def test_multivox_dsi():
data, gtab = dsi_voxels()
DS = DiffusionSpectrumModel(gtab)
get_sphere('symmetric724')
DSfit = DS.fit(data)
PDF = DSfit.pdf()
assert_equal(data.shape[:-1] + (17, 17, 17), PDF.shape)
assert_equal(np.alltrue(np.isreal(PDF)), True)
def gqi(training, category, snr, denoised, odeconv, tv, method, weight=0.1, sl=3.):
data, affine, gtab, mask, evals, S0, prefix = prepare(training,
category,
snr,
denoised,
odeconv,
tv,
method)
model = GeneralizedQSamplingModel(gtab,
method='gqi2',
sampling_length=sl,
normalize_peaks=False)
fit = model.fit(data, mask)
sphere = get_sphere('symmetric724')
odf = fit.odf(sphere)
if odeconv == True:
odf_sh = sf_to_sh(odf, sphere, sh_order=8,
basis_type='mrtrix')
# # nib.save(nib.Nifti1Image(odf_sh, affine), model_tag + 'odf_sh.nii.gz')
reg_sphere = get_sphere('symmetric724')
fodf_sh = odf_sh_to_sharp(odf_sh,
reg_sphere, basis='mrtrix', ratio=3.8 / 16.6,
sh_order=8, Lambda=1., tau=1.)
# # nib.save(nib.Nifti1Image(odf_sh, affine), model_tag + 'fodf_sh.nii.gz')
fodf_sh[np.isnan(fodf_sh)]=0
r, theta, phi = cart2sphere(sphere.x, sphere.y, sphere.z)
B_regul, m, n = real_sph_harm_mrtrix(8, theta[:, None], phi[:, None])
fodf = np.dot(fodf_sh, B_regul.T)
odf = fodf
if tv == True:
odf = tv_denoise_4d(odf, weight=weight)
save_odfs_peaks(training, odf, affine, sphere, dres, prefix)
def test_diffusivities():
psphere = get_sphere('symmetric362')
bvecs = np.concatenate(([[0, 0, 0]], psphere.vertices))
bvals = np.zeros(len(bvecs)) + 1000
bvals[0] = 0
gtab = grad.gradient_table(bvals, bvecs)
mevals = np.array(([0.0015, 0.0003, 0.0001], [0.0015, 0.0003, 0.0003]))
mevecs = [np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]),
np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]])]
S = single_tensor(gtab, 100, mevals[0], mevecs[0], snr=None)
dm = dti.TensorModel(gtab, 'LS')
dmfit = dm.fit(S)
md = mean_diffusivity(dmfit.evals)
Trace = trace(dmfit.evals)
rd = radial_diffusivity(dmfit.evals)
ad = axial_diffusivity(dmfit.evals)
lin = linearity(dmfit.evals)
plan = planarity(dmfit.evals)
spher = sphericity(dmfit.evals)
assert_almost_equal(md, (0.0015 + 0.0003 + 0.0001) / 3)
assert_almost_equal(Trace, (0.0015 + 0.0003 + 0.0001))
assert_almost_equal(ad, 0.0015)
assert_almost_equal(rd, (0.0003 + 0.0001) / 2)
assert_almost_equal(lin, (0.0015 - 0.0003)/Trace)
assert_almost_equal(plan, 2 * (0.0003 - 0.0001)/Trace)
assert_almost_equal(spher, (3 * 0.0001)/Trace)
def sdt(training, category, snr, denoised, odeconv, tv, method, weight=0.1):
data, affine, gtab, mask, evals, S0, prefix = prepare(training,
category,
snr,
denoised,
odeconv,
tv,
method)
if category == 'dti':
csdt_model = ConstrainedSDTModel(gtab, ratio = evals[1] / evals[0], sh_order=6)
if category == 'hardi':
csdt_model = ConstrainedSDTModel(gtab, ratio = evals[1] / evals[0], sh_order=8)
csdt_fit = csdt_model.fit(data, mask)
sphere = get_sphere('symmetric724')
odf = csdt_fit.odf(sphere)
if tv == True:
odf = tv_denoise_4d(odf, weight=weight)
save_odfs_peaks(training, odf, affine, sphere, dres, prefix)
def __init__(self, gtab, evals=[0.001, 0, 0], sphere=None):
"""
Initialize a signal maker
Parameters
----------
gtab : GradientTable class instance
The gradient table on which the signal is calculated.
evals : list of 3 items
The eigenvalues of the canonical tensor to use in calculating the
signal.
n_points : `dipy.core.Sphere` class instance
The discrete sphere to use as an approximation for the continuous
sphere on which the signal is represented. If integer - we will use
an instance of one of the symmetric spheres cached in
`dps.get_sphere`. If a 'dipy.core.Sphere' class instance is
provided, we will use this object. Default: the :mod:`dipy.data`
symmetric sphere with 724 vertices
"""
if sphere is None:
self.sphere = dpd.get_sphere('symmetric724')
else:
self.sphere = sphere
self.gtab = gtab
self.evals = evals
# Initialize an empty dict to fill with signals for each of the sphere
# vertices:
self.signal = np.empty((self.sphere.vertices.shape[0],
np.sum(~gtab.b0s_mask)))
# We'll need to keep track of what we've already calculated:
self._calculated = []
def _run_interface(self, runtime):
from dipy.reconst import shm
from dipy.data import get_sphere
from dipy.reconst.peaks import peaks_from_model
gtab = self._get_gradient_table()
img = nb.load(self.inputs.in_file)
data = img.get_data()
affine = img.affine
mask = None
if isdefined(self.inputs.mask_file):
mask = nb.load(self.inputs.mask_file).get_data()
# Fit it
model = shm.QballModel(gtab, 8)
sphere = get_sphere('symmetric724')
peaks = peaks_from_model(
model=model,
data=data,
relative_peak_threshold=.5,
min_separation_angle=25,
sphere=sphere,
mask=mask)
apm = shm.anisotropic_power(peaks.shm_coeff)
out_file = self._gen_filename('apm')
nb.Nifti1Image(apm.astype("float32"), affine).to_filename(out_file)
IFLOGGER.info('APM qball image saved as %s', out_file)
return runtime
def test_sfm():
fdata, fbvals, fbvecs = dpd.get_data()
data = nib.load(fdata).get_data()
gtab = grad.gradient_table(fbvals, fbvecs)
sfmodel = sfm.SparseFascicleModel(gtab)
sffit1 = sfmodel.fit(data[0, 0, 0])
sphere = dpd.get_sphere("symmetric642")
odf1 = sffit1.odf(sphere)
pred1 = sffit1.predict(gtab)
mask = np.ones(data.shape[:-1])
sffit2 = sfmodel.fit(data, mask)
pred2 = sffit2.predict(gtab)
odf2 = sffit2.odf(sphere)
sffit3 = sfmodel.fit(data)
pred3 = sffit3.predict(gtab)
odf3 = sffit3.odf(sphere)
npt.assert_almost_equal(pred3, pred2, decimal=2)
npt.assert_almost_equal(pred3[0, 0, 0], pred1, decimal=2)
npt.assert_almost_equal(odf3[0, 0, 0], odf1, decimal=2)
npt.assert_almost_equal(odf3[0, 0, 0], odf2[0, 0, 0], decimal=2)
# Fit zeros and you will get back zeros
npt.assert_almost_equal(
sfmodel.fit(np.zeros(data[0, 0, 0].shape)).beta, np.zeros(sfmodel.design_matrix[0].shape[-1])
)
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_boundaries():
"""
This test checks that the tracking will exclude seeds in both directions.
Here we create a volume of shape (50, 60, 40) and we will add 2 seeds
exactly at the volume's boundaries (49, 0, 0) and (0, 0, 0). Those should
not generate any streamlines as EuDX does not interpolate on the boundary
voxels. We also add 3 seeds not in the boundaries which should generate
streamlines without a problem.
"""
fa = np.ones((50, 60, 40))
ind = np.zeros(fa.shape)
sphere = get_sphere('repulsion724')
seed = [49., 0, 0]
seed2 = [0., 0, 0]
seed3 = [48., 0, 0]
seed4 = [1., 0, 0]
seed5 = [5., 5, 5]
eu = EuDX(a=fa, ind=ind, seeds=[seed, seed2, seed3, seed4, seed5],
odf_vertices=sphere.vertices, a_low=.2,
total_weight=0.)
track = list(eu)
assert_equal(len(track), 3)
def test_multib0_dsi():
data, gtab = dsi_voxels()
# Create a new data-set with a b0 measurement:
new_data = np.concatenate([data, data[..., 0, None]], -1)
new_bvecs = np.concatenate([gtab.bvecs, np.zeros((1, 3))])
new_bvals = np.concatenate([gtab.bvals, [0]])
new_gtab = gradient_table(new_bvals, new_bvecs)
ds = DiffusionSpectrumModel(new_gtab)
sphere = get_sphere('repulsion724')
dsfit = ds.fit(new_data)
pdf = dsfit.pdf()
dsfit.odf(sphere)
assert_equal(new_data.shape[:-1] + (17, 17, 17), pdf.shape)
assert_equal(np.alltrue(np.isreal(pdf)), True)
# And again, with one more b0 measurement (two in total):
new_data = np.concatenate([data, data[..., 0, None]], -1)
new_bvecs = np.concatenate([gtab.bvecs, np.zeros((1, 3))])
new_bvals = np.concatenate([gtab.bvals, [0]])
new_gtab = gradient_table(new_bvals, new_bvecs)
ds = DiffusionSpectrumModel(new_gtab)
dsfit = ds.fit(new_data)
pdf = dsfit.pdf()
dsfit.odf(sphere)
assert_equal(new_data.shape[:-1] + (17, 17, 17), pdf.shape)
assert_equal(np.alltrue(np.isreal(pdf)), True)
def test_dti_xval():
"""
Test k-fold cross-validation
"""
data = nib.load(fdata).get_data()
gtab = gt.gradient_table(fbval, fbvec)
dm = dti.TensorModel(gtab, "LS")
# The data has 102 directions, so will not divide neatly into 10 bits
npt.assert_raises(ValueError, xval.kfold_xval, dm, data, 10)
# But we can do this with 2 folds:
kf_xval = xval.kfold_xval(dm, data, 2)
# In simulation with no noise, COD should be perfect:
psphere = dpd.get_sphere("symmetric362")
bvecs = np.concatenate(([[0, 0, 0]], psphere.vertices))
bvals = np.zeros(len(bvecs)) + 1000
bvals[0] = 0
gtab = gt.gradient_table(bvals, bvecs)
mevals = np.array(([0.0015, 0.0003, 0.0001], [0.0015, 0.0003, 0.0003]))
mevecs = [np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]), np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]])]
S = sims.single_tensor(gtab, 100, mevals[0], mevecs[0], snr=None)
dm = dti.TensorModel(gtab, "LS")
kf_xval = xval.kfold_xval(dm, S, 2)
cod = xval.coeff_of_determination(S, kf_xval)
npt.assert_array_almost_equal(cod, np.ones(kf_xval.shape[:-1]) * 100)
# Test with 2D data for use of a mask
S = np.array([[S, S], [S, S]])
mask = np.ones(S.shape[:-1], dtype=bool)
mask[1, 1] = 0
kf_xval = xval.kfold_xval(dm, S, 2, mask=mask)
cod2d = xval.coeff_of_determination(S, kf_xval)
npt.assert_array_almost_equal(np.round(cod2d[0, 0]), cod)
def test_r2_term_odf_sharp():
SNR = None
S0 = 1
angle = 75
_, fbvals, fbvecs = get_data('small_64D') #get_data('small_64D')
bvals = np.load(fbvals)
bvecs = np.load(fbvecs)
sphere = get_sphere('symmetric724')
gtab = gradient_table(bvals, bvecs)
mevals = np.array(([0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003]))
S, sticks = multi_tensor(gtab, mevals, S0, angles=[(0, 0), (angle, 0)],
fractions=[50, 50], snr=SNR)
mevecs = [all_tensor_evecs(sticks[0]).T,
all_tensor_evecs(sticks[1]).T]
odf_gt = multi_tensor_odf(sphere.vertices, [0.5, 0.5], mevals, mevecs)
odfs_sh = sf_to_sh(odf_gt, sphere, sh_order=8, basis_type=None)
fodf_sh = odf_sh_to_sharp(odfs_sh, sphere, basis=None, ratio=3 / 15.,
sh_order=8, lambda_=1., tau=0.1, r2_term=True)
fodf = sh_to_sf(fodf_sh, sphere, sh_order=8, basis_type=None)
directions_gt, _, _ = peak_directions(odf_gt, sphere)
directions, _, _ = peak_directions(fodf, sphere)
ang_sim = angular_similarity(directions_gt, directions)
assert_equal(ang_sim > 1.9, True)
assert_equal(directions.shape[0], 2)
def __init__(self, gtab, sphere=None, response=[0.0015, 0.0005, 0.0005],
solver='ElasticNet', l1_ratio=0.5, alpha=0.001):
"""
Initialize a Sparse Fascicle Model
Parameters
----------
gtab : GradientTable class instance
sphere : Sphere class instance, optional
A sphere on which coefficients will be estimated. Default:
symmetric sphere with 362 points (from :mod:`dipy.data`).
response : (3,) array-like, optional
The eigenvalues of a canonical tensor to be used as the response
function of single-fascicle signals.
Default:[0.0015, 0.0005, 0.0005]
solver : string or SKLearnLinearSolver object, optional
This will determine the algorithm used to solve the set of linear
equations underlying this model. If it is a string it needs to be
one of the following: {'ElasticNet', 'NNLS'}. Otherwise, it can be
an object that inherits from `dipy.optimize.SKLearnLinearSolver`.
Default: 'ElasticNet'.
l1_ratio : float, optional
Sets the balance betwee L1 and L2 regularization in ElasticNet
[Zou2005]_. Default: 0.5
alpha : float, optional
Sets the balance between least-squares error and L1/L2
regularization in ElasticNet [Zou2005]_. Default: 0.001
Notes
-----
This is an implementation of the SFM, described in [Rokem2014]_.
.. [Rokem2014] Ariel Rokem, Jason D. Yeatman, Franco Pestilli, Kendrick
N. Kay, Aviv Mezer, Stefan van der Walt, Brian A. Wandell
(2014). Evaluating the accuracy of diffusion MRI models in white
matter. http://arxiv.org/abs/1411.0721
.. [Zou2005] Zou H, Hastie T (2005). Regularization and variable
selection via the elastic net. J R Stat Soc B:301-320
"""
ReconstModel.__init__(self, gtab)
if sphere is None:
sphere = dpd.get_sphere()
self.sphere = sphere
self.response = np.asarray(response)
if solver == 'ElasticNet':
self.solver = lm.ElasticNet(l1_ratio=l1_ratio, alpha=alpha,
positive=True, warm_start=True)
elif solver == 'NNLS' or solver == 'nnls':
self.solver = opt.NonNegativeLeastSquares()
elif isinstance(solver, opt.SKLearnLinearSolver):
self.solver = solver
else:
e_s = "The `solver` key-word argument needs to be: "
e_s += "'ElasticNet', 'NNLS', or a "
e_s += "`dipy.optimize.SKLearnLinearSolver` object"
raise ValueError(e_s)
def track(model, data, sphere=None, step_size=1, angle_limit=20, seeds=None,
density=[2,2,2], voxel_size=[1,1,1]):
"""
Interface for tracking based on fiber ODF models
`model` needs to have a `fit` method, such that model.fit(data).odf(sphere)
is a legitimate ODF (that is has dimensions (x,y,z, n_vertices), where
n_vertices refers to the vertices of the provided sphere.
