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# interfaces.dipy.tracks

## StreamlineTractography

Link to code

Streamline tractography using EuDX [Garyfallidis12].

[Garyfallidis12] | Garyfallidis E., “Towards an accurate brain tractography”, PhD thesis, University of Cambridge, 2012

### Example

    >>> from nipype.interfaces import dipy as ndp
    >>> track = ndp.StreamlineTractography()
    >>> track.inputs.in_file = '4d_dwi.nii'
    >>> track.inputs.in_model = 'model.pklz'
    >>> track.inputs.tracking_mask = 'dilated_wm_mask.nii'
    >>> res = track.run()

Inputs:

    [Mandatory]
    gfa_thresh: (a float, nipype default value: 0.2)
            GFA threshold to compute tracking mask
    in_file: (an existing file name)
            input diffusion data
    min_angle: (a float, nipype default value: 25.0)
            minimum separation angle
    multiprocess: (a boolean, nipype default value: True)
            use multiprocessing
    num_seeds: (an integer (int or long), nipype default value: 10000)
            desired number of tracks in tractography
    peak_threshold: (a float, nipype default value: 0.5)
            threshold to consider peaks from model
    save_seeds: (a boolean, nipype default value: False)
            save seeding voxels coordinates

    [Optional]
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    in_model: (an existing file name)
            input f/d-ODF model extracted from.
    in_peaks: (an existing file name)
            peaks computed from the odf
    out_prefix: (a unicode string)
            output prefix for file names
    seed_coord: (an existing file name)
            file containing the list of seed voxel coordinates (N,3)
    seed_mask: (an existing file name)
            input mask within which perform seeding
    tracking_mask: (an existing file name)
            input mask within which perform tracking

Outputs:

    gfa: (a file name)
            The resulting GFA (generalized FA) computed using the peaks of the
            ODF
    odf_peaks: (a file name)
            peaks computed from the odf
    out_seeds: (a file name)
            file containing the (N,3) *voxel* coordinates used in seeding.
    tracks: (a file name)
            TrackVis file containing extracted streamlines

## TrackDensityMap

Link to code

Creates a tract density image from a TrackVis track file using functions from dipy

### Example

    >>> import nipype.interfaces.dipy as dipy
    >>> trk2tdi = dipy.TrackDensityMap()
    >>> trk2tdi.inputs.in_file = 'converted.trk'
    >>> trk2tdi.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            The input TrackVis track file

    [Optional]
    data_dims: (a list of from 3 to 3 items which are an integer (int or
             long))
            The size of the image in voxels.
    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, nipype default value: tdi.nii)
            The output filename for the tracks in TrackVis (.trk) format
    points_space: (u'rasmm' or u'voxel' or None, nipype default value:
             rasmm)
            coordinates of trk file
    reference: (an existing file name)
            A reference file to define RAS coordinates space
    voxel_dims: (a list of from 3 to 3 items which are a float)
            The size of each voxel in mm.

Outputs:

    out_file: (an existing file name)