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Copying headers from input images with math_img
¶
This example shows how to copy the header information from one of
the input images to the result image when using the function
nilearn.image.math_img
.
The header information contains metadata about the image, such as the dimensions, the voxel sizes, the affine matrix, repetition time (TR), etc. Some of this information might be important for downstream analyses depending on the software used.
Let’s fetch an example fMRI dataset
from nilearn import datasets
dataset = datasets.fetch_adhd(n_subjects=2)
[get_dataset_dir] Dataset found in /home/runner/work/nilearn/nilearn/nilearn_data/adhd
[fetch_single_file] Downloading data from https://www.nitrc.org/frs/download.php/7783/adhd40_0010064.tgz ...
[_chunk_read_]
Downloaded 26591232 of 45583539 bytes (58.3%, 0.7s remaining)
[fetch_single_file] ...done. (2 seconds, 0 min)
[uncompress_file] Extracting data from /home/runner/work/nilearn/nilearn/nilearn_data/adhd/31769c9cee5cd55f045e62633d651f3d/adhd40_0010064.tgz...
[uncompress_file] .. done.
Now let’s look at the header of one of these images
from nilearn.image import load_img
subj1_img = load_img(dataset.func[0])
subj2_img = load_img(dataset.func[1])
print(f"Subject 1 header:\n{subj1_img.header}")
Subject 1 header:
<class 'nibabel.nifti1.Nifti1Header'> object, endian='<'
sizeof_hdr : 348
data_type : np.bytes_(b'')
db_name : np.bytes_(b'')
extents : 0
session_error : 0
regular : np.bytes_(b'r')
dim_info : 48
dim : [ 4 61 73 61 176 1 1 1]
intent_p1 : 0.0
intent_p2 : 0.0
intent_p3 : 0.0
intent_code : none
datatype : float32
bitpix : 32
slice_start : 0
pixdim : [-1. 3. 3. 3. 2. 0. 0. 0.]
vox_offset : 0.0
scl_slope : nan
scl_inter : nan
slice_end : 60
slice_code : unknown
xyzt_units : 10
cal_max : 0.0
cal_min : 0.0
slice_duration : 0.0
toffset : 0.0
glmax : 0
glmin : 0
descrip : np.bytes_(b'')
aux_file : np.bytes_(b'')
qform_code : scanner
sform_code : scanner
quatern_b : -0.0
quatern_c : 1.0
quatern_d : 0.0
qoffset_x : 90.0
qoffset_y : -126.0
qoffset_z : -72.0
srow_x : [-3. -0. -0. 90.]
srow_y : [ -0. 3. -0. -126.]
srow_z : [ 0. 0. 3. -72.]
intent_name : np.bytes_(b'')
magic : np.bytes_(b'n+1')
Let’s apply a simple operation using nilearn.image.math_img
from nilearn.image import math_img
result_img = math_img("img1 * 1", img1=subj1_img)
By default, nilearn.image.math_img
simply resets result image’s
header to the default Nifti1Header
.
This means that it will contain different information as compared to the input image.
We can check that as follows:
print("Following header fields do not match:")
for key in result_img.header.keys():
if not (subj1_img.header[key] == result_img.header[key]).all():
print(
f"For '{key}'\n",
"\tinput image:",
subj1_img.header[key],
"\n\tresult image:",
result_img.header[key],
)
Following header fields do not match:
For 'regular'
input image: np.bytes_(b'r')
result image: np.bytes_(b'')
For 'dim_info'
input image: 48
result image: 0
For 'pixdim'
input image: [-1. 3. 3. 3. 2. 0. 0. 0.]
result image: [-1. 3. 3. 3. 1. 1. 1. 1.]
For 'scl_slope'
input image: nan
result image: nan
For 'scl_inter'
input image: nan
result image: nan
For 'slice_end'
input image: 60
result image: 0
For 'xyzt_units'
input image: 10
result image: 0
For 'qform_code'
input image: 1
result image: 0
For 'sform_code'
input image: 1
result image: 2
This could affect some downstream analyses.
