.. AUTO-GENERATED FILE -- DO NOT EDIT!

interfaces.slicer.diffusion.diffusion
=====================================


.. _nipype.interfaces.slicer.diffusion.diffusion.DTIexport:


.. index:: DTIexport

DTIexport
---------

`Link to code <http://github.com/nipy/nipype/tree/e63e055194d62d2bdc4665688261c03a42fd0025/nipype/interfaces/slicer/diffusion/diffusion.py#L351>`__

Wraps 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::

        [Mandatory]
        terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
                Control terminal output: `stream` - displays to terminal
                immediately, `allatonce` - waits till command is finished to display
                output, `file` - writes output to file, `none` - output is ignored

        [Optional]
        args: (a string)
                Additional parameters to the command
                flag: %s
        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
                flag: %s, position: -2
        outputFile: (a boolean or a file name)
                Output DTI file
                flag: %s, position: -1

Outputs::

        outputFile: (an existing file name)
                Output DTI file

.. _nipype.interfaces.slicer.diffusion.diffusion.DTIimport:


.. index:: DTIimport

DTIimport
---------

`Link to code <http://github.com/nipy/nipype/tree/e63e055194d62d2bdc4665688261c03a42fd0025/nipype/interfaces/slicer/diffusion/diffusion.py#L245>`__

Wraps 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::

        [Mandatory]
        terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
                Control terminal output: `stream` - displays to terminal
                immediately, `allatonce` - waits till command is finished to display
                output, `file` - writes output to file, `none` - output is ignored

        [Optional]
        args: (a string)
                Additional parameters to the command
                flag: %s
        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
                flag: %s, position: -2
        outputTensor: (a boolean or a file name)
                Output DTI volume
                flag: %s, position: -1
        testingmode: (a boolean)
                Enable testing mode. Sample helix file (helix-DTI.nhdr) will be
                loaded into Slicer and converted in Nifti.
                flag: --testingmode

Outputs::

        outputTensor: (an existing file name)
                Output DTI volume

.. _nipype.interfaces.slicer.diffusion.diffusion.DWIJointRicianLMMSEFilter:


.. index:: DWIJointRicianLMMSEFilter

DWIJointRicianLMMSEFilter
-------------------------

`Link to code <http://github.com/nipy/nipype/tree/e63e055194d62d2bdc4665688261c03a42fd0025/nipype/interfaces/slicer/diffusion/diffusion.py#L173>`__

Wraps 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::

        [Mandatory]
        terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
                Control terminal output: `stream` - displays to terminal
                immediately, `allatonce` - waits till command is finished to display
                output, `file` - writes output to file, `none` - output is ignored

        [Optional]
        args: (a string)
                Additional parameters to the command
                flag: %s
        compressOutput: (a boolean)
                Compress the data of the compressed file using gzip
                flag: --compressOutput
        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.
                flag: %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).
                flag: --ng %d
        outputVolume: (a boolean or a file name)
                Output DWI volume.
                flag: %s, position: -1
        re: (an integer)
                Estimation radius.
                flag: --re %s
        rf: (an integer)
                Filtering radius.
                flag: --rf %s

Outputs::

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

.. _nipype.interfaces.slicer.diffusion.diffusion.DWIRicianLMMSEFilter:


.. index:: DWIRicianLMMSEFilter

DWIRicianLMMSEFilter
--------------------

`Link to code <http://github.com/nipy/nipype/tree/e63e055194d62d2bdc4665688261c03a42fd0025/nipype/interfaces/slicer/diffusion/diffusion.py#L84>`__

Wraps 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::

        [Mandatory]
        terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
                Control terminal output: `stream` - displays to terminal
                immediately, `allatonce` - waits till command is finished to display
                output, `file` - writes output to file, `none` - output is ignored

        [Optional]
        args: (a string)
                Additional parameters to the command
                flag: %s
        compressOutput: (a boolean)
                Compress the data of the compressed file using gzip
                flag: --compressOutput
        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.
                flag: --hrf %f
        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.
                flag: %s, position: -2
        iter: (an integer)
                Number of iterations for the noise removal filter.
                flag: --iter %d
        maxnstd: (an integer)
                Maximum allowed noise standard deviation.
                flag: --maxnstd %d
        minnstd: (an integer)
                Minimum allowed noise standard deviation.
                flag: --minnstd %d
        mnve: (an integer)
                Minimum number of voxels in kernel used for estimation.
                flag: --mnve %d
        mnvf: (an integer)
                Minimum number of voxels in kernel used for filtering.
                flag: --mnvf %d
        outputVolume: (a boolean or a file name)
                Output DWI volume.
                flag: %s, position: -1
        re: (an integer)
                Estimation radius.
                flag: --re %s
        rf: (an integer)
                Filtering radius.
                flag: --rf %s
        uav: (a boolean)
                Use absolute value in case of negative square.
                flag: --uav

Outputs::

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

.. _nipype.interfaces.slicer.diffusion.diffusion.DWIToDTIEstimation:


