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

interfaces.diffusion_toolkit.odf
================================


.. _nipype.interfaces.diffusion_toolkit.odf.HARDIMat:


.. index:: HARDIMat

HARDIMat
--------

`Link to code <http://github.com/nipy/nipype/tree/99796c15f2e157774a3f54f878fdd06ad981a80b/nipype/interfaces/diffusion_toolkit/odf.py#L54>`_

Wraps command **hardi_mat**

Use hardi_mat to calculate a reconstruction matrix from a gradient table

Inputs::

        [Mandatory]
        bvals: (an existing file name)
                b values file
        bvecs: (an existing file name)
                b vectors file

        [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
        image_info: (an existing file name)
                specify image information file. the image info file is generated
                        from original dicom image by diff_unpack program and contains image
                        orientation and other information needed for reconstruction and
                        tracking. by default will look into the image folder for .info file
        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
        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
        odf_file: (an existing file name)
                filename that contains the reconstruction points on a HEMI-sphere.
                        use the pre-set 181 points by default
        order: (an integer)
                maximum order of spherical harmonics. must be even number. default
                        is 4
        out_file: (a file name, nipype default value: recon_mat.dat)
                output matrix file
        reference_file: (an existing file name)
                provide a dicom or nifti image as the reference for the program to
                        figure out the image orientation information. if no such info was
                        found in the given image header, the next 5 options -info, etc.,
                        will be used if provided. if image orientation info can be found
                        in the given reference, all other 5 image orientation options will
                        be IGNORED

Outputs::

        out_file: (an existing file name)
                output matrix file

.. _nipype.interfaces.diffusion_toolkit.odf.ODFRecon:


.. index:: ODFRecon

ODFRecon
--------

`Link to code <http://github.com/nipy/nipype/tree/99796c15f2e157774a3f54f878fdd06ad981a80b/nipype/interfaces/diffusion_toolkit/odf.py#L126>`_

Wraps command **odf_recon**

Use odf_recon to generate tensors and other maps

Inputs::

        [Mandatory]
        DWI: (an existing file name)
                Input raw data
        matrix: (an existing file name)
                use given file as reconstruction matrix.
        n_b0: (an integer)
                number of b0 scans. by default the program gets this information
                        from the number of directions and number of volumes in
                        the raw data. useful when dealing with incomplete raw
                        data set or only using part of raw data set to reconstruct
        n_directions: (an integer)
                Number of directions
        n_output_directions: (an integer)
                Number of output directions

        [Optional]
        args: (a string)
                Additional parameters to the command
        dsi: (a boolean)
                indicates that the data is dsi
        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
        filter: (a boolean)
                apply a filter (e.g. high pass) to the raw image
        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
        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: odf)
                Output file prefix
        output_entropy: (a boolean)
                output entropy map
        output_type: ('nii' or 'analyze' or 'ni1' or 'nii.gz', nipype default value: nii)
                output file type
        sharpness: (a float)
                smooth or sharpen the raw data. factor > 0 is smoothing.
                        factor < 0 is sharpening. default value is 0
                        NOTE: this option applies to DSI study only
        subtract_background: (a boolean)
                subtract the background value before reconstruction

Outputs::

        B0: (an existing file name)
        DWI: (an existing file name)
        ODF: (an existing file name)
        entropy: (a file name)
        max: (an existing file name)

.. _nipype.interfaces.diffusion_toolkit.odf.ODFTracker:


.. index:: ODFTracker

ODFTracker
----------

`Link to code <http://github.com/nipy/nipype/tree/99796c15f2e157774a3f54f878fdd06ad981a80b/nipype/interfaces/diffusion_toolkit/odf.py#L205>`_

Wraps command **odf_tracker**

Use odf_tracker to generate track file

Inputs::

        [Mandatory]
        ODF: (an existing file name)
        mask1_file: (a file name)
                first mask image
        max: (an existing file name)

        [Optional]
        angle_threshold: (a float)
                set angle threshold. default value is 35 degree for
                        default tracking method and 25 for rk2
        args: (a string)
                Additional parameters to the command
        disc: (a boolean)
                use disc tracking
        dsi: (a boolean)
                 specify the input odf data is dsi. because dsi recon uses fixed
                        pre-calculated matrix, some special orientation patch needs to
                        be applied to keep dti/dsi/q-ball consistent.
        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
        input_data_prefix: (a string, nipype default value: odf)
                recon data prefix
        input_output_type: ('nii' or 'analyze' or 'ni1' or 'nii.gz', nipype default value: nii)
                input and output file type
        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
        limit: (an integer)
                in some special case, such as heart data, some track may go into
                        infinite circle and take long time to stop. this option allows
                        setting a limit for the longest tracking steps (voxels)
        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
        out_file: (a file name, nipype default value: tracks.trk)
                output track file
        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
        runge_kutta2: (a boolean)
                use 2nd order runge-kutta method for tracking.
                        default tracking method is non-interpolate streamline
        slice_order: (an integer)
                set the slice order. 1 means normal, -1 means reversed. default value is 1
        step_length: (a float)
                set step length, in the unit of minimum voxel size.
                        default value is 0.1.
        swap_xy: (a boolean)
                swap x and y vectors while tracking
        swap_yz: (a boolean)
                swap y and z vectors while tracking
        swap_zx: (a boolean)
                swap x and z vectors while tracking
        voxel_order: ('RAS' or 'RPS' or 'RAI' or 'RPI' or 'LAI' or 'LAS' or 'LPS' or 'LPI')
                specify the voxel order in RL/AP/IS (human brain) reference. must be
                        3 letters with no space in between.
                        for example, RAS means the voxel row is from L->R, the column
                        is from P->A and the slice order is from I->S.
                        by default voxel order is determined by the image orientation
                        (but NOT guaranteed to be correct because of various standards).
                        for example, siemens axial image is LPS, coronal image is LIP and
                        sagittal image is PIL.
                        this information also is NOT needed for tracking but will be saved
                        in the track file and is essential for track display to map onto
                        the right coordinates

Outputs::

        track_file: (an existing file name)
                output track file
