters if use Use Index Space is off. Maps to a command-line argument:
--seedspacing %f.stoppingcurvature (a float) – Tractography will stop if radius of curvature becomes smaller than this number units are degrees per mm. Maps to a command-line argument:
--stoppingcurvature %f.stoppingmode (‘LinearMeasure’ or ‘FractionalAnisotropy’) – Tensor measurement used to stop the tractography. Maps to a command-line argument:
--stoppingmode %s.stoppingvalue (a float) – Tractography will stop when the stopping measurement drops below this value. Maps to a command-line argument:
--stoppingvalue %f.useindexspace (a boolean) – Seed at IJK voxel grid. Maps to a command-line argument:
--useindexspace.writetofile (a boolean) – Write fibers to disk or create in the scene?. Maps to a command-line argument:
--writetofile.- Outputs:
OutputFibers (a pathlike object or string representing an existing file) – Tractography result.
outputdirectory (a pathlike object or string representing an existing directory) – Directory in which to save fiber(s).