def organize_stanford_data(path=None):
"""
Create the expected file-system structure for the Stanford HARDI data-set.
"""
dpd.fetch_stanford_hardi()
if path is None:
if not op.exists(afq_home):
os.mkdir(afq_home)
base_folder = op.join(afq_home, 'stanford_hardi', 'derivatives',
'dmriprep')
else:
base_folder = op.join(path, 'stanford_hardi', 'derivatives',
'dmriprep')
if not op.exists(base_folder):
anat_folder = op.join(base_folder, 'sub-01', 'sess-01', 'anat')
os.makedirs(anat_folder, exist_ok=True)
dwi_folder = op.join(base_folder, 'sub-01', 'sess-01', 'dwi')
os.makedirs(dwi_folder, exist_ok=True)
t1_img = dpd.read_stanford_t1()
nib.save(t1_img, op.join(anat_folder, 'sub-01_sess-01_T1w.nii.gz'))
seg_img = dpd.read_stanford_labels()[-1]
nib.save(seg_img,
op.join(anat_folder, 'sub-01_sess-01_aparc+aseg.nii.gz'))
dwi_img, gtab = dpd.read_stanford_hardi()
nib.save(dwi_img, op.join(dwi_folder, 'sub-01_sess-01_dwi.nii.gz'))
np.savetxt(op.join(dwi_folder, 'sub-01_sess-01_dwi.bvecs'), gtab.bvecs)
np.savetxt(op.join(dwi_folder, 'sub-01_sess-01_dwi.bvals'), gtab.bvals)
def organize_stanford_data(path=None):
"""
Create the expected file-system structure for the Stanford HARDI data-set
"""
if path is None:
if not op.exists(afq_home):
os.mkdir(afq_home)
base_folder = op.join(afq_home, 'stanford_hardi')
else:
base_folder = op.join(path, 'stanford_hardi')
if not op.exists(base_folder):
os.mkdir(base_folder)
os.mkdir(op.join(base_folder, 'sub-01'))
os.mkdir(op.join(base_folder, 'sub-01', 'sess-01'))
anat_folder = op.join(base_folder, 'sub-01', 'sess-01', 'anat')
os.mkdir(anat_folder)
dwi_folder = op.join(base_folder, 'sub-01', 'sess-01', 'dwi')
os.mkdir(dwi_folder)
t1_img = dpd.read_stanford_t1()
nib.save(t1_img, op.join(anat_folder, 'sub-01_sess-01_T1w.nii.gz'))
dwi_img, gtab = dpd.read_stanford_hardi()
nib.save(dwi_img, op.join(dwi_folder, 'sub-01_sess-01_dwi.nii.gz'))
np.savetxt(op.join(dwi_folder, 'sub-01_sess-01_dwi.bvecs'), gtab.bvecs)
np.savetxt(op.join(dwi_folder, 'sub-01_sess-01_dwi.bvals'), gtab.bvals)
def organize_stanford_data(path=None):
"""
Create the expected file-system structure for the Stanford HARDI data-set.
"""
dpd.fetch_stanford_hardi()
if path is None:
if not op.exists(afq_home):
os.mkdir(afq_home)
my_path = afq_home
else:
my_path = path
base_folder = op.join(my_path, 'stanford_hardi', 'derivatives', 'dmriprep')
if not op.exists(base_folder):
anat_folder = op.join(base_folder, 'sub-01', 'sess-01', 'anat')
os.makedirs(anat_folder, exist_ok=True)
dwi_folder = op.join(base_folder, 'sub-01', 'sess-01', 'dwi')
os.makedirs(dwi_folder, exist_ok=True)
t1_img = dpd.read_stanford_t1()
nib.save(t1_img, op.join(anat_folder, 'sub-01_sess-01_T1w.nii.gz'))
seg_img = dpd.read_stanford_labels()[-1]
nib.save(seg_img,
op.join(anat_folder, 'sub-01_sess-01_aparc+aseg.nii.gz'))
dwi_img, gtab = dpd.read_stanford_hardi()
nib.save(dwi_img, op.join(dwi_folder, 'sub-01_sess-01_dwi.nii.gz'))
np.savetxt(op.join(dwi_folder, 'sub-01_sess-01_dwi.bvecs'), gtab.bvecs)
np.savetxt(op.join(dwi_folder, 'sub-01_sess-01_dwi.bvals'), gtab.bvals)
dataset_description = {
"BIDSVersion": "1.0.0",
"Name": "Stanford HARDI",
"Subjects": ["sub-01"]
}
desc_file = op.join(my_path, 'stanford_hardi', 'dataset_description.json')
with open(desc_file, 'w') as outfile:
json.dump(dataset_description, outfile)
from dipy.tracking.local import LocalTracking
from dipy.tracking.streamline import Streamlines
streamline_generator = LocalTracking(pnm, classifier, seeds, affine,
step_size=.5)
streamlines = Streamlines(streamline_generator)
"""
Next, we will create a visualization of these streamlines, relative to this
subject's T1-weighted anatomy:
"""
from dipy.viz import window, actor, colormap as cmap
from dipy.data import read_stanford_t1
from dipy.tracking.utils import move_streamlines
from numpy.linalg import inv
t1 = read_stanford_t1()
t1_data = t1.get_data()
t1_aff = t1.affine
color = cmap.line_colors(streamlines)
# Enables/disables interactive visualization
interactive = False
"""
To speed up visualization, we will select a random sub-set of streamlines to
display. This is particularly important, if you track from seeds throughout the
entire white matter, generating many streamlines. In this case, for
demonstration purposes, we subselect 900 streamlines.
"""
from dipy.tracking.streamline import select_random_set_of_streamlines
in DIPY.
"""
import numpy as np
from dipy.data import (read_stanford_labels, fetch_stanford_t1,
read_stanford_t1)
# Fix seed
np.random.seed(1)
# Read data
hardi_img, gtab, labels_img = read_stanford_labels()
data = hardi_img.get_data()
labels = labels_img.get_data()
affine = hardi_img.get_affine()
fetch_stanford_t1()
t1 = read_stanford_t1()
t1_data = t1.get_data()
# Select a relevant part of the data (left hemisphere)
# Coordinates given in x bounds, y bounds, z bounds
dshape = data.shape[:-1]
xa, xb, ya, yb, za, zb = [15, 42, 10, 65, 18, 65]
data_small = data[xa:xb, ya:yb, za:zb]
selectionmask = np.zeros(dshape, 'bool')
selectionmask[xa:xb, ya:yb, za:zb] = True
"""
The data is first fitted to Constant Solid Angle (CDA) ODF Model. CSA is a
good choice to estimate general fractional anisotropy (GFA), which the tissue
classifier can use to restrict fiber tracking to those areas where the ODF
shows significant restricted diffusion, thus creating a region-of-interest in
