def run(self,
input_files,
bvalues_files,
bvectors_files,
mask_files,
sh_order=6,
odf_to_sh_order=8,
b0_threshold=50.0,
bvecs_tol=0.01,
extract_pam_values=False,
parallel=False,
nbr_processes=None,
out_dir='',
out_pam='peaks.pam5',
out_shm='shm.nii.gz',
out_peaks_dir='peaks_dirs.nii.gz',
out_peaks_values='peaks_values.nii.gz',
out_peaks_indices='peaks_indices.nii.gz',
out_gfa='gfa.nii.gz'):
""" Constant Solid Angle.
Parameters
----------
input_files : string
Path to the input volumes. This path may contain wildcards to
process multiple inputs at once.
bvalues_files : string
Path to the bvalues files. This path may contain wildcards to use
multiple bvalues files at once.
bvectors_files : string
Path to the bvectors files. This path may contain wildcards to use
multiple bvectors files at once.
mask_files : string
Path to the input masks. This path may contain wildcards to use
multiple masks at once. (default: No mask used)
sh_order : int, optional
Spherical harmonics order used in the CSA fit.
odf_to_sh_order : int, optional
Spherical harmonics order used for peak_from_model to compress
the ODF to spherical harmonics coefficients.
b0_threshold : float, optional
Threshold used to find b0 volumes.
bvecs_tol : float, optional
Threshold used so that norm(bvec)=1.
extract_pam_values : bool, optional
Whether or not to save pam volumes as single nifti files.
parallel : bool, optional
Whether to use parallelization in peak-finding during the
calibration procedure.
nbr_processes : int, optional
If `parallel` is True, the number of subprocesses to use
(default multiprocessing.cpu_count()).
out_dir : string, optional
Output directory. (default current directory)
out_pam : string, optional
Name of the peaks volume to be saved.
out_shm : string, optional
Name of the spherical harmonics volume to be saved.
out_peaks_dir : string, optional
Name of the peaks directions volume to be saved.
out_peaks_values : string, optional
Name of the peaks values volume to be saved.
out_peaks_indices : string, optional
Name of the peaks indices volume to be saved.
out_gfa : string, optional
Name of the generalized FA volume to be saved.
References
----------
.. [1] Aganj, I., et al. 2009. ODF Reconstruction in Q-Ball Imaging
with Solid Angle Consideration.
"""
io_it = self.get_io_iterator()
for (dwi, bval, bvec, maskfile, opam, oshm, opeaks_dir, opeaks_values,
opeaks_indices, ogfa) in io_it:
logging.info('Loading {0}'.format(dwi))
data, affine = load_nifti(dwi)
bvals, bvecs = read_bvals_bvecs(bval, bvec)
if b0_threshold < bvals.min():
warn(
"b0_threshold (value: {0}) is too low, increase your "
"b0_threshold. It should be higher than the first b0 value "
"({1}).".format(b0_threshold, bvals.min()))
gtab = gradient_table(bvals,
bvecs,
b0_threshold=b0_threshold,
atol=bvecs_tol)
mask_vol = load_nifti_data(maskfile).astype(bool)
peaks_sphere = default_sphere
logging.info('Starting CSA computations {0}'.format(dwi))
csa_model = CsaOdfModel(gtab, sh_order)
peaks_csa = peaks_from_model(model=csa_model,
data=data,
sphere=peaks_sphere,
relative_peak_threshold=.5,
min_separation_angle=25,
mask=mask_vol,
return_sh=True,
sh_order=odf_to_sh_order,
normalize_peaks=True,
parallel=parallel,
nbr_processes=nbr_processes)
peaks_csa.affine = affine
save_peaks(opam, peaks_csa)
logging.info('Finished CSA {0}'.format(dwi))
if extract_pam_values:
peaks_to_niftis(peaks_csa,
oshm,
opeaks_dir,
opeaks_values,
opeaks_indices,
ogfa,
reshape_dirs=True)
dname_ = os.path.dirname(opam)
if dname_ == '':
logging.info('Pam5 file saved in current directory')
else:
logging.info('Pam5 file saved in {0}'.format(dname_))
return io_it
def test_io_peaks():
with InTemporaryDirectory():
fname = 'test.pam5'
pam = PeaksAndMetrics()
pam.affine = np.eye(4)
pam.peak_dirs = np.random.rand(10, 10, 10, 5, 3)
pam.peak_values = np.zeros((10, 10, 10, 5))
pam.peak_indices = np.zeros((10, 10, 10, 5))
pam.shm_coeff = np.zeros((10, 10, 10, 45))
pam.sphere = default_sphere
pam.B = np.zeros((45, default_sphere.vertices.shape[0]))
pam.total_weight = 0.5
pam.ang_thr = 60
pam.gfa = np.zeros((10, 10, 10))
pam.qa = np.zeros((10, 10, 10, 5))
pam.odf = np.zeros((10, 10, 10, default_sphere.vertices.shape[0]))
save_peaks(fname, pam)
pam2 = load_peaks(fname, verbose=True)
npt.assert_array_equal(pam.peak_dirs, pam2.peak_dirs)
pam2.affine = None
fname2 = 'test2.pam5'
save_peaks(fname2, pam2, np.eye(4))
pam2_res = load_peaks(fname2, verbose=True)
