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()
os.chdir(current_directory)
perfusion_file = quantification.compute_perfusion(
out_realign.outputs.realigned_files,
ctl_scans=ctl_scans,
tag_scans=tag_scans)
# Compute CBF
quantify = mem.cache(quantification.QuantifyCBF)
out_quantify = quantify(perfusion_file=perfusion_file,
m0_file=out_smooth_m0.outputs.smoothed_files,
tr=2500.,
t1_gm=1331.)
# Mask CBF map with brain mask
brain_mask_file = preprocessing.compute_brain_mask(
out_coregister_anat.outputs.coregistered_source, frac=.2)
cbf_map = preprocessing.apply_mask(out_quantify.outputs.cbf_file,
brain_mask_file)
# Plot CBF map on top of anat
import matplotlib.pylab as plt
from nilearn import plotting
cut_coords = (
-15,
0,
15,
45,
60,
75,
)
min_cbf = 1.
max_cbf = 150.
for map_to_plot, title, vmax, threshold in zip(
out_realign.outputs.realigned_files,
ctl_scans=ctl_scans,
tag_scans=tag_scans)
# Compute CBF
quantify = mem.cache(quantification.QuantifyCBF)
out_quantify = quantify(
perfusion_file=perfusion_file,
m0_file=out_smooth_m0.outputs.smoothed_files,
tr=2500.,
t1_gm=1331.)
# Mask CBF map with brain mask
brain_mask_file = preprocessing.compute_brain_mask(
out_coregister_anat.outputs.coregistered_source, frac=.2)
cbf_map = preprocessing.apply_mask(out_quantify.outputs.cbf_file,
brain_mask_file)
os.chdir(current_directory)
# Plot CBF map on top of anat
import matplotlib.pylab as plt
from nilearn import plotting
for map_to_plot, title, vmax, threshold in zip(
[cbf_map, heroes['basal CBF'][0]], ['pipeline CBF', 'scanner CBF'],
[150., 1500.], [1., 10.]): # scanner CBF maps are scaled
plotting.plot_stat_map(
map_to_plot,
bg_img=out_coregister_anat.outputs.coregistered_source,
threshold=threshold, vmax=vmax, (-15, 0, 15, 45, 60, 75,),
display_mode='z', title=title)
plt.show()
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()
os.chdir(current_directory)
