6.1.1. Registration to common space

Here we show how to create a template from multiple anatomical scans and register all of them to it. Initially, registration is of extracted brains. Once these are reasonably aligned, whole heads are registered, weighted by masks that, if parameters are chosen well, include some scalp. The amount of scalp is hopefully enough to help in differentiating the brain-scalp boundary without including so much head tissue that it starts to contaminate the registration with the highly variable head tissue.

6.1.1.1. Retrieve the data

from sammba import data_fetchers

lemur = data_fetchers.fetch_lemur_mircen_2019_t2(subjects=[0, 1, 2])

retest contains paths to images and data description

print(lemur.anat)

Out:

['/home/salma/nilearn_data/mircen2019_t2/sub-01/sub-01_anat_sub-01_T2w.nii.gz', '/home/salma/nilearn_data/mircen2019_t2/sub-02/sub-02_anat_sub-02_T2w.nii.gz', '/home/salma/nilearn_data/mircen2019_t2/sub-03/sub-03_anat_sub-03_T2w.nii.gz']

6.1.1.2. Define the writing directory

import os

write_dir = os.path.join(os.getcwd(), 'lemur_common')
if not os.path.exists(write_dir):
    os.makedirs(write_dir)

6.1.1.3. Create the template

from sammba.registration import anats_to_common

affine_register = anats_to_common(lemur.anat, write_dir, 400,
                                  use_rats_tool=False, caching=True)

We set caching to True, so that the computations are not restarted. The paths to the registered images can be accessed easilly

registered_anats = affine_register.registered
print(registered_anats)

Out:

['/home/salma/CODE/sammba-mri/examples/01_template/lemur_common/sub-01_anat_sub-01_T2w_0_Unifized_for_brain_extraction_resample_shr_affine_catenated.nii.gz', '/home/salma/CODE/sammba-mri/examples/01_template/lemur_common/sub-02_anat_sub-02_T2w_1_Unifized_for_brain_extraction_resample_shr_affine_catenated.nii.gz', '/home/salma/CODE/sammba-mri/examples/01_template/lemur_common/sub-03_anat_sub-03_T2w_2_Unifized_for_brain_extraction_resample_shr_affine_catenated.nii.gz']

6.1.1.4. Assess the template

from nilearn import image
template_img = image.mean_img(registered_anats)

6.1.1.5. Visalize results

We plot the edges of one individual anat on top of the average image

from nilearn import plotting

average_img = image.mean_img(registered_anats)
display = plotting.plot_anat(average_img, dim=-1.6, title='affine register')
display.add_edges(registered_anats[0])
plotting.show()

6.1.1.6. Visualize pipeline steps

from sammba.graphs import create_pipeline_graph

graph_file = os.path.join(write_dir, 'affine_registration_graph')
create_pipeline_graph('anats_to_common_affine', graph_file)

Total running time of the script: ( 51 minutes 0.667 seconds)