npt.assert_array_equal(pam.peak_dirs, pam2_res.peak_dirs)
pam3 = load_peaks(fname2, verbose=False)
for attr in [
'peak_dirs', 'peak_values', 'peak_indices', 'gfa', 'qa',
'shm_coeff', 'B', 'odf'
]:
npt.assert_array_equal(getattr(pam3, attr), getattr(pam, attr))
npt.assert_equal(pam3.total_weight, pam.total_weight)
npt.assert_equal(pam3.ang_thr, pam.ang_thr)
npt.assert_array_almost_equal(pam3.sphere.vertices,
pam.sphere.vertices)
fname3 = 'test3.pam5'
pam4 = PeaksAndMetrics()
npt.assert_raises(ValueError, save_peaks, fname3, pam4)
fname4 = 'test4.pam5'
del pam.affine
save_peaks(fname4, pam, affine=None)
fname5 = 'test5.pkm'
npt.assert_raises(IOError, save_peaks, fname5, pam)
pam.affine = np.eye(4)
fname6 = 'test6.pam5'
save_peaks(fname6, pam, verbose=True)
del pam.shm_coeff
save_peaks(fname6, pam, verbose=False)
pam.shm_coeff = np.zeros((10, 10, 10, 45))
del pam.odf
save_peaks(fname6, pam)
pam_tmp = load_peaks(fname6, True)
npt.assert_equal(pam_tmp.odf, None)
fname7 = 'test7.paw'
npt.assert_raises(IOError, load_peaks, fname7)
del pam.shm_coeff
save_peaks(fname6, pam, verbose=True)
fname_shm = 'shm.nii.gz'
fname_dirs = 'dirs.nii.gz'
fname_values = 'values.nii.gz'
fname_indices = 'indices.nii.gz'
fname_gfa = 'gfa.nii.gz'
pam.shm_coeff = np.ones((10, 10, 10, 45))
peaks_to_niftis(pam,
fname_shm,
fname_dirs,
fname_values,
fname_indices,
fname_gfa,
reshape_dirs=False)
os.path.isfile(fname_shm)
os.path.isfile(fname_dirs)
os.path.isfile(fname_values)
os.path.isfile(fname_indices)
os.path.isfile(fname_gfa)
def test_io_peaks():
with InTemporaryDirectory():
fname = 'test.pam5'
sphere = get_sphere('repulsion724')
pam = PeaksAndMetrics()
pam.affine = np.eye(4)
pam.peak_dirs = np.random.rand(10, 10, 10, 5, 3)
pam.peak_values = np.zeros((10, 10, 10, 5))
pam.peak_indices = np.zeros((10, 10, 10, 5))
pam.shm_coeff = np.zeros((10, 10, 10, 45))
pam.sphere = sphere
pam.B = np.zeros((45, sphere.vertices.shape[0]))
pam.total_weight = 0.5
pam.ang_thr = 60
pam.gfa = np.zeros((10, 10, 10))
pam.qa = np.zeros((10, 10, 10, 5))
pam.odf = np.zeros((10, 10, 10, sphere.vertices.shape[0]))
save_peaks(fname, pam)
pam2 = load_peaks(fname, verbose=True)
npt.assert_array_equal(pam.peak_dirs, pam2.peak_dirs)
pam2.affine = None
fname2 = 'test2.pam5'
save_peaks(fname2, pam2, np.eye(4))
pam2_res = load_peaks(fname2, verbose=True)
npt.assert_array_equal(pam.peak_dirs, pam2_res.peak_dirs)
pam3 = load_peaks(fname2, verbose=False)
for attr in ['peak_dirs', 'peak_values', 'peak_indices',
'gfa', 'qa', 'shm_coeff', 'B', 'odf']:
npt.assert_array_equal(getattr(pam3, attr),
getattr(pam, attr))
npt.assert_equal(pam3.total_weight, pam.total_weight)
npt.assert_equal(pam3.ang_thr, pam.ang_thr)
npt.assert_array_almost_equal(pam3.sphere.vertices,
pam.sphere.vertices)
fname3 = 'test3.pam5'
pam4 = PeaksAndMetrics()
npt.assert_raises(ValueError, save_peaks, fname3, pam4)
fname4 = 'test4.pam5'
del pam.affine
save_peaks(fname4, pam, affine=None)
fname5 = 'test5.pkm'
npt.assert_raises(IOError, save_peaks, fname5, pam)
pam.affine = np.eye(4)
fname6 = 'test6.pam5'
save_peaks(fname6, pam, verbose=True)
del pam.shm_coeff
save_peaks(fname6, pam, verbose=False)
pam.shm_coeff = np.zeros((10, 10, 10, 45))
del pam.odf
save_peaks(fname6, pam)
pam_tmp = load_peaks(fname6, True)
npt.assert_equal(pam_tmp.odf, None)
fname7 = 'test7.paw'
npt.assert_raises(IOError, load_peaks, fname7)
del pam.shm_coeff
save_peaks(fname6, pam, verbose=True)
fname_shm = 'shm.nii.gz'
fname_dirs = 'dirs.nii.gz'
fname_values = 'values.nii.gz'
fname_indices = 'indices.nii.gz'
fname_gfa = 'gfa.nii.gz'
pam.shm_coeff = np.ones((10, 10, 10, 45))
peaks_to_niftis(pam, fname_shm, fname_dirs, fname_values,
fname_indices, fname_gfa, reshape_dirs=False)
os.path.isfile(fname_shm)
os.path.isfile(fname_dirs)
os.path.isfile(fname_values)
os.path.isfile(fname_indices)
os.path.isfile(fname_gfa)
def run(self,
input_files,
bvalues_files,
bvectors_files,
mask_files,
b0_threshold=50.0,
bvecs_tol=0.01,
roi_center=None,
roi_radii=10,
fa_thr=0.7,
frf=None,
extract_pam_values=False,
sh_order=8,
odf_to_sh_order=8,
parallel=False,
nbr_processes=None,
out_dir='',
out_pam='peaks.pam5',
out_shm='shm.nii.gz',
out_peaks_dir='peaks_dirs.nii.gz',
out_peaks_values='peaks_values.nii.gz',
out_peaks_indices='peaks_indices.nii.gz',
out_gfa='gfa.nii.gz'):
""" Constrained spherical deconvolution
Parameters
----------
input_files : string
Path to the input volumes. This path may contain wildcards to
process multiple inputs at once.