"""
# If no sphere is provided, we will use the dipy symmetrical sphere with
# 724 vertcies. That should be enough
if sphere is None:
sphere = dpd.get_sphere('symmetric724')
stepper = dpt.FixedSizeStepper(step_size)
interpolator = dpt.NearestNeighborInterpolator(data, voxel_size)
if seeds is None:
seeds = dpu.seeds_from_mask(mask, density, voxel_size)
pwt = dpt.ProbabilisticOdfWeightedTracker(model, interpolator, mask,
stepper, angle_limit, seeds,
sphere)
pwt_streamlines = list(pwt)
fibers = []
for f in pwt_streamlines:
fibers.append(ozf.Fiber(f))
def constrained_spherical_deconvolution(dir_src, dir_out, verbose=False):
# Load data
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')
fmask = pjoin(dir_src, 'wm_mask_' + par_b_tag + '_' + 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)
sphere = get_sphere('symmetric724')
response, ratio = auto_response(gtab, data, roi_radius=par_ar_radius,
fa_thr=par_ar_fa_th)
# print('Response function', response)
# Model fitting
csd_model = ConstrainedSphericalDeconvModel(gtab, response)
csd_fit = csd_model.fit(data, mask=mask)
# Saving Spherical Harmonic Coefficient
out_peaks = 'sh_' + par_b_tag + '_' + par_dim_tag + '.nii.gz'
save_nifti(pjoin(dir_out, out_peaks), csd_fit.shm_coeff, affine)
def test_csd_xval():
# First, let's see that it works with some data:
data = nib.load(fdata).get_data()[1:3, 1:3, 1:3] # Make it *small*
gtab = gt.gradient_table(fbval, fbvec)
S0 = np.mean(data[..., gtab.b0s_mask])
response = ([0.0015, 0.0003, 0.0001], S0)
csdm = csd.ConstrainedSphericalDeconvModel(gtab, response)
kf_xval = xval.kfold_xval(csdm, data, 2, response, sh_order=2)
# In simulation, it should work rather well (high COD):
psphere = dpd.get_sphere('symmetric362')
bvecs = np.concatenate(([[0, 0, 0]], psphere.vertices))
bvals = np.zeros(len(bvecs)) + 1000
bvals[0] = 0
gtab = gt.gradient_table(bvals, bvecs)
mevals = np.array(([0.0015, 0.0003, 0.0001], [0.0015, 0.0003, 0.0003]))
mevecs = [ np.array( [ [1, 0, 0], [0, 1, 0], [0, 0, 1] ] ),
np.array( [ [0, 0, 1], [0, 1, 0], [1, 0, 0] ] ) ]
S0 = 100
S = sims.single_tensor( gtab, S0, mevals[0], mevecs[0], snr=None )
sm = csd.ConstrainedSphericalDeconvModel(gtab, response)
smfit = sm.fit(S)
np.random.seed(12345)
response = ([0.0015, 0.0003, 0.0001], S0)
kf_xval = xval.kfold_xval(sm, S, 2, response, sh_order=2)
# Because of the regularization, COD is not going to be perfect here:
cod = xval.coeff_of_determination(S, kf_xval)
# We'll just test for regressions:
csd_cod = 97 # pre-computed by hand for this random seed
# We're going to be really lenient here:
npt.assert_array_almost_equal(np.round(cod), csd_cod)
def tracking_prob(dir_src, dir_out, verbose=False):
wm_name = 'wm_mask_' + par_b_tag + '_' + par_dim_tag + '.nii.gz'
wm_mask, affine = load_nifti(pjoin(dir_src, wm_name), verbose)
sh_name = 'sh_' + par_b_tag + '_' + par_dim_tag + '.nii.gz'
sh, _ = load_nifti(pjoin(dir_src, sh_name), verbose)
sphere = get_sphere('symmetric724')
classifier = ThresholdTissueClassifier(wm_mask.astype('f8'), .5)
classifier = BinaryTissueClassifier(wm_mask)
max_dg = ProbabilisticDirectionGetter.from_shcoeff(sh, max_angle=par_trk_max_angle, sphere=sphere)
seeds = utils.seeds_from_mask(wm_mask, density=2, affine=affine)
streamlines = LocalTracking(max_dg, classifier, seeds, affine, step_size=par_trk_step_size)
streamlines = list(streamlines)
trk_name = 'tractogram_' + par_b_tag + '_' + par_dim_tag + '_' + par_trk_prob_tag + '.trk'
trk_out = os.path.join(dir_out, trk_name)
save_trk(trk_out, streamlines, affine, wm_mask.shape)
dpy_out = trk_out.replace('.trk', '.dpy')
dpy = Dpy(dpy_out, 'w')
dpy.write_tracks(streamlines)
dpy.close()
def tracking_eudx(dir_src, dir_out, verbose=False):
# Loading FA and evecs data
fa_name = 'data_' + par_b_tag + '_' + par_dim_tag + '_FA.nii.gz'
FA, affine = load_nifti(pjoin(dir_src, fa_name), verbose)
evecs_name = 'data_' + par_b_tag + '_' + par_dim_tag + '_EV.nii.gz'
evecs, _ = load_nifti(pjoin(dir_src, evecs_name), verbose)
# Computation of streamlines
sphere = get_sphere('symmetric724')
peak_indices = quantize_evecs(evecs, sphere.vertices)
streamlines = EuDX(FA.astype('f8'),
ind=peak_indices,
seeds=par_eudx_seeds,
odf_vertices= sphere.vertices,
a_low=par_eudx_threshold)
# Saving tractography
voxel_size = (par_dim_vox,) * 3
dims = FA.shape[:3]
hdr = nib.trackvis.empty_header()
hdr['voxel_size'] = voxel_size
hdr['voxel_order'] = 'LAS'
hdr['dim'] = dims
hdr['vox_to_ras'] = affine
strm = ((sl, None, None) for sl in streamlines)
trk_name = 'tractogram_' + par_b_tag + '_' + par_dim_tag + '_' + par_rec_tag + '_' + par_eudx_tag + '.trk'
trk_out = os.path.join(dir_out, trk_name)
nib.trackvis.write(trk_out, strm, hdr, points_space='voxel')
dpy_out = trk_out.replace('.trk', '.dpy')
dpy = Dpy(dpy_out, 'w')
dpy.write_tracks(streamlines)
dpy.close()
def test_peak_finding():
vertices, faces=get_sphere('symmetric724')
odf=np.zeros(len(vertices))
odf = np.abs(vertices.sum(-1))
odf[1] = 10.
odf[505] = 505.
odf[143] = 143.
peaks, inds=peak_finding(odf.astype('f8'), faces.astype('uint16'))
print peaks, inds
edges = unique_edges(faces)
peaks, inds = local_maxima(odf, edges)
print peaks, inds
vertices_half, edges_half, faces_half = reduce_antipodal(vertices, faces)
n = len(vertices_half)
peaks, inds = local_maxima(odf[:n], edges_half)
print peaks, inds
mevals=np.array(([0.0015,0.0003,0.0003],
[0.0015,0.0003,0.0003]))
e0=np.array([1,0,0.])
e1=np.array([0.,1,0])
mevecs=[all_tensor_evecs(e0),all_tensor_evecs(e1)]
odf = multi_tensor_odf(vertices, [0.5,0.5], mevals, mevecs)
peaks, inds=peak_finding(odf, faces)
print peaks, inds
peaks2, inds2 = local_maxima(odf[:n], edges_half)
print peaks2, inds2
assert_equal(len(peaks), 2)
assert_equal(len(peaks2), 2)
def test_multi_tensor():
sphere = get_sphere('symmetric724')
vertices = sphere.vertices
mevals = np.array(([0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003]))
e0 = np.array([np.sqrt(2) / 2., np.sqrt(2) / 2., 0])
e1 = np.array([0, np.sqrt(2) / 2., np.sqrt(2) / 2.])
mevecs = [all_tensor_evecs(e0), all_tensor_evecs(e1)]
# odf = multi_tensor_odf(vertices, [0.5, 0.5], mevals, mevecs)
# assert_(odf.shape == (len(vertices),))
# assert_(np.all(odf <= 1) & np.all(odf >= 0))
fimg, fbvals, fbvecs = get_data('small_101D')
bvals, bvecs = read_bvals_bvecs(fbvals, fbvecs)
gtab = gradient_table(bvals, bvecs)
s1 = single_tensor(gtab, 100, mevals[0], mevecs[0], snr=None)
s2 = single_tensor(gtab, 100, mevals[1], mevecs[1], snr=None)
Ssingle = 0.5*s1 + 0.5*s2
S, sticks = MultiTensor(gtab, mevals, S0=100, angles=[(90, 45), (45, 90)],
fractions=[50, 50], snr=None)
assert_array_almost_equal(S, Ssingle)
def test_predict():
"""
Test model prediction API
"""
psphere = get_sphere('symmetric362')
bvecs = np.concatenate(([[1, 0, 0]], psphere.vertices))
bvals = np.zeros(len(bvecs)) + 1000
bvals[0] = 0
gtab = grad.gradient_table(bvals, bvecs)
mevals = np.array(([0.0015, 0.0003, 0.0001], [0.0015, 0.0003, 0.0003]))
mevecs = [np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]),
np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]])]
S = single_tensor(gtab, 100, mevals[0], mevecs[0], snr=None)
dm = dti.TensorModel(gtab, 'LS')
dmfit = dm.fit(S)
assert_array_almost_equal(dmfit.predict(gtab, S0=100), S)
assert_array_almost_equal(dm.predict(dmfit.model_params, S0=100), S)
fdata, fbvals, fbvecs = get_data()
data = nib.load(fdata).get_data()
# Make the data cube a bit larger:
data = np.tile(data.T, 2).T
gtab = grad.gradient_table(fbvals, fbvecs)
dtim = dti.TensorModel(gtab)
dtif = dtim.fit(data)
S0 = np.mean(data[..., gtab.b0s_mask], -1)
p = dtif.predict(gtab, S0)
assert_equal(p.shape, data.shape)
def test_local_maxima():
sphere = get_sphere('symmetric724')
vertices, faces = sphere.vertices, sphere.faces
edges = unique_edges(faces)
odf = abs(vertices.sum(-1))
odf[1] = 10.
odf[143] = 143.
odf[505] = 505
peak_values, peak_index = local_maxima(odf, edges)
npt.assert_array_equal(peak_values, [505, 143, 10])
npt.assert_array_equal(peak_index, [505, 143, 1])
hemisphere = HemiSphere(xyz=vertices, faces=faces)
vertices_half, edges_half = hemisphere.vertices, hemisphere.edges
odf = abs(vertices_half.sum(-1))
odf[1] = 10.
odf[143] = 143.
peak_value, peak_index = local_maxima(odf, edges_half)
npt.assert_array_equal(peak_value, [143, 10])
npt.assert_array_equal(peak_index, [143, 1])
odf[20] = np.nan
npt.assert_raises(ValueError, local_maxima, odf, edges_half)
def __init__(self,
gtab,
method='gqi2',
sampling_length=3.5,
normalize_peaks=True,
ratio=0.2,
sh_order=8,
lambda_=1.,
tau=0.1,
r2=True):
super(GeneralizedQSamplingDeconvModel, self).__init__(gtab,
method,
sampling_length,
normalize_peaks)
sphere = get_sphere('symmetric724')
self.invB = sf_to_sh_invB(sphere, sh_order, 'mrtrix')
self.R, self.B_reg = mats_odfdeconv(sphere,
basis='mrtrix',
ratio=ratio,
sh_order=sh_order,
lambda_=lambda_, tau=tau,
r2=r2)
self.lambda_ = lambda_
self.tau = tau
self.sh_order = sh_order
def test_minmax_normalize():
bvalue = 3000
S0 = 1
SNR = 100
sphere = get_sphere("symmetric362")
bvecs = np.concatenate(([[0, 0, 0]], sphere.vertices))
bvals = np.zeros(len(bvecs)) + bvalue
bvals[0] = 0
gtab = gradient_table(bvals, bvecs)
evals = np.array(([0.0017, 0.0003, 0.0003], [0.0017, 0.0003, 0.0003]))
S, sticks = multi_tensor(gtab, evals, S0, angles=[(0, 0), (90, 0)], fractions=[50, 50], snr=SNR)
odf = multi_tensor_odf(sphere.vertices, evals, angles=[(0, 0), (90, 0)], fractions=[50, 50])
odf2 = minmax_normalize(odf)
assert_equal(odf2.max(), 1)
assert_equal(odf2.min(), 0)
odf3 = np.empty(odf.shape)
odf3 = minmax_normalize(odf, odf3)
assert_equal(odf3.max(), 1)
assert_equal(odf3.min(), 0)
def test_exponential_iso():
fdata, fbvals, fbvecs = dpd.get_data()
data_dti = nib.load(fdata).get_data()
gtab_dti = grad.gradient_table(fbvals, fbvecs)
data_multi, gtab_multi = dpd.dsi_deconv_voxels()
for data, gtab in zip([data_dti, data_multi], [gtab_dti, gtab_multi]):
sfmodel = sfm.SparseFascicleModel(
gtab, isotropic=sfm.ExponentialIsotropicModel)
sffit1 = sfmodel.fit(data[0, 0, 0])
sphere = dpd.get_sphere()
odf1 = sffit1.odf(sphere)
pred1 = sffit1.predict(gtab)
SNR = 1000
S0 = 100
mevals = np.array(([0.0015, 0.0005, 0.0005],
[0.0015, 0.0005, 0.0005]))
angles = [(0, 0), (60, 0)]
S, sticks = sims.multi_tensor(gtab, mevals, S0, angles=angles,
fractions=[50, 50], snr=SNR)
sffit = sfmodel.fit(S)
pred = sffit.predict()
npt.assert_(xval.coeff_of_determination(pred, S) > 96)
def test_sphere_scaling_csdmodel():
"""Check that mirroring regulization sphere does not change the result of
csddeconv model"""
_, fbvals, fbvecs = get_data('small_64D')
bvals = np.load(fbvals)
bvecs = np.load(fbvecs)
gtab = gradient_table(bvals, bvecs)
mevals = np.array(([0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003]))
angles = [(0, 0), (60, 0)]
S, sticks = multi_tensor(gtab, mevals, 100., angles=angles,
fractions=[50, 50], snr=None)
sphere = get_sphere('symmetric362')
hemi = HemiSphere.from_sphere(sphere)
response = (np.array([0.0015, 0.0003, 0.0003]), 100)
model_full = ConstrainedSphericalDeconvModel(gtab, response,
reg_sphere=sphere)
model_hemi = ConstrainedSphericalDeconvModel(gtab, response,
reg_sphere=hemi)
csd_fit_full = model_full.fit(S)
csd_fit_hemi = model_hemi.fit(S)
assert_array_almost_equal(csd_fit_full.shm_coeff, csd_fit_hemi.shm_coeff)
def test_csd_predict():
"""
"""
SNR = 100
S0 = 1
_, fbvals, fbvecs = get_data('small_64D')
bvals = np.load(fbvals)
bvecs = np.load(fbvecs)
gtab = gradient_table(bvals, bvecs)
mevals = np.array(([0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003]))
angles = [(0, 0), (60, 0)]
S, sticks = multi_tensor(gtab, mevals, S0, angles=angles,
fractions=[50, 50], snr=SNR)
sphere = get_sphere('symmetric362')
odf_gt = multi_tensor_odf(sphere.vertices, mevals, angles, [50, 50])
response = (np.array([0.0015, 0.0003, 0.0003]), S0)
csd = ConstrainedSphericalDeconvModel(gtab, response)
csd_fit = csd.fit(S)
prediction = csd_predict(csd_fit.shm_coeff, gtab, response=response, S0=S0)
npt.assert_equal(prediction.shape[0], S.shape[0])
model_prediction = csd.predict(csd_fit.shm_coeff)
assert_array_almost_equal(prediction, model_prediction)
# Roundtrip tests (quite inaccurate, because of regularization):
assert_array_almost_equal(csd_fit.predict(gtab, S0=S0),S,decimal=1)
assert_array_almost_equal(csd.predict(csd_fit.shm_coeff, S0=S0),S,decimal=1)
def test_odfdeconv():
SNR = 100
S0 = 1
_, fbvals, fbvecs = get_data('small_64D')
bvals = np.load(fbvals)
bvecs = np.load(fbvecs)
gtab = gradient_table(bvals, bvecs)
mevals = np.array(([0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003]))
angles = [(0, 0), (90, 0)]
S, sticks = multi_tensor(gtab, mevals, S0, angles=angles,
fractions=[50, 50], snr=SNR)
sphere = get_sphere('symmetric362')
odf_gt = multi_tensor_odf(sphere.vertices, mevals, angles, [50, 50])
e1 = 15.0
e2 = 3.0
ratio = e2 / e1
csd = ConstrainedSDTModel(gtab, ratio, None)
csd_fit = csd.fit(S)
fodf = csd_fit.odf(sphere)
directions, _, _ = peak_directions(odf_gt, sphere)
directions2, _, _ = peak_directions(fodf, sphere)
ang_sim = angular_similarity(directions, directions2)
assert_equal(ang_sim > 1.9, True)
assert_equal(directions.shape[0], 2)
assert_equal(directions2.shape[0], 2)
with warnings.catch_warnings(record=True) as w:
ConstrainedSDTModel(gtab, ratio, sh_order=10)
assert_equal(len(w) > 0, True)
with warnings.catch_warnings(record=True) as w:
ConstrainedSDTModel(gtab, ratio, sh_order=8)
assert_equal(len(w) > 0, False)
csd_fit = csd.fit(np.zeros_like(S))
fodf = csd_fit.odf(sphere)
assert_array_equal(fodf, np.zeros_like(fodf))
odf_sh = np.zeros_like(fodf)
odf_sh[1] = np.nan
fodf, it = odf_deconv(odf_sh, csd.R, csd.B_reg)
assert_array_equal(fodf, np.zeros_like(fodf))
def test_design_matrix():
data, gtab = dpd.dsi_voxels()
sphere = dpd.get_sphere()
# Make it with NNLS, so that it gets tested regardless of sklearn
sparse_fascicle_model = sfm.SparseFascicleModel(gtab, sphere,
solver='NNLS')
npt.assert_equal(sparse_fascicle_model.design_matrix.shape,
(np.sum(~gtab.b0s_mask), sphere.vertices.shape[0]))
def test_shore_metrics():
gtab = get_gtab_taiwan_dsi()
mevals = np.array(([0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003]))
angl = [(0, 0), (60, 0)]
S, sticks = MultiTensor(gtab, mevals, S0=100.0, angles=angl,
fractions=[50, 50], snr=None)
