def test_odf_sh_to_sharp():
SNR = None
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]))
S, _ = multi_tensor(gtab,
mevals,
S0,
angles=[(10, 0), (100, 0)],
fractions=[50, 50],
snr=SNR)
sphere = default_sphere
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
qb = QballModel(gtab, sh_order=8, assume_normed=True)
qbfit = qb.fit(S)
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
odf_gt = qbfit.odf(sphere)
Z = np.linalg.norm(odf_gt)
odfs_gt = np.zeros((3, 1, 1, odf_gt.shape[0]))
odfs_gt[:, :, :] = odf_gt[:]
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
odfs_sh = sf_to_sh(odfs_gt, sphere, sh_order=8, basis_type=None)
odfs_sh /= Z
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
fodf_sh = odf_sh_to_sharp(odfs_sh,
sphere,
basis=None,
ratio=3 / 15.,
sh_order=8,
lambda_=1.,
tau=0.1)
fodf = sh_to_sf(fodf_sh, sphere, sh_order=8, basis_type=None)
directions2, _, _ = peak_directions(fodf[0, 0, 0], sphere)
assert_equal(directions2.shape[0], 2)
def test_odf_sh_to_sharp():
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]))
S, sticks = multi_tensor(gtab,
mevals,
S0,
angles=[(10, 0), (100, 0)],
fractions=[50, 50],
snr=SNR)
sphere = get_sphere('symmetric724')
qb = QballModel(gtab, sh_order=8, assume_normed=True)
qbfit = qb.fit(S)
odf_gt = qbfit.odf(sphere)
Z = np.linalg.norm(odf_gt)
odfs_gt = np.zeros((3, 1, 1, odf_gt.shape[0]))
odfs_gt[:, :, :] = odf_gt[:]
odfs_sh = sf_to_sh(odfs_gt, sphere, sh_order=8, basis_type=None)
odfs_sh /= Z
fodf_sh = odf_sh_to_sharp(odfs_sh,
sphere,
basis=None,
ratio=3 / 15.,
sh_order=8,
lambda_=1.,
tau=1.)
fodf = sh_to_sf(fodf_sh, sphere, sh_order=8, basis_type=None)
directions2, _, _ = peak_directions(fodf[0, 0, 0], sphere)
assert_equal(directions2.shape[0], 2)
def test_odf_sh_to_sharp():
SNR = None
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]))
S, sticks = multi_tensor(gtab, mevals, S0, angles=[(10, 0), (100, 0)],
fractions=[50, 50], snr=SNR)
sphere = get_sphere('symmetric724')
qb = QballModel(gtab, sh_order=8, assume_normed=True)
qbfit = qb.fit(S)
odf_gt = qbfit.odf(sphere)
Z = np.linalg.norm(odf_gt)
odfs_gt = np.zeros((3, 1, 1, odf_gt.shape[0]))
odfs_gt[:,:,:] = odf_gt[:]
odfs_sh = sf_to_sh(odfs_gt, sphere, sh_order=8, basis_type=None)
odfs_sh /= Z
fodf_sh = odf_sh_to_sharp(odfs_sh, sphere, basis=None, ratio=3 / 15.,
sh_order=8, lambda_=1., tau=0.1)
fodf = sh_to_sf(fodf_sh, sphere, sh_order=8, basis_type=None)
directions2, _, _ = peak_directions(fodf[0, 0, 0], sphere)
assert_equal(directions2.shape[0], 2)
def qball(gtab, data, name, sh_order=4):
qballmodel = QballModel(gtab, sh_order)
data_small = data[:, :, 39:40]
qball_fit = qballmodel.fit(data_small)
qball_odf = qball_fit.odf(default_sphere)
odf_spheres = actor.odf_slicer(qball_odf,
sphere=default_sphere,
scale=0.9,
norm=False,
colormap='plasma')
ren = window.Scene()
ren.add(odf_spheres)
print('Saving illustration as qball_odfs_{}.png'.format(
name)) #data.shape[-1] - 1))
window.record(ren,
out_path='results/qball_odfs_{}.png'.format(name),
size=(600, 600))
return qball_odf, qball_fit.shm_coeff
def main():
parser = _build_arg_parser()
args = parser.parse_args()
if not args.not_all:
args.gfa = args.gfa or 'gfa.nii.gz'
args.peaks = args.peaks or 'peaks.nii.gz'
args.peak_indices = args.peak_indices or 'peaks_indices.nii.gz'
args.sh = args.sh or 'sh.nii.gz'
args.nufo = args.nufo or 'nufo.nii.gz'
args.a_power = args.a_power or 'anisotropic_power.nii.gz'
arglist = [
args.gfa, args.peaks, args.peak_indices, args.sh, args.nufo,
args.a_power
]
if args.not_all and not any(arglist):
parser.error('When using --not_all, you need to specify at least ' +
'one file to output.')