bvalues_files : string
Path to the bvalues files. This path may contain wildcards to use
multiple bvalues files at once.
bvectors_files : string
Path to the bvectors files. This path may contain wildcards to use
multiple bvectors files at once.
mask_files : string
Path to the input masks. This path may contain wildcards to use
multiple masks at once. (default: No mask used)
b0_threshold : float, optional
Threshold used to find b0 volumes.
bvecs_tol : float, optional
Bvecs should be unit vectors.
roi_center : variable int, optional
Center of ROI in data. If center is None, it is assumed that it is
the center of the volume with shape `data.shape[:3]`.
roi_radii : int or array-like, optional
radii of cuboid ROI in voxels.
fa_thr : float, optional
FA threshold for calculating the response function.
frf : variable float, optional
Fiber response function can be for example inputed as 15 4 4
(from the command line) or [15, 4, 4] from a Python script to be
converted to float and multiplied by 10**-4 . If None
the fiber response function will be computed automatically.
extract_pam_values : bool, optional
Save or not to save pam volumes as single nifti files.
sh_order : int, optional
Spherical harmonics order used in the CSA fit.
odf_to_sh_order : int, optional
Spherical harmonics order used for peak_from_model to compress
the ODF to spherical harmonics coefficients.
parallel : bool, optional
Whether to use parallelization in peak-finding during the
calibration procedure.
nbr_processes : int, optional
If `parallel` is True, the number of subprocesses to use
(default multiprocessing.cpu_count()).
out_dir : string, optional
Output directory. (default current directory)
out_pam : string, optional
Name of the peaks volume to be saved.
out_shm : string, optional
Name of the spherical harmonics volume to be saved.
out_peaks_dir : string, optional
Name of the peaks directions volume to be saved.
out_peaks_values : string, optional
Name of the peaks values volume to be saved.
out_peaks_indices : string, optional
Name of the peaks indices volume to be saved.
out_gfa : string, optional
Name of the generalized FA volume to be saved.
References
----------
.. [1] Tournier, J.D., et al. NeuroImage 2007. Robust determination of
the fibre orientation distribution in diffusion MRI: Non-negativity
constrained super-resolved spherical deconvolution.
"""
io_it = self.get_io_iterator()
for (dwi, bval, bvec, maskfile, opam, oshm, opeaks_dir, opeaks_values,
opeaks_indices, ogfa) in io_it:
logging.info('Loading {0}'.format(dwi))
data, affine = load_nifti(dwi)
bvals, bvecs = read_bvals_bvecs(bval, bvec)
print(b0_threshold, bvals.min())
if b0_threshold < bvals.min():
warn(
"b0_threshold (value: {0}) is too low, increase your "
"b0_threshold. It should be higher than the first b0 value "
"({1}).".format(b0_threshold, bvals.min()))
gtab = gradient_table(bvals,
bvecs,
b0_threshold=b0_threshold,
atol=bvecs_tol)
mask_vol = load_nifti_data(maskfile).astype(bool)
n_params = ((sh_order + 1) * (sh_order + 2)) / 2
if data.shape[-1] < n_params:
raise ValueError('You need at least {0} unique DWI volumes to '
'compute fiber odfs. You currently have: {1}'
' DWI volumes.'.format(
n_params, data.shape[-1]))
if frf is None:
logging.info('Computing response function')
if roi_center is not None:
logging.info(
'Response ROI center:\n{0}'.format(roi_center))
logging.info('Response ROI radii:\n{0}'.format(roi_radii))
response, ratio = auto_response_ssst(gtab,
data,
roi_center=roi_center,
roi_radii=roi_radii,
fa_thr=fa_thr)
response = list(response)
else:
logging.info('Using response function')
if isinstance(frf, str):
l01 = np.array(literal_eval(frf), dtype=np.float64)
else:
l01 = np.array(frf, dtype=np.float64)
l01 *= 10**-4
response = np.array([l01[0], l01[1], l01[1]])
ratio = l01[1] / l01[0]
response = (response, ratio)
logging.info("Eigenvalues for the frf of the input"
" data are :{0}".format(response[0]))
logging.info(
'Ratio for smallest to largest eigen value is {0}'.format(
ratio))
peaks_sphere = default_sphere
logging.info('CSD computation started.')
