Home · Quickstart · User Guide and Examples · Interfaces Index · Developers · About · Nipy

# nipype.interfaces.dipy.tracks module

## StreamlineTractography

Link to code

> Bases: DipyBaseInterface

> 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()

> gfa_thresha float

>   GFA threshold to compute tracking mask. (Nipype default value: `0.2`)

> in_filea pathlike object or string representing an existing file

>   Input diffusion data.

> min_anglea float

>   Minimum separation angle. (Nipype default value: `25.0`)

> multiprocessa boolean

>   Use multiprocessing. (Nipype default value: `True`)

> num_seedsan integer (int or long)

>   Desired number of tracks in tractography. (Nipype default value: `10000`)

> peak_thresholda float

>   Threshold to consider peaks from model. (Nipype default value: `0.5`)

> save_seedsa boolean

>   Save seeding voxels coordinates. (Nipype default value: `False`)

> in_modela pathlike object or string representing an existing file

>   Input f/d-ODF model extracted from.

> in_peaksa pathlike object or string representing an existing file

>   Peaks computed from the odf.

> out_prefixa unicode string

>   Output prefix for file names.

> seed_coorda pathlike object or string representing an existing file

>   File containing the list of seed voxel coordinates (N,3).

> seed_maska pathlike object or string representing an existing file

>   Input mask within which perform seeding.

> tracking_maska pathlike object or string representing an existing file

>   Input mask within which perform tracking.

> gfaa pathlike object or string representing a file

>   The resulting GFA (generalized FA) computed using the peaks of the ODF.

> odf_peaksa pathlike object or string representing a file

>   Peaks computed from the odf.

> out_seedsa pathlike object or string representing a file

>   File containing the (N,3) voxel coordinates used in seeding.

> tracksa pathlike object or string representing a file

>   TrackVis file containing extracted streamlines.

## TrackDensityMap

Link to code

> Bases: DipyBaseInterface

> 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()

> in_filea pathlike object or string representing an existing file

>   The input TrackVis track file.

> data_dimsa list of from 3 to 3 items which are an integer (int or long)

>   The size of the image in voxels.

> out_filenamea pathlike object or string representing a file

>   The output filename for the tracks in TrackVis (.trk) format. (Nipype default value: `tdi.nii`)

> points_space‘rasmm’ or ‘voxel’ or None

>   Coordinates of trk file. (Nipype default value: `rasmm`)

> referencea pathlike object or string representing an existing file

>   A reference file to define RAS coordinates space.

> voxel_dimsa list of from 3 to 3 items which are a float

>   The size of each voxel in mm.

> out_file : a pathlike object or string representing an existing file