print('Fitting CSD')
print("sh_order: ", sh_order)
print("fa_thr: ", fa_thr)
response, ratio = auto_response(gtab,
data,
roi_radius=10,
fa_thr=fa_thr)
model = ConstrainedSphericalDeconvModel(gtab,
response,
sh_order=sh_order)
sh_coeffs = model.fit(data).shm_coeff
elif model_type == 'Opdt':
print('Orientation Probability Density Transform')
print("sh_order: ", sh_order)
print("smooth: ", smooth)
model = OpdtModel(gtab=gtab, sh_order=sh_order, smooth=smooth)
sh_coeffs = model.fit(data, mask=mask).shm_coeff
else:
raise ValueError(
'Model type not supported. Available models: 3D-SHORE, CSA-QBALL, SFM, CSD, Opdt'
)
if odf is not None:
odf = np.nan_to_num(odf)
print('Preparing ODF image')
odf_image = sitk.Image([data.shape[2], data.shape[1], data.shape[0]],
sitk.sitkVectorFloat32, len(sphere.vertices))
for x in range(data.shape[2]):
for y in range(data.shape[1]):
for z in range(data.shape[0]):
if mask is not None and mask[z, y, x] == 0:
qballmodel = QballModel(gtab, 4, smooth=0.006)
print 'Computing the QBALL odf...'
coeff = qballmodel._get_shm_coef(data)
print coeff.shape
print 'Computing GFA...'
print np.min(coeff[:,:,:,0]),np.max(coeff[:,:,:,0])
gfa_sh = np.sqrt(1.0 - (coeff[:,:,:,0] ** 2 / ( np.sum(np.square(coeff), axis=3) ) ) )
gfa_sh[np.isnan(gfa_sh)] = 0
print 'Saving nifti...'
nib.save(nib.Nifti1Image(gfa_sh.astype('float32'), affine), 'gfa_qball_full_brain.nii.gz')
nib.save(nib.Nifti1Image(coeff.astype('float32'), affine), 'qball_odf_sh.nii.gz')
opdtmodel = OpdtModel(gtab, 4, smooth=0.006)
print 'Computing the OPDT odf...'
coeff = opdtmodel._get_shm_coef(data)
print coeff.shape
print 'Computing GFA...'
print np.min(coeff[:,:,:,0]),np.max(coeff[:,:,:,0])
gfa_sh = np.sqrt(1.0 - (coeff[:,:,:,0] ** 2 / ( np.sum(np.square(coeff), axis=3) ) ) )
gfa_sh[np.isnan(gfa_sh)] = 0
print 'Saving nifti...'
nib.save(nib.Nifti1Image(gfa_sh.astype('float32'), affine), 'gfa_opdt_full_brain.nii.gz')
nib.save(nib.Nifti1Image(coeff.astype('float32'), affine), 'opdt_odf_sh.nii.gz')
if args.roi_nifti is not None:
roi = get_img(args.roi_nifti)
roi_data = roi.get_fdata()
else:
roi_data = np.zeros(data.shape[:3])
roi_data[FA > 0.1] = 1.
# Create seeds for ROI
seed_mask = utils.seeds_from_mask(roi_data,
density=args.sampling_density,
affine=np.eye(4))
# Setup model
print('slowadcodf')
sh_order = 6
model = OpdtModel(gtab, sh_order=sh_order, min_signal=1)
# Setup direction getter args
print('Bootstrap direction getter')
#boot_dg = BootDirectionGetter.from_data(data, model, max_angle=60., sphere=small_sphere)
b0s_mask = gtab.b0s_mask
dwi_mask = ~b0s_mask
# get fit_matrix from model
fit_matrix = model._fit_matrix
delta_b, delta_q = fit_matrix
# setup sampling matrix
sphere = small_sphere
theta = sphere.theta
phi = sphere.phi
nib.save(nib.Nifti1Image(coeff.astype('float32'), affine), 'qball_odf_sh.nii.gz')
if vizu :
odfs = sh_to_sf(coeff[20:50,55:85, 38:39], sphere, order)
fvtk.add(r, fvtk.sphere_funcs(odfs, sphere, colormap='jet'))
fvtk.show(r)
fvtk.clear(r)
#min-max normalize
odfs = (odfs - np.min(odfs, -1)[..., None]) / (np.max(odfs, -1) - np.min(odfs, -1))[..., None]
fvtk.add(r, fvtk.sphere_funcs(odfs, sphere, colormap='jet'))
fvtk.show(r)
fvtk.clear(r)
opdtmodel = OpdtModel(gtab, order, smooth=0.006)
print 'Computing the Opdt odf...'
opdtfit = opdtmodel.fit(data)
coeff = opdtfit._shm_coef
#GFA = opdtfit.gfa
#nib.save(nib.Nifti1Image(GFA.astype('float32'), affine), 'gfa_opdt.nii.gz')
nib.save(nib.Nifti1Image(coeff.astype('float32'), affine), 'opdt_odf_sh.nii.gz')
if vizu :
odfs = sh_to_sf(coeff[20:50,55:85, 38:39], sphere, order)
fvtk.add(r, fvtk.sphere_funcs(odfs, sphere, colormap='jet'))
fvtk.show(r)
fvtk.clear(r)