.. index:: DWIToDTIEstimation

DWIToDTIEstimation
------------------

`Link to code <http://github.com/nipy/nipype/tree/e63e055194d62d2bdc4665688261c03a42fd0025/nipype/interfaces/slicer/diffusion/diffusion.py#L282>`__

Wraps 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::

        [Mandatory]
        terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
                Control terminal output: `stream` - displays to terminal
                immediately, `allatonce` - waits till command is finished to display
                output, `file` - writes output to file, `none` - output is ignored

        [Optional]
        args: (a string)
                Additional parameters to the command
                flag: %s
        enumeration: ('LS' or 'WLS')
                LS: Least Squares, WLS: Weighted Least Squares
                flag: --enumeration %s
        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
                flag: %s, position: -3
        mask: (an existing file name)
                Mask where the tensors will be computed
                flag: --mask %s
        outputBaseline: (a boolean or a file name)
                Estimated baseline volume
                flag: %s, position: -1
        outputTensor: (a boolean or a file name)
                Estimated DTI volume
                flag: %s, position: -2
        shiftNeg: (a boolean)
                Shift eigenvalues so all are positive (accounts for bad tensors
                related to noise or acquisition error)
                flag: --shiftNeg

Outputs::

        outputBaseline: (an existing file name)
                Estimated baseline volume
        outputTensor: (an existing file name)
                Estimated DTI volume

.. _nipype.interfaces.slicer.diffusion.diffusion.DiffusionTensorScalarMeasurements:


.. index:: DiffusionTensorScalarMeasurements

DiffusionTensorScalarMeasurements
---------------------------------

`Link to code <http://github.com/nipy/nipype/tree/e63e055194d62d2bdc4665688261c03a42fd0025/nipype/interfaces/slicer/diffusion/diffusion.py#L319>`__

Wraps 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::

        [Mandatory]
        terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
                Control terminal output: `stream` - displays to terminal
                immediately, `allatonce` - waits till command is finished to display
                output, `file` - writes output to file, `none` - output is ignored

        [Optional]
        args: (a string)
                Additional parameters to the command
                flag: %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
                flag: --enumeration %s
        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
                flag: %s, position: -3
        outputScalar: (a boolean or a file name)
                Scalar volume derived from tensor
                flag: %s, position: -1

Outputs::

        outputScalar: (an existing file name)
                Scalar volume derived from tensor

.. _nipype.interfaces.slicer.diffusion.diffusion.DiffusionWeightedVolumeMasking:


.. index:: DiffusionWeightedVolumeMasking

DiffusionWeightedVolumeMasking
------------------------------

`Link to code <http://github.com/nipy/nipype/tree/e63e055194d62d2bdc4665688261c03a42fd0025/nipype/interfaces/slicer/diffusion/diffusion.py#L212>`__

Wraps 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::

        [Mandatory]
        terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
                Control terminal output: `stream` - displays to terminal
                immediately, `allatonce` - waits till command is finished to display
                output, `file` - writes output to file, `none` - output is ignored

        [Optional]
        args: (a string)
                Additional parameters to the command
                flag: %s
        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
                flag: %s, position: -4
        otsuomegathreshold: (a float)
                Control the sharpness of the threshold in the Otsu computation. 0:
                lower threshold, 1: higher threhold
                flag: --otsuomegathreshold %f
        outputBaseline: (a boolean or a file name)
                Estimated baseline volume
                flag: %s, position: -2
        removeislands: (a boolean)
                Remove Islands in Threshold Mask?
                flag: --removeislands
        thresholdMask: (a boolean or a file name)
                Otsu Threshold Mask
                flag: %s, position: -1

Outputs::

        outputBaseline: (an existing file name)
                Estimated baseline volume
        thresholdMask: (an existing file name)
                Otsu Threshold Mask

.. _nipype.interfaces.slicer.diffusion.diffusion.ResampleDTIVolume:


.. index:: ResampleDTIVolume

ResampleDTIVolume
-----------------

`Link to code <http://github.com/nipy/nipype/tree/e63e055194d62d2bdc4665688261c03a42fd0025/nipype/interfaces/slicer/diffusion/diffusion.py#L42>`__

Wraps 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::

        [Mandatory]
        terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
                Control terminal output: `stream` - displays to terminal
                immediately, `allatonce` - waits till command is finished to display
                output, `file` - writes output to file, `none` - output is ignored

