Resample an image to a template

The goal of this example is to illustrate the use of the function nilearn.image.resample_to_img to resample an image to a template. We use the MNI152 template as the reference for resampling a t-map image. Function nilearn.image.resample_img could also be used to achieve this.

First we load the required datasets using the nilearn datasets module.

from nilearn.datasets import fetch_neurovault_motor_task
from nilearn.datasets import load_mni152_template

template = load_mni152_template()

motor_images = fetch_neurovault_motor_task()
stat_img = motor_images.images[0]

Now, the localizer t-map image can be resampled to the MNI template image.

from nilearn.image import resample_to_img

resampled_stat_img = resample_to_img(stat_img, template)

Let’s check the shape and affine have been correctly updated.

# First load the original t-map in memory:
from nilearn.image import load_img
tmap_img = load_img(stat_img)

original_shape = tmap_img.shape
original_affine = tmap_img.affine

resampled_shape = resampled_stat_img.shape
resampled_affine = resampled_stat_img.affine

template_img = load_img(template)
template_shape = template_img.shape
template_affine = template_img.affine
print("""Shape comparison:
- Original t-map image shape : {0}
- Resampled t-map image shape: {1}
- Template image shape       : {2}
""".format(original_shape, resampled_shape, template_shape))

print("""Affine comparison:
- Original t-map image affine :\n {0}
- Resampled t-map image affine:\n {1}
- Template image affine       :\n {2}
""".format(original_affine, resampled_affine, template_affine))

Shape comparison:
- Original t-map image shape : (53, 63, 46)
- Resampled t-map image shape: (99, 117, 95)
- Template image shape       : (99, 117, 95)

Affine comparison:
- Original t-map image affine :
 [[  -3.    0.    0.   78.]
 [   0.    3.    0. -112.]
 [   0.    0.    3.  -50.]
 [   0.    0.    0.    1.]]
- Resampled t-map image affine:
 [[   2.    0.    0.  -98.]
 [   0.    2.    0. -134.]
 [   0.    0.    2.  -72.]
 [   0.    0.    0.    1.]]
- Template image affine       :
 [[   2.    0.    0.  -98.]
 [   0.    2.    0. -134.]
 [   0.    0.    2.  -72.]
 [   0.    0.    0.    1.]]

Finally, result images are displayed using nilearn plotting module.

from nilearn import plotting

plotting.plot_stat_map(stat_img,
                       bg_img=template,
                       cut_coords=(36, -27, 66),
                       threshold=3,
                       title="t-map in original resolution")
plotting.plot_stat_map(resampled_stat_img,
                       bg_img=template,
                       cut_coords=(36, -27, 66),
                       threshold=3,
                       title="Resampled t-map")
plotting.show()

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Estimated memory usage: 9 MB