# since we are testing without noise we can use higher order and lower lambdas, with respect to the default.
radial_order = 6
lambd = 1e-8
# test mapmri_indices
indices = mapmri_index_matrix(radial_order)
n_c = indices.shape[0]
F = radial_order / 2
n_gt = np.round(1 / 6.0 * (F + 1) * (F + 2) * (4 * F + 3))
assert_equal(n_c, n_gt)
# test MAPMRI fitting
mapm= MapmriModel(gtab, radial_order=radial_order, lambd=lambd)
mapfit = mapm.fit(S)
c_map=mapfit.mapmri_coeff
R = mapfit.mapmri_R
mu = mapfit.mapmri_mu
S_reconst = mapfit.predict(gtab, 1.0)
# test the signal reconstruction
S = S / S[0]
nmse_signal = np.sqrt(np.sum((S - S_reconst) ** 2)) / (S.sum())
assert_almost_equal(nmse_signal, 0.0, 3)
# test if the analytical integral of the pdf is equal to one
integral = 0
for i in range(indices.shape[0]):
n1,n2,n3 = indices[i]
integral += c_map[i] * int_func(n1) * int_func(n2) * int_func(n3)
assert_almost_equal(integral, 1.0, 3)
# compare the shore pdf with the ground truth multi_tensor pdf
sphere = get_sphere('symmetric724')
v = sphere.vertices
radius = 10e-3
r_points = v * radius
pdf_mt = multi_tensor_pdf(r_points, mevals=mevals,
angles=angl, fractions= [50, 50])
pdf_map = mapmri_EAP(r_points, radial_order, c_map, mu, R)
nmse_pdf = np.sqrt(np.sum((pdf_mt - pdf_map) ** 2)) / (pdf_mt.sum())
assert_almost_equal(nmse_pdf, 0.0, 2)
def angle_aware_bilateral_filtering_cpu(in_sh, sh_order=8,
sh_basis='descoteaux07',
in_full_basis=False,
sphere_str='repulsion724',
sigma_spatial=1.0,
sigma_angular=1.0,
sigma_range=0.5,
nbr_processes=1):
"""
Angle-aware bilateral filtering on the CPU
(optionally using multiple threads).
Parameters
----------
in_sh: ndarray (x, y, z, ncoeffs)
Input SH volume.
sh_order: int, optional
Maximum SH order of input volume.
sh_basis: str, optional
Name of SH basis used.
in_full_basis: bool, optional
True if input is expressed in full SH basis.
sphere_str: str, optional
Name of the DIPY sphere to use for sh to sf projection.
sigma_spatial: float, optional
Standard deviation for spatial filter.
sigma_angular: float, optional
Standard deviation for angular filter.
sigma_range: float, optional
Standard deviation for range filter.
nbr_processes: int, optional
Number of processes to use.
Returns
-------
out_sh: ndarray (x, y, z, ncoeffs)
Output SH coefficient array in full SH basis.
"""
# Load the sphere used for projection of SH
sphere = get_sphere(sphere_str)
# Normalized filter for each sf direction
s_weights = _get_spatial_weights(sigma_spatial)
a_weights = _get_angular_weights(s_weights.shape, sphere, sigma_angular)
weights = s_weights[..., None] * a_weights
weights /= np.sum(weights, axis=(0, 1, 2))
nb_sf = len(sphere.vertices)
B = sh_to_sf_matrix(sphere, sh_order=sh_order, basis_type=sh_basis,
return_inv=False, full_basis=in_full_basis)
if nbr_processes > 1:
# Apply filter to each sphere vertice in parallel
pool = multiprocessing.Pool(nbr_processes)
# divide the sphere directions among the processes
base_chunk_size = int(nb_sf / nbr_processes + 0.5)
first_ids = np.arange(0, nb_sf, base_chunk_size)
residuals = nb_sf - first_ids
chunk_sizes = np.where(residuals < base_chunk_size,
residuals, base_chunk_size)
res = pool.map(_process_subset_directions,
zip(itertools.repeat(weights),
itertools.repeat(in_sh),
first_ids,
chunk_sizes,
itertools.repeat(B),
itertools.repeat(sigma_range)))
pool.close()
pool.join()
# Patch chunks together.
mean_sf = np.concatenate(res, axis=-1)
else:
args = [weights, in_sh, 0, nb_sf,
B, sigma_range]
mean_sf = _process_subset_directions(args)
# Convert back to SH coefficients
_, B_inv = sh_to_sf_matrix(sphere, sh_order=sh_order, basis_type=sh_basis,
full_basis=True)
out_sh = np.array([np.dot(i, B_inv) for i in mean_sf], dtype=in_sh.dtype)
# By default, return only asymmetric SH
return out_sh
def main():
parser = _build_arg_parser()
args = parser.parse_args()
if args.verbose:
logging.basicConfig(level=logging.DEBUG)
assert_inputs_exist(
parser,
[args.in_sh, args.in_seed, args.in_map_include, args.map_exclude_file])
assert_outputs_exist(parser, args, args.out_tractogram)
if not nib.streamlines.is_supported(args.out_tractogram):
parser.error('Invalid output streamline file format (must be trk or ' +
'tck): {0}'.format(args.out_tractogram))
if not args.min_length > 0:
parser.error('minL must be > 0, {}mm was provided.'.format(
args.min_length))
if args.max_length < args.min_length:
parser.error(
'maxL must be > than minL, (minL={}mm, maxL={}mm).'.format(
args.min_length, args.max_length))
if args.compress:
if args.compress < 0.001 or args.compress > 1:
logging.warning(
'You are using an error rate of {}.\nWe recommend setting it '
'between 0.001 and 1.\n0.001 will do almost nothing to the '
'tracts while 1 will higly compress/linearize the tracts'.
format(args.compress))
if args.particles <= 0:
parser.error('--particles must be >= 1.')
if args.back_tracking <= 0:
parser.error('PFT backtracking distance must be > 0.')
if args.forward_tracking <= 0:
parser.error('PFT forward tracking distance must be > 0.')
if args.npv and args.npv <= 0:
parser.error('Number of seeds per voxel must be > 0.')
if args.nt and args.nt <= 0:
parser.error('Total number of seeds must be > 0.')
fodf_sh_img = nib.load(args.in_sh)
if not np.allclose(np.mean(fodf_sh_img.header.get_zooms()[:3]),
fodf_sh_img.header.get_zooms()[0],
atol=1e-03):
parser.error(
'SH file is not isotropic. Tracking cannot be ran robustly.')
tracking_sphere = HemiSphere.from_sphere(get_sphere('repulsion724'))
# Check if sphere is unit, since we couldn't find such check in Dipy.
if not np.allclose(np.linalg.norm(tracking_sphere.vertices, axis=1), 1.):
raise RuntimeError('Tracking sphere should be unit normed.')
sh_basis = args.sh_basis
if args.algo == 'det':
dgklass = DeterministicMaximumDirectionGetter
else:
dgklass = ProbabilisticDirectionGetter
theta = get_theta(args.theta, args.algo)
# Reminder for the future:
# pmf_threshold == clip pmf under this
# relative_peak_threshold is for initial directions filtering
# min_separation_angle is the initial separation angle for peak extraction
dg = dgklass.from_shcoeff(fodf_sh_img.get_fdata(dtype=np.float32),
max_angle=theta,
sphere=tracking_sphere,
basis_type=sh_basis,
pmf_threshold=args.sf_threshold,
relative_peak_threshold=args.sf_threshold_init)
map_include_img = nib.load(args.in_map_include)
map_exclude_img = nib.load(args.map_exclude_file)
voxel_size = np.average(map_include_img.header['pixdim'][1:4])
if not args.act:
tissue_classifier = CmcStoppingCriterion(
map_include_img.get_fdata(dtype=np.float32),
map_exclude_img.get_fdata(dtype=np.float32),
step_size=args.step_size,
average_voxel_size=voxel_size)
else:
tissue_classifier = ActStoppingCriterion(
map_include_img.get_fdata(dtype=np.float32),
map_exclude_img.get_fdata(dtype=np.float32))
if args.npv:
nb_seeds = args.npv
seed_per_vox = True
elif args.nt:
nb_seeds = args.nt
seed_per_vox = False
else:
nb_seeds = 1
seed_per_vox = True
voxel_size = fodf_sh_img.header.get_zooms()[0]
vox_step_size = args.step_size / voxel_size
seed_img = nib.load(args.in_seed)
seeds = track_utils.random_seeds_from_mask(
get_data_as_mask(seed_img, dtype=bool),
np.eye(4),
seeds_count=nb_seeds,
seed_count_per_voxel=seed_per_vox,
random_seed=args.seed)
# Note that max steps is used once for the forward pass, and
# once for the backwards. This doesn't, in fact, control the real
# max length
max_steps = int(args.max_length / args.step_size) + 1
pft_streamlines = ParticleFilteringTracking(
dg,
tissue_classifier,
seeds,
np.eye(4),
max_cross=1,
step_size=vox_step_size,
maxlen=max_steps,
pft_back_tracking_dist=args.back_tracking,
pft_front_tracking_dist=args.forward_tracking,
particle_count=args.particles,
return_all=args.keep_all,
random_seed=args.seed,
save_seeds=args.save_seeds)
scaled_min_length = args.min_length / voxel_size
scaled_max_length = args.max_length / voxel_size
if args.save_seeds:
filtered_streamlines, seeds = \
zip(*((s, p) for s, p in pft_streamlines
if scaled_min_length <= length(s) <= scaled_max_length))
data_per_streamlines = {'seeds': lambda: seeds}
else:
filtered_streamlines = \
(s for s in pft_streamlines
if scaled_min_length <= length(s) <= scaled_max_length)
data_per_streamlines = {}
if args.compress:
filtered_streamlines = (compress_streamlines(s, args.compress)
for s in filtered_streamlines)
tractogram = LazyTractogram(lambda: filtered_streamlines,
data_per_streamlines,
affine_to_rasmm=seed_img.affine)
filetype = nib.streamlines.detect_format(args.out_tractogram)
reference = get_reference_info(seed_img)
header = create_tractogram_header(filetype, *reference)
# Use generator to save the streamlines on-the-fly
nib.streamlines.save(tractogram, args.out_tractogram, header=header)
def white_matter_response_tournier13(acquisition_scheme,
data,
max_iter=5,
sh_order=10,
N_candidate_voxels=300,
peak_ratio_setting='mrtrix'):
"""
Iterative model-free white matter response function estimation according to
[1]_. Quoting the paper, the steps are the following:
- 1) The 300 brain voxels with the highest FA were identified within a
brain mask (eroded by three voxels to remove any noisy voxels at the
brain edges).
- 2) The single-fibre 'response function' was estimated within these
voxels, and used to compute the fibre orientation distribution (FOD)
employing constrained spherical deconvolution (CSD) up to lmax = 10.
- 3) Within each voxel, a peak-finding procedure was used to identify the
two largest FOD peaks, and their amplitude ratio was computed.
- 4) The 300 voxels with the lowest second to first peak amplitude ratios
were identified, and used as the current estimate of the set of
'single-fibre' voxels. It should be noted that these voxels were not
required to be a subset of the original set of 'single-fibre' voxels.
- 5) To ensure minimal bias from the initial estimate of the 'response
function', steps (2) to (4) were re-iterated until convergence (no
difference in the set of 'single-fibre' voxels). It should be noted
that, in practice, convergence was achieved within a single iteration
in all cases.
Parameters
----------
acquisition_scheme : DmipyAcquisitionScheme instance,
An acquisition scheme that has been instantiated using dMipy.
data : NDarray,
Measured diffusion signal array.
max_iter : Positive integer,
Defines the maximum amount of iterations to be done for the single-
fibre response kernel.
sh_order : Positive even integer,
Maximum spherical harmonics order to be used in the FOD estimation for
the single-fibre response kernel.
N_candidate_voxels : integer,
Number of voxels to be included in the final white matter response
estimation. Default is 300 following [1]_.
peak_ratio_setting : string,
Can be either 'ratio' or 'mrtrix', meaning the 'ratio' parameter
between two peaks is actually calculated as the ratio, or a more
complicated version as 1 / sqrt(peak1 * (1 - peak2 / peak1)) ** 2, to
avoid favouring small, yet low SNR FODs [2]_.
Returns
-------
S0_wm : positive float,
Estimated S0 tissue response value.