csd_model = ConstrainedSphericalDeconvModel(gtab,
response,
sh_order=sh_order)
peaks_csd = peaks_from_model(model=csd_model,
data=data,
sphere=peaks_sphere,
relative_peak_threshold=.5,
min_separation_angle=25,
mask=mask_vol,
return_sh=True,
sh_order=sh_order,
normalize_peaks=True,
parallel=parallel,
nbr_processes=nbr_processes)
peaks_csd.affine = affine
save_peaks(opam, peaks_csd)
logging.info('CSD computation completed.')
if extract_pam_values:
peaks_to_niftis(peaks_csd,
oshm,
opeaks_dir,
opeaks_values,
opeaks_indices,
ogfa,
reshape_dirs=True)
dname_ = os.path.dirname(opam)
if dname_ == '':
logging.info('Pam5 file saved in current directory')
else:
logging.info('Pam5 file saved in {0}'.format(dname_))
return io_it
def run(self, input_files, bvalues_files, bvectors_files, mask_files,
sh_order=6, odf_to_sh_order=8, b0_threshold=50.0, bvecs_tol=0.01,
extract_pam_values=False, parallel=False, nbr_processes=None,
out_dir='',
out_pam='peaks.pam5', out_shm='shm.nii.gz',
out_peaks_dir='peaks_dirs.nii.gz',
out_peaks_values='peaks_values.nii.gz',
out_peaks_indices='peaks_indices.nii.gz',
out_gfa='gfa.nii.gz'):
""" Constant Solid Angle.
Parameters
----------
input_files : string
Path to the input volumes. This path may contain wildcards to
process multiple inputs at once.
bvalues_files : string
Path to the bvalues files. This path may contain wildcards to use
multiple bvalues files at once.
bvectors_files : string
Path to the bvectors files. This path may contain wildcards to use
multiple bvectors files at once.
mask_files : string
Path to the input masks. This path may contain wildcards to use
multiple masks at once. (default: No mask used)
sh_order : int, optional
Spherical harmonics order (default 6) used in the CSA fit.
odf_to_sh_order : int, optional
Spherical harmonics order used for peak_from_model to compress
the ODF to spherical harmonics coefficients (default 8)
b0_threshold : float, optional
Threshold used to find b=0 directions
bvecs_tol : float, optional
Threshold used so that norm(bvec)=1 (default 0.01)
extract_pam_values : bool, optional
Wheter or not to save pam volumes as single nifti files.
parallel : bool, optional
Whether to use parallelization in peak-finding during the
calibration procedure. Default: False
nbr_processes : int, optional
If `parallel` is True, the number of subprocesses to use
(default multiprocessing.cpu_count()).
out_dir : string, optional
Output directory (default input file directory)
out_pam : string, optional
Name of the peaks volume to be saved (default 'peaks.pam5')
out_shm : string, optional
Name of the shperical harmonics volume to be saved
(default 'shm.nii.gz')
out_peaks_dir : string, optional
Name of the peaks directions volume to be saved
(default 'peaks_dirs.nii.gz')
out_peaks_values : string, optional
Name of the peaks values volume to be saved
(default 'peaks_values.nii.gz')
out_peaks_indices : string, optional
Name of the peaks indices volume to be saved
(default 'peaks_indices.nii.gz')
out_gfa : string, optional
Name of the generalise fa volume to be saved (default 'gfa.nii.gz')
References
----------
.. [1] Aganj, I., et al. 2009. ODF Reconstruction in Q-Ball Imaging
with Solid Angle Consideration.
