def test_WLS_and_LS_fit():
"""
Tests the WLS and LS fitting functions to see if they returns the correct
eigenvalues and eigenvectors.
Uses data/55dir_grad.bvec as the gradient table and 3by3by56.nii
as the data.
"""
### Defining Test Voxel (avoid nibabel dependency) ###
#Recall: D = [Dxx,Dyy,Dzz,Dxy,Dxz,Dyz,log(S_0)] and D ~ 10^-4 mm^2 /s
b0 = 1000.
bvec, bval = read_bvec_file(get_data('55dir_grad.bvec'))
B = bval[1]
#Scale the eigenvalues and tensor by the B value so the units match
D = np.array([1., 1., 1., 0., 0., 1., -np.log(b0) * B]) / B
evals = np.array([2., 1., 0.]) / B
md = evals.mean()
tensor = from_lower_triangular(D)
#Design Matrix
X = dti.design_matrix(bvec, bval)
#Signals
Y = np.exp(np.dot(X, D))
assert_almost_equal(Y[0], b0)
Y.shape = (-1,) + Y.shape
gtab = grad.gradient_table(bval, bvec)
### Testing WLS Fit on Single Voxel ###
#Estimate tensor from test signals
model = TensorModel(gtab, min_signal=1e-8, fit_method='WLS')
tensor_est = model.fit(Y)
assert_equal(tensor_est.shape, Y.shape[:-1])
assert_array_almost_equal(tensor_est.evals[0], evals)
assert_array_almost_equal(tensor_est.quadratic_form[0], tensor,
err_msg="Calculation of tensor from Y does not "
"compare to analytical solution")
assert_almost_equal(tensor_est.md[0], md)
# Test that we can fit a single voxel's worth of data (a 1d array)
y = Y[0]
tensor_est = model.fit(y)
assert_equal(tensor_est.shape, tuple())
assert_array_almost_equal(tensor_est.evals, evals)
assert_array_almost_equal(tensor_est.quadratic_form, tensor)
assert_almost_equal(tensor_est.md, md)
assert_array_almost_equal(tensor_est.lower_triangular(b0), D)
# Test using fit_method='LS'
model = TensorModel(gtab, min_signal=1e-8, fit_method='LS')
tensor_est = model.fit(y)
assert_equal(tensor_est.shape, tuple())
assert_array_almost_equal(tensor_est.evals, evals)
assert_array_almost_equal(tensor_est.quadratic_form, tensor)
assert_almost_equal(tensor_est.md, md)
assert_array_almost_equal(tensor_est.lower_triangular(b0), D)
def compute_tensor_model(dir_src, dir_out, verbose=False):
fbval = pjoin(dir_src, 'bvals_' + par_b_tag)
fbvec = pjoin(dir_src, 'bvecs_' + par_b_tag)
fdwi = pjoin(dir_src, 'data_' + par_b_tag + '_' + par_dim_tag + '.nii.gz')
fmask = pjoin(dir_src, 'nodif_brain_mask_' + par_dim_tag + '.nii.gz')
bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
gtab = gradient_table(bvals, bvecs, b0_threshold=par_b0_threshold)
data, affine = load_nifti(fdwi, verbose)
mask, _ = load_nifti(fmask, verbose)
ten_model = TensorModel(gtab)
ten_fit = ten_model.fit(data, mask)
FA = ten_fit.fa
MD = ten_fit.md
EV = ten_fit.evecs.astype(np.float32)
fa_name = 'data_' + par_b_tag + '_' + par_dim_tag + '_FA.nii.gz'
save_nifti(pjoin(dir_out, fa_name), FA, affine)
md_name = 'data_' + par_b_tag + '_' + par_dim_tag + '_MD.nii.gz'
save_nifti(pjoin(dir_out, md_name), MD, affine)
ev_name = 'data_' + par_b_tag + '_' + par_dim_tag + '_EV.nii.gz'
save_nifti(pjoin(dir_out, ev_name), EV, affine)
def test_eudx_further():
""" Cause we love testin.. ;-)
"""
fimg,fbvals,fbvecs=get_data('small_101D')
img=ni.load(fimg)
affine=img.get_affine()
data=img.get_data()
gtab = gradient_table(fbvals, fbvecs)
tensor_model = TensorModel(gtab)
ten = tensor_model.fit(data)
x,y,z=data.shape[:3]
seeds=np.zeros((10**4,3))
for i in range(10**4):
rx=(x-1)*np.random.rand()
ry=(y-1)*np.random.rand()
rz=(z-1)*np.random.rand()
seeds[i]=np.ascontiguousarray(np.array([rx,ry,rz]),dtype=np.float64)
ind = quantize_evecs(ten.evecs)
eu=EuDX(a=ten.fa, ind=ind, seeds=seeds, a_low=.2)
T=[e for e in eu]
#check that there are no negative elements
for t in T:
assert_equal(np.sum(t.ravel()<0),0)
def test_eudx_bad_seed():
"""Test passing a bad seed to eudx"""
fimg, fbvals, fbvecs = get_data('small_101D')
img = ni.load(fimg)
affine = img.affine
data = img.get_data()
gtab = gradient_table(fbvals, fbvecs)
tensor_model = TensorModel(gtab)
ten = tensor_model.fit(data)
ind = quantize_evecs(ten.evecs)
sphere = get_sphere('symmetric724')
seed = [1000000., 1000000., 1000000.]
eu = EuDX(a=ten.fa, ind=ind, seeds=[seed],
odf_vertices=sphere.vertices, a_low=.2)
assert_raises(ValueError, list, eu)
print(data.shape)
seed = [1., 5., 8.]
eu = EuDX(a=ten.fa, ind=ind, seeds=[seed],
odf_vertices=sphere.vertices, a_low=.2)
track = list(eu)
seed = [-1., 1000000., 1000000.]
eu = EuDX(a=ten.fa, ind=ind, seeds=[seed],
odf_vertices=sphere.vertices, a_low=.2)
assert_raises(ValueError, list, eu)
def test_eudx_bad_seed():
"""Test passing a bad seed to eudx"""
fimg, fbvals, fbvecs = get_data('small_101D')
img = ni.load(fimg)
affine = img.get_affine()
data = img.get_data()
gtab = gradient_table(fbvals, fbvecs)
tensor_model = TensorModel(gtab)
ten = tensor_model.fit(data)
ind = quantize_evecs(ten.evecs)
seed = [1000000., 1000000., 1000000.]
eu = EuDX(a=ten.fa, ind=ind, seeds=[seed], a_low=.2)
try:
track = list(eu)
except ValueError as ve:
if ve.args[0] == 'Seed outside boundaries':
print(ve)
print(data.shape)
seed = [1., 5., 8.]
eu = EuDX(a=ten.fa, ind=ind, seeds=[seed], a_low=.2)
track = list(eu)
seed = [-1., 1000000., 1000000.]
eu = EuDX(a=ten.fa, ind=ind, seeds=[seed], a_low=.2)
try:
track = list(eu)
except ValueError as ve:
if ve.args[0] == 'Seed outside boundaries':
print(ve)
def test_response_from_mask():
fdata, fbvals, fbvecs = get_data('small_64D')
bvals = np.load(fbvals)
bvecs = np.load(fbvecs)
data = nib.load(fdata).get_data()
gtab = gradient_table(bvals, bvecs)
ten = TensorModel(gtab)
tenfit = ten.fit(data)
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
radius = 3
for fa_thr in np.arange(0, 1, 0.1):
response_auto, ratio_auto, nvoxels = auto_response(gtab,
data,
roi_center=None,
roi_radius=radius,
fa_thr=fa_thr,
return_number_of_voxels=True)
ci, cj, ck = np.array(data.shape[:3]) / 2
mask = np.zeros(data.shape[:3])
mask[ci - radius: ci + radius,
cj - radius: cj + radius,
ck - radius: ck + radius] = 1
mask[FA <= fa_thr] = 0
response_mask, ratio_mask = response_from_mask(gtab, data, mask)
assert_equal(int(np.sum(mask)), nvoxels)
assert_array_almost_equal(response_mask[0], response_auto[0])
assert_almost_equal(response_mask[1], response_auto[1])
assert_almost_equal(ratio_mask, ratio_auto)
def test_phantom():
N = 50
vol = orbital_phantom(gtab,
func=f,
t=np.linspace(0, 2 * np.pi, N),
datashape=(10, 10, 10, len(bvals)),
origin=(5, 5, 5),
scale=(3, 3, 3),
angles=np.linspace(0, 2 * np.pi, 16),
radii=np.linspace(0.2, 2, 6),
S0=100)
m = TensorModel(gtab)
t = m.fit(vol)
FA = t.fa
# print vol
FA[np.isnan(FA)] = 0
# 686 -> expected FA given diffusivities of [1500, 400, 400]
l1, l2, l3 = 1500e-6, 400e-6, 400e-6
expected_fa = (np.sqrt(0.5) *
np.sqrt((l1 - l2)**2 + (l2-l3)**2 + (l3-l1)**2) /
np.sqrt(l1**2 + l2**2 + l3**2))
assert_array_almost_equal(FA.max(), expected_fa, decimal=2)
def test_masked_array_with_tensor():
data = np.ones((2, 4, 56))
mask = np.array([[True, False, False, True],
[True, False, True, False]])
bvec, bval = read_bvec_file(get_data('55dir_grad.bvec'))
gtab = grad.gradient_table_from_bvals_bvecs(bval, bvec.T)
tensor_model = TensorModel(gtab)
tensor = tensor_model.fit(data, mask=mask)
assert_equal(tensor.shape, (2, 4))
assert_equal(tensor.fa.shape, (2, 4))
assert_equal(tensor.evals.shape, (2, 4, 3))
assert_equal(tensor.evecs.shape, (2, 4, 3, 3))
tensor = tensor[0]
assert_equal(tensor.shape, (4,))
assert_equal(tensor.fa.shape, (4,))
assert_equal(tensor.evals.shape, (4, 3))
assert_equal(tensor.evecs.shape, (4, 3, 3))
tensor = tensor[0]
assert_equal(tensor.shape, tuple())
assert_equal(tensor.fa.shape, tuple())
assert_equal(tensor.evals.shape, (3,))
assert_equal(tensor.evecs.shape, (3, 3))
assert_equal(type(tensor.model_params), np.ndarray)
def reconstruction(dwi,bval_file,bvec_file,mask=None,type='dti',b0=0.,order=4):
""" Uses Dipy to reconstruct an fODF for each voxel.
Parameters
----------
dwi: numpy array (mandatory)
Holds the diffusion weighted image in a 4D-array (see nibabel).
bval_file: string (mandatory)
Path to the b-value file (FSL format).
bvec_file: string (mandatory)
Path to the b-vectors file (FSL format).
mask: numpy array
Holds the mask in a 3D array (see nibabel).
type: string \in {'dti','csd','csa'} (default = 'dti')
The type of the ODF reconstruction.
b0: float (default = 0)
Threshold to use for defining b0 images.
order: int (default = 4)
Order to use for constrained spherical deconvolution (csd) or constant solid angle (csa).
Returns
-----------
model_fit: Dipy Object (depends on the type)
Represents the fitted model for each voxel.
"""
#b-values and b-vectors
bvals, bvecs = read_bvals_bvecs(bval_file,bvec_file)
gtab = gradient_table(bvals, bvecs, b0_threshold=b0)
#reconstruction
if type == 'dti':
model = TensorModel(gtab,fit_method='WLS')
elif type == 'csd':
response, ratio = auto_response(gtab, dwi, roi_radius=10, fa_thr=0.7)
model = ConstrainedSphericalDeconvModel(gtab, response, sh_order=order)
elif type == 'csa':
model = CsaOdfModel(gtab, order)
if mask is not None:
model_fit = model.fit(dwi,mask=mask)
else:
model_fit = model.fit(dwi)
return model_fit
def DIPY_nii2streamlines(imgfile, maskfile, bvals, bvecs, output_prefix):
import numpy as np
import nibabel as nib
import os
from dipy.reconst.dti import TensorModel
print "nii2streamlines"
img = nib.load(imgfile)
bvals = np.genfromtxt(bvals)
bvecs = np.genfromtxt(bvecs)
if bvecs.shape[1] != 3:
bvecs = bvecs.T
print bvecs.shape
from nipype.utils.filemanip import split_filename
_, prefix, _ = split_filename(imgfile)
from dipy.data import gradient_table
gtab = gradient_table(bvals, bvecs)
data = img.get_data()
affine = img.get_affine()
zooms = img.get_header().get_zooms()[:3]
new_zooms = (2., 2., 2.)
data2, affine2 = data, affine
mask = nib.load(maskfile).get_data().astype(np.bool)
tenmodel = TensorModel(gtab)
tenfit = tenmodel.fit(data2, mask)
from dipy.reconst.dti import fractional_anisotropy
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
fa_img = nib.Nifti1Image(FA, img.get_affine())
nib.save(fa_img, experiment_dir + '/' + ('%s_tensor_fa.nii.gz' % prefix))
evecs = tenfit.evecs
evec_img = nib.Nifti1Image(evecs, img.get_affine())
nib.save(evec_img, experiment_dir + '/' + ('%s_tensor_evec.nii.gz' % prefix))
from dipy.data import get_sphere
sphere = get_sphere('symmetric724')
from dipy.reconst.dti import quantize_evecs
peak_indices = quantize_evecs(tenfit.evecs, sphere.vertices)
from dipy.tracking.eudx import EuDX
eu = EuDX(FA, peak_indices, odf_vertices = sphere.vertices, a_low=0.2, seeds=10**6, ang_thr=35)
tensor_streamlines = [streamline for streamline in eu]
hdr = nib.trackvis.empty_header()
hdr['voxel_size'] = new_zooms
hdr['voxel_order'] = 'LPS'
hdr['dim'] = data2.shape[:3]
import dipy.tracking.metrics as dmetrics
tensor_streamlines = ((sl, None, None) for sl in tensor_streamlines if dmetrics.length(sl) > 15)
ten_sl_fname = experiment_dir + '/' + ('%s_streamline.trk' % prefix)
nib.trackvis.write(ten_sl_fname, tensor_streamlines, hdr, points_space='voxel')
return ten_sl_fname
def test_eudx_further():
""" Cause we love testin.. ;-)
"""
fimg, fbvals, fbvecs = get_data('small_101D')
img = ni.load(fimg)
affine = img.affine
data = img.get_data()
gtab = gradient_table(fbvals, fbvecs)
tensor_model = TensorModel(gtab)
ten = tensor_model.fit(data)
x, y, z = data.shape[:3]
seeds = np.zeros((10**4, 3))
for i in range(10**4):
rx = (x-1)*np.random.rand()
ry = (y-1)*np.random.rand()
rz = (z-1)*np.random.rand()
seeds[i] = np.ascontiguousarray(np.array([rx, ry, rz]),
dtype=np.float64)
sphere = get_sphere('symmetric724')
ind = quantize_evecs(ten.evecs)
eu = EuDX(a=ten.fa, ind=ind, seeds=seeds,
odf_vertices=sphere.vertices, a_low=.2)
T = [e for e in eu]
# check that there are no negative elements
for t in T:
assert_equal(np.sum(t.ravel() < 0), 0)
# Test eudx with affine
def random_affine(seeds):
affine = np.eye(4)
affine[:3, :] = np.random.random((3, 4))
seeds = np.dot(seeds, affine[:3, :3].T)
seeds += affine[:3, 3]
return affine, seeds
# Make two random affines and move seeds
affine1, seeds1 = random_affine(seeds)
affine2, seeds2 = random_affine(seeds)
# Make tracks using different affines
eu1 = EuDX(a=ten.fa, ind=ind, odf_vertices=sphere.vertices,
seeds=seeds1, a_low=.2, affine=affine1)
eu2 = EuDX(a=ten.fa, ind=ind, odf_vertices=sphere.vertices,
seeds=seeds2, a_low=.2, affine=affine2)
# Move from eu2 affine2 to affine1
eu2_to_eu1 = utils.move_streamlines(eu2, output_space=affine1,
input_space=affine2)
# Check that the tracks are the same
for sl1, sl2 in zip(eu1, eu2_to_eu1):
assert_array_almost_equal(sl1, sl2)
def test_single_tensor():
evals = np.array([1.4, .35, .35]) * 10**(-3)
evecs = np.eye(3)
S = SingleTensor(gtab, 100, evals, evecs, snr=None)
assert_array_almost_equal(S[gtab.b0s_mask], 100)
assert_(np.mean(S[~gtab.b0s_mask]) < 100)
from dipy.reconst.dti import TensorModel
m = TensorModel(gtab)
t = m.fit(S)
assert_array_almost_equal(t.fa, 0.707, decimal=3)
def FA_RGB(data, gtab):
"""
Input : data, gtab taken from the load_data.py script.
Return : FA and RGB as two nd numpy array
"""
tenmodel = TensorModel(gtab)
tenfit = tenmodel.fit(data)
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
FA = np.clip(FA, 0, 1)
RGB = color_fa(FA, tenfit.evecs)
return FA, RGB
def estimate_response(gtab, data, affine, mask, fa_thr=0.7):
tenmodel = TensorModel(gtab)
tenfit = tenmodel.fit(data, mask)
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
mask[FA <= 0.1] = 0
mask[FA > 1.] = 0
indices = np.where(FA > fa_thr)
lambdas = tenfit.evals[indices][:, :2]
S0s = data[indices][:, 0]
S0 = np.mean(S0s)
l01 = np.mean(lambdas, axis=0)
evals = np.array([l01[0], l01[1], l01[1]])
ratio = evals[1] / evals[0]
print 'Response evals' , evals, ' ratio: ', ratio, '\tMean S0', S0
return (evals, S0), ratio
def prepare(training, category, snr, denoised, odeconv, tv, method):
data, affine, gtab = get_specific_data(training,
category,
snr,
denoised)
prefix = create_file_prefix(training,
category,
snr,
denoised,
odeconv,
tv,
method)
if training:
mask = nib.load('wm_mask_hardi_01.nii.gz').get_data()
else:
#mask = np.ones(data.shape[:-1])
mask = nib.load('test_hardi_30_den=1_fa_0025_dilate2_mask.nii.gz').get_data()
tenmodel = TensorModel(gtab)
tenfit = tenmodel.fit(data, mask)
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
mask[FA <= 0.1] = 0
mask[FA > 1.] = 0
indices = np.where(FA > 0.7)
lambdas = tenfit.evals[indices][:, :2]
S0s = data[indices][:, 0]
S0 = np.mean(S0s)
if S0 == 0:
print 'S0 equals to 0 switching to 1'
S0 = 1
l01 = np.mean(lambdas, axis=0)
evals = np.array([l01[0], l01[1], l01[1]])
print evals, S0
return data, affine, gtab, mask, evals, S0, prefix
def response_from_mask(gtab, data, mask):
""" Estimate the response function from a given mask.