assert_inputs_exist(parser, [args.input, args.bvals, args.bvecs])
assert_outputs_exists(parser, args, arglist)
nbr_processes = args.nbr_processes
parallel = True
if nbr_processes <= 0:
nbr_processes = None
elif nbr_processes == 1:
parallel = False
# Load data
img = nib.load(args.input)
data = img.get_data()
affine = img.get_affine()
bvals, bvecs = read_bvals_bvecs(args.bvals, args.bvecs)
if not is_normalized_bvecs(bvecs):
logging.warning('Your b-vectors do not seem normalized...')
bvecs = normalize_bvecs(bvecs)
if bvals.min() != 0:
if bvals.min() > 20:
raise ValueError(
'The minimal bvalue is greater than 20. This is highly '
'suspicious. Please check your data to ensure everything is '
'correct.\nValue found: {0}'.format(bvals.min()))
else:
logging.warning(
'Warning: no b=0 image. Setting b0_threshold to '
'bvals.min() = %s', bvals.min())
gtab = gradient_table(bvals, bvecs, b0_threshold=bvals.min())
else:
gtab = gradient_table(bvals, bvecs)
sphere = get_sphere('symmetric724')
if args.mask is None:
mask = None
else:
mask = nib.load(args.mask).get_data().astype(np.bool)
if args.use_qball:
model = QballModel(gtab, sh_order=int(args.sh_order), smooth=0.006)
else:
model = CsaOdfModel(gtab, sh_order=int(args.sh_order), smooth=0.006)
odfpeaks = peaks_from_model(model=model,
data=data,
sphere=sphere,
relative_peak_threshold=.5,
min_separation_angle=25,
mask=mask,
return_odf=False,
normalize_peaks=True,
return_sh=True,
sh_order=int(args.sh_order),
sh_basis_type=args.basis,
npeaks=5,
parallel=parallel,
nbr_processes=nbr_processes)
if args.gfa:
nib.save(nib.Nifti1Image(odfpeaks.gfa.astype(np.float32), affine),
args.gfa)
if args.peaks:
nib.save(
nib.Nifti1Image(reshape_peaks_for_visualization(odfpeaks), affine),
args.peaks)
if args.peak_indices:
nib.save(nib.Nifti1Image(odfpeaks.peak_indices, affine),
args.peak_indices)
if args.sh:
nib.save(
nib.Nifti1Image(odfpeaks.shm_coeff.astype(np.float32), affine),
args.sh)
if args.nufo:
peaks_count = (odfpeaks.peak_indices > -1).sum(3)
nib.save(nib.Nifti1Image(peaks_count.astype(np.int32), affine),
args.nufo)
if args.a_power:
odf_a_power = anisotropic_power(odfpeaks.shm_coeff)
nib.save(nib.Nifti1Image(odf_a_power.astype(np.float32), affine),
args.a_power)
data_small = data[20:50,55:85, 38:40]
csamodel = CsaOdfModel(gtab, 4, smooth=0.006)
csa_fit = csamodel.fit(data_small)
sphere = get_sphere('symmetric724')
csa_odf = csa_fit.odf(sphere)
gfa_csa = gfa(csa_odf)
odfs = csa_odf.clip(0)
gfa_csa_wo_zeros = gfa(odfs)
csa_mm = minmax_normalize(odfs)
gfa_csa_mm = gfa(csa_mm)
qballmodel = QballModel(gtab, 6, smooth=0.006)
qball_fit = qballmodel.fit(data_small)
qball_odf = qball_fit.odf(sphere)
gfa_qball = gfa(qball_odf)
gfa_qball_mm = gfa(minmax_normalize(qball_odf))
print 'Saving GFAs...'
nib.save(nib.Nifti1Image(gfa_qball.astype('float32'), affine), 'gfa.nii.gz')
nib.save(nib.Nifti1Image(gfa_qball_mm.astype('float32'), affine), 'gfa_mm.nii.gz')
nib.save(nib.Nifti1Image(gfa_csa.astype('float32'), affine), 'gfa_csa.nii.gz')
nib.save(nib.Nifti1Image(gfa_csa_wo_zeros.astype('float32'), affine), 'gfa_csa_wo_neg.nii.gz')
nib.save(nib.Nifti1Image(gfa_csa_mm.astype('float32'), affine), 'gfa_csa_mm.nii.gz')
def main():
parser = _build_arg_parser()
args = parser.parse_args()
if not args.not_all:
args.gfa = args.gfa or 'gfa.nii.gz'
args.peaks = args.peaks or 'peaks.nii.gz'
args.peak_indices = args.peak_indices or 'peaks_indices.nii.gz'
args.sh = args.sh or 'sh.nii.gz'
args.nufo = args.nufo or 'nufo.nii.gz'
args.a_power = args.a_power or 'anisotropic_power.nii.gz'
arglist = [
args.gfa, args.peaks, args.peak_indices, args.sh, args.nufo,
args.a_power
]
if args.not_all and not any(arglist):
parser.error('When using --not_all, you need to specify at least ' +
'one file to output.')