"""
io_it = self.get_io_iterator()
for (dwi, bval, bvec, maskfile, opam, oshm, opeaks_dir,
opeaks_values, opeaks_indices, ogfa) in io_it:
logging.info('Loading {0}'.format(dwi))
data, affine = load_nifti(dwi)
bvals, bvecs = read_bvals_bvecs(bval, bvec)
if b0_threshold < bvals.min():
warn("b0_threshold (value: {0}) is too low, increase your "
"b0_threshold. It should higher than the first b0 value "
"({1}).".format(b0_threshold, bvals.min()))
gtab = gradient_table(bvals, bvecs,
b0_threshold=b0_threshold, atol=bvecs_tol)
mask_vol = nib.load(maskfile).get_data().astype(np.bool)
peaks_sphere = get_sphere('repulsion724')
logging.info('Starting CSA computations {0}'.format(dwi))
csa_model = CsaOdfModel(gtab, sh_order)
peaks_csa = peaks_from_model(model=csa_model,
data=data,
sphere=peaks_sphere,
relative_peak_threshold=.5,
min_separation_angle=25,
mask=mask_vol,
return_sh=True,
sh_order=odf_to_sh_order,
normalize_peaks=True,
parallel=parallel,
nbr_processes=nbr_processes)
peaks_csa.affine = affine
save_peaks(opam, peaks_csa)
logging.info('Finished CSA {0}'.format(dwi))
if extract_pam_values:
peaks_to_niftis(peaks_csa, oshm, opeaks_dir,
opeaks_values,
opeaks_indices, ogfa, reshape_dirs=True)
dname_ = os.path.dirname(opam)
if dname_ == '':
logging.info('Pam5 file saved in current directory')
else:
logging.info(
'Pam5 file saved in {0}'.format(dname_))
return io_it
def run(self, input_files, bvalues_files, bvectors_files, mask_files,
b0_threshold=50.0, bvecs_tol=0.01, roi_center=None, roi_radius=10,
fa_thr=0.7, frf=None, extract_pam_values=False, sh_order=8,
odf_to_sh_order=8, parallel=False, nbr_processes=None,
out_dir='',
out_pam='peaks.pam5', out_shm='shm.nii.gz',
out_peaks_dir='peaks_dirs.nii.gz',
out_peaks_values='peaks_values.nii.gz',
out_peaks_indices='peaks_indices.nii.gz', out_gfa='gfa.nii.gz'):
""" Constrained spherical deconvolution
Parameters
----------
input_files : string
Path to the input volumes. This path may contain wildcards to
process multiple inputs at once.
bvalues_files : string
Path to the bvalues files. This path may contain wildcards to use
multiple bvalues files at once.
bvectors_files : string
Path to the bvectors files. This path may contain wildcards to use
multiple bvectors files at once.
mask_files : string
Path to the input masks. This path may contain wildcards to use
multiple masks at once. (default: No mask used)
b0_threshold : float, optional
Threshold used to find b=0 directions
bvecs_tol : float, optional
Bvecs should be unit vectors. (default:0.01)
roi_center : variable int, optional
Center of ROI in data. If center is None, it is assumed that it is
the center of the volume with shape `data.shape[:3]` (default None)
roi_radius : int, optional
radius of cubic ROI in voxels (default 10)
fa_thr : float, optional
FA threshold for calculating the response function (default 0.7)
frf : variable float, optional
Fiber response function can be for example inputed as 15 4 4
(from the command line) or [15, 4, 4] from a Python script to be
converted to float and mutiplied by 10**-4 . If None
the fiber response function will be computed automatically
(default: None).
extract_pam_values : bool, optional
Save or not to save pam volumes as single nifti files.
sh_order : int, optional
Spherical harmonics order (default 6) used in the CSA fit.
odf_to_sh_order : int, optional
Spherical harmonics order used for peak_from_model to compress
the ODF to spherical harmonics coefficients (default 8)
parallel : bool, optional
Whether to use parallelization in peak-finding during the
calibration procedure. Default: False
nbr_processes : int, optional
If `parallel` is True, the number of subprocesses to use
(default multiprocessing.cpu_count()).
out_dir : string, optional
Output directory (default input file directory)
out_pam : string, optional
Name of the peaks volume to be saved (default 'peaks.pam5')
out_shm : string, optional
Name of the shperical harmonics volume to be saved
(default 'shm.nii.gz')
out_peaks_dir : string, optional
Name of the peaks directions volume to be saved
(default 'peaks_dirs.nii.gz')
out_peaks_values : string, optional
Name of the peaks values volume to be saved
(default 'peaks_values.nii.gz')
out_peaks_indices : string, optional
Name of the peaks indices volume to be saved
(default 'peaks_indices.nii.gz')
out_gfa : string, optional
Name of the generalise fa volume to be saved (default 'gfa.nii.gz')
References
----------
.. [1] Tournier, J.D., et al. NeuroImage 2007. Robust determination of
the fibre orientation distribution in diffusion MRI: Non-negativity
constrained super-resolved spherical deconvolution.
"""
io_it = self.get_io_iterator()
for (dwi, bval, bvec, maskfile, opam, oshm, opeaks_dir, opeaks_values,
opeaks_indices, ogfa) in io_it:
logging.info('Loading {0}'.format(dwi))
data, affine = load_nifti(dwi)
bvals, bvecs = read_bvals_bvecs(bval, bvec)
print(b0_threshold, bvals.min())
if b0_threshold < bvals.min():
warn("b0_threshold (value: {0}) is too low, increase your "
"b0_threshold. It should higher than the first b0 value "
"({1}).".format(b0_threshold, bvals.min()))
gtab = gradient_table(bvals, bvecs, b0_threshold=b0_threshold,
atol=bvecs_tol)
mask_vol = nib.load(maskfile).get_data().astype(np.bool)
n_params = ((sh_order + 1) * (sh_order + 2)) / 2
if data.shape[-1] < n_params:
raise ValueError(
'You need at least {0} unique DWI volumes to '
'compute fiber odfs. You currently have: {1}'
' DWI volumes.'.format(n_params, data.shape[-1]))
if frf is None:
logging.info('Computing response function')
if roi_center is not None:
logging.info('Response ROI center:\n{0}'
.format(roi_center))
logging.info('Response ROI radius:\n{0}'
.format(roi_radius))
response, ratio, nvox = auto_response(
gtab, data,
roi_center=roi_center,
roi_radius=roi_radius,
fa_thr=fa_thr,
return_number_of_voxels=True)
response = list(response)
else:
logging.info('Using response function')
if isinstance(frf, str):
l01 = np.array(literal_eval(frf), dtype=np.float64)
else:
l01 = np.array(frf, dtype=np.float64)
l01 *= 10 ** -4
response = np.array([l01[0], l01[1], l01[1]])
ratio = l01[1] / l01[0]
response = (response, ratio)
logging.info("Eigenvalues for the frf of the input"
" data are :{0}".format(response[0]))
logging.info('Ratio for smallest to largest eigen value is {0}'
.format(ratio))
peaks_sphere = get_sphere('repulsion724')
logging.info('CSD computation started.')