Parameters
----------
gtab : GradientTable
data : ndarray
Diffusion data
mask : ndarray
Mask to use for the estimation of the response function. For example a
mask of the white matter voxels with FA values higher than 0.7
(see [1]_).
Returns
-------
response : tuple, (2,)
(`evals`, `S0`)
ratio : float
The ratio between smallest versus largest eigenvalue of the response.
Notes
-----
See csdeconv.auto_response() or csdeconv.recursive_response() if you don't
have a computed mask for the response function estimation.
References
----------
.. [1] Tournier, J.D., et al. NeuroImage 2004. Direct estimation of the
fiber orientation density function from diffusion-weighted MRI
data using spherical deconvolution
"""
ten = TensorModel(gtab)
indices = np.where(mask > 0)
if indices[0].size == 0:
msg = "No voxel in mask with value > 0 were found."
warnings.warn(msg, UserWarning)
return (np.nan, np.nan), np.nan
tenfit = ten.fit(data[indices])
lambdas = tenfit.evals[:, :2]
S0s = data[indices][:, np.nonzero(gtab.b0s_mask)[0]]
return _get_response(S0s, lambdas)
def test_boot_pmf():
"""This tests the local model used for the bootstrapping.
"""
hsph_updated = HemiSphere.from_sphere(unit_octahedron)
vertices = hsph_updated.vertices
bvecs = vertices
bvals = np.ones(len(vertices)) * 1000
bvecs = np.insert(bvecs, 0, np.array([0, 0, 0]), axis=0)
bvals = np.insert(bvals, 0, 0)
gtab = gradient_table(bvals, bvecs)
voxel = single_tensor(gtab)
data = np.tile(voxel, (3, 3, 3, 1))
point = np.array([1., 1., 1.])
tensor_model = TensorModel(gtab)
boot_pmf_gen = BootPmfGen(data, model=tensor_model, sphere=hsph_updated)
no_boot_pmf = boot_pmf_gen.get_pmf_no_boot(point)
model_pmf = tensor_model.fit(voxel).odf(hsph_updated)
npt.assert_equal(len(hsph_updated.vertices), no_boot_pmf.shape[0])
npt.assert_array_almost_equal(no_boot_pmf, model_pmf)
# test model sherical harminic order different than bootstrap order
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always", category=UserWarning)
csd_model = ConstrainedSphericalDeconvModel(gtab, None, sh_order=6)
assert_greater(len([lw for lw in w if issubclass(lw.category,
UserWarning)]), 0)
boot_pmf_gen_sh4 = BootPmfGen(data, model=csd_model, sphere=hsph_updated,
sh_order=4)
pmf_sh4 = boot_pmf_gen_sh4.get_pmf(point)
npt.assert_equal(len(hsph_updated.vertices), pmf_sh4.shape[0])
npt.assert_(np.sum(pmf_sh4.shape) > 0)
boot_pmf_gen_sh8 = BootPmfGen(data, model=csd_model, sphere=hsph_updated,
sh_order=8)
pmf_sh8 = boot_pmf_gen_sh8.get_pmf(point)
npt.assert_equal(len(hsph_updated.vertices), pmf_sh8.shape[0])
npt.assert_(np.sum(pmf_sh8.shape) > 0)
def single_fiber_response(diffusionData, mask, gtable, fa_thr = 0.7):
from dipy.reconst.dti import TensorModel, fractional_anisotropy
ten = TensorModel(gtable)
tenfit = ten.fit(diffusionData, mask=mask)
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
indices = np.where(FA > fa_thr)
lambdas = tenfit.evals[indices][:, :2]
S0s = diffusionData[indices][:, np.nonzero(gtable.b0s_mask)[0]]
S0 = np.mean(S0s)
l01 = np.mean(lambdas, axis=0)
evals = np.array([l01[0], l01[1], l01[1]])
response = (evals, S0)
ratio = evals[1]/evals[0]
return response, ratio
def tensor_model(
input_filename_data, input_filename_bvecs, input_filename_bvals, output_filename_fa=None, output_filename_evecs=None
):
# print 'Tensor model ...'
# print 'Loading data ...'
img = nib.load(input_filename_data)
data = img.get_data()
affine = img.get_affine()
bvals, bvecs = read_bvals_bvecs(input_filename_bvals, input_filename_bvecs)
gtab = gradient_table(bvals, bvecs)
mask = data[..., 0] > 50
tenmodel = TensorModel(gtab)
tenfit = tenmodel.fit(data, mask)
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
if output_filename_fa == None:
filename_save_fa = input_filename_data.split(".")[0] + "_tensor_fa.nii.gz"
else:
filename_save_fa = os.path.abspath(output_filename_fa)
fa_img = nib.Nifti1Image(FA, img.get_affine())
nib.save(fa_img, filename_save_fa)
print "Saving fa to:", filename_save_fa
if output_filename_evecs == None:
filename_save_evecs = input_filename_data.split(".")[0] + "_tensor_evecs.nii.gz"
else:
filename_save_evecs = os.path.abspath(output_filename_evecs)
evecs_img = nib.Nifti1Image(tenfit.evecs, img.get_affine())
nib.save(evecs_img, filename_save_evecs)
print "Saving evecs to:", filename_save_evecs
return filename_save_fa, filename_save_evecs
def eudx_basic(self, dti_file, mask_file, gtab, stop_val=0.1):
"""
Tracking with basic tensors and basic eudx - experimental
We now force seeding at every voxel in the provided mask for
simplicity. Future functionality will extend these options.
**Positional Arguments:**
dti_file:
- File (registered) to use for tensor/fiber tracking
mask_file:
- Brain mask to keep tensors inside the brain
gtab:
- dipy formatted bval/bvec Structure
**Optional Arguments:**
stop_val:
- Value to cutoff fiber track
"""
img = nb.load(dti_file)
data = img.get_data()
img = nb.load(mask_file)
mask = img.get_data()
# use all points in mask
seedIdx = np.where(mask > 0) # seed everywhere not equal to zero
seedIdx = np.transpose(seedIdx)
model = TensorModel(gtab)
ten = model.fit(data, mask)
sphere = get_sphere('symmetric724')
ind = quantize_evecs(ten.evecs, sphere.vertices)
eu = EuDX(a=ten.fa, ind=ind, seeds=seedIdx,
odf_vertices=sphere.vertices, a_low=stop_val)
tracks = [e for e in eu]
return (ten, tracks)
def auto_response(gtab, data, roi_center=None, roi_radius=10, fa_thr=0.7,
fa_callable=fa_superior, return_number_of_voxels=False):
""" Automatic estimation of response function using FA.
Parameters
----------
gtab : GradientTable
data : ndarray
diffusion data
roi_center : tuple, (3,)
Center of ROI in data. If center is None, it is assumed that it is
the center of the volume with shape `data.shape[:3]`.
roi_radius : int
radius of cubic ROI
fa_thr : float
FA threshold
fa_callable : callable
A callable that defines an operation that compares FA with the fa_thr. The operator
should have two positional arguments (e.g., `fa_operator(FA, fa_thr)`) and it should
return a bool array.
return_number_of_voxels : bool
If True, returns the number of voxels used for estimating the response
function.
Returns
-------
response : tuple, (2,)
(`evals`, `S0`)
ratio : float
The ratio between smallest versus largest eigenvalue of the response.
number of voxels : int (optional)
The number of voxels used for estimating the response function.
Notes
-----
In CSD there is an important pre-processing step: the estimation of the
fiber response function. In order to do this we look for voxels with very
anisotropic configurations. For example we can use an ROI (20x20x20) at
the center of the volume and store the signal values for the voxels with
FA values higher than 0.7. Of course, if we haven't precalculated FA we
need to fit a Tensor model to the datasets. Which is what we do in this
function.
For the response we also need to find the average S0 in the ROI. This is
possible using `gtab.b0s_mask()` we can find all the S0 volumes (which
correspond to b-values equal 0) in the dataset.
The `response` consists always of a prolate tensor created by averaging
the highest and second highest eigenvalues in the ROI with FA higher than
threshold. We also include the average S0s.
We also return the `ratio` which is used for the SDT models. If requested,
the number of voxels used for estimating the response function is also
returned, which can be used to judge the fidelity of the response function.
As a rule of thumb, at least 300 voxels should be used to estimate a good
response function (see [1]_).
References
----------
.. [1] Tournier, J.D., et al. NeuroImage 2004. Direct estimation of the
fiber orientation density function from diffusion-weighted MRI
data using spherical deconvolution
"""
ten = TensorModel(gtab)
if roi_center is None:
ci, cj, ck = np.array(data.shape[:3]) // 2
else:
ci, cj, ck = roi_center
w = roi_radius
roi = data[int(ci - w): int(ci + w),
int(cj - w): int(cj + w),
int(ck - w): int(ck + w)]
tenfit = ten.fit(roi)
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
indices = np.where(fa_callable(FA, fa_thr))
if indices[0].size == 0:
msg = "No voxel with a FA higher than " + str(fa_thr) + " were found."
msg += " Try a larger roi or a lower threshold."
warnings.warn(msg, UserWarning)
lambdas = tenfit.evals[indices][:, :2]
S0s = roi[indices][:, np.nonzero(gtab.b0s_mask)[0]]
response, ratio = _get_response(S0s, lambdas)
if return_number_of_voxels:
return response, ratio, indices[0].size
return response, ratio
#Correct b0 threshold
gtab.b0_threshold = min(bvals)
#Correct b0s_mask
gtab.b0s_mask = gtab.bvals == gtab.b0_threshold
#Get B0 indices
B0s_list = np.where(gtab.bvals == gtab.b0_threshold)[0]
print('Computing brain mask...')
b0_mask, mask = median_otsu(data)
print('Computing tensors...')
tenmodel = TensorModel(gtab)
tensorfit = tenmodel.fit(data, mask=mask)
print('Computing worst-case/best-case SNR using the corpus callosum...')
threshold = (0.5, 1, 0, 0.1, 0, 0.1)
CC_box = np.zeros_like(data[..., 0])
mins, maxs = bounding_box(mask)
mins = np.array(mins)
maxs = np.array(maxs)
diff = (maxs - mins) // 4
bounds_min = mins + diff
bounds_max = maxs - diff
CC_box[bounds_min[0]:bounds_max[0], bounds_min[1]:bounds_max[1],
bounds_min[2]:bounds_max[2]] = 1
def auto_response(gtab, data, roi_center=None, roi_radius=10, fa_thr=0.7):
""" Automatic estimation of response function using FA
Parameters
----------
gtab : GradientTable
data : ndarray
diffusion data
roi_center : tuple, (3,)
Center of ROI in data. If center is None, it is assumed that it is
the center of the volume with shape `data.shape[:3]`.
roi_radius : int
radius of cubic ROI
fa_thr : float
FA threshold
Returns
-------
response : tuple, (2,)
(`evals`, `S0`)
ratio : float
the ratio between smallest versus largest eigenvalue of the response
Notes
-----
In CSD there is an important pre-processing step: the estimation of the
fiber response function. In order to do this we look for voxels with very
anisotropic configurations. For example we can use an ROI (20x20x20) at
the center of the volume and store the signal values for the voxels with
FA values higher than 0.7. Of course, if we haven't precalculated FA we
need to fit a Tensor model to the datasets. Which is what we do in this
function.
For the response we also need to find the average S0 in the ROI. This is
possible using `gtab.b0s_mask()` we can find all the S0 volumes (which
correspond to b-values equal 0) in the dataset.
The `response` consists always of a prolate tensor created by averaging
the highest and second highest eigenvalues in the ROI with FA higher than
threshold. We also include the average S0s.
Finally, we also return the `ratio` which is used for the SDT models.
"""
ten = TensorModel(gtab)
if roi_center is None:
ci, cj, ck = np.array(data.shape[:3]) / 2
else:
ci, cj, ck = roi_center
w = roi_radius
roi = data[ci - w: ci + w, cj - w: cj + w, ck - w: ck + w]
tenfit = ten.fit(roi)
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
indices = np.where(FA > fa_thr)
lambdas = tenfit.evals[indices][:, :2]
S0s = roi[indices][:, np.nonzero(gtab.b0s_mask)[0]]
S0 = np.mean(S0s)
l01 = np.mean(lambdas, axis=0)
evals = np.array([l01[0], l01[1], l01[1]])
response = (evals, S0)
ratio = evals[1]/evals[0]
return response, ratio
def run(self,
data_file,
data_bvals,
data_bvecs,
mask=None,
bbox_threshold=(0.6, 1, 0, 0.1, 0, 0.1),
out_dir='',
out_file='product.json',
out_mask_cc='cc.nii.gz',
out_mask_noise='mask_noise.nii.gz'):
""" Workflow for computing the signal-to-noise ratio in the
corpus callosum
Parameters
----------
data_file : string
Path to the dwi.nii.gz file. This path may contain wildcards to
process multiple inputs at once.
data_bvals : string
Path of bvals.
data_bvecs : string
Path of bvecs.
mask : string, optional
Path of mask if desired. (default None)
bbox_threshold : string, optional
Threshold for bounding box, values separated with commas for ex.
[0.6,1,0,0.1,0,0.1]. (default (0.6, 1, 0, 0.1, 0, 0.1))
out_dir : string, optional
Where the resulting file will be saved. (default '')
out_file : string, optional
Name of the result file to be saved. (default 'product.json')
out_mask_cc : string, optional
Name of the CC mask volume to be saved (default 'cc.nii.gz')
out_mask_noise : string, optional
Name of the mask noise volume to be saved
(default 'mask_noise.nii.gz')
"""
if not isinstance(bbox_threshold, tuple):
b = bbox_threshold.replace("[", "")
b = b.replace("]", "")
b = b.replace("(", "")
b = b.replace(")", "")
b = b.replace(" ", "")
b = b.split(",")
for i in range(len(b)):
b[i] = float(b[i])
bbox_threshold = tuple(b)
io_it = self.get_io_iterator()
for data_path, data_bvals_path, data_bvecs_path, out_path, \
cc_mask_path, mask_noise_path in io_it:
img = nib.load('{0}'.format(data_path))
bvals, bvecs = read_bvals_bvecs('{0}'.format(data_bvals_path),
'{0}'.format(data_bvecs_path))
gtab = gradient_table(bvals, bvecs)
data = img.get_data()
affine = img.affine
logging.info('Computing brain mask...')
b0_mask, calc_mask = median_otsu(data)
if mask is None:
mask = calc_mask
else:
mask = nib.load(mask).get_data().astype(bool)
mask = np.array(calc_mask == mask).astype(int)
logging.info('Computing tensors...')
tenmodel = TensorModel(gtab)
tensorfit = tenmodel.fit(data, mask=mask)
logging.info('Computing worst-case/best-case SNR using the CC...')
threshold = bbox_threshold
if np.ndim(data) == 4:
CC_box = np.zeros_like(data[..., 0])
elif np.ndim(data) == 3:
CC_box = np.zeros_like(data)
else:
raise IOError('DWI data has invalid dimensions')
mins, maxs = bounding_box(mask)
mins = np.array(mins)
maxs = np.array(maxs)
diff = (maxs - mins) // 4
bounds_min = mins + diff
bounds_max = maxs - diff
CC_box[bounds_min[0]:bounds_max[0], bounds_min[1]:bounds_max[1],
bounds_min[2]:bounds_max[2]] = 1
mask_cc_part, cfa = segment_from_cfa(tensorfit,
CC_box,
threshold,
return_cfa=True)
cfa_img = nib.Nifti1Image((cfa * 255).astype(np.uint8), affine)
mask_cc_part_img = nib.Nifti1Image(mask_cc_part.astype(np.uint8),
affine)
nib.save(mask_cc_part_img, cc_mask_path)
logging.info('CC mask saved as {0}'.format(cc_mask_path))
mean_signal = np.mean(data[mask_cc_part], axis=0)
mask_noise = binary_dilation(mask, iterations=10)
mask_noise[..., :mask_noise.shape[-1] // 2] = 1
mask_noise = ~mask_noise
mask_noise_img = nib.Nifti1Image(mask_noise.astype(np.uint8),
affine)
nib.save(mask_noise_img, mask_noise_path)
logging.info('Mask noise saved as {0}'.format(mask_noise_path))
noise_std = np.std(data[mask_noise, :])
logging.info('Noise standard deviation sigma= ' + str(noise_std))
idx = np.sum(gtab.bvecs, axis=-1) == 0
gtab.bvecs[idx] = np.inf
axis_X = np.argmin(
np.sum((gtab.bvecs - np.array([1, 0, 0]))**2, axis=-1))
axis_Y = np.argmin(
np.sum((gtab.bvecs - np.array([0, 1, 0]))**2, axis=-1))
axis_Z = np.argmin(
np.sum((gtab.bvecs - np.array([0, 0, 1]))**2, axis=-1))
SNR_output = []
SNR_directions = []
for direction in ['b0', axis_X, axis_Y, axis_Z]:
if direction == 'b0':
SNR = mean_signal[0] / noise_std
logging.info("SNR for the b=0 image is :" + str(SNR))
else:
logging.info("SNR for direction " + str(direction) + " " +
str(gtab.bvecs[direction]) + "is :" +
str(SNR))
SNR_directions.append(direction)
SNR = mean_signal[direction] / noise_std
SNR_output.append(SNR)
data = []
data.append({
'data':
str(SNR_output[0]) + ' ' + str(SNR_output[1]) + ' ' +
str(SNR_output[2]) + ' ' + str(SNR_output[3]),
'directions':
'b0' + ' ' + str(SNR_directions[0]) + ' ' +
str(SNR_directions[1]) + ' ' + str(SNR_directions[2])
})
with open(os.path.join(out_dir, out_file), 'w') as myfile:
json.dump(data, myfile)
def test_wls_and_ls_fit():
"""
Tests the WLS and LS fitting functions to see if they returns the correct
eigenvalues and eigenvectors.
Uses data/55dir_grad.bvec as the gradient table and 3by3by56.nii
as the data.
