nipype.interfaces.dipy.tracks module¶
StreamlineTractography¶
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¶
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