For example, here the TR (given as fifth element in pixdim
)
is changed from 2 in subj1_img
to 1 in result_img
.
To fix this, we can copy the header of the input images to the result image, like this:
result_img_with_header = math_img(
"img1 * 1", img1=subj1_img, copy_header_from="img1"
)
Let’s compare the header fields again.
print("Following header fields do not match:")
for key in result_img_with_header.header.keys():
if not (subj1_img.header[key] == result_img_with_header.header[key]).all():
print(
f"For '{key}'\n",
"\tinput image:",
subj1_img.header[key],
"\n\tresult image:",
result_img_with_header.header[key],
)
Following header fields do not match:
For 'scl_slope'
input image: nan
result image: nan
For 'scl_inter'
input image: nan
result image: nan
For 'cal_max'
input image: 0.0
result image: 24359.932
We can safely ignore the fields that are still different – scl_scope
and
scl_inter
are just nan
and cal_max
is supposed to have the
maximum data value that is updated automatically by nilearn.
Modifying dimensions in the formula¶
Now let’s say we have a formula that changes the dimensions of the input images. For example, by taking the mean of the images along the time axis.
Copying the header with the copy_header_from
parameter will not work
in this case.
So, in such cases we could just use nilearn.image.math_img
without
specifying copy_header_from
and then explicitly copy the header from one
of the images using nilearn.image.new_img_like
result_img = math_img(
"np.mean(img1, axis=-1) - np.mean(img2, axis=-1)",
img1=subj1_img,
img2=subj2_img,
)
Several of the header fields are different:
print("Following header fields do not match:")
for key in result_img.header.keys():
if not (subj1_img.header[key] == result_img.header[key]).all():
print(
f"For '{key}'\n",
"\tinput image:",
subj1_img.header[key],
"\n\tresult image:",
result_img.header[key],
)
Following header fields do not match:
For 'regular'
input image: np.bytes_(b'r')
result image: np.bytes_(b'')
For 'dim_info'
input image: 48
result image: 0
For 'dim'
input image: [ 4 61 73 61 176 1 1 1]
result image: [ 3 61 73 61 1 1 1 1]
For 'pixdim'
input image: [-1. 3. 3. 3. 2. 0. 0. 0.]
result image: [-1. 3. 3. 3. 1. 1. 1. 1.]
For 'scl_slope'
input image: nan
result image: nan
For 'scl_inter'
input image: nan
result image: nan
For 'slice_end'
input image: 60
result image: 0
For 'xyzt_units'
input image: 10
result image: 0
For 'qform_code'
input image: 1
result image: 0
For 'sform_code'
input image: 1
result image: 2
Now we can copy the header explicitly like this:
from nilearn.image import new_img_like
result_img_with_header = new_img_like(
ref_niimg=subj1_img,
data=result_img.get_fdata(),
affine=result_img.affine,
copy_header=True,
)
Now, only a few not-so-important fields are different.
The modified fields can vary depending upon the formula passed into the function.
In this case, dim
and pixdim
are different because we took a mean
over the time dimension.
And again, cal_min
and cal_max
are set to minimum and maximum data
values respectively, by Nilearn.
print("Following header fields do not match:")
for key in result_img_with_header.header.keys():
if not (subj1_img.header[key] == result_img_with_header.header[key]).all():
print(
f"For '{key}'\n",
"\tinput image:",
subj1_img.header[key],
"\n\tresult image:",
result_img_with_header.header[key],
)
Following header fields do not match:
For 'dim'
input image: [ 4 61 73 61 176 1 1 1]
result image: [ 3 61 73 61 1 1 1 1]
For 'pixdim'
input image: [-1. 3. 3. 3. 2. 0. 0. 0.]
result image: [-1. 3. 3. 3. 1. 1. 1. 1.]
For 'scl_slope'
input image: nan
result image: nan
For 'scl_inter'
input image: nan
result image: nan
For 'cal_max'
input image: 0.0
result image: 17736.469
For 'cal_min'
input image: 0.0
result image: -15583.777
Total running time of the script: (0 minutes 6.453 seconds)
Estimated memory usage: 899 MB