TR2_wm_model : Dmipy Anisotropic ModelFree Model
ModelFree representation of white matter response.
selected_indices : array of size (N_candidate_voxels,),
indices of selected voxels for white matter response.
References
----------
.. [1] Tournier, J-Donald, Fernando Calamante, and Alan Connelly.
"Determination of the appropriate b value and number of gradient
directions for high-angular-resolution diffusion-weighted imaging."
NMR in Biomedicine 26.12 (2013): 1775-1786.
.. [2] MRtrix 3.0 readthedocs
"""
data_shape = np.atleast_2d(data).shape
N_voxels = int(np.prod(data_shape[:-1]))
if N_voxels < N_candidate_voxels:
msg = "The parameter N_candidate voxels is set to {} but only ".format(
N_candidate_voxels)
msg += "{} voxels are given. N_candidate_voxels".format(N_voxels)
msg += " reset to number of voxels given."
print(msg)
N_candidate_voxels = N_voxels
ratio_settings = ['ratio', 'mrtrix']
if peak_ratio_setting not in ratio_settings:
msg = 'peak_ratio_setting must be in {}'.format(ratio_settings)
raise ValueError(msg)
if data.ndim == 4:
# calculate brain mask on 4D data (x, y, z, DWI)
b0_mask, mask = median_otsu(input_volume=data,
vol_idx=np.where(
acquisition_scheme.b0_mask)[0],
median_radius=4,
numpass=4) # based on dipy default
# needs to be eroded 3 times.
mask_eroded = binary_erosion(mask, iterations=3)
data_to_fit = data[mask_eroded]
else:
# can't calculate brain mask on other than 4D data.
# assume the data was prepared.
data_to_fit = data.reshape([-1, data_shape[-1]])
gtab = gtab_dmipy2dipy(acquisition_scheme)
tenmod = dti.TensorModel(gtab)
tenfit = tenmod.fit(data_to_fit)
fa = tenfit.fa
# selected based on FA
selected_indices = np.argsort(fa)[-N_candidate_voxels:]
sphere = get_sphere('symmetric724')
hemisphere = HemiSphere(theta=sphere.theta, phi=sphere.phi)
# iterate until convergence
it = 0
while True:
print('Tournier13 white matter response iteration {}'.format(it + 1))
selected_data = data_to_fit[selected_indices]
S0_wm, TR2_wm_model = estimate_TR2_anisotropic_tissue_response_model(
acquisition_scheme, selected_data)
sh_model = MultiCompartmentSphericalHarmonicsModel([TR2_wm_model],
sh_order=sh_order)
sh_fit = sh_model.fit(acquisition_scheme,
data_to_fit,
solver='csd_tournier07',
use_parallel_processing=False,
lambda_lb=0.)
peaks, values, indices = sh_fit.peaks_directions(
hemisphere, max_peaks=2, relative_peak_threshold=0.)
if peak_ratio_setting == 'ratio':
ratio = values[..., 1] / values[..., 0]
elif peak_ratio_setting == 'mrtrix':
ratio = 1. / np.sqrt(values[..., 0] *
(1 - values[..., 1] / values[..., 0]))**2
selected_indices_old = selected_indices
selected_indices = np.argsort(ratio)[:N_candidate_voxels]
percentage_overlap = 100 * float(
len(np.intersect1d(selected_indices,
selected_indices_old))) / N_candidate_voxels
print('{:.1f} percent candidate voxel overlap.'.format(
percentage_overlap))
if percentage_overlap == 100.:
print('White matter response converged')
break
it += 1
if it == max_iter:
print('Maximum iterations reached without convergence')
break
return S0_wm, TR2_wm_model, selected_indices
def __init__(self, gtab, ratio, reg_sphere=None, sh_order=8, lambda_=1.,
tau=0.1):
r""" Spherical Deconvolution Transform (SDT) [1]_.
The SDT computes a fiber orientation distribution (FOD) as opposed to a
diffusion ODF as the QballModel or the CsaOdfModel. This results in a
sharper angular profile with better angular resolution. The Constrained
SDTModel is similar to the Constrained CSDModel but mathematically it
deconvolves the q-ball ODF as oppposed to the HARDI signal (see [1]_
for a comparison and a through discussion).
A sharp fODF is obtained because a single fiber *response* function is
injected as *a priori* knowledge. In the SDTModel, this response is a
single fiber q-ball ODF as opposed to a single fiber signal function
for the CSDModel. The response function will be used as deconvolution
kernel.
Parameters
----------
gtab : GradientTable
ratio : float
ratio of the smallest vs the largest eigenvalue of the single
prolate tensor response function
reg_sphere : Sphere
sphere used to build the regularization B matrix
sh_order : int
maximal spherical harmonics order
lambda_ : float
weight given to the constrained-positivity regularization part of
the deconvolution equation
tau : float
threshold (tau *mean(fODF)) controlling the amplitude below
which the corresponding fODF is assumed to be zero.
References
----------
.. [1] Descoteaux, M., et al. IEEE TMI 2009. Deterministic and
Probabilistic Tractography Based on Complex Fibre Orientation
Distributions.
"""
SphHarmModel.__init__(self, gtab)
m, n = sph_harm_ind_list(sh_order)
self.m, self.n = m, n
self._where_b0s = lazy_index(gtab.b0s_mask)
self._where_dwi = lazy_index(~gtab.b0s_mask)
no_params = ((sh_order + 1) * (sh_order + 2)) / 2
if no_params > np.sum(~gtab.b0s_mask):
msg = "Number of parameters required for the fit are more "
msg += "than the actual data points"
warnings.warn(msg, UserWarning)
x, y, z = gtab.gradients[self._where_dwi].T
r, theta, phi = cart2sphere(x, y, z)
# for the gradient sphere
self.B_dwi = real_sph_harm(m, n, theta[:, None], phi[:, None])
# for the odf sphere
if reg_sphere is None:
self.sphere = get_sphere('symmetric362')
else:
self.sphere = reg_sphere
r, theta, phi = cart2sphere(
self.sphere.x,
self.sphere.y,
self.sphere.z
)
self.B_reg = real_sph_harm(m, n, theta[:, None], phi[:, None])
self.R, self.P = forward_sdt_deconv_mat(ratio, n)
# scale lambda_ to account for differences in the number of
# SH coefficients and number of mapped directions
self.lambda_ = (lambda_ * self.R.shape[0] * self.R[0, 0] /
self.B_reg.shape[0])
self.tau = tau
self.sh_order = sh_order
def test_odf_slicer(interactive=False):
sphere = get_sphere('symmetric362')
shape = (11, 11, 11, sphere.vertices.shape[0])
fid, fname = mkstemp(suffix='_odf_slicer.mmap')
print(fid)
print(fname)
odfs = np.memmap(fname, dtype=np.float64, mode='w+', shape=shape)
odfs[:] = 1
affine = np.eye(4)
renderer = window.Renderer()
mask = np.ones(odfs.shape[:3])
mask[:4, :4, :4] = 0
odfs[..., 0] = 1
odf_actor = actor.odf_slicer(odfs,
affine,
mask=mask,
sphere=sphere,
scale=.25,
colormap='jet')
fa = 0. * np.zeros(odfs.shape[:3])
fa[:, 0, :] = 1.
fa[:, -1, :] = 1.
fa[0, :, :] = 1.
fa[-1, :, :] = 1.
fa[5, 5, 5] = 1
k = 5
I, J, K = odfs.shape[:3]
fa_actor = actor.slicer(fa, affine)
fa_actor.display_extent(0, I, 0, J, k, k)
renderer.add(odf_actor)
renderer.reset_camera()
renderer.reset_clipping_range()
odf_actor.display_extent(0, I, 0, J, k, k)
odf_actor.GetProperty().SetOpacity(1.0)
if interactive:
window.show(renderer, reset_camera=False)
arr = window.snapshot(renderer)
report = window.analyze_snapshot(arr, find_objects=True)
npt.assert_equal(report.objects, 11 * 11)
renderer.clear()
renderer.add(fa_actor)
renderer.reset_camera()
renderer.reset_clipping_range()
if interactive:
window.show(renderer)
mask[:] = 0
mask[5, 5, 5] = 1
fa[5, 5, 5] = 0
fa_actor = actor.slicer(fa, None)
fa_actor.display(None, None, 5)
odf_actor = actor.odf_slicer(odfs,
None,
mask=mask,
sphere=sphere,
scale=.25,
colormap='jet',
norm=False,
global_cm=True)
renderer.clear()
renderer.add(fa_actor)
renderer.add(odf_actor)
renderer.reset_camera()
renderer.reset_clipping_range()
if interactive:
window.show(renderer)
renderer.clear()
renderer.add(odf_actor)
renderer.add(fa_actor)
odfs[:, :, :] = 1
mask = np.ones(odfs.shape[:3])
odf_actor = actor.odf_slicer(odfs,
None,
mask=mask,
sphere=sphere,
scale=.25,
colormap='jet',
norm=False,
global_cm=True)
renderer.clear()
renderer.add(odf_actor)
renderer.add(fa_actor)
renderer.add(actor.axes((11, 11, 11)))
for i in range(11):
odf_actor.display(i, None, None)
fa_actor.display(i, None, None)
if interactive:
window.show(renderer)
for j in range(11):
odf_actor.display(None, j, None)
fa_actor.display(None, j, None)
if interactive:
window.show(renderer)
# with mask equal to zero everything should be black
mask = np.zeros(odfs.shape[:3])
odf_actor = actor.odf_slicer(odfs,
None,
mask=mask,
sphere=sphere,
scale=.25,
colormap='plasma',
norm=False,
global_cm=True)
renderer.clear()
renderer.add(odf_actor)
renderer.reset_camera()
renderer.reset_clipping_range()
if interactive:
window.show(renderer)
report = window.analyze_renderer(renderer)
npt.assert_equal(report.actors, 1)
npt.assert_equal(report.actors_classnames[0], 'vtkLODActor')
del odf_actor
odfs._mmap.close()
del odfs
os.close(fid)
os.remove(fname)
def diffusion_components(dki_params,
sphere='repulsion100',
awf=None,
mask=None):
""" Extracts the restricted and hindered diffusion tensors of well aligned
fibers from diffusion kurtosis imaging parameters [1]_.
Parameters
----------
dki_params : ndarray (x, y, z, 27) or (n, 27)
All parameters estimated from the diffusion kurtosis model.
Parameters are ordered as follows:
1) Three diffusion tensor's eigenvalues
2) Three lines of the eigenvector matrix each containing the first,
second and third coordinates of the eigenvector
3) Fifteen elements of the kurtosis tensor
sphere : Sphere class instance, optional
The sphere providing sample directions to sample the restricted and
hindered cellular diffusion tensors. For more details see Fieremans
et al., 2011.
awf : ndarray (optional)
Array containing values of the axonal water fraction that has the shape
dki_params.shape[:-1]. If not given this will be automatically computed
using :func:`axonal_water_fraction`" with function's default precision.
mask : ndarray (optional)
A boolean array used to mark the coordinates in the data that should be
analyzed that has the shape dki_params.shape[:-1]
Returns
--------
edt : ndarray (x, y, z, 6) or (n, 6)
Parameters of the hindered diffusion tensor.
idt : ndarray (x, y, z, 6) or (n, 6)
Parameters of the restricted diffusion tensor.
Note
----
In the original article of DKI microstructural model [1]_, the hindered and
restricted tensors were definde as the intra-cellular and extra-cellular
diffusion compartments respectively.
References
----------
.. [1] Fieremans E, Jensen JH, Helpern JA, 2011. White matter
characterization with diffusional kurtosis imaging.
Neuroimage 58(1):177-88. doi: 10.1016/j.neuroimage.2011.06.006
"""
shape = dki_params.shape[:-1]
# load gradient directions
if not isinstance(sphere, dps.Sphere):
sphere = get_sphere(sphere)
# select voxels where to apply the single fiber model
if mask is None:
mask = np.ones(shape, dtype='bool')
else:
if mask.shape != shape:
raise ValueError("Mask is not the same shape as dki_params.")
else:
mask = np.array(mask, dtype=bool, copy=False)
# check or compute awf values
if awf is None:
awf = axonal_water_fraction(dki_params, sphere=sphere, mask=mask)
else:
if awf.shape != shape:
raise ValueError("awf array is not the same shape as dki_params.")
# Initialize hindered and restricted diffusion tensors
edt_all = np.zeros(shape + (6, ))
idt_all = np.zeros(shape + (6, ))
# Generate matrix that converts apparent diffusion coefficients to tensors
B = np.zeros((sphere.x.size, 6))
B[:, 0] = sphere.x * sphere.x # Bxx
B[:, 1] = sphere.x * sphere.y * 2. # Bxy
B[:, 2] = sphere.y * sphere.y # Byy
B[:, 3] = sphere.x * sphere.z * 2. # Bxz
B[:, 4] = sphere.y * sphere.z * 2. # Byz
B[:, 5] = sphere.z * sphere.z # Bzz
pinvB = np.linalg.pinv(B)
# Compute hindered and restricted diffusion tensors for all voxels
evals, evecs, kt = split_dki_param(dki_params)
dt = lower_triangular(vec_val_vect(evecs, evals))
md = mean_diffusivity(evals)
index = ndindex(mask.shape)
for idx in index:
if not mask[idx]:
continue
# sample apparent diffusion and kurtosis values
di = directional_diffusion(dt[idx], sphere.vertices)
ki = directional_kurtosis(dt[idx],
md[idx],
kt[idx],
sphere.vertices,
adc=di,
min_kurtosis=0)
edi = di * (1 + np.sqrt(ki * awf[idx] / (3.0 - 3.0 * awf[idx])))
edt = np.dot(pinvB, edi)
edt_all[idx] = edt
# We only move on if there is an axonal water fraction.
# Otherwise, remaining params are already zero, so move on
if awf[idx] == 0:
continue
# Convert apparent diffusion and kurtosis values to apparent diffusion
# values of the hindered and restricted diffusion
idi = di * (1 - np.sqrt(ki * (1.0 - awf[idx]) / (3.0 * awf[idx])))
# generate hindered and restricted diffusion tensors
idt = np.dot(pinvB, idi)
idt_all[idx] = idt
return edt_all, idt_all
def _run_interface(self, runtime):
from dipy.reconst.peaks import peaks_from_model
from dipy.tracking.eudx import EuDX
from dipy.data import get_sphere
# import marshal as pickle
import pickle as pickle
import gzip
if (not (isdefined(self.inputs.in_model)
or isdefined(self.inputs.in_peaks))):
raise RuntimeError(('At least one of in_model or in_peaks should '
'be supplied'))
img = nb.load(self.inputs.in_file)
imref = nb.four_to_three(img)[0]
affine = img.affine
data = img.get_data().astype(np.float32)
hdr = imref.header.copy()
hdr.set_data_dtype(np.float32)
hdr['data_type'] = 16
sphere = get_sphere('symmetric724')
self._save_peaks = False
if isdefined(self.inputs.in_peaks):
IFLOGGER.info('Peaks file found, skipping ODF peaks search...')
