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

interfaces.diffusion_toolkit.dti
================================


.. _nipype.interfaces.diffusion_toolkit.dti.DTIRecon:


.. index:: DTIRecon

DTIRecon
--------

`Link to code <http://github.com/nipy/nipype/tree/b1b78251dfd6f3b60c6bc63f79f86b356a8fe9cc/nipype/interfaces/diffusion_toolkit/dti.py#L57>`__

Wraps command **dti_recon**

Use dti_recon to generate tensors and other maps

Inputs::

        [Mandatory]
        DWI: (an existing file name)
                Input diffusion volume
        bvals: (an existing file name)
                b values file
        bvecs: (an existing file name)
                b vectors file
        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]
        DWI: (an existing file name)
                Input diffusion volume
        args: (a string)
                Additional parameters to the command
        b0_threshold: (a float)
                program will use b0 image with the given threshold to mask out high
                 background of fa/adc maps. by default it will calculate threshold
                 automatically. but if it failed, you need to set it manually.
        bvals: (an existing file name)
                b values file
        bvecs: (an existing file name)
                b vectors file
        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
        image_orientation_vectors: (a list of from 6 to 6 items which are a
                 float)
                specify image orientation vectors. if just one argument given,
                 will treat it as filename and read the orientation vectors from
                 the file. if 6 arguments are given, will treat them as 6 float
                 numbers and construct the 1st and 2nd vector and calculate the 3rd
                 one automatically.
                 this information will be used to determine image orientation,
                 as well as to adjust gradient vectors with oblique angle when
        n_averages: (an integer)
                Number of averages
        oblique_correction: (a boolean)
                when oblique angle(s) applied, some SIEMENS dti protocols do not
                 adjust gradient accordingly, thus it requires adjustment for
                correct
                 diffusion tensor calculation
        out_prefix: (a string, nipype default value: dti)
                Output file prefix
        output_type: ('nii' or 'analyze' or 'ni1' or 'nii.gz', nipype default
                 value: nii)
                output file type
        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

Outputs::

        ADC: (an existing file name)
        B0: (an existing file name)
        FA: (an existing file name)
        FA_color: (an existing file name)
        L1: (an existing file name)
        L2: (an existing file name)
        L3: (an existing file name)
        V1: (an existing file name)
        V2: (an existing file name)
        V3: (an existing file name)
        exp: (an existing file name)
        tensor: (an existing file name)

.. _nipype.interfaces.diffusion_toolkit.dti.DTITracker:


.. index:: DTITracker

DTITracker
----------

`Link to code <http://github.com/nipy/nipype/tree/b1b78251dfd6f3b60c6bc63f79f86b356a8fe9cc/nipype/interfaces/diffusion_toolkit/dti.py#L147>`__

Wraps command **dti_tracker**


Inputs::

        [Mandatory]
        mask1_file: (a file name)
                first mask image
        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]
        angle_threshold: (a float)
                set angle threshold. default value is 35 degree
        angle_threshold_weight: (a float)
                set angle threshold weighting factor. weighting will be be applied
                on top of the angle_threshold
        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
        input_data_prefix: (a string, nipype default value: dti)
                for internal naming use only
        input_type: ('nii' or 'analyze' or 'ni1' or 'nii.gz')
                input and output file type. accepted values are:
                 analyze -> analyze format 7.5
                 ni1 -> nifti format saved in seperate .hdr and .img file
                 nii -> nifti format with one .nii file
                 nii.gz -> nifti format with compression
                 default type is 'nii'
        invert_x: (a boolean)
                invert x component of the vector
        invert_y: (a boolean)
                invert y component of the vector
        invert_z: (a boolean)
                invert z component of the vector
        mask1_file: (a file name)
                first mask image
        mask1_threshold: (a float)
                threshold value for the first mask image, if not given, the program
                will try automatically find the threshold
        mask2_file: (a file name)
                second mask image
        mask2_threshold: (a float)
                threshold value for the second mask image, if not given, the program
                will try automatically find the threshold
        output_file: (a file name, nipype default value: tracks.trk)
        output_mask: (a file name)
                output a binary mask file in analyze format
        primary_vector: ('v2' or 'v3')
                which vector to use for fibre tracking: v2 or v3. If not set use v1
        random_seed: (an integer)
                use random location in a voxel instead of the center of the voxel to
                seed. can also define number of seed per voxel. default is 1
        step_length: (a float)
                set step length, in the unit of minimum voxel size.
                 default value is 0.5 for interpolated streamline method
                 and 0.1 for other methods
        swap_xy: (a boolean)
                swap x & y vectors while tracking
        swap_yz: (a boolean)
                swap y & z vectors while tracking
        swap_zx: (a boolean)
                swap x & z vectors while tracking
        tensor_file: (an existing file name)
                reconstructed tensor file
        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
        tracking_method: ('fact' or 'rk2' or 'tl' or 'sl')
                fact -> use FACT method for tracking. this is the default method.
                 rk2 -> use 2nd order runge-kutta method for tracking.
                 tl -> use tensorline method for tracking.
                 sl -> use interpolated streamline method with fixed step-length

Outputs::

        mask_file: (an existing file name)
        track_file: (an existing file name)