csd_model = ConstrainedSphericalDeconvModel(gtab, response,
sh_order=sh_order)
peaks_csd = peaks_from_model(model=csd_model,
data=data,
sphere=peaks_sphere,
relative_peak_threshold=.5,
min_separation_angle=25,
mask=mask_vol,
return_sh=True,
sh_order=sh_order,
normalize_peaks=True,
parallel=parallel,
nbr_processes=nbr_processes)
peaks_csd.affine = affine
save_peaks(opam, peaks_csd)
logging.info('CSD computation completed.')
if extract_pam_values:
peaks_to_niftis(peaks_csd, oshm, opeaks_dir, opeaks_values,
opeaks_indices, ogfa, reshape_dirs=True)
dname_ = os.path.dirname(opam)
if dname_ == '':
logging.info('Pam5 file saved in current directory')
else:
logging.info(
'Pam5 file saved in {0}'.format(dname_))
return io_it
def run(self, input_files, bvalues, bvectors, mask_files, sh_order=6,
odf_to_sh_order=8, b0_threshold=0.0, bvecs_tol=0.01,
extract_pam_values=False,
out_dir='',
out_pam='peaks.pam5', out_shm='shm.nii.gz',
out_peaks_dir='peaks_dirs.nii.gz',
out_peaks_values='peaks_values.nii.gz',
out_peaks_indices='peaks_indices.nii.gz',
out_gfa='gfa.nii.gz'):
""" Constant Solid Angle.
Parameters
----------
input_files : string
Path to the input volumes. This path may contain wildcards to
process multiple inputs at once.
bvalues : string
Path to the bvalues files. This path may contain wildcards to use
multiple bvalues files at once.
bvectors : string
Path to the bvectors files. This path may contain wildcards to use
multiple bvectors files at once.
mask_files : string
Path to the input masks. This path may contain wildcards to use
multiple masks at once. (default: No mask used)
sh_order : int, optional
Spherical harmonics order (default 6) used in the CSA fit.
odf_to_sh_order : int, optional
Spherical harmonics order used for peak_from_model to compress
the ODF to spherical harmonics coefficients (default 8)
b0_threshold : float, optional
Threshold used to find b=0 directions
bvecs_tol : float, optional
Threshold used so that norm(bvec)=1 (default 0.01)
extract_pam_values : bool, optional
Wheter or not to save pam volumes as single nifti files.
out_dir : string, optional
Output directory (default input file directory)
out_pam : string, optional
Name of the peaks volume to be saved (default 'peaks.pam5')
out_shm : string, optional
Name of the shperical harmonics volume to be saved
(default 'shm.nii.gz')
out_peaks_dir : string, optional
Name of the peaks directions volume to be saved
(default 'peaks_dirs.nii.gz')
out_peaks_values : string, optional
Name of the peaks values volume to be saved
(default 'peaks_values.nii.gz')
out_peaks_indices : string, optional
Name of the peaks indices volume to be saved
(default 'peaks_indices.nii.gz')
out_gfa : string, optional
Name of the generalise fa volume to be saved (default 'gfa.nii.gz')
References
----------
.. [1] Aganj, I., et. al. 2009. ODF Reconstruction in Q-Ball Imaging
with Solid Angle Consideration.