f = gzip.open(self.inputs.in_peaks, 'rb')
peaks = pickle.load(f)
f.close()
else:
self._save_peaks = True
IFLOGGER.info('Loading model and computing ODF peaks')
f = gzip.open(self.inputs.in_model, 'rb')
odf_model = pickle.load(f)
f.close()
peaks = peaks_from_model(
model=odf_model,
data=data,
sphere=sphere,
relative_peak_threshold=self.inputs.peak_threshold,
min_separation_angle=self.inputs.min_angle,
parallel=self.inputs.multiprocess)
f = gzip.open(self._gen_filename('peaks', ext='.pklz'), 'wb')
pickle.dump(peaks, f, -1)
f.close()
hdr.set_data_shape(peaks.gfa.shape)
nb.Nifti1Image(peaks.gfa.astype(np.float32), affine,
hdr).to_filename(self._gen_filename('gfa'))
IFLOGGER.info('Performing tractography')
if isdefined(self.inputs.tracking_mask):
msk = nb.load(self.inputs.tracking_mask).get_data()
msk[msk > 0] = 1
msk[msk < 0] = 0
else:
msk = np.ones(imref.shape)
gfa = peaks.gfa * msk
seeds = self.inputs.num_seeds
if isdefined(self.inputs.seed_coord):
seeds = np.loadtxt(self.inputs.seed_coord)
elif isdefined(self.inputs.seed_mask):
seedmsk = nb.load(self.inputs.seed_mask).get_data()
assert (seedmsk.shape == data.shape[:3])
seedmsk[seedmsk > 0] = 1
seedmsk[seedmsk < 1] = 0
seedps = np.array(np.where(seedmsk == 1), dtype=np.float32).T
vseeds = seedps.shape[0]
nsperv = (seeds // vseeds) + 1
IFLOGGER.info(
'Seed mask is provided (%d voxels inside '
'mask), computing seeds (%d seeds/voxel).', vseeds, nsperv)
if nsperv > 1:
IFLOGGER.info('Needed %d seeds per selected voxel (total %d).',
nsperv, vseeds)
seedps = np.vstack(np.array([seedps] * nsperv))
voxcoord = seedps + np.random.uniform(-1, 1, size=seedps.shape)
nseeds = voxcoord.shape[0]
seeds = affine.dot(
np.vstack((voxcoord.T, np.ones((1, nseeds)))))[:3, :].T
if self.inputs.save_seeds:
np.savetxt(self._gen_filename('seeds', ext='.txt'), seeds)
if isdefined(self.inputs.tracking_mask):
tmask = msk
a_low = 0.1
else:
tmask = gfa
a_low = self.inputs.gfa_thresh
eu = EuDX(tmask,
peaks.peak_indices[..., 0],
seeds=seeds,
affine=affine,
odf_vertices=sphere.vertices,
a_low=a_low)
ss_mm = [np.array(s) for s in eu]
trkfilev = nb.trackvis.TrackvisFile([(s, None, None) for s in ss_mm],
points_space='rasmm',
affine=np.eye(4))
trkfilev.to_file(self._gen_filename('tracked', ext='.trk'))
return runtime
bvecs = np.c_[bvecs[:, 0], bvecs[:, 1], -bvecs[:, 2]]
gtab = gradient_table(bvals, bvecs)
data, affine, vox_size = load_nifti(fdwi, return_voxsize=True)
scale = [0.5, 0.5, 0.5, 1.]
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
def test_bootstap_peak_tracker():
"""This tests that the Bootstrat Peak Direction Getter plays nice
LocalTracking and produces reasonable streamlines in a simple example.
"""
sphere = get_sphere('repulsion100')
# A simple image with three possible configurations, a vertical tract,
# a horizontal tract and a crossing
simple_image = np.array([
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0],
[2, 3, 2, 2, 2, 0],
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0],
])
simple_image = simple_image[..., None]
bvecs = sphere.vertices
bvals = np.ones(len(bvecs)) * 1000
bvecs = np.insert(bvecs, 0, np.array([0, 0, 0]), axis=0)
bvals = np.insert(bvals, 0, 0)
gtab = gradient_table(bvals, bvecs)
angles = [(90, 90), (90, 0)]
fracs = [50, 50]
mevals = np.array([[1.5, 0.4, 0.4], [1.5, 0.4, 0.4]]) * 1e-3
mevecs = [
np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]),
np.array([[0, 1, 0], [1, 0, 0], [0, 0, 1]])
]
voxel1 = single_tensor(gtab, 1, mevals[0], mevecs[0], snr=None)
voxel2 = single_tensor(gtab, 1, mevals[0], mevecs[1], snr=None)
voxel3, _ = multi_tensor(gtab,
mevals,
fractions=fracs,
angles=angles,
snr=None)
data = np.tile(voxel3, [5, 6, 1, 1])
data[simple_image == 1] = voxel1
data[simple_image == 2] = voxel2
response = (np.array(mevals[1]), 1)
csd_model = ConstrainedSphericalDeconvModel(gtab, response, sh_order=6)
seeds = [np.array([0., 1., 0.]), np.array([2., 4., 0.])]
tc = BinaryTissueClassifier((simple_image > 0).astype(float))
boot_dg = BootDirectionGetter.from_data(data, csd_model, 60)
streamlines_generator = LocalTracking(boot_dg, tc, seeds, np.eye(4), 1.)
streamlines = Streamlines(streamlines_generator)
expected = [
np.array([[0., 1., 0.], [1., 1., 0.], [2., 1., 0.], [3., 1., 0.],
[4., 1., 0.]]),
np.array([
[2., 5., 0.],
[2., 4., 0.],
[2., 3., 0.],
[2., 2., 0.],
[2., 1., 0.],
[2., 0., 0.],
])
]
def allclose(x, y):
return x.shape == y.shape and np.allclose(x, y, atol=0.5)
if not allclose(streamlines[0], expected[0]):
raise AssertionError()
if not allclose(streamlines[1], expected[1]):
raise AssertionError()
def run_track(B0_mask, gm_in_dwi, vent_csf_in_dwi, wm_in_dwi, tiss_class,
labels_im_file_wm_gm_int, labels_im_file, target_samples,
curv_thr_list, step_list, track_type, max_length, maxcrossing,
directget, conn_model, gtab_file, dwi_file, network, node_size,
dens_thresh, ID, roi, min_span_tree, disp_filt, parc, prune,
atlas, uatlas, labels, coords, norm, binary, atlas_mni, life_run,
min_length, fa_path):
'''
Run all ensemble tractography and filtering routines.
Parameters
----------
B0_mask : str
File path to B0 brain mask.
gm_in_dwi : str
File path to grey-matter tissue segmentation Nifti1Image.
vent_csf_in_dwi : str
File path to ventricular CSF tissue segmentation Nifti1Image.
wm_in_dwi : str
File path to white-matter tissue segmentation Nifti1Image.
tiss_class : str
Tissue classification method.
labels_im_file_wm_gm_int : str
File path to atlas parcellation Nifti1Image in T1w-warped native diffusion space, restricted to wm-gm interface.
labels_im_file : str
File path to atlas parcellation Nifti1Image in T1w-warped native diffusion space.
target_samples : int
Total number of streamline samples specified to generate streams.
curv_thr_list : list
List of integer curvature thresholds used to perform ensemble tracking.
step_list : list
List of float step-sizes used to perform ensemble tracking.
track_type : str
Tracking algorithm used (e.g. 'local' or 'particle').
max_length : int
Maximum fiber length threshold in mm to restrict tracking.
maxcrossing : int
Maximum number if diffusion directions that can be assumed per voxel while tracking.
directget : str
The statistical approach to tracking. Options are: det (deterministic), closest (clos), boot (bootstrapped),
and prob (probabilistic).
conn_model : str
Connectivity reconstruction method (e.g. 'csa', 'tensor', 'csd').
gtab_file : str
File path to pickled DiPy gradient table object.
dwi_file : str
File path to diffusion weighted image.
network : str
Resting-state network based on Yeo-7 and Yeo-17 naming (e.g. 'Default')
used to filter nodes in the study of brain subgraphs.
node_size : int
Spherical centroid node size in the case that coordinate-based centroids
are used as ROI's for tracking.
dens_thresh : bool
Indicates whether a target graph density is to be used as the basis for
thresholding.
ID : str
A subject id or other unique identifier.
roi : str
File path to binarized/boolean region-of-interest Nifti1Image file.
min_span_tree : bool
Indicates whether local thresholding from the Minimum Spanning Tree
should be used.
disp_filt : bool
Indicates whether local thresholding using a disparity filter and
'backbone network' should be used.
parc : bool
Indicates whether to use parcels instead of coordinates as ROI nodes.
prune : bool
Indicates whether to prune final graph of disconnected nodes/isolates.
atlas : str
Name of atlas parcellation used.
uatlas : str
File path to atlas parcellation Nifti1Image in MNI template space.
labels : list
List of string labels corresponding to graph nodes.
coords : list
List of (x, y, z) tuples corresponding to a coordinate atlas used or
which represent the center-of-mass of each parcellation node.
norm : int
Indicates method of normalizing resulting graph.
binary : bool
Indicates whether to binarize resulting graph edges to form an
unweighted graph.
atlas_mni : str
File path to atlas parcellation Nifti1Image in T1w-warped MNI space.
life_run : bool
Indicates whether to perform Linear Fascicle Evaluation (LiFE).
min_length : int
Minimum fiber length threshold in mm.
fa_path : str
File path to FA Nifti1Image.
Returns
-------
streams : str
File path to save streamline array sequence in .trk format.
track_type : str
Tracking algorithm used (e.g. 'local' or 'particle').
target_samples : int
Total number of streamline samples specified to generate streams.
conn_model : str
Connectivity reconstruction method (e.g. 'csa', 'tensor', 'csd').
dir_path : str
Path to directory containing subject derivative data for a given pynets run.
network : str
Resting-state network based on Yeo-7 and Yeo-17 naming (e.g. 'Default')
used to filter nodes in the study of brain subgraphs.
node_size : int
Spherical centroid node size in the case that coordinate-based centroids
are used as ROI's for tracking.
dens_thresh : bool
Indicates whether a target graph density is to be used as the basis for
thresholding.
ID : str
A subject id or other unique identifier.
roi : str
File path to binarized/boolean region-of-interest Nifti1Image file.
min_span_tree : bool
Indicates whether local thresholding from the Minimum Spanning Tree
should be used.
disp_filt : bool
Indicates whether local thresholding using a disparity filter and
'backbone network' should be used.
parc : bool
Indicates whether to use parcels instead of coordinates as ROI nodes.
prune : bool
Indicates whether to prune final graph of disconnected nodes/isolates.
atlas : str
Name of atlas parcellation used.
uatlas : str
File path to atlas parcellation Nifti1Image in MNI template space.
labels : list
List of string labels corresponding to graph nodes.
coords : list
List of (x, y, z) tuples corresponding to a coordinate atlas used or
which represent the center-of-mass of each parcellation node.
norm : int
Indicates method of normalizing resulting graph.
binary : bool
Indicates whether to binarize resulting graph edges to form an
unweighted graph.
atlas_mni : str
File path to atlas parcellation Nifti1Image in T1w-warped MNI space.
curv_thr_list : list
List of integer curvature thresholds used to perform ensemble tracking.
step_list : list
List of float step-sizes used to perform ensemble tracking.
fa_path : str
File path to FA Nifti1Image.
dm_path : str
File path to fiber density map Nifti1Image.
'''
try:
import cPickle as pickle
except ImportError:
import _pickle as pickle
from dipy.io import load_pickle
from colorama import Fore, Style
from dipy.data import get_sphere
from pynets import utils
from pynets.dmri.track import prep_tissues, reconstruction, filter_streamlines, track_ensemble
# Load gradient table
gtab = load_pickle(gtab_file)
# Fit diffusion model
mod_fit = reconstruction(conn_model, gtab, dwi_file, wm_in_dwi)
# Load atlas parcellation (and its wm-gm interface reduced version for seeding)
atlas_img = nib.load(labels_im_file)
atlas_data = atlas_img.get_fdata().astype('int')
atlas_img_wm_gm_int = nib.load(labels_im_file_wm_gm_int)
atlas_data_wm_gm_int = atlas_img_wm_gm_int.get_fdata().astype('int')
# Build mask vector from atlas for later roi filtering
parcels = []
i = 0
for roi_val in np.unique(atlas_data)[1:]:
parcels.append(atlas_data == roi_val)
i = i + 1
# Get sphere
sphere = get_sphere('repulsion724')
# Instantiate tissue classifier
tiss_classifier = prep_tissues(B0_mask, gm_in_dwi, vent_csf_in_dwi,
wm_in_dwi, tiss_class)
if np.sum(atlas_data) == 0:
raise ValueError(
'ERROR: No non-zero voxels found in atlas. Check any roi masks and/or wm-gm interface images '
'to verify overlap with dwi-registered atlas.')
# Iteratively build a list of streamlines for each ROI while tracking
print(
"%s%s%s%s" %
(Fore.GREEN, 'Target number of samples: ', Fore.BLUE, target_samples))
print(Style.RESET_ALL)
print("%s%s%s%s" % (Fore.GREEN, 'Using curvature threshold(s): ',
Fore.BLUE, curv_thr_list))
print(Style.RESET_ALL)
print("%s%s%s%s" %
(Fore.GREEN, 'Using step size(s): ', Fore.BLUE, step_list))
print(Style.RESET_ALL)
print("%s%s%s%s" % (Fore.GREEN, 'Tracking type: ', Fore.BLUE, track_type))
print(Style.RESET_ALL)
if directget == 'prob':
print("%s%s%s" %
('Using ', Fore.MAGENTA, 'Probabilistic Direction...'))
elif directget == 'boot':
print("%s%s%s" % ('Using ', Fore.MAGENTA, 'Bootstrapped Direction...'))
elif directget == 'closest':
print("%s%s%s" % ('Using ', Fore.MAGENTA, 'Closest Peak Direction...'))
elif directget == 'det':
print("%s%s%s" %
('Using ', Fore.MAGENTA, 'Deterministic Maximum Direction...'))
print(Style.RESET_ALL)
# Commence Ensemble Tractography
streamlines = track_ensemble(target_samples, atlas_data_wm_gm_int, parcels,
mod_fit, tiss_classifier, sphere, directget,
curv_thr_list, step_list, track_type,
maxcrossing, max_length)
print('Tracking Complete')
# Perform streamline filtering routines
dir_path = utils.do_dir_path(atlas, dwi_file)
[streams, dir_path,
dm_path] = filter_streamlines(dwi_file, dir_path, gtab, streamlines,
life_run, min_length, conn_model,
target_samples, node_size, curv_thr_list,
step_list, network, roi)
return streams, track_type, target_samples, conn_model, dir_path, network, node_size, dens_thresh, ID, roi, min_span_tree, disp_filt, parc, prune, atlas, uatlas, labels, coords, norm, binary, atlas_mni, curv_thr_list, step_list, fa_path, dm_path
def angle_aware_bilateral_filtering_gpu(in_sh, sh_order=8,
sh_basis='descoteaux07',
in_full_basis=False,
sphere_str='repulsion724',
sigma_spatial=1.0,
sigma_angular=1.0,
sigma_range=0.5):
"""
Angle-aware bilateral filtering using OpenCL for GPU computing.
Parameters
----------
in_sh: ndarray (x, y, z, ncoeffs)
Input SH volume.
sh_order: int, optional
Maximum SH order of input volume.
sh_basis: str, optional
Name of SH basis used.
in_full_basis: bool, optional
True if input is expressed in full SH basis.
sphere_str: str, optional
Name of the DIPY sphere to use for sh to sf projection.
sigma_spatial: float, optional
Standard deviation for spatial filter.
sigma_angular: float, optional
Standard deviation for angular filter.
sigma_range: float, optional
Standard deviation for range filter.
Returns
-------
out_sh: ndarray (x, y, z, ncoeffs)
Output SH coefficient array in full SH basis.
