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# interfaces.slicer.diffusion.diffusion

## DTIexport

Link to code

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

    [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

## DTIimport

Link to code

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

    [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

## DWIJointRicianLMMSEFilter

Link to code

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

    [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.

## DWIRicianLMMSEFilter

Link to code

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

    [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.

## DWIToDTIEstimation

Link to code

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

    [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

## DiffusionTensorScalarMeasurements

Link to code

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

    [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

## DiffusionWeightedVolumeMasking

Link to code

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

    [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

## ResampleDTIVolume

Link to code

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

    [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

## TractographyLabelMapSeeding

Link to code

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

    [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)