"""
# Defining Test Voxel (avoid nibabel dependency) ###
# Recall: D = [Dxx,Dyy,Dzz,Dxy,Dxz,Dyz,log(S_0)] and D ~ 10^-4 mm^2 /s
b0 = 1000.
bvec, bval = read_bvec_file(get_fnames('55dir_grad.bvec'))
B = bval[1]
# Scale the eigenvalues and tensor by the B value so the units match
D = np.array([1., 1., 1., 0., 0., 1., -np.log(b0) * B]) / B
evals = np.array([2., 1., 0.]) / B
md = evals.mean()
tensor = from_lower_triangular(D)
# Design Matrix
gtab = grad.gradient_table(bval, bvec)
X = dti.design_matrix(gtab)
# Signals
Y = np.exp(np.dot(X, D))
npt.assert_almost_equal(Y[0], b0)
Y.shape = (-1,) + Y.shape
# Testing WLS Fit on Single Voxel
# If you do something wonky (passing min_signal<0), you should get an
# error:
npt.assert_raises(ValueError, TensorModel, gtab, fit_method='WLS',
min_signal=-1)
# Estimate tensor from test signals
model = TensorModel(gtab, fit_method='WLS', return_S0_hat=True)
tensor_est = model.fit(Y)
npt.assert_equal(tensor_est.shape, Y.shape[:-1])
npt.assert_array_almost_equal(tensor_est.evals[0], evals)
npt.assert_array_almost_equal(tensor_est.quadratic_form[0], tensor,
err_msg="Calculation of tensor from Y does "
"not compare to analytical solution")
npt.assert_almost_equal(tensor_est.md[0], md)
npt.assert_array_almost_equal(tensor_est.S0_hat[0], b0, decimal=3)
# Test that we can fit a single voxel's worth of data (a 1d array)
y = Y[0]
tensor_est = model.fit(y)
npt.assert_equal(tensor_est.shape, tuple())
npt.assert_array_almost_equal(tensor_est.evals, evals)
npt.assert_array_almost_equal(tensor_est.quadratic_form, tensor)
npt.assert_almost_equal(tensor_est.md, md)
npt.assert_array_almost_equal(tensor_est.lower_triangular(b0), D)
# Test using fit_method='LS'
model = TensorModel(gtab, fit_method='LS')
tensor_est = model.fit(y)
npt.assert_equal(tensor_est.shape, tuple())
npt.assert_array_almost_equal(tensor_est.evals, evals)
npt.assert_array_almost_equal(tensor_est.quadratic_form, tensor)
npt.assert_almost_equal(tensor_est.md, md)
npt.assert_array_almost_equal(tensor_est.lower_triangular(b0), D)
npt.assert_array_almost_equal(tensor_est.linearity, linearity(evals))
npt.assert_array_almost_equal(tensor_est.planarity, planarity(evals))
npt.assert_array_almost_equal(tensor_est.sphericity, sphericity(evals))
def run(self,
data_files,
bvals_files,
bvecs_files,
mask_files,
bbox_threshold=[0.6, 1, 0, 0.1, 0, 0.1],
out_dir='',
out_file='product.json',
out_mask_cc='cc.nii.gz',
out_mask_noise='mask_noise.nii.gz'):
"""Compute the signal-to-noise ratio in the corpus callosum.
Parameters
----------
data_files : string
Path to the dwi.nii.gz file. This path may contain wildcards to
process multiple inputs at once.
bvals_files : string
Path of bvals.
bvecs_files : string
Path of bvecs.
mask_files : string
Path of brain mask
bbox_threshold : variable float, optional
Threshold for bounding box, values separated with commas for ex.
[0.6,1,0,0.1,0,0.1]. (default (0.6, 1, 0, 0.1, 0, 0.1))
out_dir : string, optional
Where the resulting file will be saved. (default '')
out_file : string, optional
Name of the result file to be saved. (default 'product.json')
out_mask_cc : string, optional
Name of the CC mask volume to be saved (default 'cc.nii.gz')
out_mask_noise : string, optional
Name of the mask noise volume to be saved
(default 'mask_noise.nii.gz')
"""
io_it = self.get_io_iterator()
for dwi_path, bvals_path, bvecs_path, mask_path, out_path, \
cc_mask_path, mask_noise_path in io_it:
data, affine = load_nifti(dwi_path)
bvals, bvecs = read_bvals_bvecs(bvals_path, bvecs_path)
gtab = gradient_table(bvals=bvals, bvecs=bvecs)
logging.info('Computing brain mask...')
_, calc_mask = median_otsu(data)
mask, affine = load_nifti(mask_path)
mask = np.array(calc_mask == mask.astype(bool)).astype(int)
logging.info('Computing tensors...')
tenmodel = TensorModel(gtab)
tensorfit = tenmodel.fit(data, mask=mask)
logging.info('Computing worst-case/best-case SNR using the CC...')
if np.ndim(data) == 4:
CC_box = np.zeros_like(data[..., 0])
elif np.ndim(data) == 3:
CC_box = np.zeros_like(data)
else:
raise IOError('DWI data has invalid dimensions')
mins, maxs = bounding_box(mask)
mins = np.array(mins)
maxs = np.array(maxs)
diff = (maxs - mins) // 4
bounds_min = mins + diff
bounds_max = maxs - diff
CC_box[bounds_min[0]:bounds_max[0], bounds_min[1]:bounds_max[1],
bounds_min[2]:bounds_max[2]] = 1
if len(bbox_threshold) != 6:
raise IOError('bbox_threshold should have 6 float values')
mask_cc_part, cfa = segment_from_cfa(tensorfit,
CC_box,
bbox_threshold,
return_cfa=True)
save_nifti(cc_mask_path, mask_cc_part.astype(np.uint8), affine)
logging.info('CC mask saved as {0}'.format(cc_mask_path))
mean_signal = np.mean(data[mask_cc_part], axis=0)
mask_noise = binary_dilation(mask, iterations=10)
mask_noise[..., :mask_noise.shape[-1] // 2] = 1
mask_noise = ~mask_noise
save_nifti(mask_noise_path, mask_noise.astype(np.uint8), affine)
logging.info('Mask noise saved as {0}'.format(mask_noise_path))
noise_std = np.std(data[mask_noise, :])
logging.info('Noise standard deviation sigma= ' + str(noise_std))
idx = np.sum(gtab.bvecs, axis=-1) == 0
gtab.bvecs[idx] = np.inf
axis_X = np.argmin(
np.sum((gtab.bvecs - np.array([1, 0, 0]))**2, axis=-1))
axis_Y = np.argmin(
np.sum((gtab.bvecs - np.array([0, 1, 0]))**2, axis=-1))
axis_Z = np.argmin(
np.sum((gtab.bvecs - np.array([0, 0, 1]))**2, axis=-1))
SNR_output = []
SNR_directions = []
for direction in ['b0', axis_X, axis_Y, axis_Z]:
if direction == 'b0':
SNR = mean_signal[0] / noise_std
logging.info("SNR for the b=0 image is :" + str(SNR))
else:
logging.info("SNR for direction " + str(direction) + " " +
str(gtab.bvecs[direction]) + "is :" +
str(SNR))
SNR_directions.append(direction)
SNR = mean_signal[direction] / noise_std
SNR_output.append(SNR)
data = []
data.append({
'data':
str(SNR_output[0]) + ' ' + str(SNR_output[1]) + ' ' +
str(SNR_output[2]) + ' ' + str(SNR_output[3]),
'directions':
'b0' + ' ' + str(SNR_directions[0]) + ' ' +
str(SNR_directions[1]) + ' ' + str(SNR_directions[2])
})
with open(os.path.join(out_dir, out_path), 'w') as myfile:
json.dump(data, myfile)
csd_peaks = peaks_from_model(model=csd_model,
data=data,
sphere=sphere,
mask=mask,
relative_peak_threshold=.5,
min_separation_angle=25,
parallel=True)
"""
For the tracking part, we will use the fiber directions from the ``csd_model``
but stop tracking in areas where fractional anisotropy is low (< 0.1).
To derive the FA, used here as a stopping criterion, we would need to fit a
tensor model first. Here, we fit the tensor using weighted least squares (WLS).
"""
tensor_model = TensorModel(gtab, fit_method='WLS')
tensor_fit = tensor_model.fit(data, mask)
fa = tensor_fit.fa
"""
In this simple example we can use FA to stop tracking. Here we stop tracking
when FA < 0.1.
"""
tissue_classifier = ThresholdTissueClassifier(fa, 0.1)
"""
Now, we need to set starting points for propagating each track. We call those
seeds. Using ``random_seeds_from_mask`` we can select a specific number of
seeds (``seeds_count``) in each voxel where the mask ``fa > 0.3`` is true.
"""
seeds = random_seeds_from_mask(fa > 0.3, seeds_count=1)
def dwi_dipy_run(dwi_dir,
node_size,
dir_path,
conn_model,
parc,
atlas_select,
network,
wm_mask=None):
import os
import glob
import re
import nipype.interfaces.fsl as fsl
from dipy.reconst.dti import TensorModel, quantize_evecs
from dipy.reconst.csdeconv import ConstrainedSphericalDeconvModel, recursive_response
from dipy.tracking.local import LocalTracking, ThresholdTissueClassifier
from dipy.tracking import utils
from dipy.direction import peaks_from_model
from dipy.tracking.eudx import EuDX
from dipy.data import get_sphere
from dipy.core.gradients import gradient_table
from dipy.io import read_bvals_bvecs
def atoi(text):
return int(text) if text.isdigit() else text
def natural_keys(text):
return [atoi(c) for c in re.split('(\d+)', text)]
dwi_img = "%s%s" % (dwi_dir, '/dwi.nii.gz')
nodif_brain_mask_path = "%s%s" % (dwi_dir, '/nodif_brain_mask.nii.gz')
bvals = "%s%s" % (dwi_dir, '/bval')
bvecs = "%s%s" % (dwi_dir, '/bvec')
img = nib.load(dwi_img)
data = img.get_data()
# Loads mask and ensures it's a true binary mask
img = nib.load(nodif_brain_mask_path)
mask = img.get_data()
mask = mask > 0
[bvals, bvecs] = read_bvals_bvecs(bvals, bvecs)
gtab = gradient_table(bvals, bvecs)
# Estimates some tensors
model = TensorModel(gtab)
ten = model.fit(data, mask)
sphere = get_sphere('symmetric724')
ind = quantize_evecs(ten.evecs, sphere.vertices)
# Tractography
if conn_model == 'csd':
trac_mod = 'csd'
else:
conn_model = 'tensor'
trac_mod = ten.fa
affine = img.affine
print('Tracking with tensor model...')
if wm_mask is None:
mask = nib.load(mask).get_data()
mask[0, :, :] = False
mask[:, 0, :] = False
mask[:, :, 0] = False
seeds = utils.seeds_from_mask(mask, density=2)
else:
wm_mask_data = nib.load(wm_mask).get_data()
wm_mask_data[0, :, :] = False
wm_mask_data[:, 0, :] = False
wm_mask_data[:, :, 0] = False
seeds = utils.seeds_from_mask(wm_mask_data, density=2)
#seeds = random_seeds_from_mask(ten.fa > 0.3, seeds_count=num_total_samples)
if conn_model == 'tensor':
eu = EuDX(a=trac_mod,
ind=ind,
seeds=seeds,
odf_vertices=sphere.vertices,
a_low=0.05,
step_sz=.5)
tracks = [e for e in eu]
elif conn_model == 'csd':
print('Tracking with CSD model...')
if wm_mask is None:
response = recursive_response(gtab,
data,
mask=mask.astype('bool'),
sh_order=8,
peak_thr=0.01,
init_fa=0.08,
init_trace=0.0021,
iter=8,
convergence=0.001,
parallel=True)
else:
response = recursive_response(gtab,
data,
mask=wm_mask_data.astype('bool'),
sh_order=8,
peak_thr=0.01,
init_fa=0.08,
init_trace=0.0021,
iter=8,
convergence=0.001,
parallel=True)
csd_model = ConstrainedSphericalDeconvModel(gtab, response)
csd_peaks = peaks_from_model(model=csd_model,
data=data,
sphere=sphere,
relative_peak_threshold=.5,
min_separation_angle=25,
parallel=True)
tissue_classifier = ThresholdTissueClassifier(ten.fa, 0.1)
streamline_generator = LocalTracking(csd_peaks,
tissue_classifier,
seeds,
affine=affine,
step_size=0.5)
tracks = [e for e in streamline_generator]
if parc is True:
node_size = 'parc'
if network:
seeds_dir = "%s%s%s%s%s%s%s" % (dir_path, '/seeds_', network, '_',
atlas_select, '_', str(node_size))
else:
seeds_dir = "%s%s%s%s%s" % (dir_path, '/seeds_', atlas_select, '_',
str(node_size))
seed_files = glob.glob("%s%s" % (seeds_dir, '/*diff.nii.gz'))
seed_files.sort(key=natural_keys)
# Binarize ROIs
print('\nBinarizing seed masks...')
j = 1
for i in seed_files:
args = ' -bin '
out_file = "%s%s" % (i.split('.nii.gz')[0], '_bin.nii.gz')
maths = fsl.ImageMaths(in_file=i, op_string=args, out_file=out_file)
os.system(maths.cmdline)
args = ' -mul ' + str(j)
maths = fsl.ImageMaths(in_file=out_file,
op_string=args,
out_file=out_file)
os.system(maths.cmdline)
j = j + 1
# Create atlas from ROIs
seed_files = glob.glob("%s%s" % (seeds_dir, '/*diff_bin.nii.gz'))
seed_files.sort(key=natural_keys)
print('\nMerging seed masks into single labels image...')
label_sum = "%s%s" % (seeds_dir, '/all_rois.nii.gz')
args = ' -add ' + i
maths = fsl.ImageMaths(in_file=seed_files[0],
op_string=args,
out_file=label_sum)
os.system(maths.cmdline)
for i in seed_files:
args = ' -add ' + i
maths = fsl.ImageMaths(in_file=label_sum,
op_string=args,
out_file=label_sum)
os.system(maths.cmdline)
labels_im = nib.load(label_sum)
labels_data = labels_im.get_data().astype('int')
conn_matrix, grouping = utils.connectivity_matrix(
tracks,
labels_data,
affine=affine,
return_mapping=True,
mapping_as_streamlines=True)
conn_matrix[:3, :] = 0
conn_matrix[:, :3] = 0
return conn_matrix
def auto_response(gtab,
data,
roi_center=None,
roi_radius=10,
fa_thr=0.7,
fa_callable=fa_superior,
return_number_of_voxels=False):
""" Automatic estimation of response function using FA.
Parameters
----------
gtab : GradientTable
data : ndarray
diffusion data
roi_center : tuple, (3,)
Center of ROI in data. If center is None, it is assumed that it is
the center of the volume with shape `data.shape[:3]`.
roi_radius : int
radius of cubic ROI
fa_thr : float
FA threshold
fa_callable : callable
A callable that defines an operation that compares FA with the fa_thr. The operator
should have two positional arguments (e.g., `fa_operator(FA, fa_thr)`) and it should
return a bool array.
return_number_of_voxels : bool
If True, returns the number of voxels used for estimating the response
function.
Returns
-------
response : tuple, (2,)
(`evals`, `S0`)
ratio : float
The ratio between smallest versus largest eigenvalue of the response.
number of voxels : int (optional)
The number of voxels used for estimating the response function.
Notes
-----
In CSD there is an important pre-processing step: the estimation of the
fiber response function. In order to do this we look for voxels with very
anisotropic configurations. For example we can use an ROI (20x20x20) at
the center of the volume and store the signal values for the voxels with
FA values higher than 0.7. Of course, if we haven't precalculated FA we
need to fit a Tensor model to the datasets. Which is what we do in this
function.
For the response we also need to find the average S0 in the ROI. This is
possible using `gtab.b0s_mask()` we can find all the S0 volumes (which
correspond to b-values equal 0) in the dataset.
The `response` consists always of a prolate tensor created by averaging
the highest and second highest eigenvalues in the ROI with FA higher than
threshold. We also include the average S0s.
We also return the `ratio` which is used for the SDT models. If requested,
the number of voxels used for estimating the response function is also
returned, which can be used to judge the fidelity of the response function.
As a rule of thumb, at least 300 voxels should be used to estimate a good
response function (see [1]_).
