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_sfm_mod_est(dmri_estimation_data):
"""Test SFM model estimation."""
gtab = dmri_estimation_data['gtab']
dwi_file = dmri_estimation_data['dwi_file_small']
dwi_data_small = nib.load(dwi_file).get_fdata()
B0_mask_file = dmri_estimation_data['B0_mask_small']
[sf_odf, model] = sfm_mod_est(gtab, dwi_data_small, B0_mask_file)
assert sf_odf is not None
assert model is not None
def test_sfm_mod_est(dmri_estimation_data):
"""Test SFM model estimation."""
gtab = dmri_estimation_data['gtab']
dwi_data = 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_data.get_fdata()
[sf_odf, model] = sfm_mod_est(gtab, dwi_data, B0_mask_file.name)
assert sf_odf is not None
assert model is not None
B0_mask_file.close()
