/
experiment_deconv_bundles.py
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/
experiment_deconv_bundles.py
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import numpy as np
import nibabel as nib
from dipy.reconst.dti import fractional_anisotropy
from dipy.reconst.dti import TensorModel
from dipy.reconst.gqi import GeneralizedQSamplingModel
from dipy.reconst.dsi import DiffusionSpectrumDeconvModel
from dipy.reconst.csdeconv import ConstrainedSphericalDeconvModel, ConstrainedSDTModel
from dipy.data import get_sphere
from dipy.reconst.shm import sf_to_sh
from load_data import get_jc_hardi, get_test_mask, get_test_wm_mask, get_test_hardi
from show_streamlines import show_streamlines
from conn_mat import connectivity_matrix
from dipy.io.pickles import save_pickle, load_pickle
from time import time
threshold = 0.75
from dipy.data import get_sphere
sphere = get_sphere('symmetric724')
dname = 'SNR20/'
if __name__ == '__main__':
data, affine, gtab = get_test_hardi(snr=20, denoised=0)
mask = get_test_mask()
tenmodel = TensorModel(gtab)
tenfit = tenmodel.fit(data, mask)
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
nib.save(nib.Nifti1Image(FA.astype('float32'), affine),
'FA.nii.gz')
for i in range(27) :
print 'White matter bundle: ', i
wm_mask = get_test_wm_mask(i)
print(FA[wm_mask].max())
indicesAniso = np.where(np.logical_and(FA > threshold, wm_mask))
print ' Response function'
S0s = data[indicesAniso][:, np.nonzero(gtab.b0s_mask)[0]]
S0 = np.mean(S0s)
if S0 == 0 :
S0 = 1
lambdas = tenfit.evals[indicesAniso][:, :2]
mean_evals = np.mean(lambdas, axis=0)
evals = np.array([mean_evals[0], mean_evals[1], mean_evals[1]])
response = (evals, S0)
ratio = mean_evals[1] / mean_evals[0]
print ' ... Valued ', response
response_ar = np.array([mean_evals[0], mean_evals[1], mean_evals[1], S0])
if i < 10 :
np.savetxt(dname + 'response_0' + str(i) + '.txt', response_ar)
else :
np.savetxt(dname + 'response_' + str(i) + '.txt', response_ar)
csd_model = ConstrainedSphericalDeconvModel(gtab, (evals, S0))
from dipy.reconst.odf import peaks_from_model
peaks = peaks_from_model(model=csd_model,
data=data,
sphere=sphere,
relative_peak_threshold=0.25,
min_separation_angle=45,
mask=wm_mask,
return_odf=False,
return_sh=True,
normalize_peaks=False,
sh_order=8,
sh_basis_type='mrtrix',
npeaks=5,
parallel=True, nbr_process=8)
shm_coeff = peaks.shm_coeff
myPeaksDirs = peaks.peak_dirs
test = np.reshape(myPeaksDirs, [myPeaksDirs.shape[0],
myPeaksDirs.shape[1],
myPeaksDirs.shape[2],
myPeaksDirs.shape[3]*myPeaksDirs.shape[4]])
if i < 10 :
nib.save(nib.Nifti1Image(shm_coeff.astype('float32'), affine),
dname + 'fodf_csd_sh_0' + str(i) + '.nii.gz')
nib.save(nib.Nifti1Image(test.astype('float32'), affine),
dname + 'peaks_csd._0' + str(i) + '.nii.gz')
else :
nib.save(nib.Nifti1Image(shm_coeff.astype('float32'), affine),
dname + 'fodf_csd_sh_' + str(i) + '.nii.gz')
nib.save(nib.Nifti1Image(test.astype('float32'), affine),
dname + 'peaks_csd._' + str(i) + '.nii.gz')
sdt_model = ConstrainedSDTModel(gtab, ratio)
from dipy.reconst.odf import peaks_from_model
peaks = peaks_from_model(model=sdt_model,
data=data,
sphere=sphere,
relative_peak_threshold=0.25,
min_separation_angle=45,
mask=wm_mask,
return_odf=False,
return_sh=True,
normalize_peaks=False,
sh_order=8,
sh_basis_type='mrtrix',
npeaks=5,
parallel=True, nbr_process=8)
shm_coeff = peaks.shm_coeff
myPeaksDirs = peaks.peak_dirs
test = np.reshape(myPeaksDirs, [myPeaksDirs.shape[0],
myPeaksDirs.shape[1],
myPeaksDirs.shape[2],
myPeaksDirs.shape[3]*myPeaksDirs.shape[4]])
if i < 10 :
nib.save(nib.Nifti1Image(shm_coeff.astype('float32'), affine),
dname + 'fodf_sdt_sh_0' + str(i) + '.nii.gz')
nib.save(nib.Nifti1Image(test.astype('float32'), affine),
dname + 'peaks_sdt._0' + str(i) + '.nii.gz')
else :
nib.save(nib.Nifti1Image(shm_coeff.astype('float32'), affine),
dname + 'fodf_sdt_sh_' + str(i) + '.nii.gz')
nib.save(nib.Nifti1Image(test.astype('float32'), affine),
dname + 'peaks_sdt._' + str(i) + '.nii.gz')