"""
s_weights = _get_spatial_weights(sigma_spatial)
h_half_width = len(s_weights) // 2
sphere = get_sphere(sphere_str)
a_weights = _get_angular_weights(s_weights.shape, sphere, sigma_angular)
h_weights = s_weights[..., None] * a_weights
h_weights /= np.sum(h_weights, axis=(0, 1, 2))
sh_to_sf_mat = sh_to_sf_matrix(sphere, sh_order=sh_order,
basis_type=sh_basis,
full_basis=in_full_basis,
return_inv=False)
_, sf_to_sh_mat = sh_to_sf_matrix(sphere, sh_order=sh_order,
basis_type=sh_basis,
full_basis=True,
return_inv=True)
out_n_coeffs = sf_to_sh_mat.shape[1]
n_dirs = len(sphere.vertices)
volume_shape = in_sh.shape
in_sh = np.pad(in_sh, ((h_half_width, h_half_width),
(h_half_width, h_half_width),
(h_half_width, h_half_width),
(0, 0)))
cl_kernel = CLKernel('correlate', 'denoise', 'angle_aware_bilateral.cl')
cl_kernel.set_define('IM_X_DIM', volume_shape[0])
cl_kernel.set_define('IM_Y_DIM', volume_shape[1])
cl_kernel.set_define('IM_Z_DIM', volume_shape[2])
cl_kernel.set_define('H_X_DIM', h_weights.shape[0])
cl_kernel.set_define('H_Y_DIM', h_weights.shape[1])
cl_kernel.set_define('H_Z_DIM', h_weights.shape[2])
cl_kernel.set_define('SIGMA_RANGE', float(sigma_range))
cl_kernel.set_define('IN_N_COEFFS', volume_shape[-1])
cl_kernel.set_define('OUT_N_COEFFS', out_n_coeffs)
cl_kernel.set_define('N_DIRS', n_dirs)
cl_manager = CLManager(cl_kernel, 4, 1)
cl_manager.add_input_buffer(0, in_sh)
cl_manager.add_input_buffer(1, h_weights)
cl_manager.add_input_buffer(2, sh_to_sf_mat)
cl_manager.add_input_buffer(3, sf_to_sh_mat)
cl_manager.add_output_buffer(0, volume_shape[:3] + (out_n_coeffs,),
np.float32)
outputs = cl_manager.run(volume_shape[:3])
return outputs[0]
def __init__(self,
gtab,
sh_order=8,
lambda_lb=1e-3,
dec_alg='CSD',
sphere=None,
lambda_csd=1.0):
r""" Analytical and continuous modeling of the diffusion signal with
respect to the FORECAST basis [1,2,3]_.
This implementation is a modification of the original FORECAST
model presented in [1]_ adapted for multi-shell data as in [2,3]_ .
The main idea is to model the diffusion signal as the combination of a
single fiber response function $F(\mathbf{b})$ times the fODF
$\rho(\mathbf{v})$
..math::
:nowrap:
\begin{equation}
E(\mathbf{b}) = \int_{\mathbf{v} \in \mathcal{S}^2} \rho(\mathbf{v}) F({\mathbf{b}} | \mathbf{v}) d \mathbf{v}
\end{equation}
where $\mathbf{b}$ is the b-vector (b-value times gradient direction)
and $\mathbf{v}$ is an unit vector representing a fiber direction.
In FORECAST $\rho$ is modeled using real symmetric Spherical Harmonics
(SH) and $F(\mathbf(b))$ is an axially symmetric tensor.
Parameters
----------
gtab : GradientTable,
gradient directions and bvalues container class.
sh_order : unsigned int,
an even integer that represent the SH order of the basis (max 12)
lambda_lb: float,
Laplace-Beltrami regularization weight.
dec_alg : str,
Spherical deconvolution algorithm. The possible values are Weighted Least Squares ('WLS'),
Positivity Constraints using CVXPY ('POS') and the Constraint
Spherical Deconvolution algorithm ('CSD'). Default is 'CSD'.
sphere : array, shape (N,3),
sphere points where to enforce positivity when 'POS' or 'CSD'
dec_alg are selected.
lambda_csd : float,
CSD regularization weight.
References
----------
.. [1] Anderson A. W., "Measurement of Fiber Orientation Distributions
Using High Angular Resolution Diffusion Imaging", Magnetic
Resonance in Medicine, 2005.
.. [2] Kaden E. et al., "Quantitative Mapping of the Per-Axon Diffusion
Coefficients in Brain White Matter", Magnetic Resonance in
Medicine, 2016.
.. [3] Zucchelli M. et al., "A generalized SMT-based framework for
Diffusion MRI microstructural model estimation", MICCAI Workshop
on Computational DIFFUSION MRI (CDMRI), 2017.
Examples
--------
In this example, where the data, gradient table and sphere tessellation
used for reconstruction are provided, we model the diffusion signal
with respect to the FORECAST and compute the fODF, parallel and
perpendicular diffusivity.
>>> from dipy.data import get_sphere, get_3shell_gtab
>>> gtab = get_3shell_gtab()
>>> from dipy.sims.voxel import MultiTensor
>>> mevals = np.array(([0.0017, 0.0003, 0.0003],
... [0.0017, 0.0003, 0.0003]))
>>> angl = [(0, 0), (60, 0)]
>>> data, sticks = MultiTensor(gtab,
... mevals,
... S0=100.0,
... angles=angl,
... fractions=[50, 50],
... snr=None)
>>> from dipy.reconst.forecast import ForecastModel
>>> fm = ForecastModel(gtab, sh_order=6)
>>> f_fit = fm.fit(data)
>>> d_par = f_fit.dpar
>>> d_perp = f_fit.dperp
>>> sphere = get_sphere('symmetric724')
>>> fodf = f_fit.odf(sphere)
"""
OdfModel.__init__(self, gtab)
# round the bvals in order to avoid numerical errors
self.bvals = np.round(gtab.bvals / 100) * 100
self.bvecs = gtab.bvecs
if sh_order >= 0 and not (bool(sh_order % 2)) and sh_order <= 12:
self.sh_order = sh_order
else:
msg = "sh_order must be a non-zero even positive number "
msg += "between 2 and 12"
raise ValueError(msg)
if sphere is None:
sphere = get_sphere('repulsion724')
self.vertices = sphere.vertices[0:int(sphere.vertices.shape[0] /
2), :]
else:
self.vertices = sphere
self.b0s_mask = self.bvals == 0
self.one_0_bvals = np.r_[0, self.bvals[~self.b0s_mask]]
self.one_0_bvecs = np.r_[np.array([0, 0, 0]).reshape(1, 3),
self.bvecs[~self.b0s_mask, :]]
self.rho = rho_matrix(self.sh_order, self.one_0_bvecs)
# signal regularization matrix
self.srm = rho_matrix(4, self.one_0_bvecs)
self.lb_matrix_signal = lb_forecast(4)
self.b_unique = np.sort(np.unique(self.bvals[self.bvals > 0]))
self.wls = True
self.csd = False
self.pos = False
if dec_alg.upper() == 'POS':
if have_cvxpy:
self.wls = False
self.pos = True
else:
msg = 'cvxpy is needed to inforce positivity constraints.'
raise ValueError(msg)
if dec_alg.upper() == 'CSD':
self.csd = True
self.lb_matrix = lb_forecast(self.sh_order)
self.lambda_lb = lambda_lb
self.lambda_csd = lambda_csd
self.fod = rho_matrix(sh_order, self.vertices)
def test_predict():
"""
Test model prediction API
"""
psphere = get_sphere('symmetric362')
bvecs = np.concatenate(([[1, 0, 0]], psphere.vertices))
bvals = np.zeros(len(bvecs)) + 1000
bvals[0] = 0
gtab = grad.gradient_table(bvals, bvecs)
mevals = np.array(([0.0015, 0.0003, 0.0001], [0.0015, 0.0003, 0.0003]))
mevecs = [
np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]),
np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]])
]
S = single_tensor(gtab, 100, mevals[0], mevecs[0], snr=None)
dm = dti.TensorModel(gtab, 'LS', return_S0_hat=True)
dmfit = dm.fit(S)
npt.assert_array_almost_equal(dmfit.predict(gtab, S0=100), S)
npt.assert_array_almost_equal(dmfit.predict(gtab), S)
npt.assert_array_almost_equal(dm.predict(dmfit.model_params, S0=100), S)
fdata, fbvals, fbvecs = get_fnames()
data = load_nifti_data(fdata)
# Make the data cube a bit larger:
data = np.tile(data.T, 2).T
gtab = grad.gradient_table(fbvals, fbvecs)
dtim = dti.TensorModel(gtab)
dtif = dtim.fit(data)
S0 = np.mean(data[..., gtab.b0s_mask], -1)
p = dtif.predict(gtab, S0)
npt.assert_equal(p.shape, data.shape)
# Predict using S0_hat:
dtim = dti.TensorModel(gtab, return_S0_hat=True)
dtif = dtim.fit(data)
p = dtif.predict(gtab)
npt.assert_equal(p.shape, data.shape)
p = dtif.predict(gtab, S0)
npt.assert_equal(p.shape, data.shape)
# Test iter_fit_tensor with S0_hat
dtim = dti.TensorModel(gtab, step=2, return_S0_hat=True)
dtif = dtim.fit(data)
S0 = np.mean(data[..., gtab.b0s_mask], -1)
p = dtif.predict(gtab, S0)
npt.assert_equal(p.shape, data.shape)
# Use a smaller step in predicting:
dtim = dti.TensorModel(gtab, step=2)
dtif = dtim.fit(data)
S0 = np.mean(data[..., gtab.b0s_mask], -1)
p = dtif.predict(gtab, S0)
npt.assert_equal(p.shape, data.shape)
# And with a scalar S0:
S0 = 1
p = dtif.predict(gtab, S0)
npt.assert_equal(p.shape, data.shape)
# Assign the step through kwarg:
p = dtif.predict(gtab, S0, step=1)
npt.assert_equal(p.shape, data.shape)
# And without S0:
p = dtif.predict(gtab, step=1)
npt.assert_equal(p.shape, data.shape)
def run_tractography(fdwi,
fbval,
fbvec,
fwmparc,
mod_func,
mod_type,
seed_density=20):
"""
mod_func : 'str'
'csd' or 'csa'
mod_type : 'str'
'det' or 'prob'
seed_density : int, default=20
Seeding density for tractography
"""
# Getting default params
sphere = get_sphere("repulsion724")
stream_affine = np.eye(4)
# Loading data
print("Loading Data...")
dwi, gtab, wm_mask = load_data(fdwi, fbval, fbvec, fwmparc)
# Make tissue classifier
tiss_classifier = BinaryStoppingCriterion(wm_mask)
if mod_func == "csd":
mod = csd_mod_est(gtab, dwi, wm_mask)
elif mod_func == "csa":
mod = odf_mod_est(gtab)
# Build seed list
seeds = utils.random_seeds_from_mask(
wm_mask,
affine=stream_affine,
seeds_count=int(seed_density),
seed_count_per_voxel=True,
)
# Make streamlines
if mod_type == "det":
print("Obtaining peaks from model...")
direction_getter = peaks_from_model(
mod,
dwi,
sphere,
relative_peak_threshold=0.5,
min_separation_angle=25,
mask=wm_mask,
npeaks=5,
normalize_peaks=True,
)
elif mod_type == "prob":
print("Preparing probabilistic tracking...")
print("Fitting model to data...")
mod_fit = mod.fit(dwi, wm_mask)
print("Building direction-getter...")
try:
print(
"Proceeding using spherical harmonic coefficient from model estimation..."
)
direction_getter = ProbabilisticDirectionGetter.from_shcoeff(
mod_fit.shm_coeff, max_angle=60.0, sphere=sphere)
except:
print("Proceeding using FOD PMF from model estimation...")
fod = mod_fit.odf(sphere)
pmf = fod.clip(min=0)
direction_getter = ProbabilisticDirectionGetter.from_pmf(
pmf, max_angle=60.0, sphere=sphere)
print("Running Local Tracking")
streamline_generator = LocalTracking(
direction_getter,
tiss_classifier,
seeds,
stream_affine,
step_size=0.5,
return_all=True,
)
print("Reconstructing tractogram streamlines...")
streamlines = Streamlines(streamline_generator)
tracks = Streamlines([track for track in streamlines if len(track) > 60])
return tracks
def _get_direction_getter(args, mask_data):
sh_data = nib.load(args.in_sh).get_fdata(dtype=np.float32)
sphere = HemiSphere.from_sphere(get_sphere(args.sphere))
theta = get_theta(args.theta, args.algo)
non_zeros_count = np.count_nonzero(np.sum(sh_data, axis=-1))
non_first_val_count = np.count_nonzero(np.argmax(sh_data, axis=-1))
if args.algo in ['det', 'prob']:
if non_first_val_count / non_zeros_count > 0.5:
logging.warning('Input detected as peaks. Input should be'
'fodf for det/prob, verify input just in case.')
if args.algo == 'det':
dg_class = DeterministicMaximumDirectionGetter
else:
dg_class = ProbabilisticDirectionGetter
return dg_class.from_shcoeff(shcoeff=sh_data,
max_angle=theta,
sphere=sphere,
basis_type=args.sh_basis,
relative_peak_threshold=args.sf_threshold)
elif args.algo == 'eudx':
# Code for type EUDX. We don't use peaks_from_model
# because we want the peaks from the provided sh.
sh_shape_3d = sh_data.shape[:-1]
dg = PeaksAndMetrics()
dg.sphere = sphere
dg.ang_thr = theta
dg.qa_thr = args.sf_threshold
# Heuristic to find out if the input are peaks or fodf
# fodf are always around 0.15 and peaks around 0.75
if non_first_val_count / non_zeros_count > 0.5:
logging.info('Input detected as peaks.')
nb_peaks = sh_data.shape[-1] // 3
slices = np.arange(0, 15 + 1, 3)
peak_values = np.zeros(sh_shape_3d + (nb_peaks, ))
peak_indices = np.zeros(sh_shape_3d + (nb_peaks, ))
for idx in np.argwhere(np.sum(sh_data, axis=-1)):
idx = tuple(idx)
for i in range(nb_peaks):
peak_values[idx][i] = np.linalg.norm(
sh_data[idx][slices[i]:slices[i + 1]], axis=-1)
peak_indices[idx][i] = sphere.find_closest(
sh_data[idx][slices[i]:slices[i + 1]])
dg.peak_dirs = sh_data
else:
logging.info('Input detected as fodf.')
npeaks = 5
peak_dirs = np.zeros((sh_shape_3d + (npeaks, 3)))
peak_values = np.zeros((sh_shape_3d + (npeaks, )))
peak_indices = np.full((sh_shape_3d + (npeaks, )), -1, dtype='int')
b_matrix = get_b_matrix(find_order_from_nb_coeff(sh_data), sphere,
args.sh_basis)
for idx in np.argwhere(np.sum(sh_data, axis=-1)):
idx = tuple(idx)
directions, values, indices = get_maximas(
sh_data[idx], sphere, b_matrix, args.sf_threshold, 0)
if values.shape[0] != 0:
n = min(npeaks, values.shape[0])
peak_dirs[idx][:n] = directions[:n]
peak_values[idx][:n] = values[:n]
peak_indices[idx][:n] = indices[:n]
dg.peak_dirs = peak_dirs
dg.peak_values = peak_values
dg.peak_indices = peak_indices
return dg
def dMRI2ODF_DTI(PATH):
'''
Input the dMRI data
return the ODF
'''
dMRI_path = PATH + 'data.nii.gz'
mask_path = PATH + 'nodif_brain_mask.nii.gz'
dMRI_img = nib.load(dMRI_path)
dMRI_data = dMRI_img.get_fdata()
mask_img = nib.load(mask_path)
mask = mask_img.get_fdata()
########## subsample ##########
# dMRI_data = dMRI_data[45:-48,50:-65,51:-54,...]
# mask = mask[45:-48,50:-65,51:-54]
# breakpoint()
dMRI_data = dMRI_data[:, 87, ...]
