def organize_cfin_data(path=None):
"""
Create the expected file-system structure for the
CFIN multi b-value diffusion data-set.
"""
dpd.fetch_cfin_multib()
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, 'cfin_multib', '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_cfin_t1()
nib.save(t1_img, op.join(anat_folder, 'sub-01_sess-01_T1w.nii.gz'))
dwi_img, gtab = dpd.read_cfin_dwi()
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": "CFIN",
"Subjects": ["sub-01"]
}
desc_file = op.join(my_path, 'cfin_multib', 'dataset_description.json')
with open(desc_file, 'w') as outfile:
json.dump(dataset_description, outfile)
import dipy.reconst.dki_micro as dki_micro
from dipy.data import fetch_cfin_multib
from dipy.data import read_cfin_dwi
from dipy.segment.mask import median_otsu
from scipy.ndimage.filters import gaussian_filter
"""
DKI requires multi-shell data, i.e. data acquired from more than one non-zero
b-value. Here, we use fetch to download a multi-shell dataset which was kindly
provided by Hansen and Jespersen (more details about the data are provided in
their paper [Hansen2016]_). The total size of the downloaded data is 192
MBytes, however you only need to fetch it once.
"""
fetch_cfin_multib()
img, gtab = read_cfin_dwi()
data = img.get_data()
affine = img.affine
"""
Function ``read_cfin_multib`` return img and gtab which contains respectively
a nibabel Nifti1Image object (where the data can be extracted) and a
GradientTable object with information about the b-values and b-vectors.
Before fitting the data, we preform some data pre-processing. We first compute
a brain mask to avoid unnecessary calculations on the background of the image.
"""
maskdata, mask = median_otsu(data,
vol_idx=[0, 1],
fetch_cfin_multib()
"""
``data`` contains the voxel data and ``gtab`` contains a ``GradientTable``
object (gradient information e.g. b-values). For example, to show the b-values
it is possible to write::
print(gtab.bvals)
For the values of the q-space indices to make sense it is necessary to
explicitly state the ``big_delta`` and ``small_delta`` parameters in the
gradient table.
"""
img, gtab = read_cfin_dwi()
big_delta = 0.0365 # seconds
small_delta = 0.0157 # seconds
gtab = gradient_table(bvals=gtab.bvals, bvecs=gtab.bvecs,
big_delta=big_delta,
small_delta=small_delta)
data = img.get_data()
data_small = data[40:65, 50:51]
print('data.shape (%d, %d, %d, %d)' % data.shape)
"""
The MAPMRI Model can now be instantiated. The ``radial_order`` determines the
expansion order of the basis, i.e., how many basis functions are used to
approximate the signal.