References
----------
.. [1] Tournier, J.D., et al. NeuroImage 2004. Direct estimation of the
fiber orientation density function from diffusion-weighted MRI
data using spherical deconvolution
"""
ten = TensorModel(gtab)
if roi_center is None:
ci, cj, ck = np.array(data.shape[:3]) // 2
else:
ci, cj, ck = roi_center
w = roi_radius
roi = data[int(ci - w):int(ci + w),
int(cj - w):int(cj + w),
int(ck - w):int(ck + w)]
tenfit = ten.fit(roi)
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
indices = np.where(fa_callable(FA, fa_thr))
if indices[0].size == 0:
msg = "No voxel with a FA higher than " + str(fa_thr) + " were found."
msg += " Try a larger roi or a lower threshold."
warnings.warn(msg, UserWarning)
lambdas = tenfit.evals[indices][:, :2]
S0s = roi[indices][:, np.nonzero(gtab.b0s_mask)[0]]
response, ratio = _get_response(S0s, lambdas)
if return_number_of_voxels:
return response, ratio, indices[0].size
return response, ratio
def dmri_recon(sid, data_dir, out_dir, resolution, recon='csd', num_threads=2):
import tempfile
#tempfile.tempdir = '/om/scratch/Fri/ksitek/'
import os
oldval = None
if 'MKL_NUM_THREADS' in os.environ:
oldval = os.environ['MKL_NUM_THREADS']
os.environ['MKL_NUM_THREADS'] = '%d' % num_threads
ompoldval = None
if 'OMP_NUM_THREADS' in os.environ:
ompoldval = os.environ['OMP_NUM_THREADS']
os.environ['OMP_NUM_THREADS'] = '%d' % num_threads
import nibabel as nib
import numpy as np
from glob import glob
if resolution == '0.2mm':
filename = 'Reg_S64550_nii4d.nii'
fimg = os.path.abspath(glob(os.path.join(data_dir, filename))[0])
else:
filename = 'Reg_S64550_nii4d_resamp-%s.nii.gz'%(resolution)
fimg = os.path.abspath(glob(os.path.join(data_dir, 'resample', filename))[0])
print("dwi file = %s"%fimg)
fbvec = os.path.abspath(glob(os.path.join(data_dir, 'bvecs',
'camino_120_RAS_flipped-xy.bvecs'))[0])
print("bvec file = %s"%fbvec)
fbval = os.path.abspath(glob(os.path.join(data_dir, 'bvecs',
'camino_120_RAS.bvals'))[0])
print("bval file = %s"%fbval)
img = nib.load(fimg)
data = img.get_data()
affine = img.get_affine()
prefix = sid
from dipy.io import read_bvals_bvecs
from dipy.core.gradients import vector_norm
bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
b0idx = []
for idx, val in enumerate(bvals):
if val < 1:
pass
#bvecs[idx] = [1, 0, 0]
else:
b0idx.append(idx)
#print "b0idx=%d"%idx
#print "input bvecs:"
#print bvecs
bvecs[b0idx, :] = bvecs[b0idx, :]/vector_norm(bvecs[b0idx])[:, None]
#print "bvecs after normalization:"
#print bvecs
from dipy.core.gradients import gradient_table
gtab = gradient_table(bvals, bvecs)
gtab.bvecs.shape == bvecs.shape
gtab.bvecs
gtab.bvals.shape == bvals.shape
gtab.bvals
from dipy.reconst.csdeconv import auto_response
response, ratio = auto_response(gtab, data, roi_radius=10, fa_thr=0.1) # 0.7
#from dipy.segment.mask import median_otsu
#b0_mask, mask = median_otsu(data[:, :, :, b0idx].mean(axis=3).squeeze(), 4, 4)
if resolution == '0.2mm':
mask_name = 'Reg_S64550_nii_b0-slice_mask.nii.gz'
fmask1 = os.path.join(data_dir, mask_name)
else:
mask_name = 'Reg_S64550_nii_b0-slice_mask_resamp-%s.nii.gz'%(resolution)
fmask1 = os.path.join(data_dir, 'resample', mask_name)
print("fmask file = %s"%fmask1)
mask = nib.load(fmask1).get_data()
useFA = True
print("creating model")
if recon == 'csd':
from dipy.reconst.csdeconv import ConstrainedSphericalDeconvModel
model = ConstrainedSphericalDeconvModel(gtab, response)
useFA = True
elif recon == 'csa':
from dipy.reconst.shm import CsaOdfModel, normalize_data
model = CsaOdfModel(gtab, 4)
useFA = False
else:
raise ValueError('only csd, csa supported currently')
from dipy.reconst.dsi import (DiffusionSpectrumDeconvModel,
DiffusionSpectrumModel)
model = DiffusionSpectrumDeconvModel(gtab)
fit = model.fit(data)
from dipy.data import get_sphere
sphere = get_sphere('symmetric724')
#odfs = fit.odf(sphere)
from dipy.reconst.peaks import peaks_from_model
print("running peaks_from_model")
peaks = peaks_from_model(model=model,
data=data,
sphere=sphere,
mask=mask,
return_sh=True,
return_odf=False,
normalize_peaks=True,
npeaks=5,
relative_peak_threshold=.5,
min_separation_angle=25,
parallel=num_threads > 1,
nbr_processes=num_threads)
from dipy.reconst.dti import TensorModel
print("running tensor model")
tenmodel = TensorModel(gtab)
tenfit = tenmodel.fit(data, mask)
from dipy.reconst.dti import fractional_anisotropy
print("running FA")
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
fa_img = nib.Nifti1Image(FA, img.get_affine())
tensor_fa_file = os.path.abspath('%s_tensor_fa.nii.gz' % (prefix))
nib.save(fa_img, tensor_fa_file)
from dipy.reconst.dti import axial_diffusivity
print("running AD")
AD = axial_diffusivity(tenfit.evals)
AD[np.isnan(AD)] = 0
ad_img = nib.Nifti1Image(AD, img.get_affine())
tensor_ad_file = os.path.abspath('%s_tensor_ad.nii.gz' % (prefix))
nib.save(ad_img, tensor_ad_file)
from dipy.reconst.dti import radial_diffusivity
print("running RD")
RD = radial_diffusivity(tenfit.evals)
RD[np.isnan(RD)] = 0
rd_img = nib.Nifti1Image(RD, img.get_affine())
tensor_rd_file = os.path.abspath('%s_tensor_rd.nii.gz' % (prefix))
nib.save(rd_img, tensor_rd_file)
from dipy.reconst.dti import mean_diffusivity
print("running MD")
MD = mean_diffusivity(tenfit.evals)
MD[np.isnan(MD)] = 0
md_img = nib.Nifti1Image(MD, img.get_affine())
tensor_md_file = os.path.abspath('%s_tensor_md.nii.gz' % (prefix))
nib.save(md_img, tensor_md_file)
evecs = tenfit.evecs
evec_img = nib.Nifti1Image(evecs, img.get_affine())
tensor_evec_file = os.path.abspath('%s_tensor_evec.nii.gz' % (prefix))
nib.save(evec_img, tensor_evec_file)
shm_coeff = fit.shm_coeff
shm_coeff_file = os.path.abspath('%s_shm_coeff.nii.gz' % (prefix))
nib.save(nib.Nifti1Image(shm_coeff, img.get_affine()), shm_coeff_file)
#from dipy.reconst.dti import quantize_evecs
#peak_indices = quantize_evecs(tenfit.evecs, sphere.vertices)
#eu = EuDX(FA, peak_indices, odf_vertices = sphere.vertices,
#a_low=0.2, seeds=10**6, ang_thr=35)
fa_img = nib.Nifti1Image(peaks.gfa, img.get_affine())
model_gfa_file = os.path.abspath('%s_%s_gfa.nii.gz' % (prefix, recon))
nib.save(fa_img, model_gfa_file)
from dipy.tracking.eudx import EuDX
print("reconstructing with EuDX")
if useFA:
eu = EuDX(FA, peaks.peak_indices[..., 0],
odf_vertices = sphere.vertices,
#a_low=0.1,
seeds=10**6,
ang_thr=45)
else:
eu = EuDX(peaks.gfa, peaks.peak_indices[..., 0],
odf_vertices = sphere.vertices,
#a_low=0.1,
seeds=10**6,
ang_thr=45)
sl_fname = os.path.abspath('%s_%s_streamline.trk' % (prefix, recon))
"""
#import dipy.tracking.metrics as dmetrics
streamlines = ((sl, None, None) for sl in eu) # if dmetrics.length(sl) > 15)
hdr = nib.trackvis.empty_header()
hdr['voxel_size'] = fa_img.get_header().get_zooms()[:3]
hdr['voxel_order'] = 'RAS' #LAS
hdr['dim'] = FA.shape[:3]
nib.trackvis.write(sl_fname, streamlines, hdr, points_space='voxel')
"""
# trying new dipy.io.streamline module, per email to neuroimaging list
# 2018.04.05
from nibabel.streamlines import Field
from nibabel.orientations import aff2axcodes
affine = img.get_affine()
vox_size=fa_img.get_header().get_zooms()[:3]
fov_shape=FA.shape[:3]
if vox_size is not None and fov_shape is not None:
hdr = {}
hdr[Field.VOXEL_TO_RASMM] = affine.copy()
hdr[Field.VOXEL_SIZES] = vox_size
hdr[Field.DIMENSIONS] = fov_shape
hdr[Field.VOXEL_ORDER] = "".join(aff2axcodes(affine))
tractogram = nib.streamlines.Tractogram(eu)
tractogram.affine_to_rasmm = affine
trk_file = nib.streamlines.TrkFile(tractogram, header=hdr)
nib.streamlines.save(trk_file, sl_fname)
if oldval:
os.environ['MKL_NUM_THREADS'] = oldval
else:
del os.environ['MKL_NUM_THREADS']
if ompoldval:
os.environ['OMP_NUM_THREADS'] = ompoldval
else:
del os.environ['OMP_NUM_THREADS']
assert tensor_fa_file
assert tensor_evec_file
assert model_gfa_file
assert tensor_ad_file
assert tensor_rd_file
assert tensor_md_file
assert shm_coeff_file
print('all output files created')
return tensor_fa_file, tensor_evec_file, model_gfa_file, sl_fname, affine, tensor_ad_file, tensor_rd_file, tensor_md_file, shm_coeff_file
def mask_for_response_msmt(gtab,
data,
roi_center=None,
roi_radii=10,
wm_fa_thr=0.7,
gm_fa_thr=0.2,
csf_fa_thr=0.1,
gm_md_thr=0.0007,
csf_md_thr=0.002):
""" Computation of masks for multi-shell multi-tissue (msmt) response
function using FA and MD.
Parameters
----------
gtab : GradientTable
data : ndarray
diffusion data (4D)
roi_center : array-like, (3,)
Center of ROI in data. If center is None, it is assumed that it is
the center of the volume with shape `data.shape[:3]`.
roi_radii : int or array-like, (3,)
radii of cuboid ROI
wm_fa_thr : float
FA threshold for WM.
gm_fa_thr : float
FA threshold for GM.
csf_fa_thr : float
FA threshold for CSF.
gm_md_thr : float
MD threshold for GM.
csf_md_thr : float
MD threshold for CSF.
Returns
-------
mask_wm : ndarray
Mask of voxels within the ROI and with FA above the FA threshold
for WM.
mask_gm : ndarray
Mask of voxels within the ROI and with FA below the FA threshold
for GM and with MD above the MD threshold for GM.
mask_csf : ndarray
Mask of voxels within the ROI and with FA below the FA threshold
for CSF and with MD above the MD threshold for CSF.
Notes
-----
In msmt-CSD there is an important pre-processing step: the estimation of
every tissue's response function. In order to do this, we look for voxels
corresponding to WM, GM and CSF. This function aims to accomplish that by
returning a mask of voxels within a ROI and who respect some threshold
constraints, for each tissue. More precisely, the WM mask must have a FA
value above a given threshold. The GM mask and CSF mask must have a FA
below given thresholds and a MD below other thresholds. To get the FA and
MD, we need to fit a Tensor model to the datasets.
"""
if len(data.shape) < 4:
msg = """Data must be 4D (3D image + directions). To use a 2D image,
please reshape it into a (N, N, 1, ndirs) array."""
raise ValueError(msg)
if isinstance(roi_radii, numbers.Number):
roi_radii = (roi_radii, roi_radii, roi_radii)
if roi_center is None:
roi_center = np.array(data.shape[:3]) // 2
roi_radii = _roi_in_volume(data.shape, np.asarray(roi_center),
np.asarray(roi_radii))
roi_mask = _mask_from_roi(data.shape[:3], roi_center, roi_radii)
list_bvals = unique_bvals_tolerance(gtab.bvals)
if not np.all(list_bvals <= 1200):
msg_bvals = """Some b-values are higher than 1200.
The DTI fit might be affected."""
warnings.warn(msg_bvals, UserWarning)
ten = TensorModel(gtab)
tenfit = ten.fit(data, mask=roi_mask)
fa = fractional_anisotropy(tenfit.evals)
fa[np.isnan(fa)] = 0
md = mean_diffusivity(tenfit.evals)
md[np.isnan(md)] = 0
mask_wm = np.zeros(fa.shape, dtype=np.int64)
mask_wm[fa > wm_fa_thr] = 1
mask_wm *= roi_mask
md_mask_gm = np.zeros(md.shape, dtype=np.int64)
md_mask_gm[(md < gm_md_thr)] = 1
fa_mask_gm = np.zeros(fa.shape, dtype=np.int64)
fa_mask_gm[(fa < gm_fa_thr) & (fa > 0)] = 1
mask_gm = md_mask_gm * fa_mask_gm
mask_gm *= roi_mask
md_mask_csf = np.zeros(md.shape, dtype=np.int64)
md_mask_csf[(md < csf_md_thr) & (md > 0)] = 1
fa_mask_csf = np.zeros(fa.shape, dtype=np.int64)
fa_mask_csf[(fa < csf_fa_thr) & (fa > 0)] = 1
mask_csf = md_mask_csf * fa_mask_csf
mask_csf *= roi_mask
msg = """No voxel with a {0} than {1} were found.
Try a larger roi or a {2} threshold for {3}."""
if np.sum(mask_wm) == 0:
msg_fa = msg.format('FA higher', str(wm_fa_thr), 'lower FA', 'WM')
warnings.warn(msg_fa, UserWarning)
if np.sum(mask_gm) == 0:
msg_fa = msg.format('FA lower', str(gm_fa_thr), 'higher FA', 'GM')
msg_md = msg.format('MD lower', str(gm_md_thr), 'higher MD', 'GM')
warnings.warn(msg_fa, UserWarning)
warnings.warn(msg_md, UserWarning)
if np.sum(mask_csf) == 0:
msg_fa = msg.format('FA lower', str(csf_fa_thr), 'higher FA', 'CSF')
msg_md = msg.format('MD lower', str(csf_md_thr), 'higher MD', 'CSF')
warnings.warn(msg_fa, UserWarning)
warnings.warn(msg_md, UserWarning)
return mask_wm, mask_gm, mask_csf
def mask_for_response_ssst(gtab,
data,
roi_center=None,
roi_radii=10,
fa_thr=0.7):
""" Computation of mask for single-shell single-tissue (ssst) response
function using FA.
Parameters
----------
gtab : GradientTable
data : ndarray
diffusion data (4D)
roi_center : array-like, (3,)
Center of ROI in data. If center is None, it is assumed that it is
the center of the volume with shape `data.shape[:3]`.
roi_radii : int or array-like, (3,)
radii of cuboid ROI
fa_thr : float
FA threshold
Returns
-------
mask : ndarray
Mask of voxels within the ROI and with FA above the FA threshold.
Notes
-----
In CSD there is an important pre-processing step: the estimation of the
fiber response function. In order to do this, we look for voxels with very
anisotropic configurations. This function aims to accomplish that by
returning a mask of voxels within a ROI, that have a FA value above a
given threshold. For example we can use a ROI (20x20x20) at
the center of the volume and store the signal values for the voxels with
FA values higher than 0.7 (see [1]_).
References
----------
.. [1] Tournier, J.D., et al. NeuroImage 2004. Direct estimation of the
fiber orientation density function from diffusion-weighted MRI
data using spherical deconvolution
"""
if len(data.shape) < 4:
msg = """Data must be 4D (3D image + directions). To use a 2D image,
please reshape it into a (N, N, 1, ndirs) array."""
raise ValueError(msg)
if isinstance(roi_radii, numbers.Number):
roi_radii = (roi_radii, roi_radii, roi_radii)
if roi_center is None:
roi_center = np.array(data.shape[:3]) // 2
roi_radii = _roi_in_volume(data.shape, np.asarray(roi_center),
np.asarray(roi_radii))
roi_mask = _mask_from_roi(data.shape[:3], roi_center, roi_radii)
ten = TensorModel(gtab)
tenfit = ten.fit(data, mask=roi_mask)
fa = fractional_anisotropy(tenfit.evals)
fa[np.isnan(fa)] = 0
mask = np.zeros(fa.shape, dtype=np.int64)
mask[fa > fa_thr] = 1
if np.sum(mask) == 0:
msg = """No voxel with a FA higher than {} were found.
Try a larger roi or a lower threshold.""".format(str(fa_thr))
warnings.warn(msg, UserWarning)
return mask
**Stopping States**
- 'ENDPOINT': stops at a position where metric_map < threshold; the streamline
reached the target stopping area.
- 'OUTSIDEIMAGE': stops at a position outside of metric_map; the streamline
reached an area outside the image where no direction data is available.
- 'TRACKPOINT': stops at a position because no direction is available; the
streamline is stopping where metric_map >= threshold, but there is no valid
direction to follow.
- 'INVALIDPOINT': N/A.
"""
tensor_model = TensorModel(gtab)
tenfit = tensor_model.fit(data, mask=labels > 0)
FA = fractional_anisotropy(tenfit.evals)
threshold_criterion = ThresholdStoppingCriterion(FA, .2)
fig = plt.figure()
mask_fa = FA.copy()
mask_fa[mask_fa < 0.2] = 0
plt.xticks([])
plt.yticks([])
plt.imshow(mask_fa[:, :, data.shape[2] // 2].T, cmap='gray', origin='lower',
interpolation='nearest')
fig.tight_layout()
fig.savefig('threshold_fa.png')
"""
del data, affine, zooms
print(data2.shape)
print(affine2)
print(nib.aff2axcodes(affine2))
print('>>> Save resampled data, masks and S0...')
# Save as nii (not nii.gz) to reduce saving and loading time
fname2 = join(dname, 'dwi_1x1x1.nii')
nib.save(nib.Nifti1Image(data2, affine2), fname2)
fname2_mask = join(dname, 'dwi_mask_1x1x1.nii.gz')
nib.save(nib.Nifti1Image(mask2.astype(np.uint8), affine2), fname2_mask)
fname2_S0 = join(dname, 'dwi_S0_1x1x1.nii.gz')
S0s = data2[..., b0_index]
S0 = np.mean(S0s, axis=-1)
nib.save(nib.Nifti1Image(S0, affine2), fname2_S0)
print('>>> Calculate FA...')
ten = TensorModel(gtab)
tenfit = ten.fit(data2, mask2)
fname2_fa = join(dname, 'dwi_fa_1x1x1.nii.gz')
nib.save(nib.Nifti1Image(tenfit.fa, affine2), fname2_fa)
del data2, mask2
lap = through_label_sl.label_position(labels, labelValue=1)
dataslice = data[40:80, 20:80, lap[2][2] / 2]
#print lap[2][2]/2
#get_csd_gfa(f_name,data,gtab,dataslice)
maskdata, mask = median_otsu(data,
2,
1,
False,
vol_idx=range(10, 50),
dilate=2) #不去背景
""" get fa and tensor evecs and ODF"""
from dipy.reconst.dti import TensorModel, mean_diffusivity
tenmodel = TensorModel(gtab)
tenfit = tenmodel.fit(data, mask)
sphere = get_sphere('symmetric724')
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
np.save(os.getcwd() + '\zhibiao' + f_name + '_FA.npy', FA)
fa_img = nib.Nifti1Image(FA.astype(np.float32), affine)
print FA.shape
nib.save(fa_img, os.getcwd() + '/zhibiao/' + f_name + '_FA.nii.gz')
print('Saving "DTI_tensor_fa.nii.gz" sucessful.')
evecs_img = nib.Nifti1Image(tenfit.evecs.astype(np.float32), affine)
nib.save(
evecs_img,
os.getcwd() + '/zhibiao/' + f_name + '_DTI_tensor_evecs.nii.gz')
def _run_interface(self, runtime):
from scipy.special import gamma
from dipy.reconst.dti import TensorModel
import gc
img = nb.load(self.inputs.in_file)
hdr = img.get_header().copy()
affine = img.get_affine()
data = img.get_data()
gtab = self._get_gradient_table()
if isdefined(self.inputs.in_mask):
msk = nb.load(self.inputs.in_mask).get_data().astype(np.uint8)
else:
msk = np.ones(data.shape[:3], dtype=np.uint8)
try_b0 = True
if isdefined(self.inputs.noise_mask):
noise_msk = nb.load(self.inputs.noise_mask).get_data().reshape(-1)
noise_msk[noise_msk > 0.5] = 1
noise_msk[noise_msk < 1.0] = 0
noise_msk = noise_msk.astype(np.uint8)
try_b0 = False
elif np.all(data[msk == 0, 0] == 0):
IFLOGGER.info('Input data are masked.')
noise_msk = msk.reshape(-1).astype(np.uint8)
else:
noise_msk = (1 - msk).reshape(-1).astype(np.uint8)
nb0 = np.sum(gtab.b0s_mask)
dsample = data.reshape(-1, data.shape[-1])
if try_b0 and (nb0 > 1):
noise_data = dsample.take(np.where(gtab.b0s_mask),
axis=-1)[noise_msk == 0, ...]
n = nb0
else:
nodiff = np.where(~gtab.b0s_mask)
nodiffidx = nodiff[0].tolist()
n = 20 if len(nodiffidx) >= 20 else len(nodiffidx)
idxs = np.random.choice(nodiffidx, size=n, replace=False)
noise_data = dsample.take(idxs, axis=-1)[noise_msk == 1, ...]
