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# interfaces.mrtrix.preprocess

## DWI2Tensor

Link to code

Wraps command dwi2tensor

Converts diffusion-weighted images to tensor images.

### Example

    >>> import nipype.interfaces.mrtrix as mrt
    >>> dwi2tensor = mrt.DWI2Tensor()
    >>> dwi2tensor.inputs.in_file = 'dwi.mif'
    >>> dwi2tensor.inputs.encoding_file = 'encoding.txt'
    >>> dwi2tensor.run()

Inputs:

    [Mandatory]
    in_file
            Diffusion-weighted images
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output

    [Optional]
    args: (a string)
            Additional parameters to the command
    debug: (a boolean)
            Display debugging messages.
    encoding_file: (a file name)
            Encoding file, , supplied as a 4xN text file with each line is in the format [ X Y Z b
            ], where [ X Y Z ] describe the direction of the applied gradient, and b gives the
            b-value in units (1000 s/mm^2). See FSL2MRTrix()
    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
    ignore_slice_by_volume: (a list of from 2 to 2 items which are an integer)
            Requires two values (i.e. [34 1] for [Slice Volume] Ignores the image slices specified
            when computing the tensor. Slice here means the z coordinate of the slice to be ignored.
    ignore_volumes: (a list of at least 1 items which are an integer)
            Requires two values (i.e. [2 5 6] for [Volumes] Ignores the image volumes specified when
            computing the tensor.
    out_filename: (a file name)
            Output tensor filename
    quiet: (a boolean)
            Do not display information messages or progress status.

Outputs:

    tensor: (an existing file name)
            path/name of output diffusion tensor image

## Erode

Link to code

Wraps command erode

Erode (or dilates) a mask (i.e. binary) image

### Example

    >>> import nipype.interfaces.mrtrix as mrt
    >>> erode = mrt.Erode()
    >>> erode.inputs.in_file = 'mask.mif'
    >>> erode.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            Input mask image to be eroded
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output

    [Optional]
    args: (a string)
            Additional parameters to the command
    debug: (a boolean)
            Display debugging messages.
    dilate: (a boolean)
            Perform dilation rather than erosion
    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
    number_of_passes: (an integer)
            the number of passes (default: 1)
    out_filename: (a file name)
            Output image filename
    quiet: (a boolean)
            Do not display information messages or progress status.

Outputs:

    out_file: (an existing file name)
            the output image

## GenerateWhiteMatterMask

Link to code

Wraps command gen_WM_mask

Generates a white matter probability mask from the DW images.

### Example

    >>> import nipype.interfaces.mrtrix as mrt
    >>> genWM = mrt.GenerateWhiteMatterMask()
    >>> genWM.inputs.in_file = 'dwi.mif'
    >>> genWM.inputs.encoding_file = 'encoding.txt'
    >>> genWM.run()

Inputs:

    [Mandatory]
    binary_mask: (an existing file name)
            Binary brain mask
    encoding_file: (an existing file name)
            Gradient encoding, supplied as a 4xN text file with each line is in the format [ X Y Z b
            ], where [ X Y Z ] describe the direction of the applied gradient, and b gives the
            b-value in units (1000 s/mm^2). See FSL2MRTrix
    in_file: (an existing file name)
            Diffusion-weighted images
    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
    noise_level_margin: (a float)
            Specify the width of the margin on either side of the image to be used to estimate the
            noise level (default = 10)
    out_WMProb_filename: (a file name)
            Output WM probability image filename

Outputs:

    WMprobabilitymap: (an existing file name)
            WMprobabilitymap

## MRConvert

Link to code

Wraps command mrconvert

Perform conversion between different file types and optionally extract a subset of the input image.

If used correctly, this program can be a very useful workhorse. In addition to converting images between different formats, it can be used to extract specific studies from a data set, extract a specific region of interest, flip the images, or to scale the intensity of the images.