        [Optional]
        Inverse_ITK_Transformation: (a boolean)
                Inverse the transformation before applying it from output image to
                input image (only for rigid and affine transforms)
                flag: --Inverse_ITK_Transformation
        Reference: (an existing file name)
                Reference Volume (spacing,size,orientation,origin)
                flag: --Reference %s
        args: (a string)
                Additional parameters to the command
                flag: %s
        centered_transform: (a boolean)
                Set the center of the transformation to the center of the input
                image (only for rigid and affine transforms)
                flag: --centered_transform
        correction: ('zero' or 'none' or 'abs' or 'nearest')
                Correct the tensors if computed tensor is not semi-definite positive
                flag: --correction %s
        defField: (an existing file name)
                File containing the deformation field (3D vector image containing
                vectors with 3 components)
                flag: --defField %s
        default_pixel_value: (a float)
                Default pixel value for samples falling outside of the input region
                flag: --default_pixel_value %f
        direction_matrix: (a float)
                9 parameters of the direction matrix by rows (ijk to LPS if LPS
                transform, ijk to RAS if RAS transform)
                flag: --direction_matrix %s
        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
                flag: --hfieldtype %s
        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)
                flag: --image_center %s
        inputVolume: (an existing file name)
                Input volume to be resampled
                flag: %s, position: -2
        interpolation: ('linear' or 'nn' or 'ws' or 'bs')
                Sampling algorithm (linear , nn (nearest neighborhoor), ws
                (WindowedSinc), bs (BSpline) )
                flag: --interpolation %s
        notbulk: (a boolean)
                The transform following the BSpline transform is not set as a bulk
                transform for the BSpline transform
                flag: --notbulk
        number_of_thread: (an integer)
                Number of thread used to compute the output image
                flag: --number_of_thread %d
        origin: (a list of items which are any value)
                Origin of the output Image
                flag: --origin %s
        outputVolume: (a boolean or a file name)
                Resampled Volume
                flag: %s, position: -1
        rotation_point: (a list of items which are any value)
                Center of rotation (only for rigid and affine transforms)
                flag: --rotation_point %s
        size: (a float)
                Size along each dimension (0 means use input size)
                flag: --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)
                flag: --spaceChange
        spacing: (a float)
                Spacing along each dimension (0 means use input spacing)
                flag: --spacing %s
        spline_order: (an integer)
                Spline Order (Spline order may be from 0 to 5)
                flag: --spline_order %d
        transform: ('rt' or 'a')
                Transform algorithm, rt = Rigid Transform, a = Affine Transform
                flag: --transform %s
        transform_matrix: (a float)
                12 parameters of the transform matrix by rows ( --last 3 being
                translation-- )
                flag: --transform_matrix %s
        transform_order: ('input-to-output' or 'output-to-input')
                Select in what order the transforms are read
                flag: --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)
                flag: --transform_tensor_method %s
        transformationFile: (an existing file name)
                flag: --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
                flag: --window_function %s

Outputs::

        outputVolume: (an existing file name)
                Resampled Volume

.. _nipype.interfaces.slicer.diffusion.diffusion.TractographyLabelMapSeeding:


.. index:: TractographyLabelMapSeeding

TractographyLabelMapSeeding
---------------------------

`Link to code <http://github.com/nipy/nipype/tree/e63e055194d62d2bdc4665688261c03a42fd0025/nipype/interfaces/slicer/diffusion/diffusion.py#L135>`__

Wraps 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::

        [Mandatory]
        terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
                Control terminal output: `stream` - displays to terminal
                immediately, `allatonce` - waits till command is finished to display
                output, `file` - writes output to file, `none` - output is ignored

        [Optional]
        InputVolume: (an existing file name)
                Input DTI volume
                flag: %s, position: -2
        OutputFibers: (a boolean or a file name)
                Tractography result
                flag: %s, position: -1
        args: (a string)
                Additional parameters to the command
                flag: %s
        clthreshold: (a float)
                Minimum Linear Measure for the seeding to start.
                flag: --clthreshold %f
        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
                flag: --inputroi %s
        integrationsteplength: (a float)
                Distance between points on the same fiber in mm
                flag: --integrationsteplength %f
        label: (an integer)
                Label value that defines seeding region.
                flag: --label %d
        maximumlength: (a float)
                Maximum length of fibers (in mm)
                flag: --maximumlength %f
        minimumlength: (a float)
                Minimum length of the fibers (in mm)
                flag: --minimumlength %f
        name: (a string)
                Name to use for fiber files
                flag: --name %s
        outputdirectory: (a boolean or a directory name)
                Directory in which to save fiber(s)
                flag: --outputdirectory %s
        randomgrid: (a boolean)
                Enable random placing of seeds
                flag: --randomgrid
        seedspacing: (a float)
                Spacing (in mm) between seed points, only matters if use Use Index
                Space is off
                flag: --seedspacing %f
        stoppingcurvature: (a float)
                Tractography will stop if radius of curvature becomes smaller than
                this number units are degrees per mm
                flag: --stoppingcurvature %f
        stoppingmode: ('LinearMeasure' or 'FractionalAnisotropy')
                Tensor measurement used to stop the tractography
                flag: --stoppingmode %s
        stoppingvalue: (a float)
                Tractography will stop when the stopping measurement drops below
                this value
                flag: --stoppingvalue %f
        useindexspace: (a boolean)
                Seed at IJK voxel grid
                flag: --useindexspace
        writetofile: (a boolean)
                Write fibers to disk or create in the scene?
                flag: --writetofile

Outputs::

        OutputFibers: (an existing file name)
                Tractography result
        outputdirectory: (an existing directory name)
                Directory in which to save fiber(s)