mask = mask[:, 87, ...]
for cnt in range(10):
fig = plt.imshow(dMRI_data[:, :, cnt].transpose(1, 0),
cmap='Greys',
interpolation='nearest')
plt.axis('off')
# plt.imshow(dMRI_data[:,15,:,cnt].transpose(1,0),cmap='Greys')
plt.savefig(str(cnt) + '.png',
bbox_inches='tight',
dpi=300,
transparent=True,
pad_inches=0)
# breakpoint()
bval = PATH + "bvals"
bvec = PATH + "bvecs"
radial_order = 6
zeta = 700
lambdaN = 1e-8
lambdaL = 1e-8
gtab = gradient_table(bvals=bval, bvecs=bvec)
asm = ShoreModel(gtab,
radial_order=radial_order,
zeta=zeta,
lambdaN=lambdaN,
lambdaL=lambdaL)
asmfit = asm.fit(dMRI_data, mask=mask)
sphere = get_sphere('symmetric362')
dMRI_odf = asmfit.odf(sphere)
dMRI_odf[dMRI_odf <= 0] = 0
tenmodel = dti.TensorModel(gtab)
tenfit = tenmodel.fit(dMRI_data, mask)
dMRI_dti = tenfit.quadratic_form
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
FA = np.clip(FA, 0, 1)
RGB = color_fa(FA, tenfit.evecs)
evals = tenfit.evals + 1e-20
evecs = tenfit.evecs
cfa = RGB + 1e-20
cfa /= cfa.max()
evals = np.expand_dims(evals, 2)
evecs = np.expand_dims(evecs, 2)
cfa = np.expand_dims(cfa, 2)
ren = window.Scene()
sphere = get_sphere('symmetric362')
ren.add(
actor.tensor_slicer(evals,
evecs,
scalar_colors=cfa,
sphere=sphere,
scale=0.5))
window.record(ren,
n_frames=1,
out_path='../data/tensor.png',
size=(5000, 5000))
odf_ = dMRI_odf
ren = window.Scene()
sfu = actor.odf_slicer(np.expand_dims(odf_, 2),
sphere=sphere,
colormap="plasma",
scale=0.5)
ren.add(sfu)
window.record(ren,
n_frames=1,
out_path='../data/odfs.png',
size=(5000, 5000))
return None
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 test_particle_filtering_tractography():
"""This tests that the ParticleFilteringTracking produces
more streamlines connecting the gray matter than LocalTracking.
"""
sphere = get_sphere('repulsion100')
step_size = 0.2
# Simple tissue masks
simple_wm = np.array([[0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0],
[0, 1, 1, 1, 0, 0], [0, 1, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 0]])
simple_wm = np.dstack([
np.zeros(simple_wm.shape), simple_wm, simple_wm, simple_wm,
np.zeros(simple_wm.shape)
])
simple_gm = np.array([[1, 1, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 1, 0], [0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0]])
simple_gm = np.dstack([
np.zeros(simple_gm.shape), simple_gm, simple_gm, simple_gm,
np.zeros(simple_gm.shape)
])
simple_csf = np.ones(simple_wm.shape) - simple_wm - simple_gm
tc = ActTissueClassifier.from_pve(simple_wm, simple_gm, simple_csf)
seeds = seeds_from_mask(simple_wm, density=2)
# Random pmf in every voxel
shape_img = list(simple_wm.shape)
shape_img.extend([sphere.vertices.shape[0]])
np.random.seed(0) # Random number generator initialization
pmf = np.random.random(shape_img)
# Test that PFT recover equal or more streamlines than localTracking
dg = ProbabilisticDirectionGetter.from_pmf(pmf, 60, sphere)
local_streamlines_generator = LocalTracking(dg,
tc,
seeds,
np.eye(4),
step_size,
max_cross=1,
return_all=False)
local_streamlines = Streamlines(local_streamlines_generator)
pft_streamlines_generator = ParticleFilteringTracking(
dg,
tc,
seeds,
np.eye(4),
step_size,
max_cross=1,
return_all=False,
pft_back_tracking_dist=1,
pft_front_tracking_dist=0.5)
pft_streamlines = Streamlines(pft_streamlines_generator)
npt.assert_(np.array([len(pft_streamlines) > 0]))
npt.assert_(np.array([len(pft_streamlines) >= len(local_streamlines)]))
# Test that all points are equally spaced
for l in [1, 2, 5, 10, 100]:
pft_streamlines = ParticleFilteringTracking(dg,
tc,
seeds,
np.eye(4),
step_size,
max_cross=1,
return_all=True,
maxlen=l)
for s in pft_streamlines:
for i in range(len(s) - 1):
npt.assert_almost_equal(np.linalg.norm(s[i] - s[i + 1]),
step_size)
# Test that all points are within the image volume
seeds = seeds_from_mask(np.ones(simple_wm.shape), density=1)
pft_streamlines_generator = ParticleFilteringTracking(dg,
tc,
seeds,
np.eye(4),
step_size,
max_cross=1,
return_all=True)
pft_streamlines = Streamlines(pft_streamlines_generator)
for s in pft_streamlines:
npt.assert_(np.all((s + 0.5).astype(int) >= 0))
npt.assert_(np.all((s + 0.5).astype(int) < simple_wm.shape))
# Test that the number of streamline return with return_all=True equal the
# number of seeds places
npt.assert_(np.array([len(pft_streamlines) == len(seeds)]))
# Test non WM seed position
seeds = [[0, 5, 4], [0, 0, 1], [50, 50, 50]]
pft_streamlines_generator = ParticleFilteringTracking(dg,
tc,
seeds,
np.eye(4),
step_size,
max_cross=1,
return_all=True)
pft_streamlines = Streamlines(pft_streamlines_generator)
npt.assert_equal(len(pft_streamlines[0]), 3) # INVALIDPOINT
npt.assert_equal(len(pft_streamlines[1]), 3) # ENDPOINT
npt.assert_equal(len(pft_streamlines[2]), 1) # OUTSIDEIMAGE
# Test with wrong tissueclassifier type
tc_bin = BinaryTissueClassifier(simple_wm)
npt.assert_raises(
ValueError, lambda: ParticleFilteringTracking(dg, tc_bin, seeds,
np.eye(4), step_size))
# Test with invalid back/front tracking distances
npt.assert_raises(
ValueError,
lambda: ParticleFilteringTracking(dg,
tc,
seeds,
np.eye(4),
step_size,
pft_back_tracking_dist=0,
pft_front_tracking_dist=0))
npt.assert_raises(
ValueError, lambda: ParticleFilteringTracking(
dg, tc, seeds, np.eye(4), step_size, pft_back_tracking_dist=-1))
npt.assert_raises(
ValueError,
lambda: ParticleFilteringTracking(dg,
tc,
seeds,
np.eye(4),
step_size,
pft_back_tracking_dist=0,
pft_front_tracking_dist=-2))
# Test with invalid affine shape
npt.assert_raises(
ValueError,
lambda: ParticleFilteringTracking(dg, tc, seeds, np.eye(3), step_size))
# Test with invalid maxlen
npt.assert_raises(
ValueError, lambda: ParticleFilteringTracking(
dg, tc, seeds, np.eye(4), step_size, maxlen=0))
npt.assert_raises(
ValueError, lambda: ParticleFilteringTracking(
dg, tc, seeds, np.eye(4), step_size, maxlen=-1))
# Test with invalid particle count
npt.assert_raises(
ValueError, lambda: ParticleFilteringTracking(
dg, tc, seeds, np.eye(4), step_size, particle_count=0))
npt.assert_raises(
ValueError, lambda: ParticleFilteringTracking(
dg, tc, seeds, np.eye(4), step_size, particle_count=-1))
# Test reproducibility
tracking_1 = Streamlines(
ParticleFilteringTracking(dg,
tc,
seeds,
np.eye(4),
step_size,
random_seed=0)).data
tracking_2 = Streamlines(
ParticleFilteringTracking(dg,
tc,
seeds,
np.eye(4),
step_size,
random_seed=0)).data
npt.assert_equal(tracking_1, tracking_2)
this response.
It is a very good practice to always validate the result of auto_response. For,
this purpose we can print it and have a look at its values.
"""
print(response)
"""
(array([ 0.0014, 0.00029, 0.00029]), 416.206)
We initialize an SFM model object, using these values. We will use the default
sphere (362 vertices, symmetrically distributed on the surface of the sphere),
as a set of putative fascicle directions that are considered in the model
"""
sphere = dpd.get_sphere()
sf_model = sfm.SparseFascicleModel(gtab,
sphere=sphere,
l1_ratio=0.5,
alpha=0.001,
response=response[0])
"""
For the purpose of the example, we will consider a small volume of data
containing parts of the corpus callosum and of the centrum semiovale
"""
data_small = data[20:50, 55:85, 38:39]
"""
Fitting the model to this small volume of data, we calculate the ODF of this
model on the sphere, and plot it.
"""
def run_track(nodif_B0_mask, gm_in_dwi, vent_csf_in_dwi, wm_in_dwi, tiss_class, labels_im_file_wm_gm_int,
labels_im_file, target_samples, curv_thr_list, step_list, track_type, max_length, maxcrossing, directget,
conn_model, gtab_file, dwi_file, network, node_size, dens_thresh, ID, roi, min_span_tree, disp_filt, parc,
prune, atlas_select, uatlas_select, label_names, coords, norm, binary, atlas_mni, life_run, min_length,
fa_path):
try:
import cPickle as pickle
except ImportError:
import _pickle as pickle
from dipy.io import load_pickle
from colorama import Fore, Style
from dipy.data import get_sphere
from pynets import utils
from pynets.dmri.track import prep_tissues, reconstruction, filter_streamlines, track_ensemble
# Load gradient table
gtab = load_pickle(gtab_file)
# Fit diffusion model
mod_fit = reconstruction(conn_model, gtab, dwi_file, wm_in_dwi)
# Load atlas parcellation (and its wm-gm interface reduced version for seeding)
atlas_img = nib.load(labels_im_file)
atlas_data = atlas_img.get_fdata().astype('int')
atlas_img_wm_gm_int = nib.load(labels_im_file_wm_gm_int)
atlas_data_wm_gm_int = atlas_img_wm_gm_int.get_fdata().astype('int')
# Build mask vector from atlas for later roi filtering
parcels = []
i = 0
for roi_val in np.unique(atlas_data)[1:]:
parcels.append(atlas_data == roi_val)
i = i + 1
parcel_vec = np.ones(len(parcels))
# Get sphere
sphere = get_sphere('repulsion724')
# Instantiate tissue classifier
tiss_classifier = prep_tissues(nodif_B0_mask, gm_in_dwi, vent_csf_in_dwi, wm_in_dwi, tiss_class)
if np.sum(atlas_data) == 0:
raise ValueError('ERROR: No non-zero voxels found in atlas. Check any roi masks and/or wm-gm interface images '
'to verify overlap with dwi-registered atlas.')
# Iteratively build a list of streamlines for each ROI while tracking
print("%s%s%s%s" % (Fore.GREEN, 'Target number of samples: ', Fore.BLUE, target_samples))
print(Style.RESET_ALL)
print("%s%s%s%s" % (Fore.GREEN, 'Using curvature threshold(s): ', Fore.BLUE, curv_thr_list))
print(Style.RESET_ALL)
print("%s%s%s%s" % (Fore.GREEN, 'Using step size(s): ', Fore.BLUE, step_list))
print(Style.RESET_ALL)
print("%s%s%s%s" % (Fore.GREEN, 'Tracking type: ', Fore.BLUE, track_type))
print(Style.RESET_ALL)
if directget == 'prob':
print("%s%s%s" % ('Using ', Fore.MAGENTA, 'Probabilistic Direction...'))
elif directget == 'boot':
print("%s%s%s" % ('Using ', Fore.MAGENTA, 'Bootstrapped Direction...'))
elif directget == 'closest':
print("%s%s%s" % ('Using ', Fore.MAGENTA, 'Closest Peak Direction...'))
elif directget == 'det':
print("%s%s%s" % ('Using ', Fore.MAGENTA, 'Deterministic Maximum Direction...'))