# Estimate sigma required by RESTORE
mean_std = np.median(noise_data.std(-1))
try:
bias = (1. - np.sqrt(2. / (n - 1)) *
(gamma(n / 2.) / gamma((n - 1) / 2.)))
except:
bias = .0
pass
sigma = mean_std * (1 + bias)
if sigma == 0:
IFLOGGER.warn(
('Noise std is 0.0, looks like data was masked and noise'
' cannot be estimated correctly. Using default tensor '
'model instead of RESTORE.'))
dti = TensorModel(gtab)
else:
IFLOGGER.info(('Performing RESTORE with noise std=%.4f.') % sigma)
dti = TensorModel(gtab, fit_method='RESTORE', sigma=sigma)
try:
fit_restore = dti.fit(data, msk)
except TypeError:
dti = TensorModel(gtab)
fit_restore = dti.fit(data, msk)
hdr.set_data_dtype(np.float32)
hdr['data_type'] = 16
for k in self._outputs().get():
scalar = getattr(fit_restore, k)
hdr.set_data_shape(np.shape(scalar))
nb.Nifti1Image(scalar.astype(np.float32),
affine, hdr).to_filename(self._gen_filename(k))
return runtime
def main():
parser = buildArgsParser()
args = parser.parse_args()
if args.par is None:
print('Need output name for parallel curve')
return None
if args.perp is None:
print('Need output name for perpendicular curve')
return None
if args.diff is None:
print('Need output name for perpendicular difference curve')
return None
# load and concatenate all the bval
print('Loading bval')
bvals = [np.genfromtxt(fname) for fname in args.bval]
bval = np.concatenate(bvals, axis=0)
print('{:} b-values'.format(bval.shape[0]))
del bvals
# load and concatenate all the bvec
print('Loading bvec')
bvecs = []
for fname in args.bvec:
tmp = np.genfromtxt(fname)
if tmp.shape[1] != 3:
tmp = tmp.T
bvecs.append(tmp)
bvec = np.concatenate(bvecs, axis=0)
print('{:} b-vectors'.format(bvec.shape[0]))
del bvecs
if bval.shape[0] != bvec.shape[0]:
print('Mismatch of bval and bvec')
return None
# load and concatenate all the data
print('Loading data')
data_img = [nib.load(fname) for fname in args.data]
affine = data_img[0].affine
data_data = []
for img in data_img:
tmp = img.get_fdata()
print('data shape = {:}'.format(tmp.shape))
# need 4D data for the concatenate
if tmp.ndim == 3:
tmp = tmp[..., None]
data_data.append(tmp)
data = np.concatenate(data_data, axis=3)
print('Full data shape = {:}'.format(data.shape))
del data_data
if bval.shape[0] != data.shape[3]:
print('Mismatch of bval/bvec and data')
return None
# load and multiply all the mask
print('Loading Mask')
mask = np.ones(data.shape[:3], dtype=np.bool)
mask_data = [nib.load(fname).get_fdata().astype(np.bool) for fname in args.mask]
for tmp in mask_data:
mask = np.logical_and(mask, tmp)
print('Final mask has {:} voxels ({:.1f} % of total)'.format(mask.sum(), 100*mask.sum()/np.prod(data.shape[:3])))
del mask_data
b0_th = 50 # threshold below which a bvalues is considered a b0
round_th = 250 # threshold used to round the bvals into shells
print('bval below {:} are round to 0'.format(b0_th))
print('rounding bval to nearest {:}'.format(round_th))
bval[bval<b0_th] = 0
bval = round_th*np.round(bval/round_th, decimals=0)
bval_shell = sorted(list(set(bval)))
print('#Vol | bval Shell')
for b in bval_shell:
print('{:} at b = {:.0f}'.format((bval==b).sum(), b))
# clean data
data = np.clip(data, 0, np.inf)
data[np.isinf(data)] = 0
data[np.isnan(data)] = 0
# Fit DTI with all data
# keep eigenvectors
print('Fit DTI on full data')
gtab = gradient_table(bval, bvec)
tenmodel = TensorModel(gtab, fit_method='WLS', min_signal=1e-16)
start_time = time()
tenfit = tenmodel.fit(data, mask)
end_time = time()
print('elapsed time = {:.0f} sec'.format(end_time-start_time))
eigenvectors1 = tenfit.evecs[..., :, 0]
eigenvectors2 = tenfit.evecs[..., :, 1]
eigenvectors3 = tenfit.evecs[..., :, 2]
S_par_list = []
S_perp1_list = []
S_perp2_list = []
# Fit DTI on each shell
for bshell in bval_shell:
if bshell > 0:
print('Fitting b = {:}'.format(bshell))
shell_mask = np.logical_or(bval==0, bval==bshell)
shell_gtab = gradient_table(bval[shell_mask], bvec[shell_mask])
shell_tenmodel = TensorModel(shell_gtab, fit_method='WLS', min_signal=1e-16)
start_time = time()
shell_tenfit = shell_tenmodel.fit(data[..., shell_mask], mask)
end_time = time()
adc_par = np.einsum('...i,...i->...', np.einsum('...i,...ij->...j', eigenvectors1, tenfit.quadratic_form), eigenvectors1)
adc_perp1 = np.einsum('...i,...i->...', np.einsum('...i,...ij->...j', eigenvectors2, tenfit.quadratic_form), eigenvectors2)
adc_perp2 = np.einsum('...i,...i->...', np.einsum('...i,...ij->...j', eigenvectors3, tenfit.quadratic_form), eigenvectors3)
S_par = np.exp(-bshell*adc_par)
S_perp1 = np.exp(-bshell*adc_perp1)
S_perp2 = np.exp(-bshell*adc_perp2)
S_par_list.append(S_par[...,None])
S_perp1_list.append(S_perp1[...,None])
S_perp2_list.append(S_perp2[...,None])
data_par = np.concatenate(S_par_list, axis=3)
data_perp1 = np.concatenate(S_perp1_list, axis=3)
data_perp2 = np.concatenate(S_perp2_list, axis=3)
data_perp = (data_perp1 + data_perp2) / 2.0
data_diff = np.abs(data_perp1 - data_perp2)
nib.Nifti1Image(data_par*mask[...,None], affine).to_filename(args.par)
nib.Nifti1Image(data_perp*mask[...,None], affine).to_filename(args.perp)
nib.Nifti1Image(data_diff*mask[...,None], affine).to_filename(args.diff)
def main():
parser = _build_args_parser()
args = parser.parse_args()
img = nib.load(args.input)
data = img.get_data()
bmax = int(args.bmax)
print('\ndata shape ({}, {}, {}, {})'.format(data.shape[0], data.shape[1],
data.shape[2], data.shape[3]))
print('total voxels {}'.format(np.prod(data.shape[:3])))
# remove negatives
print('\ncliping negative ({} voxels, {:.2f} % of total)'.format(
(data < 0).sum(),
100 * (data < 0).sum() / float(np.prod(data.shape[:3]))))
data = np.clip(data, 0, np.inf)
affine = img.affine
if args.mask is None:
mask = None
masksum = np.prod(data.shape[:3])
else:
mask = nib.load(args.mask).get_data().astype(np.bool)
masksum = mask.sum()
print('\nMask has {} voxels, {:.2f} % of total'.format(
masksum, 100 * masksum / float(np.prod(data.shape[:3]))))
# Validate bvals and bvecs
bvals, bvecs = read_bvals_bvecs(args.bvals, args.bvecs)
if not is_normalized_bvecs(bvecs):
print('Your b-vectors do not seem normalized...')
bvecs = normalize_bvecs(bvecs)
# Get tensors
method = 'WLS'
min_signal = 1e-16
print('\nUsing fitting method {}'.format(method))
# print('Using minimum signal = {}'.format(min_signal)
b0_thr = bvals.min() + 10
print('\nassuming existence of b0 (thr = {})\n'.format(b0_thr))
# restricted gtab
gtab = gradient_table(bvals[bvals < bmax + 22],
bvecs[bvals < bmax + 22],
b0_threshold=b0_thr)
tenmodel = TensorModel(gtab, fit_method=method, min_signal=min_signal)
tenfit = tenmodel.fit(data[..., bvals < bmax + 22], mask)
MD = tenfit.md
FA = tenfit.fa
evalmax = np.max(tenfit.evals, axis=3)
invevalmax = evalmax**-1
invevalmax[np.isnan(invevalmax)] = 0
invevalmax[np.isinf(invevalmax)] = 0
evalmin = np.min(tenfit.evals, axis=3)
weird_contrast = np.exp(-bmax * evalmin) - np.exp(-bmax * evalmax)
invMD = MD**-1
invMD[np.isnan(invMD)] = 0
invMD[np.isinf(invMD)] = 0
nib.nifti1.Nifti1Image(MD,
img.affine).to_filename('./MD_bmax_{}'.format(bmax))
nib.nifti1.Nifti1Image(invMD, img.affine).to_filename(
'./invMD_bmax_{}'.format(bmax))
nib.nifti1.Nifti1Image(FA,
img.affine).to_filename('./FA_bmax_{}'.format(bmax))
nib.nifti1.Nifti1Image(invevalmax, img.affine).to_filename(
'./inv_e1_bmax_{}'.format(bmax))
nib.nifti1.Nifti1Image(weird_contrast, img.affine).to_filename(
'./minmax_contrast_bmax_{}'.format(bmax))
def compute_tensors(self, dti_vol, atlas_file, gtab):
# WGR:TODO figure out how to organize tensor options and formats
# WGR:TODO figure out how to deal with files on disk vs. in workspace
"""
Takes registered DTI image and produces tensors
**Positional Arguments:**
dti_vol:
- Registered DTI volume, from workspace.
atlas_file:
- File containing an atlas (or brain mask).
gtab:
- Structure containing dipy formatted bval/bvec information
"""
labeldata = nib.load(atlas_file)
label = labeldata.get_data()
"""
Create a brain mask. Here we just threshold labels.
"""
mask = (label > 0)
gtab.info
print data.shape
"""
For the constrained spherical deconvolution we need to estimate the
response function (see :ref:`example_reconst_csd`) and create a model.
"""
response, ratio = auto_response(gtab, dti_vol, roi_radius=10,
fa_thr=0.7)
csd_model = ConstrainedSphericalDeconvModel(gtab, response)
"""
Next, we use ``peaks_from_model`` to fit the data and calculated
the fiber directions in all voxels.
"""
sphere = get_sphere('symmetric724')
csd_peaks = peaks_from_model(model=csd_model,
data=data,
sphere=sphere,
mask=mask,
relative_peak_threshold=.5,
min_separation_angle=25,
parallel=True)
"""
For the tracking part, we will use ``csd_model`` fiber directions
but stop tracking where fractional anisotropy (FA) is low (< 0.1).
To derive the FA, used as a stopping criterion, we need to fit a
tensor model first. Here, we use weighted least squares (WLS).
"""
print 'tensors...'
tensor_model = TensorModel(gtab, fit_method='WLS')
tensor_fit = tensor_model.fit(data, mask)
FA = fractional_anisotropy(tensor_fit.evals)
"""
In order for the stopping values to be used with our tracking
algorithm we need to have the same dimensions as the
``csd_peaks.peak_values``. For this reason, we can assign the
same FA value to every peak direction in the same voxel in
the following way.
"""
stopping_values = np.zeros(csd_peaks.peak_values.shape)
stopping_values[:] = FA[..., None]
print datetime.now() - startTime
pass
def test_wls_and_ls_fit():
"""
Tests the WLS and LS fitting functions to see if they returns the correct
eigenvalues and eigenvectors.
Uses data/55dir_grad.bvec as the gradient table and 3by3by56.nii
as the data.
"""
# Defining Test Voxel (avoid nibabel dependency) ###
# Recall: D = [Dxx,Dyy,Dzz,Dxy,Dxz,Dyz,log(S_0)] and D ~ 10^-4 mm^2 /s
b0 = 1000.
bvec, bval = read_bvec_file(get_fnames('55dir_grad.bvec'))
B = bval[1]
# Scale the eigenvalues and tensor by the B value so the units match
D = np.array([1., 1., 1., 0., 0., 1., -np.log(b0) * B]) / B
evals = np.array([2., 1., 0.]) / B
md = evals.mean()
tensor = from_lower_triangular(D)
# Design Matrix
gtab = grad.gradient_table(bval, bvec)
X = dti.design_matrix(gtab)
# Signals
Y = np.exp(np.dot(X, D))
npt.assert_almost_equal(Y[0], b0)
Y.shape = (-1, ) + Y.shape
# Testing WLS Fit on Single Voxel
# If you do something wonky (passing min_signal<0), you should get an
# error:
npt.assert_raises(ValueError,
TensorModel,
gtab,
fit_method='WLS',
min_signal=-1)
# Estimate tensor from test signals
model = TensorModel(gtab, fit_method='WLS', return_S0_hat=True)
tensor_est = model.fit(Y)
npt.assert_equal(tensor_est.shape, Y.shape[:-1])
npt.assert_array_almost_equal(tensor_est.evals[0], evals)
npt.assert_array_almost_equal(tensor_est.quadratic_form[0],
tensor,
err_msg="Calculation of tensor from Y does "
"not compare to analytical solution")
npt.assert_almost_equal(tensor_est.md[0], md)
npt.assert_array_almost_equal(tensor_est.S0_hat[0], b0, decimal=3)
# Test that we can fit a single voxel's worth of data (a 1d array)
y = Y[0]
tensor_est = model.fit(y)
npt.assert_equal(tensor_est.shape, tuple())
npt.assert_array_almost_equal(tensor_est.evals, evals)
npt.assert_array_almost_equal(tensor_est.quadratic_form, tensor)
npt.assert_almost_equal(tensor_est.md, md)
npt.assert_array_almost_equal(tensor_est.lower_triangular(b0), D)
# Test using fit_method='LS'
model = TensorModel(gtab, fit_method='LS')
tensor_est = model.fit(y)
npt.assert_equal(tensor_est.shape, tuple())
npt.assert_array_almost_equal(tensor_est.evals, evals)
npt.assert_array_almost_equal(tensor_est.quadratic_form, tensor)
npt.assert_almost_equal(tensor_est.md, md)
npt.assert_array_almost_equal(tensor_est.lower_triangular(b0), D)
npt.assert_array_almost_equal(tensor_est.linearity, linearity(evals))
npt.assert_array_almost_equal(tensor_est.planarity, planarity(evals))
npt.assert_array_almost_equal(tensor_est.sphericity, sphericity(evals))
def eudx_advanced(self, dti_file, mask_file, gtab,
seed_num=100000, stop_val=0.1):
"""
Tracking with more complex tensors - experimental
Initializes the graph with nodes corresponding to the number of ROIs
**Positional Arguments:**
dti_file:
- File (registered) to use for tensor/fiber tracking
mask_file:
- Brain mask to keep tensors inside the brain
gtab:
- dipy formatted bval/bvec Structure
**Optional Arguments:**
seed_num:
- Number of seeds to use for fiber tracking
stop_val:
- Value to cutoff fiber track
"""
img = nb.load(dti_file)
data = img.get_data()
img = nb.load(mask_file)
mask = img.get_data()
mask = mask > 0 # to ensure binary mask
"""
For the constrained spherical deconvolution we need to estimate the
response function (see :ref:`example_reconst_csd`) and create a model.
"""
response, ratio = auto_response(gtab, data, roi_radius=10,
fa_thr=0.7)
csd_model = ConstrainedSphericalDeconvModel(gtab, response)
"""
Next, we use ``peaks_from_model`` to fit the data and calculated
the fiber directions in all voxels.
"""
sphere = get_sphere('symmetric724')
csd_peaks = peaks_from_model(model=csd_model,
data=data,
sphere=sphere,
mask=mask,
relative_peak_threshold=.5,
min_separation_angle=25,
parallel=True)
"""
For the tracking part, we will use ``csd_model`` fiber directions
but stop tracking where fractional anisotropy (FA) is low (< 0.1).
To derive the FA, used as a stopping criterion, we need to fit a
tensor model first. Here, we use weighted least squares (WLS).
"""
print 'tensors...'
tensor_model = TensorModel(gtab, fit_method='WLS')
tensor_fit = tensor_model.fit(data, mask)
FA = fractional_anisotropy(tensor_fit.evals)
"""
In order for the stopping values to be used with our tracking
algorithm we need to have the same dimensions as the
``csd_peaks.peak_values``. For this reason, we can assign the
same FA value to every peak direction in the same voxel in
the following way.