"""
io_it = self.get_io_iterator()
for (dwi, bval, bvec, maskfile, opam, oshm, opeaks_dir,
opeaks_values, opeaks_indices, ogfa) in io_it:
logging.info('Loading {0}'.format(dwi))
vol = nib.load(dwi)
data = vol.get_data()
affine = vol.affine
bvals, bvecs = read_bvals_bvecs(bval, bvec)
gtab = gradient_table(bvals, bvecs,
b0_threshold=b0_threshold, atol=bvecs_tol)
mask_vol = nib.load(maskfile).get_data().astype(np.bool)
peaks_sphere = get_sphere('repulsion724')
logging.info('Starting CSA computations {0}'.format(dwi))
csa_model = CsaOdfModel(gtab, sh_order)
peaks_csa = peaks_from_model(model=csa_model,
data=data,
sphere=peaks_sphere,
relative_peak_threshold=.5,
min_separation_angle=25,
mask=mask_vol,
return_sh=True,
sh_order=odf_to_sh_order,
normalize_peaks=True,
parallel=False)
peaks_csa.affine = affine
save_peaks(opam, peaks_csa)
logging.info('Finished CSA {0}'.format(dwi))
if extract_pam_values:
peaks_to_niftis(peaks_csa, oshm, opeaks_dir,
opeaks_values,
opeaks_indices, ogfa, reshape_dirs=True)
dname_ = os.path.dirname(opam)
if dname_ == '':
logging.info('Pam5 file saved in current directory')
else:
logging.info(
'Pam5 file saved in {0}'.format(dname_))
return io_it
def run(self, input_files, bvalues, bvectors, mask_files,
b0_threshold=0.0,
bvecs_tol=0.01,
roi_center=None,
roi_radius=10,
fa_thr=0.7,
frf=None, extract_pam_values=False,
sh_order=8,
odf_to_sh_order=8,
out_dir='',
out_pam='peaks.pam5', out_shm='shm.nii.gz',
out_peaks_dir='peaks_dirs.nii.gz',
out_peaks_values='peaks_values.nii.gz',
out_peaks_indices='peaks_indices.nii.gz', out_gfa='gfa.nii.gz'):
""" Constrained spherical deconvolution
Parameters
----------
input_files : string
Path to the input volumes. This path may contain wildcards to
process multiple inputs at once.
bvalues : string
Path to the bvalues files. This path may contain wildcards to use
multiple bvalues files at once.
bvectors : string
Path to the bvectors files. This path may contain wildcards to use
multiple bvectors files at once.
mask_files : string
Path to the input masks. This path may contain wildcards to use
multiple masks at once. (default: No mask used)
b0_threshold : float, optional
Threshold used to find b=0 directions
bvecs_tol : float, optional
Bvecs should be unit vectors. (default:0.01)
roi_center : variable int, optional
Center of ROI in data. If center is None, it is assumed that it is
the center of the volume with shape `data.shape[:3]` (default None)
roi_radius : int, optional
radius of cubic ROI in voxels (default 10)
fa_thr : float, optional
FA threshold for calculating the response function (default 0.7)
frf : variable float, optional
Fiber response function can be for example inputed as 15 4 4
(from the command line) or [15, 4, 4] from a Python script to be
converted to float and mutiplied by 10**-4 . If None
the fiber response function will be computed automatically
(default: None).
extract_pam_values : bool, optional
Save or not to save pam volumes as single nifti files.
sh_order : int, optional
Spherical harmonics order (default 6) used in the CSA fit.
odf_to_sh_order : int, optional
Spherical harmonics order used for peak_from_model to compress
the ODF to spherical harmonics coefficients (default 8)
out_dir : string, optional
Output directory (default input file directory)
out_pam : string, optional
Name of the peaks volume to be saved (default 'peaks.pam5')
out_shm : string, optional
Name of the shperical harmonics volume to be saved
(default 'shm.nii.gz')
out_peaks_dir : string, optional
Name of the peaks directions volume to be saved
(default 'peaks_dirs.nii.gz')
out_peaks_values : string, optional
Name of the peaks values volume to be saved
(default 'peaks_values.nii.gz')
out_peaks_indices : string, optional
Name of the peaks indices volume to be saved
(default 'peaks_indices.nii.gz')
out_gfa : string, optional
Name of the generalise fa volume to be saved (default 'gfa.nii.gz')
References
----------
.. [1] Tournier, J.D., et al. NeuroImage 2007. Robust determination of
the fibre orientation distribution in diffusion MRI: Non-negativity
constrained super-resolved spherical deconvolution.
"""
io_it = self.get_io_iterator()
for (dwi, bval, bvec, maskfile, opam, oshm, opeaks_dir, opeaks_values,
opeaks_indices, ogfa) in io_it:
logging.info('Loading {0}'.format(dwi))
img = nib.load(dwi)
data = img.get_data()
affine = img.affine
bvals, bvecs = read_bvals_bvecs(bval, bvec)
gtab = gradient_table(bvals, bvecs, b0_threshold=b0_threshold,
atol=bvecs_tol)
mask_vol = nib.load(maskfile).get_data().astype(np.bool)
sh_order = 8
if data.shape[-1] < 15:
raise ValueError(
'You need at least 15 unique DWI volumes to '
'compute fiber odfs. You currently have: {0}'
' DWI volumes.'.format(data.shape[-1]))
elif data.shape[-1] < 30:
sh_order = 6
if frf is None:
logging.info('Computing response function')
if roi_center is not None:
logging.info('Response ROI center:\n{0}'
.format(roi_center))
logging.info('Response ROI radius:\n{0}'
.format(roi_radius))
response, ratio, nvox = auto_response(
gtab, data,
roi_center=roi_center,
roi_radius=roi_radius,
fa_thr=fa_thr,
return_number_of_voxels=True)
response = list(response)
else:
logging.info('Using response function')
if isinstance(frf, str):
l01 = np.array(literal_eval(frf), dtype=np.float64)
else:
l01 = np.array(frf, dtype=np.float64)
l01 *= 10 ** -4
response = np.array([l01[0], l01[1], l01[1]])
ratio = l01[1] / l01[0]
response = (response, ratio)
logging.info(
'Eigenvalues for the frf of the input data are :{0}'
.format(response[0]))
logging.info('Ratio for smallest to largest eigen value is {0}'
.format(ratio))
peaks_sphere = get_sphere('repulsion724')
logging.info('CSD computation started.')
