def test_normalize_grads():
"""
Test make_gtab_and_bmask functionality
"""
base_dir = str(Path(__file__).parent / "examples")
fbval = f"{base_dir}/BIDS/sub-25659/ses-1/dwi/final_bval.bval"
fbvec = f"{base_dir}/BIDS/sub-25659/ses-1/dwi/final_bvec.bvec"
bvals = np.loadtxt(fbval)
bvecs = np.loadtxt(fbvec)
b0_threshold = 50
bvecs_normed, bvals_normed = dmriutils.normalize_gradients(
bvecs, bvals, b0_threshold, bvec_norm_epsilon=0.1, b_scale=True)
assert bvecs_normed is not None
assert bvals_normed is not None
def test_normalize_grads():
"""
Test make_gtab_and_bmask functionality
"""
base_dir = str(Path(__file__).parent / "examples")
dwi_path = base_dir + '/002/dmri'
fbval = dwi_path + '/bval.bval'
fbvec = dwi_path + '/bvec.bvec'
bvals = np.loadtxt(fbval)
bvecs = np.loadtxt(fbvec)
b0_threshold = 50
bvecs_normed, bvals_normed = dmriutils.normalize_gradients(
bvecs, bvals, b0_threshold, bvec_norm_epsilon=0.1, b_scale=True)
assert bvecs_normed is not None
assert bvals_normed is not None
def make_gtab_and_bmask(fbval,
fbvec,
dwi_file,
network,
node_size,
atlas,
b0_thr=50):
"""
Create gradient table from bval/bvec, and a mean B0 brain mask.
Parameters
----------
fbval : str
File name of the b-values file.
fbvec : str
File name of the b-vectors file.
dwi_file : str
File path to diffusion weighted image.
network : str
Resting-state network based on Yeo-7 and Yeo-17 naming (e.g. 'Default') used to filter nodes in the study of
brain subgraphs.
node_size : int
Spherical centroid node size in the case that coordinate-based centroids
are used as ROI's.
atlas : str
Name of a Nilearn-hosted coordinate or parcellation/label-based atlas supported for fetching.
See Nilearn's datasets.atlas module for more detailed reference.
Returns
-------
gtab_file : str
File path to pickled DiPy gradient table object.
nodif_b0_bet : str
File path to mean brain-extracted B0 image.
B0_mask : str
File path to mean B0 brain mask.
dwi_file : str
File path to diffusion weighted image.
"""
import os
from dipy.io import save_pickle
import os.path as op
from dipy.io import read_bvals_bvecs
from dipy.core.gradients import gradient_table
from pynets.dmri.dmri_utils import make_mean_b0, normalize_gradients
outdir = op.dirname(dwi_file)
namer_dir = outdir + '/dmri_tmp'
if not os.path.isdir(namer_dir):
os.makedirs(namer_dir, exist_ok=True)
B0_bet = "%s%s" % (namer_dir, "/mean_B0_bet.nii.gz")
B0_mask = "%s%s" % (namer_dir, "/mean_B0_bet_mask.nii.gz")
fbvec_norm = "%s%s" % (namer_dir, "/bvec_normed.bvec")
fbval_norm = "%s%s" % (namer_dir, "/bval_normed.bvec")
gtab_file = "%s%s" % (namer_dir, "/gtab.pkl")
all_b0s_file = "%s%s" % (namer_dir, "/all_b0s.nii.gz")
# loading bvecs/bvals
bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
bvecs_norm, bvals_norm = normalize_gradients(bvecs,
bvals,
b0_threshold=b0_thr)
# Save corrected
np.savetxt(fbval_norm, bvals_norm)
np.savetxt(fbvec_norm, bvecs_norm)
# Creating the gradient table
gtab = gradient_table(bvals_norm, bvecs_norm)
# Correct b0 threshold
gtab.b0_threshold = b0_thr
# Get b0 indices
b0s = np.where(gtab.bvals <= gtab.b0_threshold)[0]
print("%s%s" % ('b0\'s found at: ', b0s))
# Correct bvals to set 0's for B0 based on thresh
gtab_bvals = gtab.bvals.copy()
b0_thr_ixs = np.where(gtab_bvals < gtab.b0_threshold)[0]
gtab_bvals[b0_thr_ixs] = 0
gtab.b0s_mask = gtab_bvals == 0
# Show info
print(gtab.info)
# Save gradient table to pickle
save_pickle(gtab_file, gtab)
# Extract and Combine all b0s collected, make mean b0
print("Extracting b0's...")
b0_vols = []
dwi_img = nib.load(dwi_file)
all_b0s_aff = dwi_img.affine.copy()
dwi_data = np.asarray(dwi_img.dataobj)
dwi_img.uncache()
for b0 in b0s:
print(b0)
b0_vols.append(dwi_data[:, :, :, b0])
all_b0s_aff[3][3] = len(b0_vols)
nib.save(nib.Nifti1Image(np.stack(b0_vols, axis=3), affine=all_b0s_aff),
all_b0s_file)
mean_b0_file = make_mean_b0(all_b0s_file)
# Create mean b0 brain mask
cmd = 'bet ' + mean_b0_file + ' ' + B0_bet + ' -m -f 0.2'
os.system(cmd)
del dwi_data
return gtab_file, B0_bet, B0_mask, dwi_file