### Example

    >>> import nipype.interfaces.mrtrix as mrt
    >>> mrconvert = mrt.MRConvert()
    >>> mrconvert.inputs.in_file = 'dwi_FA.mif'
    >>> mrconvert.inputs.out_filename = 'dwi_FA.nii'
    >>> mrconvert.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            voxel-order data filename
    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
    extension: ('mif' or 'nii' or 'float' or 'char' or 'short' or 'int' or 'long' or
             'double', nipype default value: mif)
            "i.e. Bfloat". Can be "char", "short", "int", "long", "float" or "double"
    extract_at_axis: (1 or 2 or 3)
            "Extract data only at the coordinates specified. This option specifies the Axis. Must be
            used in conjunction with extract_at_coordinate.
    extract_at_coordinate: (a list of from 1 to 3 items which are a float)
            "Extract data only at the coordinates specified. This option specifies the coordinates.
            Must be used in conjunction with extract_at_axis. Three comma-separated numbers giving
            the size of each voxel in mm.
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    layout: ('nii' or 'float' or 'char' or 'short' or 'int' or 'long' or 'double')
            specify the layout of the data in memory. The actual layout produced will depend on
            whether the output image format can support it.
    offset_bias: (a float)
            Apply offset to the intensity values.
    out_filename: (a file name)
            Output filename
    output_datatype: ('nii' or 'float' or 'char' or 'short' or 'int' or 'long' or 'double')
            "i.e. Bfloat". Can be "char", "short", "int", "long", "float" or "double"
    prs: (a boolean)
            Assume that the DW gradients are specified in the PRS frame (Siemens DICOM only).
    replace_NaN_with_zero: (a boolean)
            Replace all NaN values with zero.
    resample: (a float)
            Apply scaling to the intensity values.
    voxel_dims: (a list of from 3 to 3 items which are a float)
            Three comma-separated numbers giving the size of each voxel in mm.

Outputs:

    converted: (an existing file name)
            path/name of 4D volume in voxel order

## MRMultiply

Link to code

Wraps command mrmult

Multiplies two images.

### Example

    >>> import nipype.interfaces.mrtrix as mrt
    >>> MRmult = mrt.MRMultiply()
    >>> MRmult.inputs.in_files = ['dwi.mif', 'dwi_WMProb.mif']
    >>> MRmult.run()

Inputs:

    [Mandatory]
    in_files
            Input images to be multiplied
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output

    [Optional]
    args: (a string)
            Additional parameters to the command
    debug: (a boolean)
            Display debugging messages.
    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
    out_filename: (a file name)
            Output image filename
    quiet: (a boolean)
            Do not display information messages or progress status.

Outputs:

    out_file: (an existing file name)
            the output image of the multiplication

## MRTransform

Link to code

Wraps command mrtransform

Apply spatial transformations or reslice images

### Example

    >>> MRxform = MRTransform()
    >>> MRxform.inputs.in_files = 'anat_coreg.mif'
    >>> MRxform.run()

Inputs:

    [Mandatory]
    in_files
            Input images to be transformed
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output

    [Optional]
    args: (a string)
            Additional parameters to the command
    debug: (a boolean)
            Display debugging messages.
    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
    flip_x: (a boolean)
            assume the transform is supplied assuming a coordinate system with the x-axis reversed
            relative to the MRtrix convention (i.e. x increases from right to left). This is
            required to handle transform matrices produced by FSL's FLIRT command. This is only used
            in conjunction with the -reference option.
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    invert: (a boolean)
            Invert the specified transform before using it
    out_filename: (a file name)
            Output image
    quiet: (a boolean)
            Do not display information messages or progress status.
    reference_image: (an existing file name)
            in case the transform supplied maps from the input image onto a reference image, use
            this option to specify the reference. Note that this implicitly sets the -replace
            option.
    replace_transform: (a boolean)
            replace the current transform by that specified, rather than applying it to the current
            transform
    template_image: (an existing file name)
            Reslice the input image to match the specified template image.
    transformation_file: (an existing file name)
            The transform to apply, in the form of a 4x4 ascii file.

Outputs:

    out_file: (an existing file name)
            the output image of the transformation

## MRTrixViewer

Link to code

Wraps command mrview

Loads the input images in the MRTrix Viewer.

### Example

    >>> import nipype.interfaces.mrtrix as mrt
    >>> MRview = mrt.MRTrixViewer()
    >>> MRview.inputs.in_files = 'dwi.mif'
    >>> MRview.run()

Inputs:

    [Mandatory]
    in_files
            Input images to be viewed
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output

    [Optional]
    args: (a string)
            Additional parameters to the command
    debug: (a boolean)
            Display debugging messages.
    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
    quiet: (a boolean)
            Do not display information messages or progress status.

