def reconstruction(conn_model, gtab, dwi_data, B0_mask):
"""
Estimate a tensor model from dwi data.
Parameters
----------
conn_model : str
Connectivity reconstruction method (e.g. 'csa', 'tensor', 'csd',
'sfm').
gtab : Obj
DiPy object storing diffusion gradient information.
dwi_data : array
4D array of dwi data.
B0_mask : str
File path to B0 brain mask.
Returns
-------
mod_fit : ndarray
Fitted connectivity reconstruction model.
mod : obj
Connectivity reconstruction model.
References
----------
.. [1] Soares, J. M., Marques, P., Alves, V., & Sousa, N. (2013).
A hitchhiker’s guide to diffusion tensor imaging.
Frontiers in Neuroscience. https://doi.org/10.3389/fnins.2013.00031
"""
from pynets.dmri.estimation import (
csa_mod_est,
csd_mod_est,
sfm_mod_est,
tens_mod_est,
)
if conn_model == "csa" or conn_model == "CSA":
[mod_fit, mod] = csa_mod_est(gtab, dwi_data, B0_mask)
elif conn_model == "csd" or conn_model == "CSD":
[mod_fit, mod] = csd_mod_est(gtab, dwi_data, B0_mask)
elif conn_model == "sfm" or conn_model == "SFM":
[mod_fit, mod] = sfm_mod_est(gtab, dwi_data, B0_mask)
elif conn_model == "ten" or conn_model == "tensor" or \
conn_model == "TEN":
[mod_fit, mod] = tens_mod_est(gtab, dwi_data, B0_mask)
else:
try:
raise ValueError(
"Error: No valid reconstruction model specified. See the "
"`-mod` flag."
)
except ValueError:
import sys
sys.exit(0)
del dwi_data
return mod_fit, mod
def test_tens_mod_est(dmri_estimation_data):
"""Test tensor ODF model estimation."""
gtab = dmri_estimation_data['gtab']
dwi_file = dmri_estimation_data['dwi_file']
dwi_img = nib.load(dwi_file)
B0_mask_file = dmri_estimation_data['B0_mask']
dwi_data = dwi_img.get_fdata()
[mod_odf, model] = \
tens_mod_est(gtab, dwi_data, B0_mask_file)
assert mod_odf is not None
assert model is not None
def test_tens_mod_est(dmri_estimation_data):
"""Test tensor ODF model estimation."""
gtab = dmri_estimation_data['gtab']
dwi_img = dmri_estimation_data['dwi_img']
B0_mask_img = dmri_estimation_data['B0_mask_img']
B0_mask_file = tempfile.NamedTemporaryFile(mode='w+', suffix='.nii.gz')
nib.save(B0_mask_img, B0_mask_file.name)
dwi_data = dwi_img.get_fdata()
[mod_odf, model] = \
tens_mod_est(gtab, dwi_data, B0_mask_file.name)
assert mod_odf is not None
assert model is not None
B0_mask_file.close()
def reconstruction(conn_model, gtab, dwi_file, wm_in_dwi):
try:
import cPickle as pickle
except ImportError:
import _pickle as pickle
from pynets.dmri.estimation import tens_mod_est, csa_mod_est, csd_mod_est
dwi_img = nib.load(dwi_file)
data = dwi_img.get_fdata()
if conn_model == 'tensor':
mod = tens_mod_est(gtab, data, wm_in_dwi)
elif conn_model == 'csa':
mod = csa_mod_est(gtab, data, wm_in_dwi)
elif conn_model == 'csd':
mod = csd_mod_est(gtab, data, wm_in_dwi)
else:
raise ValueError('Error: Either no seeds supplied, or no valid seeds found in white-matter interface')
return mod
def reconstruction(conn_model, gtab, dwi_file, wm_in_dwi):
'''
Estimate a tensor model from dwi data.
Parameters
----------
conn_model : str
Connectivity reconstruction method (e.g. 'csa', 'tensor', 'csd').
gtab : Obj
DiPy object storing diffusion gradient information.
dwi_file : str
File path to diffusion weighted image.
wm_in_dwi : str
File path to white-matter tissue segmentation Nifti1Image.
Returns
-------
mod : obj
Connectivity reconstruction model.
'''
try:
import cPickle as pickle
except ImportError:
import _pickle as pickle
from pynets.dmri.estimation import tens_mod_est, csa_mod_est, csd_mod_est
dwi_img = nib.load(dwi_file)
data = dwi_img.get_fdata()
if conn_model == 'tensor':
mod = tens_mod_est(gtab, data, wm_in_dwi)
elif conn_model == 'csa':
mod = csa_mod_est(gtab, data, wm_in_dwi)
elif conn_model == 'csd':
mod = csd_mod_est(gtab, data, wm_in_dwi)
else:
raise ValueError(
'Error: Either no seeds supplied, or no valid seeds found in white-matter interface'
)
return mod