This example explains how to coregister a set of bundles to a common space that is not biased by any specific bundle. This is useful when we want to align all the bundles but do not have a target reference space defined by an atlas.
The bundle groupwise registration framework in DIPY relies on streamline linear registration (SLR) [Garyfallidis15] and an iterative process.
In each iteration, bundles are shuffled and matched in pairs. Then, each pair of bundles are simultaneously moved to a common space in between both.
After all pairs have been aligned, a group distance metric is computed as the mean pairwise distance between all bundle pairs. With each iteration, bundles get closer to each other and the group distance decreases.
When the reduction in the group distance reaches a tolerance level the process ends.
To reduce computational time, by default both registration and distance computation are performed on streamline centroids (obtained with Quickbundles) [Garyfallidis12].
We start by importing and creating the necessary functions:
from dipy.align.streamlinear import groupwise_slr from dipy.data import read_five_af_bundles from dipy.viz.streamline import show_bundles import logging logging.basicConfig(level=logging.INFO)
To run groupwise registration we need to have our input bundles stored in a list.
Here we load 5 left arcuate fasciculi and store them in a list.
bundles = read_five_af_bundles()
Let’s now visualize our input bundles:
colors = [[0.91, 0.26, 0.35], [0.99, 0.50, 0.38], [0.99, 0.88, 0.57], [0.69, 0.85, 0.64], [0.51, 0.51, 0.63]] show_bundles(bundles, interactive=False, colors=colors, save_as='before_group_registration.png')
They are in native space and, therefore, not aligned.
Now running groupwise registration is as simple as:
bundles_reg, aff, d = groupwise_slr(bundles, verbose=True)
Finally, we visualize the registered bundles to confirm that they are now in a common space:
show_bundles(bundles_reg, interactive=False, colors=colors, save_as='after_group_registration.png')
In addition to the registered bundles, groupwise_slr also returns a list with the individual transformation matrices as well as the pairwise distances computed in each iteration.
By changing the input arguments the user can modify the transformation (up to affine), the number of maximum number of streamlines per bundle, the level of clustering, or the tolerance of the method.
Garyfallidis et al., “Robust and efficient linear registration of white-matter fascicles in the space of streamlines”, Neuroimage, 117:124-140, 2015.
Garyfallidis E. et al., “QuickBundles a method for tractography simplification”, Frontiers in Neuroscience, vol 6, no 175, 2012.
Romero-Bascones D. et al., “BundleAtlasing: unbiased population-specific atlasing of bundles in streamline space”, ISMRM, 2022.
Total running time of the script: ( 0 minutes 3.769 seconds)