Outputs:

    None

## MedianFilter3D

Link to code

Wraps command median3D

Smooth images using a 3x3x3 median filter.

### Example

    >>> import nipype.interfaces.mrtrix as mrt
    >>> median3d = mrt.MedianFilter3D()
    >>> median3d.inputs.in_file = 'mask.mif'
    >>> median3d.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            Input images to be smoothed
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output

    [Optional]
    args: (a string)
            Additional parameters to the command
    debug: (a boolean)
            Display debugging messages.
    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
    out_filename: (a file name)
            Output image filename
    quiet: (a boolean)
            Do not display information messages or progress status.

Outputs:

    out_file: (an existing file name)
            the output image

## Tensor2ApparentDiffusion

Link to code

Wraps command tensor2ADC

Generates a map of the apparent diffusion coefficient (ADC) in each voxel

### Example

    >>> import nipype.interfaces.mrtrix as mrt
    >>> tensor2ADC = mrt.Tensor2ApparentDiffusion()
    >>> tensor2ADC.inputs.in_file = 'dwi_tensor.mif'
    >>> tensor2ADC.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            Diffusion tensor image
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output

    [Optional]
    args: (a string)
            Additional parameters to the command
    debug: (a boolean)
            Display debugging messages.
    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
    out_filename: (a file name)
            Output Fractional Anisotropy filename
    quiet: (a boolean)
            Do not display information messages or progress status.

Outputs:

    ADC: (an existing file name)
            the output image of the major eigenvectors of the diffusion tensor image.

## Tensor2FractionalAnisotropy

Link to code

Wraps command tensor2FA

Generates a map of the fractional anisotropy in each voxel.

### Example

    >>> import nipype.interfaces.mrtrix as mrt
    >>> tensor2FA = mrt.Tensor2FractionalAnisotropy()
    >>> tensor2FA.inputs.in_file = 'dwi_tensor.mif'
    >>> tensor2FA.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            Diffusion tensor image
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output

    [Optional]
    args: (a string)
            Additional parameters to the command
    debug: (a boolean)
            Display debugging messages.
    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
    out_filename: (a file name)
            Output Fractional Anisotropy filename
    quiet: (a boolean)
            Do not display information messages or progress status.

Outputs:

    FA: (an existing file name)
            the output image of the major eigenvectors of the diffusion tensor image.

## Tensor2Vector

Link to code

Wraps command tensor2vector

Generates a map of the major eigenvectors of the tensors in each voxel.

### Example

    >>> import nipype.interfaces.mrtrix as mrt
    >>> tensor2vector = mrt.Tensor2Vector()
    >>> tensor2vector.inputs.in_file = 'dwi_tensor.mif'
    >>> tensor2vector.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            Diffusion tensor image
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output

    [Optional]
    args: (a string)
            Additional parameters to the command
    debug: (a boolean)
            Display debugging messages.
    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
    out_filename: (a file name)
            Output vector filename
    quiet: (a boolean)
            Do not display information messages or progress status.

Outputs:

    vector: (an existing file name)
            the output image of the major eigenvectors of the diffusion tensor image.

## Threshold

Link to code

Wraps command threshold

Create bitwise image by thresholding image intensity.

By default, the threshold level is determined using a histogram analysis to cut out the background. Otherwise, the threshold intensity can be specified using command line options. Note that only the first study is used for thresholding.

### Example

    >>> import nipype.interfaces.mrtrix as mrt
    >>> thresh = mrt.Threshold()
    >>> thresh.inputs.in_file = 'wm_mask.mif'
    >>> thresh.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            The input image to be thresholded
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output

    [Optional]
    absolute_threshold_value: (a float)
            Specify threshold value as absolute intensity.
    args: (a string)
            Additional parameters to the command
    debug: (a boolean)
            Display debugging messages.
    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
    invert: (a boolean)
            Invert output binary mask
    out_filename: (a file name)
            The output binary image mask.
    percentage_threshold_value: (a float)
            Specify threshold value as a percentage of the peak intensity in the input image.
    quiet: (a boolean)
            Do not display information messages or progress status.
    replace_zeros_with_NaN: (a boolean)
            Replace all zero values with NaN

Outputs:

    out_file: (an existing file name)
            The output binary image mask.