"""
stopping_values = np.zeros(csd_peaks.peak_values.shape)
stopping_values[:] = FA[..., None]
streamline_generator = EuDX(stopping_values,
csd_peaks.peak_indices,
seeds=seed_num,
odf_vertices=sphere.vertices,
a_low=stop_val)
streamlines = [streamline for streamline in streamline_generator]
return streamlines
def run(context):
####################################################
# Get the path to input files and other parameter #
####################################################
analysis_data = context.fetch_analysis_data()
settings = analysis_data['settings']
postprocessing = settings['postprocessing']
hcpl_dwi_file_handle = context.get_files('input', modality='HARDI')[0]
hcpl_dwi_file_path = hcpl_dwi_file_handle.download('/root/')
hcpl_bvalues_file_handle = context.get_files(
'input', reg_expression='.*prep.bvalues.hcpl.txt')[0]
hcpl_bvalues_file_path = hcpl_bvalues_file_handle.download('/root/')
hcpl_bvecs_file_handle = context.get_files(
'input', reg_expression='.*prep.gradients.hcpl.txt')[0]
hcpl_bvecs_file_path = hcpl_bvecs_file_handle.download('/root/')
dwi_file_handle = context.get_files('input', modality='DSI')[0]
dwi_file_path = dwi_file_handle.download('/root/')
bvalues_file_handle = context.get_files(
'input', reg_expression='.*prep.bvalues.txt')[0]
bvalues_file_path = bvalues_file_handle.download('/root/')
bvecs_file_handle = context.get_files(
'input', reg_expression='.*prep.gradients.txt')[0]
bvecs_file_path = bvecs_file_handle.download('/root/')
inject_file_handle = context.get_files(
'input', reg_expression='.*prep.inject.nii.gz')[0]
inject_file_path = inject_file_handle.download('/root/')
VUMC_ROIs_file_handle = context.get_files(
'input', reg_expression='.*VUMC_ROIs.nii.gz')[0]
VUMC_ROIs_file_path = VUMC_ROIs_file_handle.download('/root/')
###############################
# _____ _____ _______ __ #
# | __ \_ _| __ \ \ / / #
# | | | || | | |__) \ \_/ / #
# | | | || | | ___/ \ / #
# | |__| || |_| | | | #
# |_____/_____|_| |_| #
# #
# dipy.org/documentation #
###############################
# IronTract Team #
# TrackyMcTrackface #
###############################
#################
# Load the data #
#################
dwi_img = nib.load(hcpl_dwi_file_path)
bvals, bvecs = read_bvals_bvecs(hcpl_bvalues_file_path,
hcpl_bvecs_file_path)
gtab = gradient_table(bvals, bvecs)
############################################
# Extract the brain mask from the b0 image #
############################################
_, brain_mask = median_otsu(dwi_img.get_data()[:, :, :, 0],
median_radius=2,
numpass=1)
##################################################################
# Fit the tensor model and compute the fractional anisotropy map #
##################################################################
context.set_progress(message='Processing voxel-wise DTI metrics.')
tenmodel = TensorModel(gtab)
tenfit = tenmodel.fit(dwi_img.get_data(), mask=brain_mask)
FA = fractional_anisotropy(tenfit.evals)
# fa_file_path = "/root/fa.nii.gz"
# nib.Nifti1Image(FA,dwi_img.affine).to_filename(fa_file_path)
################################################
# Compute Fiber Orientation Distribution (CSD) #
################################################
context.set_progress(message='Processing voxel-wise FOD estimation.')
response, _ = auto_response_ssst(gtab,
dwi_img.get_data(),
roi_radii=10,
fa_thr=0.7)
csd_model = ConstrainedSphericalDeconvModel(gtab, response, sh_order=6)
csd_fit = csd_model.fit(dwi_img.get_data(), mask=brain_mask)
# fod_file_path = "/root/fod.nii.gz"
# nib.Nifti1Image(csd_fit.shm_coeff,dwi_img.affine).to_filename(fod_file_path)
###########################################
# Compute DIPY Probabilistic Tractography #
###########################################
context.set_progress(message='Processing tractography.')
sphere = get_sphere("repulsion724")
seed_mask_img = nib.load(inject_file_path)
affine = seed_mask_img.affine
seeds = utils.seeds_from_mask(seed_mask_img.get_data(), affine, density=5)
stopping_criterion = ThresholdStoppingCriterion(FA, 0.2)
prob_dg = ProbabilisticDirectionGetter.from_shcoeff(csd_fit.shm_coeff,
max_angle=20.,
sphere=sphere)
streamline_generator = LocalTracking(prob_dg,
stopping_criterion,
seeds,
affine,
step_size=.2,
max_cross=1)
streamlines = Streamlines(streamline_generator)
# sft = StatefulTractogram(streamlines, seed_mask_img, Space.RASMM)
# streamlines_file_path = "/root/streamlines.trk"
# save_trk(sft, streamlines_file_path)
###########################################################################
# Compute 3D volumes for the IronTract Challenge. For 'EPFL', we only #
# keep streamlines with length > 1mm. We compute the visitation count #
# image and apply a small gaussian smoothing. The gaussian smoothing #
# is especially usefull to increase voxel coverage of deterministic #
# algorithms. The log of the smoothed visitation count map is then #
# iteratively thresholded producing 200 volumes/operation points. #
# For VUMC, additional streamline filtering is done using anatomical #
# priors (keeping only streamlines that intersect with at least one ROI). #
###########################################################################
if postprocessing in ["EPFL", "ALL"]:
context.set_progress(message='Processing density map (EPFL)')
volume_folder = "/root/vol_epfl"
output_epfl_zip_file_path = "/root/TrackyMcTrackface_EPFL_example.zip"
os.mkdir(volume_folder)
lengths = length(streamlines)
streamlines = streamlines[lengths > 1]
density = utils.density_map(streamlines, affine, seed_mask_img.shape)
density = scipy.ndimage.gaussian_filter(density.astype("float32"), 0.5)
log_density = np.log10(density + 1)
max_density = np.max(log_density)
for i, t in enumerate(np.arange(0, max_density, max_density / 200)):
nbr = str(i)
nbr = nbr.zfill(3)
mask = log_density >= t
vol_filename = os.path.join(
volume_folder, "vol" + nbr + "_t" + str(t) + ".nii.gz")
nib.Nifti1Image(mask.astype("int32"), affine,
seed_mask_img.header).to_filename(vol_filename)
shutil.make_archive(output_epfl_zip_file_path[:-4], 'zip',
volume_folder)
if postprocessing in ["VUMC", "ALL"]:
context.set_progress(message='Processing density map (VUMC)')
ROIs_img = nib.load(VUMC_ROIs_file_path)
volume_folder = "/root/vol_vumc"
output_vumc_zip_file_path = "/root/TrackyMcTrackface_VUMC_example.zip"
os.mkdir(volume_folder)
lengths = length(streamlines)
streamlines = streamlines[lengths > 1]
rois = ROIs_img.get_fdata().astype(int)
_, grouping = utils.connectivity_matrix(streamlines,
affine,
rois,
inclusive=True,
return_mapping=True,
mapping_as_streamlines=False)
streamlines = streamlines[grouping[(0, 1)]]
density = utils.density_map(streamlines, affine, seed_mask_img.shape)
density = scipy.ndimage.gaussian_filter(density.astype("float32"), 0.5)
log_density = np.log10(density + 1)
max_density = np.max(log_density)
for i, t in enumerate(np.arange(0, max_density, max_density / 200)):
nbr = str(i)
nbr = nbr.zfill(3)
mask = log_density >= t
vol_filename = os.path.join(
volume_folder, "vol" + nbr + "_t" + str(t) + ".nii.gz")
nib.Nifti1Image(mask.astype("int32"), affine,
seed_mask_img.header).to_filename(vol_filename)
shutil.make_archive(output_vumc_zip_file_path[:-4], 'zip',
volume_folder)
###################
# Upload the data #
###################
context.set_progress(message='Uploading results...')
# context.upload_file(fa_file_path, 'fa.nii.gz')
# context.upload_file(fod_file_path, 'fod.nii.gz')
# context.upload_file(streamlines_file_path, 'streamlines.trk')
if postprocessing in ["EPFL", "ALL"]:
context.upload_file(output_epfl_zip_file_path,
'TrackyMcTrackface_EPFL_example.zip')
if postprocessing in ["VUMC", "ALL"]:
context.upload_file(output_vumc_zip_file_path,
'TrackyMcTrackface_VUMC_example.zip')
import nibabel as nib
img = nib.load('t0.nii.gz')
data = img.get_data()
print('data.shape (%d, %d, %d, %d)' % data.shape)
mask = data[..., 0] > 50
from dipy.io import read_bvals_bvecs
bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
from dipy.core.gradients import gradient_table
gtab = gradient_table(bvals, bvecs)
from dipy.reconst.dti import TensorModel
ten = TensorModel(gtab)
tenfit = ten.fit(data,mask)
from dipy.reconst.dti import fractional_anisotropy
fa = fractional_anisotropy(tenfit.evals)
fa[np.isnan(fa)] = 0
from dipy.reconst.dti import color_fa
Rgbv = color_fa(fa, tenfit.evecs)
fa = np.clip(fa, 0,1)
#save FA to image
nib.save(nib.Nifti1Image(np.array(255*Rgbv,'uint8'),img.get_affine()),'tensor_rgb.nii.gz')
# Separate the b-values and find the indices.
bval_list, b_inds, unique_b, bvals_scaled = ozu.separate_bvals(bvals)
all_b_idx = np.where(bvals_scaled != 0)
ad_arr = np.zeros(3)
rd_arr = np.zeros(3)
for b_idx in np.arange(1, len(unique_b)):
# Separate data by b-value and create a b0 mask.
bnk_b0_inds = np.concatenate((b_inds[0], b_inds[b_idx]))
bnk_data = data[..., bnk_b0_inds]
b0_mask, mask = median_otsu(data[..., b_inds[0][0]], 4, 4)
# Fit a tensor for generating a color FA map
gtab = gradient_table(bvals[bnk_b0_inds], bvecs[:, bnk_b0_inds])
tenmodel = TensorModel(gtab)
tensorfit = tenmodel.fit(bnk_data, mask=mask)
# Now segment the corpus callosum
threshold = (0.5, 1, 0, 0.2, 0, 0.2)
CC_box = np.zeros_like(data[..., b_inds[0][0]])
# Create a bounding box in which to look for the corpus
# callosum.
mins, maxs = bounding_box(mask)
mins = np.array(mins)
maxs = np.array(maxs)
diff = (maxs - mins) // 5
bounds_min = mins + diff
bounds_max = maxs - diff
CC_box[bounds_min[0]:bounds_max[0], bounds_min[1]:bounds_max[1],
def prepare_data_for_actors(dwi_filename,
bvals_filename,
bvecs_filename,
target_template_filename,
slices_choice,
shells=None):
# Load and prepare the data
dwi_img = nib.load(dwi_filename)
dwi_data = dwi_img.get_data()
dwi_affine = dwi_img.get_affine()
bvals, bvecs = read_bvals_bvecs(bvals_filename, bvecs_filename)
target_template_img = nib.load(target_template_filename)
target_template_data = target_template_img.get_data()
target_template_affine = target_template_img.affine
mask_data = np.zeros(target_template_data.shape)
mask_data[target_template_data > 0] = 1
# Prepare mask for tensors fit
x_slice, y_slice, z_slice = slices_choice
mask_data = prepare_slices_mask(mask_data, x_slice, y_slice, z_slice)
# Extract B0
gtab = gradient_table(bvals, normalize_bvecs(bvecs), b0_threshold=10)
b0_idx = np.where(gtab.b0s_mask)[0]
mean_b0 = np.mean(dwi_data[..., b0_idx], axis=3, dtype=dwi_data.dtype)
if shells:
indices = [get_shell_indices(bvals, shell) for shell in shells]
indices = np.sort(np.hstack(indices))
if len(indices) < 1:
raise ValueError(
'There are no volumes that have the supplied b-values.')
shell_data = np.zeros((dwi_data.shape[:-1] + (len(indices), )),
dtype=dwi_data.dtype)
shell_bvecs = np.zeros((len(indices), 3))
shell_bvals = np.zeros((len(indices), ))
for i, indice in enumerate(indices):
shell_data[..., i] = dwi_data[..., indice]
shell_bvals[i] = bvals[indice]
shell_bvecs[i, :] = bvecs[indice, :]
else:
shell_data = dwi_data
shell_bvals = bvals
shell_bvecs = bvecs
# Register the DWI data to the template
transformed_dwi, transformation = register_image(
target_template_data,
target_template_affine,
mean_b0,
dwi_affine,
transformation_type='rigid',
dwi=shell_data)
# Rotate gradients
rotated_bvecs = np.dot(shell_bvecs, transformation[0:3, 0:3])
rotated_bvecs = normalize_bvecs(rotated_bvecs)
rotated_gtab = gradient_table(shell_bvals, rotated_bvecs, b0_threshold=10)
# Get tensors
tensor_model = TensorModel(rotated_gtab, fit_method='LS')
tensor_fit = tensor_model.fit(transformed_dwi, mask_data)
# Get FA
fa_map = np.clip(fractional_anisotropy(tensor_fit.evals), 0, 1)
# Get eigen vals/vecs
evals = np.zeros(target_template_data.shape + (1, ))
evals[..., 0] = tensor_fit.evals[..., 0] / np.max(tensor_fit.evals[..., 0])
evecs = np.zeros(target_template_data.shape + (1, 3))
evecs[:, :, :, 0, :] = tensor_fit.evecs[..., 0]
return fa_map, evals, evecs
data = zoom(data, zoom=scale, order=1, mode='constant')
labels = zoom(labels, zoom=scale[:3], order=1, mode='constant')
labels = labels.astype(int)
# Build Brain Mask
#bm = np.where(labels == 0, False, True)
bm = (labels != 0) * 1
mask = bm
#sphere = get_sphere('repulsion724')
sphere = get_sphere('repulsion200')
from dipy.reconst.dti import TensorModel
tensor_model = TensorModel(gtab)
t1 = time()
tensor_fit = tensor_model.fit(data, bm)
# save_nifti('bmfa.nii.gz', tensor_fit.fa, affine)
# wenlin make this change-adress name to each animal
affine = affine @ np.diag(scale)
save_nifti(outpath + 'bmfa' + runno + '.nii.gz', tensor_fit.fa, affine)
fa = tensor_fit.fa
duration1 = time() - t1
#wenlin make this change-adress name to each animal
# print('DTI duration %.3f' % (duration1,))
print(runno + ' DTI duration %.3f' % (duration1, ))
#response : 3.96154132e-04, 9.23377324e-05, 9.23377324e-05
#replace CSA with CSD
# Build Brain Mask
def nii2streamlines(imgfile, maskfile, bvals, bvecs):
import numpy as np
import nibabel as nib
import os
from dipy.reconst.dti import TensorModel
img = nib.load(imgfile)
bvals = np.genfromtxt(bvals)
bvecs = np.genfromtxt(bvecs)
if bvecs.shape[1] != 3:
bvecs = bvecs.T
from nipype.utils.filemanip import split_filename
_, prefix, _ = split_filename(imgfile)
from dipy.data import gradient_table
gtab = gradient_table(bvals, bvecs)
data = img.get_data()
affine = img.get_affine()
zooms = img.get_header().get_zooms()[:3]
new_zooms = (2., 2., 2.)
data2, affine2 = data, affine
mask = nib.load(maskfile).get_data().astype(np.bool)
tenmodel = TensorModel(gtab)
tenfit = tenmodel.fit(data2, mask)
from dipy.reconst.dti import fractional_anisotropy
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
fa_img = nib.Nifti1Image(FA, img.get_affine())
nib.save(fa_img, '%s_tensor_fa.nii.gz' % prefix)
evecs = tenfit.evecs
evec_img = nib.Nifti1Image(evecs, img.get_affine())
nib.save(evec_img, '%s_tensor_evec.nii.gz' % prefix)
from dipy.data import get_sphere
sphere = get_sphere('symmetric724')
from dipy.reconst.dti import quantize_evecs
peak_indices = quantize_evecs(tenfit.evecs, sphere.vertices)
from dipy.tracking.eudx import EuDX
eu = EuDX(FA,
peak_indices,
odf_vertices=sphere.vertices,
a_low=0.2,
seeds=10**6,
ang_thr=35)
tensor_streamlines = [streamline for streamline in eu]
hdr = nib.trackvis.empty_header()
hdr['voxel_size'] = new_zooms
hdr['voxel_order'] = 'LPS'
hdr['dim'] = data2.shape[:3]
import dipy.tracking.metrics as dmetrics
tensor_streamlines = ((sl, None, None) for sl in tensor_streamlines
if dmetrics.length(sl) > 15)
ten_sl_fname = '%s_streamline.trk' % prefix
nib.trackvis.write(ten_sl_fname,
tensor_streamlines,
hdr,
points_space='voxel')
return ten_sl_fname
def dodata(f_name, data_path):
dipy_home = pjoin(os.path.expanduser('~'), 'dipy_data')
folder = pjoin(dipy_home, data_path)
fraw = pjoin(folder, f_name + '.nii.gz')
fbval = pjoin(folder, f_name + '.bval')
fbvec = pjoin(folder, f_name + '.bvec')
flabels = pjoin(folder, f_name + '.nii-label.nii.gz')
bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
gtab = gradient_table(bvals, bvecs)
img = nib.load(fraw)
data = img.get_data()
affine = img.get_affine()
label_img = nib.load(flabels)
labels = label_img.get_data()
lap = through_label_sl.label_position(labels, labelValue=1)
dataslice = data[40:80, 20:80, lap[2][2] / 2]
#print lap[2][2]/2
#get_csd_gfa(f_name,data,gtab,dataslice)
maskdata, mask = median_otsu(data,
2,
1,
False,
vol_idx=range(10, 50),
dilate=2) #不去背景
""" get fa and tensor evecs and ODF"""
from dipy.reconst.dti import TensorModel, mean_diffusivity
tenmodel = TensorModel(gtab)
tenfit = tenmodel.fit(data, mask)
sphere = get_sphere('symmetric724')
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
np.save(os.getcwd() + '\zhibiao' + f_name + '_FA.npy', FA)
fa_img = nib.Nifti1Image(FA.astype(np.float32), affine)
nib.save(fa_img, os.getcwd() + '\zhibiao' + f_name + '_FA.nii.gz')
print('Saving "DTI_tensor_fa.nii.gz" sucessful.')
evecs_img = nib.Nifti1Image(tenfit.evecs.astype(np.float32), affine)
nib.save(evecs_img,
os.getcwd() + '\zhibiao' + f_name + '_DTI_tensor_evecs.nii.gz')
print('Saving "DTI_tensor_evecs.nii.gz" sucessful.')
