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 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)
