Automatic Fiber Bundle Extraction with RecoBundles

Automatic Fiber Bundle Extraction with RecoBundles

This example explains how we can use RecoBundles [Garyfallidis17] to extract bundles from tractograms.

First import the necessary modules.

import numpy as np
from dipy.segment.bundles import RecoBundles
from dipy.align.streamlinear import whole_brain_slr
from dipy.viz import window, actor
from dipy.io.streamline import load_trk, save_trk

Download and read data for this tutorial

from dipy.data.fetcher import (fetch_target_tractogram_hcp,
                               fetch_bundle_atlas_hcp842,
                               get_bundle_atlas_hcp842,
                               get_target_tractogram_hcp)

target_file, target_folder = fetch_target_tractogram_hcp()
atlas_file, atlas_folder = fetch_bundle_atlas_hcp842()

atlas_file, all_bundles_files = get_bundle_atlas_hcp842()
target_file = get_target_tractogram_hcp()

atlas, atlas_header = load_trk(atlas_file)
target, target_header = load_trk(target_file)

let’s visualize atlas tractogram and target tractogram before registration

interactive = False

ren = window.Renderer()
ren.SetBackground(1, 1, 1)
ren.add(actor.line(atlas, colors=(1,0,1)))
ren.add(actor.line(target, colors=(1,1,0)))
window.record(ren, out_path='tractograms_initial.png', size=(600, 600))
if interactive:
    window.show(ren)
../../_images/tractograms_initial.png

Atlas and target before registration.

We will register target tractogram to model atlas’ space using streamlinear registeration (SLR) [Garyfallidis15]

moved, transform, qb_centroids1, qb_centroids2 = whole_brain_slr(
            atlas, target, x0='affine', verbose=True, progressive=True)

We save the transform generated in this registration, so that we can use it in the bundle profiles example

np.save("slr_transform.npy", transform)

let’s visualize atlas tractogram and target tractogram after registration

interactive = False

ren = window.Renderer()
ren.SetBackground(1, 1, 1)
ren.add(actor.line(atlas, colors=(1,0,1)))
ren.add(actor.line(moved, colors=(1,1,0)))
window.record(ren, out_path='tractograms_after_registration.png',
              size=(600, 600))
if interactive:
    window.show(ren)
../../_images/tractograms_after_registration.png

Atlas and target after registration.

Read AF left and CST left bundles from already fetched atlas data to use them as model bundles

from dipy.data.fetcher import get_two_hcp842_bundles
model_af_l_file, model_cst_l_file = get_two_hcp842_bundles()

Extracting bundles using recobundles [Garyfallidis17]

model_af_l, hdr = load_trk(model_af_l_file)

rb = RecoBundles(moved, verbose=True)

recognized_af_l, af_l_labels = rb.recognize(model_bundle=model_af_l,
                                            model_clust_thr=5.,
                                            reduction_thr=10,
                                            reduction_distance='mam',
                                            slr=True,
                                            slr_metric='asymmetric',
                                            pruning_distance='mam')

let’s visualize extracted Arcuate Fasciculus Left bundle and model bundle together

interactive = False

ren = window.Renderer()
ren.SetBackground(1, 1, 1)
ren.add(actor.line(model_af_l, colors=(.1,.7,.26)))
ren.add(actor.line(recognized_af_l, colors=(.1,.1,6)))
ren.set_camera(focal_point=(320.21296692, 21.28884506,  17.2174015),
               position=(2.11, 200.46, 250.44) , view_up=(0.1, -1.028, 0.18))
window.record(ren, out_path='AF_L_recognized_bundle.png',
              size=(600, 600))
if interactive:
    window.show(ren)
../../_images/AF_L_recognized_bundle.png

Extracted Arcuate Fasciculus Left bundle and model bundle

Save the bundle as a trk file. Rather than saving the recognized streamlines in the space of the atlas, we save the streamlines that are in the original space of the subject anatomy.

save_trk( "AF_L.trk", target[af_l_labels], hdr['voxel_to_rasmm'])

model_cst_l, hdr = load_trk(model_cst_l_file)

recognized_cst_l, cst_l_labels = rb.recognize(model_bundle=model_cst_l,
                                              model_clust_thr=5.,
                                              reduction_thr=10,
                                              reduction_distance='mam',
                                              slr=True,
                                              slr_metric='asymmetric',
                                              pruning_distance='mam')

let’s visualize extracted Corticospinal Tract (CST) Left bundle and model bundle together

interactive = False

ren = window.Renderer()
ren.SetBackground(1, 1, 1)
ren.add(actor.line(model_cst_l, colors=(.1,.7,.26)))
ren.add(actor.line(recognized_cst_l, colors=(.1,.1,6)))
ren.set_camera(focal_point=(-18.17281532, -19.55606842, 6.92485857),
               position=(-360.11, -340.46, -40.44),
               view_up=(-0.03, 0.028, 0.89))
window.record(ren, out_path='CST_L_recognized_bundle.png',
              size=(600, 600))
if interactive:
    window.show(ren)
../../_images/CST_L_recognized_bundle.png

Extracted Corticospinal Tract (CST) Left bundle and model bundle

Save the bundle as a trk file:

save_trk("CST_L.trk", target[cst_l_labels], hdr['voxel_to_rasmm'])

References

[Garyfallidis17](1, 2) Garyfallidis et al. Recognition of white matter bundles using local and global streamline-based registration and clustering, Neuroimage, 2017.

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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 doc/examples/ directory.