Tracking requires a per-voxel model. Here, the model is the Sparse Fascicle Model (SFM), described in [Rokem2015]. This model reconstructs the diffusion signal as a combination of the signals from different fascicles (see also Reconstruction with the Sparse Fascicle Model).
To begin, we read the Stanford HARDI data set into memory:
from dipy.data import read_stanford_labels hardi_img, gtab, labels_img = read_stanford_labels() data = hardi_img.get_data() labels = labels_img.get_data() affine = hardi_img.affine
This data set provides a label map (generated using FreeSurfer), in which the white matter voxels are labeled as either 1 or 2:
white_matter = (labels == 1) | (labels == 2)
The first step in tracking is generating a model from which tracking directions can be extracted in every voxel.
For the SFM, this requires first that we define a canonical response function that will be used to deconvolve the signal in every voxel
from dipy.reconst.csdeconv import auto_response response, ratio = auto_response(gtab, data, roi_radius=10, fa_thr=0.7)
We initialize an SFM model object, using this response function and using the default sphere (362 vertices, symmetrically distributed on the surface of the sphere):
from dipy.data import get_sphere sphere = get_sphere() from dipy.reconst import sfm sf_model = sfm.SparseFascicleModel(gtab, sphere=sphere, l1_ratio=0.5, alpha=0.001, response=response)
We fit this model to the data in each voxel in the white-matter mask, so that we can use these directions in tracking:
from dipy.direction.peaks import peaks_from_model pnm = peaks_from_model(sf_model, data, sphere, relative_peak_threshold=.5, min_separation_angle=25, mask=white_matter, parallel=True )
A ThresholdTissueClassifier object is used to segment the data to track only through areas in which the Generalized Fractional Anisotropy (GFA) is sufficiently high.
from dipy.tracking.local import ThresholdTissueClassifier classifier = ThresholdTissueClassifier(pnm.gfa, .25)
Tracking will be started from a set of seeds evenly distributed in the white matter:
from dipy.tracking import utils seeds = utils.seeds_from_mask(white_matter, density=[2, 2, 2], affine=affine)
For the sake of brevity, we will take only the first 1000 seeds, generating only 1000 streamlines. Remove this line to track from many more points in all of the white matter
seeds = seeds[:1000]
We now have the necessary components to construct a tracking pipeline and execute the tracking
from dipy.tracking.local import LocalTracking from dipy.tracking.streamline import Streamlines streamlines_generator = LocalTracking(pnm, classifier, seeds, affine, step_size=.5) streamlines = Streamlines(streamlines_generator)
Next, we will create a visualization of these streamlines, relative to this subject’s T1-weighted anatomy:
from dipy.viz import window, actor, colormap as cmap from dipy.data import read_stanford_t1 from dipy.tracking.utils import move_streamlines from numpy.linalg import inv t1 = read_stanford_t1() t1_data = t1.get_data() t1_aff = t1.affine color = cmap.line_colors(streamlines) # Enables/disables interactive visualization interactive = False
To speed up visualization, we will select a random sub-set of streamlines to display. This is particularly important, if you track from seeds throughout the entire white matter, generating many streamlines. In this case, for demonstration purposes, we subselect 900 streamlines.
from dipy.tracking.streamline import select_random_set_of_streamlines plot_streamlines = select_random_set_of_streamlines(streamlines, 900) streamlines_actor = actor.streamtube( list(move_streamlines(plot_streamlines, inv(t1_aff))), cmap.line_colors(streamlines), linewidth=0.1) vol_actor = actor.slicer(t1_data) vol_actor.display(40, None, None) vol_actor2 = vol_actor.copy() vol_actor2.display(None, None, 35) ren = window.Renderer() ren.add(streamlines_actor) ren.add(vol_actor) ren.add(vol_actor2) window.record(ren, out_path='sfm_streamlines.png', size=(800, 800)) if interactive: window.show(ren)
Finally, we can save these streamlines to a ‘trk’ file, for use in other software, or for further analysis.
from dipy.io.trackvis import save_trk save_trk("sfm_detr.trk", streamlines, affine, labels.shape)
|[Rokem2015]||Ariel Rokem, Jason D. Yeatman, Franco Pestilli, Kendrick N. Kay, Aviv Mezer, Stefan van der Walt, Brian A. Wandell (2015). Evaluating the accuracy of diffusion MRI models in white matter. PLoS ONE 10(4): e0123272. doi:10.1371/journal.pone.0123272|
Example source code
You can download
the full source code of this example. This same script is also included in the dipy source distribution under the