MD1 = mean_diffusivity(tenfit.evals)
nib.save(nib.Nifti1Image(MD1.astype(np.float32), img.get_affine()),
os.getcwd() + '\zhibiao' + f_name + '_MD.nii.gz')
#tensor_odfs = tenmodel.fit(data[20:50, 55:85, 38:39]).odf(sphere)
#from dipy.reconst.odf import gfa
#dti_gfa=gfa(tensor_odfs)
wm_mask = (np.logical_or(FA >= 0.4,
(np.logical_and(FA >= 0.15, MD >= 0.0011))))
response = recursive_response(gtab,
data,
mask=wm_mask,
sh_order=8,
peak_thr=0.01,
init_fa=0.08,
init_trace=0.0021,
iter=8,
convergence=0.001,
parallel=False)
from dipy.reconst.csdeconv import ConstrainedSphericalDeconvModel
csd_model = ConstrainedSphericalDeconvModel(gtab, response)
#csd_fit = csd_model.fit(data)
from dipy.direction import peaks_from_model
csd_peaks = peaks_from_model(model=csd_model,
data=data,
sphere=sphere,
relative_peak_threshold=.5,
min_separation_angle=25,
parallel=False)
GFA = csd_peaks.gfa
nib.save(GFA, os.getcwd() + '\zhibiao' + f_name + '_MSD.nii.gz')
print('Saving "GFA.nii.gz" sucessful.')
from dipy.reconst.shore import ShoreModel
asm = ShoreModel(gtab)
print('Calculating...SHORE msd')
asmfit = asm.fit(data, mask)
msd = asmfit.msd()
msd[np.isnan(msd)] = 0
#print GFA[:,:,slice].T
print('Saving msd_img.png')
nib.save(msd, os.getcwd() + '\zhibiao' + f_name + '_GFA.nii.gz')
data, affine, gtab = get_train_dti(30) elif datat == 1: print 'fitting with hardi' data, affine, gtab = get_train_hardi(30) elif datat == 2: print 'fitting with dsi' data, affine, gtab = get_train_dsi(30) mask, affine = get_train_mask() data.shape mask.shape model = TensorModel(gtab) fit = model.fit(data, mask) print 'done!' fa = fit.fa slice_z = 25 Th = [0.05, 0.075, 0.1,0.15] figure(2*datat+1) imshow(fa[:, :, slice_z], interpolation='nearest') colorbar() title(mask.sum()) figure(2*datat + 2) for i in range(4):
def test_WLS_and_LS_fit():
"""
Tests the WLS and LS fitting functions to see if they returns the correct
eigenvalues and eigenvectors.
Uses data/55dir_grad.bvec as the gradient table and 3by3by56.nii
as the data.
"""
### Defining Test Voxel (avoid nibabel dependency) ###
#Recall: D = [Dxx,Dyy,Dzz,Dxy,Dxz,Dyz,log(S_0)] and D ~ 10^-4 mm^2 /s
b0 = 1000.
bvec, bval = read_bvec_file(get_data('55dir_grad.bvec'))
B = bval[1]
#Scale the eigenvalues and tensor by the B value so the units match
D = np.array([1., 1., 1., 0., 0., 1., -np.log(b0) * B]) / B
evals = np.array([2., 1., 0.]) / B
md = evals.mean()
tensor = from_lower_triangular(D)
#Design Matrix
X = dti.design_matrix(bvec, bval)
#Signals
Y = np.exp(np.dot(X, D))
assert_almost_equal(Y[0], b0)
Y.shape = (-1,) + Y.shape
gtab = grad.gradient_table(bval, bvec)
### Testing WLS Fit on Single Voxel ###
#Estimate tensor from test signals
model = TensorModel(gtab, min_signal=1e-8, fit_method='WLS')
tensor_est = model.fit(Y)
assert_equal(tensor_est.shape, Y.shape[:-1])
assert_array_almost_equal(tensor_est.evals[0], evals)
assert_array_almost_equal(tensor_est.quadratic_form[0], tensor,
err_msg="Calculation of tensor from Y does not "
"compare to analytical solution")
assert_almost_equal(tensor_est.md[0], md)
# Test that we can fit a single voxel's worth of data (a 1d array)
y = Y[0]
tensor_est = model.fit(y)
assert_equal(tensor_est.shape, tuple())
assert_array_almost_equal(tensor_est.evals, evals)
assert_array_almost_equal(tensor_est.quadratic_form, tensor)
assert_almost_equal(tensor_est.md, md)
assert_array_almost_equal(tensor_est.lower_triangular(b0), D)
# Test using fit_method='LS'
model = TensorModel(gtab, min_signal=1e-8, fit_method='LS')
tensor_est = model.fit(y)
assert_equal(tensor_est.shape, tuple())
assert_array_almost_equal(tensor_est.evals, evals)
assert_array_almost_equal(tensor_est.quadratic_form, tensor)
assert_almost_equal(tensor_est.md, md)
assert_array_almost_equal(tensor_est.lower_triangular(b0), D)
assert_array_almost_equal(tensor_est.linearity, linearity(evals))
assert_array_almost_equal(tensor_est.planarity, planarity(evals))
assert_array_almost_equal(tensor_est.sphericity, sphericity(evals))
def test_eudx_further():
""" Cause we love testin.. ;-)
"""
fimg, fbvals, fbvecs = get_fnames('small_101D')
img = ni.load(fimg)
data = img.get_data()
gtab = gradient_table(fbvals, fbvecs)
tensor_model = TensorModel(gtab)
ten = tensor_model.fit(data)
x, y, z = data.shape[:3]
seeds = np.zeros((10**4, 3))
for i in range(10**4):
rx = (x - 1) * np.random.rand()
ry = (y - 1) * np.random.rand()
rz = (z - 1) * np.random.rand()
seeds[i] = np.ascontiguousarray(np.array([rx, ry, rz]),
dtype=np.float64)
sphere = get_sphere('symmetric724')
ind = quantize_evecs(ten.evecs)
eu = EuDX(a=ten.fa,
ind=ind,
seeds=seeds,
odf_vertices=sphere.vertices,
a_low=.2)
T = [e for e in eu]
# check that there are no negative elements
for t in T:
assert_equal(np.sum(t.ravel() < 0), 0)
# Test eudx with affine
def random_affine(seeds):
affine = np.eye(4)
affine[:3, :] = np.random.random((3, 4))
seeds = np.dot(seeds, affine[:3, :3].T)
seeds += affine[:3, 3]
return affine, seeds
# Make two random affines and move seeds
affine1, seeds1 = random_affine(seeds)
affine2, seeds2 = random_affine(seeds)
# Make tracks using different affines
eu1 = EuDX(a=ten.fa,
ind=ind,
odf_vertices=sphere.vertices,
seeds=seeds1,
a_low=.2,
affine=affine1)
eu2 = EuDX(a=ten.fa,
ind=ind,
odf_vertices=sphere.vertices,
seeds=seeds2,
a_low=.2,
affine=affine2)
# Move from eu2 affine2 to affine1
eu2_to_eu1 = utils.move_streamlines(eu2,
output_space=affine1,
input_space=affine2)
# Check that the tracks are the same
for sl1, sl2 in zip(eu1, eu2_to_eu1):
assert_array_almost_equal(sl1, sl2)
def test_recursive_response_calibration():
"""
Test the recursive response calibration method.
"""
SNR = 100
S0 = 1
_, fbvals, fbvecs = get_fnames('small_64D')
bvals, bvecs = read_bvals_bvecs(fbvals, fbvecs)
sphere = default_sphere
gtab = gradient_table(bvals, bvecs)
evals = np.array([0.0015, 0.0003, 0.0003])
evecs = np.array([[0, 1, 0], [0, 0, 1], [1, 0, 0]]).T
mevals = np.array(([0.0015, 0.0003, 0.0003], [0.0015, 0.0003, 0.0003]))
angles = [(0, 0), (90, 0)]
where_dwi = lazy_index(~gtab.b0s_mask)
S_cross, _ = multi_tensor(gtab,
mevals,
S0,
angles=angles,
fractions=[50, 50],
snr=SNR)
S_single = single_tensor(gtab, S0, evals, evecs, snr=SNR)
data = np.concatenate((np.tile(S_cross, (8, 1)), np.tile(S_single,
(2, 1))),
axis=0)
odf_gt_cross = multi_tensor_odf(sphere.vertices, mevals, angles, [50, 50])
odf_gt_single = single_tensor_odf(sphere.vertices, evals, evecs)
response = recursive_response(gtab,
data,
mask=None,
sh_order=8,
peak_thr=0.01,
init_fa=0.05,
init_trace=0.0021,
iter=8,
convergence=0.001,
parallel=False)
csd = ConstrainedSphericalDeconvModel(gtab, response)
csd_fit = csd.fit(data)
assert_equal(np.all(csd_fit.shm_coeff[:, 0] >= 0), True)
fodf = csd_fit.odf(sphere)
directions_gt_single, _, _ = peak_directions(odf_gt_single, sphere)
directions_gt_cross, _, _ = peak_directions(odf_gt_cross, sphere)
directions_single, _, _ = peak_directions(fodf[8, :], sphere)
directions_cross, _, _ = peak_directions(fodf[0, :], sphere)
ang_sim = angular_similarity(directions_cross, directions_gt_cross)
assert_equal(ang_sim > 1.9, True)
assert_equal(directions_cross.shape[0], 2)
assert_equal(directions_gt_cross.shape[0], 2)
ang_sim = angular_similarity(directions_single, directions_gt_single)
assert_equal(ang_sim > 0.9, True)
assert_equal(directions_single.shape[0], 1)
assert_equal(directions_gt_single.shape[0], 1)
with warnings.catch_warnings(record=True) as w:
sphere = Sphere(xyz=gtab.gradients[where_dwi])
npt.assert_equal(len(w), 1)
npt.assert_(issubclass(w[0].category, UserWarning))
npt.assert_("Vertices are not on the unit sphere" in str(w[0].message))
sf = response.on_sphere(sphere)
S = np.concatenate(([response.S0], sf))
tenmodel = TensorModel(gtab, min_signal=0.001)
tenfit = tenmodel.fit(S)
FA = fractional_anisotropy(tenfit.evals)
FA_gt = fractional_anisotropy(evals)
assert_almost_equal(FA, FA_gt, 1)
def main():
parser = _build_args_parser()
args = parser.parse_args()
if not args.not_all:
args.fa = args.fa or 'fa.nii.gz'
args.ga = args.ga or 'ga.nii.gz'
args.rgb = args.rgb or 'rgb.nii.gz'
args.md = args.md or 'md.nii.gz'
args.ad = args.ad or 'ad.nii.gz'
args.rd = args.rd or 'rd.nii.gz'
args.mode = args.mode or 'mode.nii.gz'
args.norm = args.norm or 'tensor_norm.nii.gz'
args.tensor = args.tensor or 'tensor.nii.gz'
args.evecs = args.evecs or 'tensor_evecs.nii.gz'
args.evals = args.evals or 'tensor_evals.nii.gz'
args.residual = args.residual or 'dti_residual.nii.gz'
args.p_i_signal =\
args.p_i_signal or 'physically_implausible_signals_mask.nii.gz'
args.pulsation = args.pulsation or 'pulsation_and_misalignment.nii.gz'
outputs = [
args.fa, args.ga, args.rgb, args.md, args.ad, args.rd, args.mode,
args.norm, args.tensor, args.evecs, args.evals, args.residual,
args.p_i_signal, args.pulsation
]
if args.not_all and not any(outputs):
parser.error('When using --not_all, you need to specify at least ' +
'one metric to output.')
assert_inputs_exist(parser, [args.input, args.bvals, args.bvecs],
[args.mask])
assert_outputs_exist(parser, args, outputs)
img = nib.load(args.input)
data = img.get_data()
affine = img.get_affine()
if args.mask is None:
mask = None
else:
mask = nib.load(args.mask).get_data().astype(np.bool)
# Validate bvals and bvecs
logging.info('Tensor estimation with the %s method...', args.method)
bvals, bvecs = read_bvals_bvecs(args.bvals, args.bvecs)
if not is_normalized_bvecs(bvecs):
logging.warning('Your b-vectors do not seem normalized...')
bvecs = normalize_bvecs(bvecs)
check_b0_threshold(args, bvals.min())
gtab = gradient_table(bvals, bvecs, b0_threshold=bvals.min())
# Get tensors
if args.method == 'restore':
sigma = ne.estimate_sigma(data)
tenmodel = TensorModel(gtab,
fit_method=args.method,
sigma=sigma,
min_signal=_get_min_nonzero_signal(data))
else:
tenmodel = TensorModel(gtab,
fit_method=args.method,
min_signal=_get_min_nonzero_signal(data))
tenfit = tenmodel.fit(data, mask)
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
FA = np.clip(FA, 0, 1)
if args.tensor:
# Get the Tensor values and format them for visualisation
# in the Fibernavigator.
tensor_vals = lower_triangular(tenfit.quadratic_form)
correct_order = [0, 1, 3, 2, 4, 5]
tensor_vals_reordered = tensor_vals[..., correct_order]
fiber_tensors = nib.Nifti1Image(
tensor_vals_reordered.astype(np.float32), affine)
nib.save(fiber_tensors, args.tensor)
if args.fa:
fa_img = nib.Nifti1Image(FA.astype(np.float32), affine)
nib.save(fa_img, args.fa)
if args.ga:
GA = geodesic_anisotropy(tenfit.evals)
GA[np.isnan(GA)] = 0
ga_img = nib.Nifti1Image(GA.astype(np.float32), affine)
nib.save(ga_img, args.ga)
if args.rgb:
RGB = color_fa(FA, tenfit.evecs)
rgb_img = nib.Nifti1Image(np.array(255 * RGB, 'uint8'), affine)
nib.save(rgb_img, args.rgb)
if args.md:
MD = mean_diffusivity(tenfit.evals)
md_img = nib.Nifti1Image(MD.astype(np.float32), affine)
nib.save(md_img, args.md)
if args.ad:
AD = axial_diffusivity(tenfit.evals)
ad_img = nib.Nifti1Image(AD.astype(np.float32), affine)
nib.save(ad_img, args.ad)
if args.rd:
RD = radial_diffusivity(tenfit.evals)
rd_img = nib.Nifti1Image(RD.astype(np.float32), affine)
nib.save(rd_img, args.rd)
if args.mode:
# Compute tensor mode
inter_mode = dipy_mode(tenfit.quadratic_form)
# Since the mode computation can generate NANs when not masked,
# we need to remove them.
non_nan_indices = np.isfinite(inter_mode)
mode = np.zeros(inter_mode.shape)
mode[non_nan_indices] = inter_mode[non_nan_indices]
mode_img = nib.Nifti1Image(mode.astype(np.float32), affine)
nib.save(mode_img, args.mode)
if args.norm:
NORM = norm(tenfit.quadratic_form)
norm_img = nib.Nifti1Image(NORM.astype(np.float32), affine)
nib.save(norm_img, args.norm)
if args.evecs:
evecs = tenfit.evecs.astype(np.float32)
evecs_img = nib.Nifti1Image(evecs, affine)
nib.save(evecs_img, args.evecs)
# save individual e-vectors also
e1_img = nib.Nifti1Image(evecs[..., 0], affine)
e2_img = nib.Nifti1Image(evecs[..., 1], affine)
e3_img = nib.Nifti1Image(evecs[..., 2], affine)
nib.save(e1_img, add_filename_suffix(args.evecs, '_v1'))
nib.save(e2_img, add_filename_suffix(args.evecs, '_v2'))
nib.save(e3_img, add_filename_suffix(args.evecs, '_v3'))
if args.evals:
evals = tenfit.evals.astype(np.float32)
evals_img = nib.Nifti1Image(evals, affine)
nib.save(evals_img, args.evals)
# save individual e-values also
e1_img = nib.Nifti1Image(evals[..., 0], affine)
e2_img = nib.Nifti1Image(evals[..., 1], affine)
e3_img = nib.Nifti1Image(evals[..., 2], affine)
nib.save(e1_img, add_filename_suffix(args.evals, '_e1'))
nib.save(e2_img, add_filename_suffix(args.evals, '_e2'))
nib.save(e3_img, add_filename_suffix(args.evals, '_e3'))
if args.p_i_signal:
S0 = np.mean(data[..., gtab.b0s_mask], axis=-1, keepdims=True)
DWI = data[..., ~gtab.b0s_mask]
pis_mask = np.max(S0 < DWI, axis=-1)
if args.mask is not None:
pis_mask *= mask
pis_img = nib.Nifti1Image(pis_mask.astype(np.int16), affine)
nib.save(pis_img, args.p_i_signal)
if args.pulsation:
STD = np.std(data[..., ~gtab.b0s_mask], axis=-1)
if args.mask is not None:
STD *= mask
std_img = nib.Nifti1Image(STD.astype(np.float32), affine)
nib.save(std_img, add_filename_suffix(args.pulsation, '_std_dwi'))
if np.sum(gtab.b0s_mask) <= 1:
logger.info('Not enough b=0 images to output standard '
'deviation map')
else:
if len(np.where(gtab.b0s_mask)) == 2:
logger.info('Only two b=0 images. Be careful with the '
'interpretation of this std map')
STD = np.std(data[..., gtab.b0s_mask], axis=-1)
if args.mask is not None:
STD *= mask
std_img = nib.Nifti1Image(STD.astype(np.float32), affine)
nib.save(std_img, add_filename_suffix(args.pulsation, '_std_b0'))
if args.residual:
if args.mask is None:
logger.info("Outlier detection will not be performed, since no "
"mask was provided.")
S0 = np.mean(data[..., gtab.b0s_mask], axis=-1)
data_p = tenfit.predict(gtab, S0)
R = np.mean(np.abs(data_p[..., ~gtab.b0s_mask] -
data[..., ~gtab.b0s_mask]),
axis=-1)
if args.mask is not None:
R *= mask
R_img = nib.Nifti1Image(R.astype(np.float32), affine)
nib.save(R_img, args.residual)
R_k = np.zeros(data.shape[-1]) # mean residual per DWI
std = np.zeros(data.shape[-1]) # std residual per DWI
q1 = np.zeros(data.shape[-1]) # first quartile
q3 = np.zeros(data.shape[-1]) # third quartile
iqr = np.zeros(data.shape[-1]) # interquartile
for i in range(data.shape[-1]):
x = np.abs(data_p[..., i] - data[..., i])[mask]
R_k[i] = np.mean(x)
std[i] = np.std(x)
q3[i], q1[i] = np.percentile(x, [75, 25])
iqr[i] = q3[i] - q1[i]
# Outliers are observations that fall below Q1 - 1.5(IQR) or
# above Q3 + 1.5(IQR) We check if a volume is an outlier only if
# we have a mask, else we are biased.
if args.mask is not None and R_k[i] < (q1[i] - 1.5 * iqr[i]) \
or R_k[i] > (q3[i] + 1.5 * iqr[i]):
logger.warning(
'WARNING: Diffusion-Weighted Image i=%s is an '
'outlier', i)
residual_basename, _ = split_name_with_nii(args.residual)
res_stats_basename = residual_basename + ".npy"
np.save(add_filename_suffix(res_stats_basename, "_mean_residuals"),
R_k)
np.save(add_filename_suffix(res_stats_basename, "_q1_residuals"), q1)
np.save(add_filename_suffix(res_stats_basename, "_q3_residuals"), q3)
np.save(add_filename_suffix(res_stats_basename, "_iqr_residuals"), iqr)
np.save(add_filename_suffix(res_stats_basename, "_std_residuals"), std)
# To do: I would like to have an error bar with q1 and q3.
# Now, q1 acts as a std
dwi = np.arange(R_k[~gtab.b0s_mask].shape[0])
plt.bar(dwi,
R_k[~gtab.b0s_mask],
0.75,
color='y',
yerr=q1[~gtab.b0s_mask])
plt.xlabel('DW image')
plt.ylabel('Mean residuals +- q1')
plt.title('Residuals')
plt.savefig(residual_basename + '_residuals_stats.png')
def tens_mod_fa_est(gtab_file, dwi_file, B0_mask):
"""
Estimate a tensor FA image to use for registrations.
