To show the concept we will use two pre-saved cingulum bundles. The algorithm used here is called Streamline-based Linear Registration (SLR) [Garyfallidis15].
from dipy.viz import window, actor from time import sleep from dipy.data import two_cingulum_bundles cb_subj1, cb_subj2 = two_cingulum_bundles() from dipy.align.streamlinear import StreamlineLinearRegistration from dipy.tracking.streamline import set_number_of_points
An important step before running the registration is to resample the streamlines so that they both have the same number of points per streamline. Here we will use 20 points. This step is not optional. Inputting streamlines with different number of points will break the theoretical advantages of using the SLR as explained in [Garyfallidis15].
cb_subj1 = set_number_of_points(cb_subj1, 20) cb_subj2 = set_number_of_points(cb_subj2, 20)
Let’s say now that we want to move the
cb_subj2 (moving) so that it can be
cb_subj1 (static). Here is how this is done.
srr = StreamlineLinearRegistration() srm = srr.optimize(static=cb_subj1, moving=cb_subj2)
After the optimization is finished we can apply the transformation to
cb_subj2_aligned = srm.transform(cb_subj2) def show_both_bundles(bundles, colors=None, show=True, fname=None): ren = window.Renderer() ren.SetBackground(1., 1, 1) for (i, bundle) in enumerate(bundles): color = colors[i] lines_actor = actor.streamtube(bundle, color, linewidth=0.3) lines_actor.RotateX(-90) lines_actor.RotateZ(90) ren.add(lines_actor) if show: window.show(ren) if fname is not None: sleep(1) window.record(ren, n_frames=1, out_path=fname, size=(900, 900)) show_both_bundles([cb_subj1, cb_subj2], colors=[window.colors.orange, window.colors.red], show=False, fname='before_registration.png')
show_both_bundles([cb_subj1, cb_subj2_aligned], colors=[window.colors.orange, window.colors.red], show=False, fname='after_registration.png')
As you can see the two cingulum bundles are well aligned although they contain many streamlines of different length and shape.
|[Garyfallidis15]||(1, 2, 3) Garyfallidis et al., “Robust and efficient linear registration of white-matter fascicles in the space of streamlines”, Neuroimage, 117:124-140, 2015.|
|[Garyfallidis14]||Garyfallidis et al., “Direct native-space fiber bundle alignment for group comparisons”, ISMRM, 2014.|
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