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

interfaces.mrtrix.preprocess
============================


.. _nipype.interfaces.mrtrix.preprocess.DWI2Tensor:


.. index:: DWI2Tensor

DWI2Tensor
----------

`Link to code <http://github.com/nipy/nipype/tree/9595f272aa4086ea28f7534a8bd05690f60bf6b8/nipype/interfaces/mrtrix/preprocess.py#L99>`__

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()                                   # doctest: +SKIP

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

.. _nipype.interfaces.mrtrix.preprocess.Erode:


.. index:: Erode

Erode
-----

`Link to code <http://github.com/nipy/nipype/tree/9595f272aa4086ea28f7534a8bd05690f60bf6b8/nipype/interfaces/mrtrix/preprocess.py#L406>`__

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()                                     # doctest: +SKIP

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

.. _nipype.interfaces.mrtrix.preprocess.GenerateWhiteMatterMask:


.. index:: GenerateWhiteMatterMask

GenerateWhiteMatterMask
-----------------------

`Link to code <http://github.com/nipy/nipype/tree/9595f272aa4086ea28f7534a8bd05690f60bf6b8/nipype/interfaces/mrtrix/preprocess.py#L362>`__

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()                                     # doctest: +SKIP

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

.. _nipype.interfaces.mrtrix.preprocess.MRConvert:


.. index:: MRConvert

MRConvert
---------

`Link to code <http://github.com/nipy/nipype/tree/9595f272aa4086ea28f7534a8bd05690f60bf6b8/nipype/interfaces/mrtrix/preprocess.py#L43>`__

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()                                 # doctest: +SKIP

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

.. _nipype.interfaces.mrtrix.preprocess.MRMultiply:


.. index:: MRMultiply

MRMultiply
----------

`Link to code <http://github.com/nipy/nipype/tree/9595f272aa4086ea28f7534a8bd05690f60bf6b8/nipype/interfaces/mrtrix/preprocess.py#L264>`__

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()                                             # doctest: +SKIP

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

.. _nipype.interfaces.mrtrix.preprocess.MRTransform:


.. index:: MRTransform

MRTransform
-----------

`Link to code <http://github.com/nipy/nipype/tree/9595f272aa4086ea28f7534a8bd05690f60bf6b8/nipype/interfaces/mrtrix/preprocess.py#L546>`__

Wraps command **mrtransform**

Apply spatial transformations or reslice images

Example
~~~~~~~

>>> MRxform = MRTransform()
>>> MRxform.inputs.in_files = 'anat_coreg.mif'
>>> MRxform.run()                                   # doctest: +SKIP

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

.. _nipype.interfaces.mrtrix.preprocess.MRTrixViewer:


.. index:: MRTrixViewer

MRTrixViewer
------------

`Link to code <http://github.com/nipy/nipype/tree/9595f272aa4086ea28f7534a8bd05690f60bf6b8/nipype/interfaces/mrtrix/preprocess.py#L304>`__

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()                                    # doctest: +SKIP

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

.. _nipype.interfaces.mrtrix.preprocess.MedianFilter3D:


.. index:: MedianFilter3D

MedianFilter3D
--------------

`Link to code <http://github.com/nipy/nipype/tree/9595f272aa4086ea28f7534a8bd05690f60bf6b8/nipype/interfaces/mrtrix/preprocess.py#L496>`__

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()                                  # doctest: +SKIP

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

.. _nipype.interfaces.mrtrix.preprocess.Tensor2ApparentDiffusion:


.. index:: Tensor2ApparentDiffusion

Tensor2ApparentDiffusion
------------------------

`Link to code <http://github.com/nipy/nipype/tree/9595f272aa4086ea28f7534a8bd05690f60bf6b8/nipype/interfaces/mrtrix/preprocess.py#L223>`__

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()                                # doctest: +SKIP

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.

.. _nipype.interfaces.mrtrix.preprocess.Tensor2FractionalAnisotropy:


.. index:: Tensor2FractionalAnisotropy

Tensor2FractionalAnisotropy
---------------------------

`Link to code <http://github.com/nipy/nipype/tree/9595f272aa4086ea28f7534a8bd05690f60bf6b8/nipype/interfaces/mrtrix/preprocess.py#L182>`__

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()                                 # doctest: +SKIP

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.

.. _nipype.interfaces.mrtrix.preprocess.Tensor2Vector:


.. index:: Tensor2Vector

Tensor2Vector
-------------

`Link to code <http://github.com/nipy/nipype/tree/9595f272aa4086ea28f7534a8bd05690f60bf6b8/nipype/interfaces/mrtrix/preprocess.py#L141>`__

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()                             # doctest: +SKIP

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.

.. _nipype.interfaces.mrtrix.preprocess.Threshold:


.. index:: Threshold

Threshold
---------

`Link to code <http://github.com/nipy/nipype/tree/9595f272aa4086ea28f7534a8bd05690f60bf6b8/nipype/interfaces/mrtrix/preprocess.py#L450>`__

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()                                             # doctest: +SKIP

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.