assert_inputs_exist(parser, [args.in_dwi, args.in_bval, args.in_bvec])
assert_outputs_exist(parser, args, arglist)
validate_nbr_processes(parser, args)
nbr_processes = args.nbr_processes
parallel = nbr_processes > 1
# Load data
img = nib.load(args.in_dwi)
data = img.get_fdata(dtype=np.float32)
bvals, bvecs = read_bvals_bvecs(args.in_bval, args.in_bvec)
if not is_normalized_bvecs(bvecs):
logging.warning('Your b-vectors do not seem normalized...')
bvecs = normalize_bvecs(bvecs)
check_b0_threshold(args, bvals.min())
gtab = gradient_table(bvals, bvecs, b0_threshold=bvals.min())
sphere = get_sphere('symmetric724')
mask = None
if args.mask:
mask = get_data_as_mask(nib.load(args.mask))
# Sanity check on shape of mask
if mask.shape != data.shape[:-1]:
raise ValueError('Mask shape does not match data shape.')
if args.use_qball:
model = QballModel(gtab, sh_order=args.sh_order, smooth=DEFAULT_SMOOTH)
else:
model = CsaOdfModel(gtab,
sh_order=args.sh_order,
smooth=DEFAULT_SMOOTH)
odfpeaks = peaks_from_model(model=model,
data=data,
sphere=sphere,
relative_peak_threshold=.5,
min_separation_angle=25,
mask=mask,
return_odf=False,
normalize_peaks=True,
return_sh=True,
sh_order=int(args.sh_order),
sh_basis_type=args.sh_basis,
npeaks=5,
parallel=parallel,
nbr_processes=nbr_processes)
if args.gfa:
nib.save(nib.Nifti1Image(odfpeaks.gfa.astype(np.float32), img.affine),
args.gfa)
if args.peaks:
nib.save(
nib.Nifti1Image(reshape_peaks_for_visualization(odfpeaks),
img.affine), args.peaks)
if args.peak_indices:
nib.save(nib.Nifti1Image(odfpeaks.peak_indices, img.affine),
args.peak_indices)
if args.sh:
nib.save(
nib.Nifti1Image(odfpeaks.shm_coeff.astype(np.float32), img.affine),
args.sh)
if args.nufo:
peaks_count = (odfpeaks.peak_indices > -1).sum(3)
nib.save(nib.Nifti1Image(peaks_count.astype(np.int32), img.affine),
args.nufo)
if args.a_power:
odf_a_power = anisotropic_power(odfpeaks.shm_coeff)
nib.save(nib.Nifti1Image(odf_a_power.astype(np.float32), img.affine),
args.a_power)
csamodel = CsaOdfModel(gtab, 4, smooth=0.006)
print 'Computing the CSA odf...'
coeff = csamodel._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_full_brain.nii.gz')
nib.save(nib.Nifti1Image(coeff.astype('float32'), affine), 'csa_odf_sh.nii.gz')
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)
#GFA = csafit.gfa
#nib.save(nib.Nifti1Image(GFA.astype('float32'), affine), 'gfa_csa.nii.gz')
nib.save(nib.Nifti1Image(coeff.astype('float32'), affine), 'csa_odf_sh.nii.gz')
sphere = get_sphere('symmetric724')
odfs = sh_to_sf(coeff[20:50,55:85, 38:39], sphere, order)
if vizu :
from dipy.viz import fvtk
r = fvtk.ren()
fvtk.add(r, fvtk.sphere_funcs(odfs, sphere, colormap='jet'))
fvtk.show(r)
fvtk.clear(r)
qballmodel = QballModel(gtab, order, smooth=0.006)
print 'Computing the QBALL odf...'
qballfit = qballmodel.fit(data)
coeff = qballfit._shm_coef
#GFA = qballfit.gfa
# dipy 0.6 compatible
GFA = np.sqrt(1.0 - (coeff[:,:,:,0] ** 2 / ( np.sum(np.square(coeff), axis=3) ) ) )
GFA[np.isnan(GFA)] = 0
nib.save(nib.Nifti1Image(GFA.astype('float32'), affine), 'gfa_qball.nii.gz')
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'))