Parameters
----------
gtab_file : str
File path to pickled DiPy gradient table object.
dwi_file : str
File path to diffusion weighted image.
B0_mask : str
File path to B0 brain mask.
Returns
-------
fa_path : str
File path to FA Nifti1Image.
B0_mask : str
File path to B0 brain mask Nifti1Image.
gtab_file : str
File path to pickled DiPy gradient table object.
dwi_file : str
File path to diffusion weighted Nifti1Image.
fa_md_path : str
File path to FA/MD mask Nifti1Image.
"""
import os
from dipy.io import load_pickle
from dipy.reconst.dti import TensorModel
from dipy.reconst.dti import fractional_anisotropy, mean_diffusivity
gtab = load_pickle(gtab_file)
data = nib.load(dwi_file, mmap=False).get_fdata()
print("Generating tensor FA image to use for registrations...")
nodif_B0_img = nib.load(B0_mask, mmap=False)
nodif_B0_mask_data = nodif_B0_img.get_fdata().astype("bool")
model = TensorModel(gtab)
mod = model.fit(data, nodif_B0_mask_data)
FA = fractional_anisotropy(mod.evals)
# MD = mean_diffusivity(mod.evals)
# FA_MD = np.logical_or(
# FA >= 0.2, (np.logical_and(
# FA >= 0.08, MD >= 0.0011)))
# FA_MD[np.isnan(FA_MD)] = 0
FA = np.nan_to_num(np.asarray(FA.astype('float32')))
fa_path = f"{os.path.dirname(B0_mask)}{'/tensor_fa.nii.gz'}"
nib.save(
nib.Nifti1Image(
FA,
nodif_B0_img.affine),
fa_path)
# md_path = f"{os.path.dirname(B0_mask)}{'/tensor_md.nii.gz'}"
# nib.save(
# nib.Nifti1Image(
# MD.astype(
# np.float32),
# nodif_B0_img.affine),
# md_path)
nodif_B0_img.uncache()
del FA
return fa_path, B0_mask, gtab_file, dwi_file
def dwi_dipy_run(dwi_dir,
node_size,
dir_path,
conn_model,
parc,
atlas_select,
network,
wm_mask=None):
from dipy.reconst.dti import TensorModel, quantize_evecs
from dipy.reconst.csdeconv import ConstrainedSphericalDeconvModel, recursive_response
from dipy.tracking.local import LocalTracking, ActTissueClassifier
from dipy.tracking import utils
from dipy.direction import peaks_from_model
from dipy.tracking.eudx import EuDX
from dipy.data import get_sphere, default_sphere
from dipy.core.gradients import gradient_table
from dipy.io import read_bvals_bvecs
from dipy.tracking.streamline import Streamlines
from dipy.direction import ProbabilisticDirectionGetter, ClosestPeakDirectionGetter, BootDirectionGetter
from nibabel.streamlines import save as save_trk
from nibabel.streamlines import Tractogram
##
dwi_dir = '/Users/PSYC-dap3463/Downloads/bedpostx_s002'
img_pve_csf = nib.load(
'/Users/PSYC-dap3463/Downloads/002_all/tmp/reg_a/t1w_vent_csf_diff_dwi.nii.gz'
)
img_pve_wm = nib.load(
'/Users/PSYC-dap3463/Downloads/002_all/tmp/reg_a/t1w_wm_in_dwi_bin.nii.gz'
)
img_pve_gm = nib.load(
'/Users/PSYC-dap3463/Downloads/002_all/tmp/reg_a/t1w_gm_mask_dwi.nii.gz'
)
labels_img = nib.load(
'/Users/PSYC-dap3463/Downloads/002_all/tmp/reg_a/dwi_aligned_atlas.nii.gz'
)
num_total_samples = 10000
tracking_method = 'boot' # Options are 'boot', 'prob', 'peaks', 'closest'
procmem = [2, 4]
##
if parc is True:
node_size = 'parc'
dwi_img = "%s%s" % (dwi_dir, '/dwi.nii.gz')
nodif_brain_mask_path = "%s%s" % (dwi_dir, '/nodif_brain_mask.nii.gz')
bvals = "%s%s" % (dwi_dir, '/bval')
bvecs = "%s%s" % (dwi_dir, '/bvec')
dwi_img = nib.load(dwi_img)
data = dwi_img.get_data()
[bvals, bvecs] = read_bvals_bvecs(bvals, bvecs)
gtab = gradient_table(bvals, bvecs)
gtab.b0_threshold = min(bvals)
sphere = get_sphere('symmetric724')
# Loads mask and ensures it's a true binary mask
mask_img = nib.load(nodif_brain_mask_path)
mask = mask_img.get_data()
mask = mask > 0
# Fit a basic tensor model first
model = TensorModel(gtab)
ten = model.fit(data, mask)
fa = ten.fa
# Tractography
if conn_model == 'csd':
print('Tracking with csd model...')
elif conn_model == 'tensor':
print('Tracking with tensor model...')
else:
raise RuntimeError("%s%s" % (conn_model, ' is not a valid model.'))
# Combine seed counts from voxel with seed counts total
wm_mask_data = img_pve_wm.get_data()
wm_mask_data[0, :, :] = False
wm_mask_data[:, 0, :] = False
wm_mask_data[:, :, 0] = False
seeds = utils.seeds_from_mask(wm_mask_data,
density=1,
affine=dwi_img.get_affine())
seeds_rnd = utils.random_seeds_from_mask(ten.fa > 0.02,
seeds_count=num_total_samples,
seed_count_per_voxel=True)
seeds_all = np.vstack([seeds, seeds_rnd])
# Load tissue maps and prepare tissue classifier (Anatomically-Constrained Tractography (ACT))
background = np.ones(img_pve_gm.shape)
background[(img_pve_gm.get_data() + img_pve_wm.get_data() +
img_pve_csf.get_data()) > 0] = 0
include_map = img_pve_gm.get_data()
include_map[background > 0] = 1
exclude_map = img_pve_csf.get_data()
act_classifier = ActTissueClassifier(include_map, exclude_map)
if conn_model == 'tensor':
ind = quantize_evecs(ten.evecs, sphere.vertices)
streamline_generator = EuDX(a=fa,
ind=ind,
seeds=seeds_all,
odf_vertices=sphere.vertices,
a_low=0.05,
step_sz=.5)
elif conn_model == 'csd':
print('Tracking with CSD model...')
response = recursive_response(
gtab,
data,
mask=img_pve_wm.get_data().astype('bool'),
sh_order=8,
peak_thr=0.01,
init_fa=0.05,
init_trace=0.0021,
iter=8,
convergence=0.001,
parallel=True)
csd_model = ConstrainedSphericalDeconvModel(gtab, response)
if tracking_method == 'boot':
dg = BootDirectionGetter.from_data(data,
csd_model,
max_angle=30.,
sphere=default_sphere)
elif tracking_method == 'prob':
try:
print(
'First attempting to build the direction getter directly from the spherical harmonic representation of the FOD...'
)
csd_fit = csd_model.fit(
data, mask=img_pve_wm.get_data().astype('bool'))
dg = ProbabilisticDirectionGetter.from_shcoeff(
csd_fit.shm_coeff, max_angle=30., sphere=default_sphere)
except:
print(
'Sphereical harmonic not available for this model. Using peaks_from_model to represent the ODF of the model on a spherical harmonic basis instead...'
)
peaks = peaks_from_model(
csd_model,
data,
default_sphere,
.5,
25,
mask=img_pve_wm.get_data().astype('bool'),
return_sh=True,
parallel=True,
nbr_processes=procmem[0])
dg = ProbabilisticDirectionGetter.from_shcoeff(
peaks.shm_coeff, max_angle=30., sphere=default_sphere)
elif tracking_method == 'peaks':
dg = peaks_from_model(model=csd_model,
data=data,
sphere=default_sphere,
relative_peak_threshold=.5,
min_separation_angle=25,
mask=img_pve_wm.get_data().astype('bool'),
parallel=True,
nbr_processes=procmem[0])
elif tracking_method == 'closest':
csd_fit = csd_model.fit(data,
mask=img_pve_wm.get_data().astype('bool'))
pmf = csd_fit.odf(default_sphere).clip(min=0)
dg = ClosestPeakDirectionGetter.from_pmf(pmf,
max_angle=30.,
sphere=default_sphere)
streamline_generator = LocalTracking(dg,
act_classifier,
seeds_all,
affine=dwi_img.affine,
step_size=0.5)
del dg
try:
del csd_fit
except:
pass
try:
del response
except:
pass
try:
del csd_model
except:
pass
streamlines = Streamlines(streamline_generator, buffer_size=512)
save_trk(Tractogram(streamlines, affine_to_rasmm=dwi_img.affine),
'prob_streamlines.trk')
tracks = [sl for sl in streamlines if len(sl) > 1]
labels_data = labels_img.get_data().astype('int')
labels_affine = labels_img.affine
conn_matrix, grouping = utils.connectivity_matrix(
tracks,
labels_data,
affine=labels_affine,
return_mapping=True,
mapping_as_streamlines=True,
symmetric=True)
conn_matrix[:3, :] = 0
conn_matrix[:, :3] = 0
return conn_matrix
def _run_interface(self, runtime):
from scipy.special import gamma
from dipy.reconst.dti import TensorModel
import gc
img = nb.load(self.inputs.in_file)
hdr = img.get_header().copy()
affine = img.get_affine()
data = img.get_data()
gtab = self._get_gradient_table()
if isdefined(self.inputs.in_mask):
msk = nb.load(self.inputs.in_mask).get_data().astype(np.uint8)
else:
msk = np.ones(data.shape[:3], dtype=np.uint8)
try_b0 = True
if isdefined(self.inputs.noise_mask):
noise_msk = nb.load(self.inputs.noise_mask).get_data().reshape(-1)
noise_msk[noise_msk > 0.5] = 1
noise_msk[noise_msk < 1.0] = 0
noise_msk = noise_msk.astype(np.uint8)
try_b0 = False
elif np.all(data[msk == 0, 0] == 0):
IFLOGGER.info('Input data are masked.')
noise_msk = msk.reshape(-1).astype(np.uint8)
else:
noise_msk = (1 - msk).reshape(-1).astype(np.uint8)
nb0 = np.sum(gtab.b0s_mask)
dsample = data.reshape(-1, data.shape[-1])
if try_b0 and (nb0 > 1):
noise_data = dsample.take(np.where(gtab.b0s_mask),
axis=-1)[noise_msk == 0, ...]
n = nb0
else:
nodiff = np.where(~gtab.b0s_mask)
nodiffidx = nodiff[0].tolist()
n = 20 if len(nodiffidx) >= 20 else len(nodiffidx)
idxs = np.random.choice(nodiffidx, size=n, replace=False)
noise_data = dsample.take(idxs, axis=-1)[noise_msk == 1, ...]
# Estimate sigma required by RESTORE
mean_std = np.median(noise_data.std(-1))
try:
bias = (1. - np.sqrt(2. / (n - 1)) * (gamma(n / 2.) / gamma(
(n - 1) / 2.)))
except:
bias = .0
pass
sigma = mean_std * (1 + bias)
if sigma == 0:
IFLOGGER.warn(
('Noise std is 0.0, looks like data was masked and noise'
' cannot be estimated correctly. Using default tensor '
'model instead of RESTORE.'))
dti = TensorModel(gtab)
else:
IFLOGGER.info(('Performing RESTORE with noise std=%.4f.') % sigma)
dti = TensorModel(gtab, fit_method='RESTORE', sigma=sigma)
try:
fit_restore = dti.fit(data, msk)
except TypeError:
dti = TensorModel(gtab)
fit_restore = dti.fit(data, msk)
hdr.set_data_dtype(np.float32)
hdr['data_type'] = 16
for k in self._outputs().get():
scalar = getattr(fit_restore, k)
hdr.set_data_shape(np.shape(scalar))
nb.Nifti1Image(scalar.astype(np.float32), affine,
hdr).to_filename(self._gen_filename(k))
return runtime
from conn_mat import connectivity_matrix
from dipy.io.pickles import save_pickle, load_pickle
from time import time
threshold = 0.75
from dipy.data import get_sphere
sphere = get_sphere('symmetric724')
dname = 'SNR20/'
if __name__ == '__main__':
data, affine, gtab = get_test_hardi(snr=20, denoised=0)
mask = get_test_mask()
tenmodel = TensorModel(gtab)
tenfit = tenmodel.fit(data, mask)
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
nib.save(nib.Nifti1Image(FA.astype('float32'), affine),
'FA.nii.gz')
for i in range(27) :
print 'White matter bundle: ', i
wm_mask = get_test_wm_mask(i)
print(FA[wm_mask].max())
indicesAniso = np.where(np.logical_and(FA > threshold, wm_mask))
print ' Response function'
S0s = data[indicesAniso][:, np.nonzero(gtab.b0s_mask)[0]]
S0 = np.mean(S0s)
if S0 == 0 :
def main():
parser = _build_args_parser()
args = parser.parse_args()
img = nib.load(args.input)
data = img.get_fdata()
print('\ndata shape ({}, {}, {}, {})'.format(data.shape[0], data.shape[1], data.shape[2], data.shape[3]))
print('total voxels {}'.format(np.prod(data.shape[:3])))
# remove negatives
print('\ncliping negative ({} voxels, {:.2f} % of total)'.format((data<0).sum(),100*(data<0).sum()/float(np.prod(data.shape[:3]))))
data = np.clip(data, 0, np.inf)
affine = img.affine
if args.mask is None:
mask = None
masksum = np.prod(data.shape[:3])
else:
mask = nib.load(args.mask).get_data().astype(np.bool)
masksum = mask.sum()
print('\nMask has {} voxels, {:.2f} % of total'.format(masksum,100*masksum/float(np.prod(data.shape[:3]))))
# Validate bvals and bvecs
bvals, bvecs = read_bvals_bvecs(args.bvals, args.bvecs)
if not is_normalized_bvecs(bvecs):
print('Your b-vectors do not seem normalized...')
bvecs = normalize_bvecs(bvecs)
# detect unique b-shell and assign shell id to each volume
# sort bvals to get monotone increasing bvalue
bvals_argsort = np.argsort(bvals)
bvals_sorted = bvals[bvals_argsort]
b_shell_threshold = 25.
unique_bvalues = []
shell_idx = []
unique_bvalues.append(bvals_sorted[0])
shell_idx.append(0)
for newb in bvals_sorted[1:]:
# check if volume is in existing shell
done = False
for i,b in enumerate(unique_bvalues):
if (newb - b_shell_threshold < b) and (newb + b_shell_threshold > b):
shell_idx.append(i)
done = True
if not done:
unique_bvalues.append(newb)
shell_idx.append(i+1)
unique_bvalues = np.array(unique_bvalues)
# un-sort shells
shells = np.zeros_like(bvals)
shells[bvals_argsort] = shell_idx
print('\nWe have {} shells'.format(len(unique_bvalues)))
print('with b-values {}\n'.format(unique_bvalues))
for i in range(len(unique_bvalues)):
shell_b = bvals[shells==i]
print('shell {}: n = {}, min/max {} {}'.format(i, len(shell_b), shell_b.min(), shell_b.max()))
# Get tensors
method = 'WLS'
min_signal = 1e-16
print('\nUsing fitting method {}'.format(method))
# print('Using minimum signal = {}'.format(min_signal)
b0_thr = bvals.min() + 10
print('\nassuming existence of b0 (thr = {})\n'.format(b0_thr))
fas = []
mds = []
lams_max = []
lams_min = []
delta_S = []
raw_signal = []
for i in range(len(unique_bvalues)-1):
# max_shell = i+1
print('fitting using {} th shells (bmax = {})'.format(i+2, bvals[shells==i+1].max()))
# restricted gtab
# gtab = gradient_table(bvals[shells <= i+1], bvecs[shells <= i+1], b0_threshold=b0_thr)
gtab = gradient_table(bvals[np.logical_or(shells == i+1, shells == 0)], bvecs[np.logical_or(shells == i+1, shells == 0)], b0_threshold=b0_thr)
tenmodel = TensorModel(gtab, fit_method=method, min_signal=min_signal)
tenfit = tenmodel.fit(data[..., np.logical_or(shells == i+1, shells == 0)], mask)
raw_signal.append(data[..., np.logical_or(shells == i+1, shells == 0)][mask].mean(axis=1))
evalmax = np.max(tenfit.evals, axis=3)
evalmin = np.min(tenfit.evals, axis=3)
evalmax[np.isnan(evalmax)] = 0
evalmin[np.isnan(evalmin)] = 0
evalmax[np.isinf(evalmax)] = 0
evalmin[np.isinf(evalmin)] = 0
weird_contrast = np.exp(-unique_bvalues[i+1]*evalmin) - np.exp(-unique_bvalues[i+1]*evalmax)
mds.append(tenfit.md[mask])
fas.append(tenfit.fa[mask])
lams_max.append(evalmax[mask])
lams_min.append(evalmin[mask])
delta_S.append(weird_contrast[mask])
bmaxs = np.array([bvals[shells==i+1].max() for i in range(len(unique_bvalues)-1)])
names = ['FA',
'MD',
'eval_max',
'eval_min',
'delta_S',
'eval_max_minus_eval_min',
'raw_signal']
units = ['a.u.',
'mm^2/s',
'mm^2/s',
'mm^2/s',
'contrast (a.u.)',
'mm^2/s',
'raw signal (a.u.)']
datas = [np.array(fas).mean(axis=1),
np.array(mds).mean(axis=1),
np.array(lams_max).mean(axis=1),
np.array(lams_min).mean(axis=1),
np.array(delta_S).mean(axis=1),
(np.array(lams_max)-np.array(lams_min)).mean(axis=1),
np.array(raw_signal).mean(axis=1)]
for i in range(len(names)):
plt.figure()
plt.plot(bmaxs, datas[i])
plt.title(names[i])
plt.xlabel('bval (s/mm^2)')
plt.ylabel(units[i])
plt.savefig('./roi_plot_'+names[i]+'.png', dpi=150)
plt.show()