csd_model = ConstrainedSphericalDeconvModel(gtab, response,
sh_order=sh_order)
peaks_csd = peaks_from_model(model=csd_model,
data=data,
sphere=peaks_sphere,
relative_peak_threshold=.5,
min_separation_angle=25,
mask=mask_vol,
return_sh=True,
sh_order=sh_order,
normalize_peaks=True,
parallel=False)
peaks_csd.affine = affine
save_peaks(opam, peaks_csd)
logging.info('CSD computation completed.')
if extract_pam_values:
peaks_to_niftis(peaks_csd, oshm, opeaks_dir, opeaks_values,
opeaks_indices, ogfa, reshape_dirs=True)
dname_ = os.path.dirname(opam)
if dname_ == '':
logging.info('Pam5 file saved in current directory')
else:
logging.info(
'Pam5 file saved in {0}'.format(dname_))
return io_it
def run(self,
input_files,
bvalues,
bvectors,
mask_files,
b0_threshold=0.0,
extract_pam_values=False,
out_dir='',
out_pam='peaks.pam5',
out_shm='shm.nii.gz',
out_peaks_dir='peaks_dirs.nii.gz',
out_peaks_values='peaks_values.nii.gz',
out_peaks_indices='peaks_indices.nii.gz',
out_gfa='gfa.nii.gz'):
""" Workflow for peaks computation. Peaks computation is done by 'globing'
``input_files`` and saves the peaks in a directory specified by
``out_dir``.
Parameters
----------
input_files : string
Path to the input volumes. This path may contain wildcards to
process multiple inputs at once.
bvalues : string
Path to the bvalues files. This path may contain wildcards to use
multiple bvalues files at once.
bvectors : string
Path to the bvalues files. This path may contain wildcards to use
multiple bvalues files at once.
mask_files : string
Path to the input masks. This path may contain wildcards to use
multiple masks at once. (default: No mask used)
b0_threshold : float, optional
Threshold used to find b=0 directions
extract_pam_values : bool, optional
Wheter or not to save pam volumes as single nifti files.
out_dir : string, optional
Output directory (default input file directory)
out_pam : string, optional
Name of the peaks volume to be saved (default 'peaks.pam5')
out_shm : string, optional
Name of the shperical harmonics volume to be saved
(default 'shm.nii.gz')
out_peaks_dir : string, optional
Name of the peaks directions volume to be saved
(default 'peaks_dirs.nii.gz')
out_peaks_values : string, optional
Name of the peaks values volume to be saved
(default 'peaks_values.nii.gz')
out_peaks_indices : string, optional
Name of the peaks indices volume to be saved
(default 'peaks_indices.nii.gz')
out_gfa : string, optional
Name of the generalise fa volume to be saved (default 'gfa.nii.gz')
"""
io_it = self.get_io_iterator()
for dwi, bval, bvec, maskfile, opam, oshm, opeaks_dir, \
opeaks_values, opeaks_indices, ogfa in io_it:
logging.info('Computing fiber odfs for {0}'.format(dwi))
vol = nib.load(dwi)
data = vol.get_data()
affine = vol.get_affine()
bvals, bvecs = read_bvals_bvecs(bval, bvec)
gtab = gradient_table(bvals, bvecs, b0_threshold=b0_threshold)
mask_vol = nib.load(maskfile).get_data().astype(np.bool)
sh_order = 8
if data.shape[-1] < 15:
raise ValueError('You need at least 15 unique DWI volumes to '
'compute fiber odfs. You currently have: {0}'
' DWI volumes.'.format(data.shape[-1]))
elif data.shape[-1] < 30:
sh_order = 6
response, ratio = auto_response(gtab, data)
response = list(response)
logging.info(
'Eigenvalues for the frf of the input data are :{0}'.format(
response[0]))
logging.info(
'Ratio for smallest to largest eigen value is {0}'.format(
ratio))
peaks_sphere = get_sphere('symmetric362')
csa_model = CsaOdfModel(gtab, sh_order)
peaks_csa = peaks_from_model(model=csa_model,
data=data,
sphere=peaks_sphere,
relative_peak_threshold=.5,
min_separation_angle=25,
mask=mask_vol,
return_sh=True,
sh_order=sh_order,
normalize_peaks=True,
parallel=False)
peaks_csa.affine = affine
save_peaks(opam, peaks_csa)
if extract_pam_values:
peaks_to_niftis(peaks_csa,
oshm,
opeaks_dir,
opeaks_values,
opeaks_indices,
ogfa,
reshape_dirs=True)
logging.info('Peaks saved in {0}'.format(os.path.dirname(opam)))
return io_it
