def test_get_voxel_dims():
data = np.zeros((91, 91, 60))
affine = 3 * np.eye(4)
affine[:, 3] = np.ones(4)
in_file = '/tmp/file1.nii'
nibabel.Nifti1Image(data, affine).to_filename(in_file)
assert_array_equal(_utils.get_voxel_dims(in_file), [3., 3., 3.])
def test_get_voxel_dims():
data = np.zeros((91, 91, 60))
affine = 3 * np.eye(4)
affine[:, 3] = np.ones(4)
in_file = '/tmp/file1.nii'
nibabel.Nifti1Image(data, affine).to_filename(in_file)
assert_array_equal(_utils.get_voxel_dims(in_file), [3., 3., 3.])
perfusion_file=perfusion_file,
m0_file=out_smooth_m0.outputs.smoothed_files,
tr=2500.,
t1_gm=1331.)
# Compute brain mask
brain_mask_file = preprocessing.compute_brain_mask(
out_coregister_anat.outputs.coregistered_source, frac=.2)
# Normalize CBF
normalize = mem.cache(spm.Normalize)
out_normalize = normalize(
parameter_file=out_segment.outputs.transformation_mat,
apply_to_files=[out_quantify.outputs.cbf_file,
brain_mask_file],
write_voxel_sizes=_utils.get_voxel_dims(func_file),
write_interp=2,
jobtype='write')
# Mask CBF map with brain mask
cbf_map = preprocessing.apply_mask(
out_normalize.outputs.normalized_files[0],
out_normalize.outputs.normalized_files[1])
# Plot CBF map on top of MNI template
plotting.plot_stat_map(
cbf_map,
bg_img='/usr/share/fsl/5.0/data/standard/MNI152_T1_2mm.nii.gz',
threshold=.1, vmax=150.,
display_mode='z')
plt.show()
jobtype='estimate')
# Segment anat
segment = mem.cache(spm.Segment)
out_segment = segment(data=anat_file,
gm_output_type=[True, False, True],
wm_output_type=[True, False, True],
csf_output_type=[True, False, True],
save_bias_corrected=True)
# Normalize anat
normalize_anat = mem.cache(spm.Normalize)
out_normalize_anat = normalize_anat(
parameter_file=out_segment.outputs.transformation_mat,
apply_to_files=[out_coregister.outputs.coregistered_source],
write_voxel_sizes=_utils.get_voxel_dims(anat_file),
write_interp=1,
jobtype='write')
# Normalize EPIs
normalize_func = mem.cache(spm.Normalize)
out_normalize_func = normalize_func(
parameter_file=out_segment.outputs.transformation_mat,
apply_to_files=[
out_realign.outputs.realigned_files,
out_segment.outputs.native_gm_image,
out_segment.outputs.native_wm_image,
out_segment.outputs.native_csf_image
],
write_voxel_sizes=_utils.get_voxel_dims(func_file),
write_interp=1,
csf_output_type=[True, False, True],
save_bias_corrected=True)
# Coregister anat to mean ASL
coregister_anat = mem.cache(spm.Coregister)
out_coregister_anat = coregister_anat(
target=out_average.outputs.mean_image,
source=anat_file,
apply_to_files=[out_segment.outputs.native_gm_image,
out_segment.outputs.native_wm_image],
write_interp=3,
jobtype='estwrite')
# Normalize func
normalize = mem.cache(spm.Normalize)
write_voxel_sizes = np.round(_utils.get_voxel_dims(func_file), 2).tolist()
out_normalize = normalize(
parameter_file=out_segment.outputs.transformation_mat,
apply_to_files=[out_realign.outputs.realigned_files,
out_average.outputs.mean_image],
write_voxel_sizes=write_voxel_sizes,
write_interp=2,
jobtype='write')
# Smooth func
smooth = mem.cache(spm.Smooth)
out_smooth = smooth(
in_files=out_normalize.outputs.normalized_files,
fwhm=[5., 5., 5.])
os.chdir(current_directory)