print(Style.RESET_ALL)
# Commence Ensemble Tractography
streamlines = track_ensemble(target_samples, atlas_data_wm_gm_int, parcels, parcel_vec,
mod_fit, tiss_classifier, sphere, directget, curv_thr_list, step_list, track_type,
maxcrossing, max_length)
print('Tracking Complete')
# Perform streamline filtering routines
dir_path = utils.do_dir_path(atlas_select, dwi_file)
[streams, dir_path] = filter_streamlines(dwi_file, dir_path, gtab, streamlines, life_run, min_length, conn_model,
target_samples, node_size, curv_thr_list, step_list)
return streams, track_type, target_samples, conn_model, dir_path, network, node_size, dens_thresh, ID, roi, min_span_tree, disp_filt, parc, prune, atlas_select, uatlas_select, label_names, coords, norm, binary, atlas_mni, curv_thr_list, step_list, fa_path
def execution(self, context):
sh_coeff_vol = aims.read(self.sh_coefficients.fullPath())
header = sh_coeff_vol.header()
#transformation from Aims LPI mm space to RAS mm (reference space)
aims_mm_to_ras_mm = np.array(header['transformations'][0]).reshape((4, 4))
voxel_size = np.array(header['voxel_size'])
if len(voxel_size) == 4:
voxel_size = voxel_size[:-1]
scaling = np.concatenate((voxel_size, np.ones(1)))
context.write(voxel_size.shape)
scaling_mat = np.diag(scaling)
context.write(scaling_mat.shape, aims_mm_to_ras_mm.shape)
aims_voxel_to_ras_mm = np.dot(scaling_mat, aims_mm_to_ras_mm)
affine_tracking = np.eye(4)
sh = np.array(sh_coeff_vol, copy=True)
sh = sh.astype(np.float64)
vol_shape = sh.shape[:-1]
if self.sphere is not None:
sphere = read_sphere(self.sphere.fullPath())
else:
context.write(
'No Projection Sphere provided. Default dipy sphere symmetric 362 is used'
)
sphere = get_sphere()
dg = DirectionGetter[self.type].from_shcoeff(
sh,
self.max_angle,
sphere,
basis_type=None,
relative_peak_threshold=self.relative_peak_threshold,
min_separation_angle=self.min_separation_angle)
#Handling seeds in both deterministic and probabilistic framework
s = np.loadtxt(self.seeds.fullPath())
s = s.astype(np.float32)
i = np.arange(self.nb_samples)
if self.nb_samples <= 1:
seeds = s
else:
seeds = np.zeros((self.nb_samples, ) + s.shape)
seeds[i] = s
seeds = seeds.reshape((-1, 3))
seeds = nib.affines.apply_affine(np.linalg.inv(scaling_mat), seeds)
#building classifier
csf_vol = aims.read(self.csf_pve.fullPath())
grey_vol = aims.read(self.gm_pve.fullPath())
white_vol = aims.read(self.wm_pve.fullPath())
csf = np.array(csf_vol)
csf = csf[..., 0]
gm = np.array(grey_vol)
gm = gm[..., 0]
wm = np.array(white_vol)
wm = wm[..., 0]
#rethreshold volumes due to interpolation (eg values >1)
total = (csf + gm + wm).copy()
csf[total <= 0] = 0
gm[total <= 0] = 0
wm[total <= 0] = 0
csf[total != 0] = (csf[total != 0]) / (total[total != 0])
wm[total != 0] = (wm[total != 0]) / (total[total != 0])
gm[total != 0] = gm[total != 0] / (total[total != 0])
classif = Classifiers[self.constraint]
classifier = classif.from_pve(wm_map=wm, gm_map=gm, csf_map=csf)
#Tracking is made in the LPI voxel space in order no to imposes affine to data. The seeds are supposed to also be in LPI voxel space
streamlines_generator = ParticleFilteringTracking(
dg,
classifier,
seeds,
affine_tracking,
step_size=self.step_size,
max_cross=self.crossing_max,
maxlen=self.nb_iter_max,
pft_back_tracking_dist=self.back_tracking_dist,
pft_front_tracking_dist=self.front_tracking_dist,
pft_max_trial=self.max_trial,
particle_count=self.nb_particles,
return_all=self.return_all)
#Store Fibers directly in LPI orientation with appropriate transformation
save_trk(self.streamlines.fullPath(),
streamlines_generator,
affine=aims_voxel_to_ras_mm,
vox_size=voxel_size,
shape=vol_shape)
transformManager = getTransformationManager()
transformManager.copyReferential(self.sh_coefficients, self.streamlines)
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 test_csdeconv():
SNR = 100
S0 = 1
_, fbvals, fbvecs = get_fnames('small_64D')
bvals, bvecs = read_bvals_bvecs(fbvals, fbvecs)
gtab = gradient_table(bvals, bvecs, b0_threshold=0)
mevals = np.array(([0.0015, 0.0003, 0.0003], [0.0015, 0.0003, 0.0003]))
angles = [(0, 0), (60, 0)]
S, sticks = multi_tensor(gtab,
mevals,
S0,
angles=angles,
fractions=[50, 50],
snr=SNR)
sphere = get_sphere('symmetric362')
odf_gt = multi_tensor_odf(sphere.vertices, mevals, angles, [50, 50])
response = (np.array([0.0015, 0.0003, 0.0003]), S0)
csd = ConstrainedSphericalDeconvModel(gtab, response)
csd_fit = csd.fit(S)
assert_equal(csd_fit.shm_coeff[0] > 0, True)
fodf = csd_fit.odf(sphere)
directions, _, _ = peak_directions(odf_gt, sphere)
directions2, _, _ = peak_directions(fodf, sphere)
ang_sim = angular_similarity(directions, directions2)
assert_equal(ang_sim > 1.9, True)
assert_equal(directions.shape[0], 2)
assert_equal(directions2.shape[0], 2)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always", category=UserWarning)
_ = ConstrainedSphericalDeconvModel(gtab, response, sh_order=10)
assert_greater(
len([lw for lw in w if issubclass(lw.category, UserWarning)]), 0)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always", category=UserWarning)
ConstrainedSphericalDeconvModel(gtab, response, sh_order=8)
assert_equal(
len([lw for lw in w if issubclass(lw.category, UserWarning)]), 0)
mevecs = []
for s in sticks:
mevecs += [all_tensor_evecs(s).T]
S2 = single_tensor(gtab, 100, mevals[0], mevecs[0], snr=None)
big_S = np.zeros((10, 10, 10, len(S2)))
big_S[:] = S2
aresponse, aratio = auto_response_ssst(gtab,
big_S,
roi_center=(5, 5, 4),
roi_radii=3,
fa_thr=0.5)
assert_array_almost_equal(aresponse[0], response[0])
assert_almost_equal(aresponse[1], 100)
assert_almost_equal(aratio, response[0][1] / response[0][0])
auto_response_ssst(gtab, big_S, roi_radii=3, fa_thr=0.5)
assert_array_almost_equal(aresponse[0], response[0])
def test_odfdeconv():
SNR = 100
S0 = 1
_, fbvals, fbvecs = get_fnames('small_64D')
bvals, bvecs = read_bvals_bvecs(fbvals, fbvecs)
gtab = gradient_table(bvals, bvecs)
mevals = np.array(([0.0015, 0.0003, 0.0003], [0.0015, 0.0003, 0.0003]))
angles = [(0, 0), (90, 0)]
S, _ = multi_tensor(gtab,
mevals,
S0,
angles=angles,
fractions=[50, 50],
snr=SNR)
sphere = get_sphere('symmetric362')
odf_gt = multi_tensor_odf(sphere.vertices, mevals, angles, [50, 50])
e1 = 15.0
e2 = 3.0
ratio = e2 / e1
csd = ConstrainedSDTModel(gtab, ratio, None)
csd_fit = csd.fit(S)
fodf = csd_fit.odf(sphere)
directions, _, _ = peak_directions(odf_gt, sphere)
directions2, _, _ = peak_directions(fodf, sphere)
ang_sim = angular_similarity(directions, directions2)
assert_equal(ang_sim > 1.9, True)
assert_equal(directions.shape[0], 2)
assert_equal(directions2.shape[0], 2)
with warnings.catch_warnings(record=True) as w:
ConstrainedSDTModel(gtab, ratio, sh_order=10)
w_count = len(w)
# A warning is expected from the ConstrainedSDTModel constructor
# and additionnal warnings should be raised where legacy SH bases
# are used
assert_equal(w_count > 1, True)
with warnings.catch_warnings(record=True) as w:
ConstrainedSDTModel(gtab, ratio, sh_order=8)
# Test that the warning from ConstrainedSDTModel
# constructor is no more raised
assert_equal(len(w) == w_count - 1, True)
csd_fit = csd.fit(np.zeros_like(S))
fodf = csd_fit.odf(sphere)
assert_array_equal(fodf, np.zeros_like(fodf))
odf_sh = np.zeros_like(fodf)
odf_sh[1] = np.nan
fodf, _ = odf_deconv(odf_sh, csd.R, csd.B_reg)
assert_array_equal(fodf, np.zeros_like(fodf))
def test_csdeconv():
SNR = 100
S0 = 1
_, fbvals, fbvecs = get_data('small_64D')
bvals = np.load(fbvals)
bvecs = np.load(fbvecs)
gtab = gradient_table(bvals, bvecs)
mevals = np.array(([0.0015, 0.0003, 0.0003], [0.0015, 0.0003, 0.0003]))
angles = [(0, 0), (60, 0)]
S, sticks = multi_tensor(gtab,
mevals,
S0,
angles=angles,
fractions=[50, 50],
snr=SNR)
sphere = get_sphere('symmetric362')
odf_gt = multi_tensor_odf(sphere.vertices, mevals, angles, [50, 50])
response = (np.array([0.0015, 0.0003, 0.0003]), S0)
csd = ConstrainedSphericalDeconvModel(gtab, response)
csd_fit = csd.fit(S)
assert_equal(csd_fit.shm_coeff[0] > 0, True)
fodf = csd_fit.odf(sphere)
directions, _, _ = peak_directions(odf_gt, sphere)
directions2, _, _ = peak_directions(fodf, sphere)
ang_sim = angular_similarity(directions, directions2)
assert_equal(ang_sim > 1.9, True)
assert_equal(directions.shape[0], 2)
assert_equal(directions2.shape[0], 2)
assert_warns(UserWarning,
ConstrainedSphericalDeconvModel,
gtab,
response,
sh_order=10)
with warnings.catch_warnings(record=True) as w:
ConstrainedSphericalDeconvModel(gtab, response, sh_order=8)
assert_equal(
len([
local_warn for local_warn in w
if issubclass(local_warn.category, UserWarning)
]) > 0, False)
mevecs = []
for s in sticks:
mevecs += [all_tensor_evecs(s).T]
S2 = single_tensor(gtab, 100, mevals[0], mevecs[0], snr=None)
big_S = np.zeros((10, 10, 10, len(S2)))
big_S[:] = S2
aresponse, aratio = auto_response(gtab,
big_S,
roi_center=(5, 5, 4),
roi_radius=3,
fa_thr=0.5)
assert_array_almost_equal(aresponse[0], response[0])
assert_almost_equal(aresponse[1], 100)
assert_almost_equal(aratio, response[0][1] / response[0][0])
auto_response(gtab, big_S, roi_radius=3, fa_thr=0.5)
assert_array_almost_equal(aresponse[0], response[0])
_, _, nvoxels = auto_response(gtab,
big_S,
roi_center=(5, 5, 4),
roi_radius=30,
fa_thr=0.5,
return_number_of_voxels=True)
assert_equal(nvoxels, 1000)
_, _, nvoxels = auto_response(gtab,
big_S,
roi_center=(5, 5, 4),
roi_radius=30,
fa_thr=1,
return_number_of_voxels=True)
assert_equal(nvoxels, 0)
if bvecs.shape[1] != 3:
bvecs = bvecs.T
bvals = np.genfromtxt(dpath + 'bvals_b10.txt')
# remove b0
bvecs = bvecs[bvals > 10]
bvals = bvals[bvals > 10]
# sym
bvecs = np.concatenate((bvecs, -bvecs), axis=0)
bvals = np.concatenate((bvals, bvals), axis=0)
# sample spherical distribution
ODFS_low = []
sphere_low = get_sphere('repulsion724')
for mu1 in [np.array([0, 0, 1])]:
for k1 in [1, 2, 4, 16]:
# comp1 (symmetric)
# d1 = sphPDF(k1, mu1, sphere_low.vertices)
# d2 = sphPDF(k1, mu1, -sphere_low.vertices)
# dd1 = (d1+d2)/2.
# dd1 = dd1/dd1.sum()
dd1 = sphPDF_sym(k1, mu1, sphere_low.vertices, True)
for mu2 in [
np.array([1, 0, 0]),
np.array([0, 1, 0]),
np.array([0, 0, 1])
]:
for k2 in [1, 2, 4, 16]:
# comp2 (symmetric)
# memory for storing NNLS weights
if sparse:
sparsity = 0.01 # expected proportion of nnz atom weights per fascicle
nnz_pred = int(np.ceil(sparsity * num_atoms * num_samples * num_fasc))
# Store row and column indices of the dense weight matrix
w_idx = np.zeros((nnz_pred, 2), dtype=np.int64) # 2 is 2 !
# Store weights themselves
w_data = np.zeros(nnz_pred, dtype=np.float64)
else:
w_store = np.zeros((num_samples, num_fasc*num_atoms), dtype=np.float)
nnz_hist = np.zeros(num_samples) # always useful even in non sparse mode
# Quantities used repeatedly for CSD peak estimation
# use largest sphere available in Dipy
odf_sphere = get_sphere('repulsion724')
gam = util.get_gyromagnetic_ratio('H')
G = sch_mat_b0[:, 3]
Deltas = sch_mat_b0[:, 4]
deltas = sch_mat_b0[:, 5]
bvals = (gam*G*deltas)**2*(Deltas-deltas/3) # in SI units s/m^2
bvecs = sch_mat_b0[:, :3]
gtab = gradient_table(bvals/1e6, bvecs) # bvals in s/mm^2
num_dwi = np.sum(bvals > 0)
MAX_SH_ORDER = 12
sh_max_vals = np.arange(2, MAX_SH_ORDER+1, 2)
# base sizes is the number of free coefficients to estimate, i.e. the
# degrees of freedom of the model
base_sizes = (sh_max_vals+1)*(sh_max_vals+2)//2
plt.show()
plt.savefig('simulated_signal.png')
"""
.. figure:: simulated_signal.png
:align: center
**Simulated MultiTensor signal**
"""
"""
For the ODF simulation we will need a sphere. Because we are interetested in a
simulation of only a single voxel, we can use a sphere with very high
resolution. We generate that by subdividing the triangles of one of Dipy's
cached spheres, which we can read in the following way.
"""
sphere = get_sphere('symmetric724')
sphere = sphere.subdivide(2)
odf = multi_tensor_odf(sphere.vertices, mevals, angles, fractions)
from dipy.viz import fvtk
ren = fvtk.ren()
odf_actor = fvtk.sphere_funcs(odf, sphere)
odf_actor.RotateX(90)
fvtk.add(ren, odf_actor)
print('Saving illustration as multi_tensor_simulation')
fvtk.record(ren, out_path='multi_tensor_simulation.png', size=(300, 300))
def __init__(self,
a,
ind,
seeds=10000,
odf_vertices=None,
a_low=0.0239,
step_sz=0.5,
ang_thr=60.,
length_thr=0.,
total_weight=.5):
''' Euler integration with multiple stopping criteria and supporting multiple peaks
Parameters
------------
a : array, shape(x,y,z,Np)
magnitude of the peak of a scalar anisotropic function e.g. QA
(quantitative anisotropy) or a different function of shape(x,y,z)
e.g FA or GFA.
ind : array, shape(x,y,z,Np)
indices of orientations of the scalar anisotropic peaks found on the
resampling sphere
seeds : int or sequence, optional
number of random seeds or list of seeds
odf_vertices : None or ndarray, optional
sphere points which define a discrete representation of orientations
for the peaks, the same for all voxels. None results in
a_low : float, optional
low threshold for QA(typical 0.023) or FA(typical 0.2) or any other
anisotropic function
step_sz : float, optional
euler propagation step size
ang_thr : float, optional
if turning angle is bigger than this threshold then tracking stops.
length_thr: float, optional
total_weight : float, optional
total weighting threshold
Examples
--------
>>> import nibabel as nib
>>> from dipy.reconst.dti import Tensor
>>> from dipy.data import get_data
>>> fimg,fbvals,fbvecs=get_data('small_101D')
>>> img=nib.load(fimg)
>>> affine=img.get_affine()
>>> bvals=np.loadtxt(fbvals)
>>> gradients=np.loadtxt(fbvecs).T
>>> data=img.get_data()
>>> ten=Tensor(data,bvals,gradients,thresh=50)
>>> eu=EuDX(a=ten.fa(),ind=ten.ind(),seeds=100,a_low=.2)
>>> tracks=[e for e in eu]
Notes
-------
This works as an iterator class because otherwise it could fill your entire memory if you generate many tracks.
Something very common as you can easily generate millions of tracks if you have many seeds.
'''
self.a = a.copy()
self.ind = ind.copy()
self.a_low = a_low
self.ang_thr = ang_thr
self.step_sz = step_sz
self.length_thr = length_thr
self.total_weight = total_weight
if len(self.a.shape) == 3:
#tmpa=np.zeros(self.a.shape+(2,))
#tmpi=np.zeros(self.ind.shape+(2,))
#self.a=tmpa.copy()
#self.ind=tmpi.copy()
#self.a[...,0]=a.copy()
#self.ind[...,0]=ind.copy()
self.a.shape = self.a.shape + (1, )
self.ind.shape = self.ind.shape + (1, )
#store number of maximum peacks
x, y, z, g = self.a.shape
self.Np = g
if odf_vertices == None:
vertices, faces = get_sphere('symmetric362')
self.odf_vertices = vertices
try:
if len(seeds) > 0:
self.seed_list = seeds
self.seed_no = len(seeds)
except TypeError:
self.seed_no = seeds
self.seed_list = None
self.ind = self.ind.astype(np.double)
def test_recursive_response_calibration():
"""
Test the recursive response calibration method.
"""
SNR = 100
S0 = 1
_, fbvals, fbvecs = get_data('small_64D')
bvals = np.load(fbvals)
bvecs = np.load(fbvecs)
sphere = get_sphere('symmetric724')
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, sticks_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)
sphere = Sphere(xyz=gtab.gradients[where_dwi])
sf = response.on_sphere(sphere)
S = np.concatenate(([response.S0], sf))
tenmodel = dti.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)