def main():
parser = _build_arg_parser()
args = parser.parse_args()
assert_inputs_exist(parser, args.in_sh)
assert_outputs_exist(parser, args, args.out_sh)
sphere = get_sphere('repulsion724').subdivide(1)
img = nib.load(args.in_sh)
data = img.get_fdata(dtype=np.float32)
new_data = convert_sh_basis(data,
sphere,
input_basis=args.sh_basis,
nbr_processes=args.nbr_processes)
nib.save(nib.Nifti1Image(new_data, img.affine, header=img.header),
args.out_sh)
def main():
parser = _build_arg_parser()
args = parser.parse_args()
logging.basicConfig(level=logging.INFO)
if not args.not_all:
args.wm_out_fODF = args.wm_out_fODF or 'wm_fodf.nii.gz'
args.gm_out_fODF = args.gm_out_fODF or 'gm_fodf.nii.gz'
args.csf_out_fODF = args.csf_out_fODF or 'csf_fodf.nii.gz'
args.vf = args.vf or 'vf.nii.gz'
args.vf_rgb = args.vf_rgb or 'vf_rgb.nii.gz'
arglist = [args.wm_out_fODF, args.gm_out_fODF, args.csf_out_fODF,
args.vf, args.vf_rgb]
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,
args.in_wm_frf, args.in_gm_frf,
args.in_csf_frf])
assert_outputs_exist(parser, args, arglist)
# Loading data
wm_frf = np.loadtxt(args.in_wm_frf)
gm_frf = np.loadtxt(args.in_gm_frf)
csf_frf = np.loadtxt(args.in_csf_frf)
vol = nib.load(args.in_dwi)
data = vol.get_fdata(dtype=np.float32)
bvals, bvecs = read_bvals_bvecs(args.in_bval, args.in_bvec)
# Checking mask
if args.mask is None:
mask = None
else:
mask = get_data_as_mask(nib.load(args.mask), dtype=bool)
if mask.shape != data.shape[:-1]:
raise ValueError("Mask is not the same shape as data.")
sh_order = args.sh_order
# Checking data and sh_order
b0_thr = check_b0_threshold(
args.force_b0_threshold, bvals.min(), bvals.min())
if data.shape[-1] < (sh_order + 1) * (sh_order + 2) / 2:
logging.warning(
'We recommend having at least {} unique DWIs volumes, but you '
'currently have {} volumes. Try lowering the parameter --sh_order '
'in case of non convergence.'.format(
(sh_order + 1) * (sh_order + 2) / 2, data.shape[-1]))
# Checking bvals, bvecs values and loading gtab
if not is_normalized_bvecs(bvecs):
logging.warning('Your b-vectors do not seem normalized...')
bvecs = normalize_bvecs(bvecs)
gtab = gradient_table(bvals, bvecs, b0_threshold=b0_thr)
# Checking response functions and computing msmt response function
if not wm_frf.shape[1] == 4:
raise ValueError('WM frf file did not contain 4 elements. '
'Invalid or deprecated FRF format')
if not gm_frf.shape[1] == 4:
raise ValueError('GM frf file did not contain 4 elements. '
'Invalid or deprecated FRF format')
if not csf_frf.shape[1] == 4:
raise ValueError('CSF frf file did not contain 4 elements. '
'Invalid or deprecated FRF format')
ubvals = unique_bvals_tolerance(bvals, tol=20)
msmt_response = multi_shell_fiber_response(sh_order, ubvals,
wm_frf, gm_frf, csf_frf)
# Loading spheres
reg_sphere = get_sphere('symmetric362')
# Computing msmt-CSD
msmt_model = MultiShellDeconvModel(gtab, msmt_response,
reg_sphere=reg_sphere,
sh_order=sh_order)
# Computing msmt-CSD fit
msmt_fit = fit_from_model(msmt_model, data,
mask=mask, nbr_processes=args.nbr_processes)
shm_coeff = msmt_fit.all_shm_coeff
nan_count = len(np.argwhere(np.isnan(shm_coeff[..., 0])))
voxel_count = np.prod(shm_coeff.shape[:-1])
if nan_count / voxel_count >= 0.05:
msg = """There are {} voxels out of {} that could not be solved by
the solver, reaching a critical amount of voxels. Make sure to tune the
response functions properly, as the solving process is very sensitive
to it. Proceeding to fill the problematic voxels by 0.
"""
logging.warning(msg.format(nan_count, voxel_count))
elif nan_count > 0:
msg = """There are {} voxels out of {} that could not be solved by
the solver. Make sure to tune the response functions properly, as the
solving process is very sensitive to it. Proceeding to fill the
problematic voxels by 0.
"""
logging.warning(msg.format(nan_count, voxel_count))
shm_coeff = np.where(np.isnan(shm_coeff), 0, shm_coeff)
# Saving results
if args.wm_out_fODF:
wm_coeff = shm_coeff[..., 2:]
if args.sh_basis == 'tournier07':
wm_coeff = convert_sh_basis(wm_coeff, reg_sphere, mask=mask,
nbr_processes=args.nbr_processes)
nib.save(nib.Nifti1Image(wm_coeff.astype(np.float32),
vol.affine), args.wm_out_fODF)
if args.gm_out_fODF:
gm_coeff = shm_coeff[..., 1]
if args.sh_basis == 'tournier07':
gm_coeff = gm_coeff.reshape(gm_coeff.shape + (1,))
gm_coeff = convert_sh_basis(gm_coeff, reg_sphere, mask=mask,
nbr_processes=args.nbr_processes)
nib.save(nib.Nifti1Image(gm_coeff.astype(np.float32),
vol.affine), args.gm_out_fODF)
if args.csf_out_fODF:
csf_coeff = shm_coeff[..., 0]
if args.sh_basis == 'tournier07':
csf_coeff = csf_coeff.reshape(csf_coeff.shape + (1,))
csf_coeff = convert_sh_basis(csf_coeff, reg_sphere, mask=mask,
nbr_processes=args.nbr_processes)
nib.save(nib.Nifti1Image(csf_coeff.astype(np.float32),
vol.affine), args.csf_out_fODF)
if args.vf:
nib.save(nib.Nifti1Image(msmt_fit.volume_fractions.astype(np.float32),
vol.affine), args.vf)
if args.vf_rgb:
vf = msmt_fit.volume_fractions
vf_rgb = vf / np.max(vf) * 255
vf_rgb = np.clip(vf_rgb, 0, 255)
nib.save(nib.Nifti1Image(vf_rgb.astype(np.uint8),
vol.affine), args.vf_rgb)
def main():
parser = _build_arg_parser()
args = parser.parse_args()
logging.basicConfig(level=logging.INFO)
assert_inputs_exist(parser, [args.in_dwi, args.in_bval, args.in_bvec,
args.frf_file])
assert_outputs_exist(parser, args, args.out_fODF)
# Loading data
full_frf = np.loadtxt(args.frf_file)
vol = nib.load(args.in_dwi)
data = vol.get_fdata(dtype=np.float32)
bvals, bvecs = read_bvals_bvecs(args.in_bval, args.in_bvec)
# Checking mask
if args.mask is None:
mask = None
else:
mask = get_data_as_mask(nib.load(args.mask), dtype=bool)
if mask.shape != data.shape[:-1]:
raise ValueError("Mask is not the same shape as data.")
sh_order = args.sh_order
# Checking data and sh_order
b0_thr = check_b0_threshold(
args.force_b0_threshold, bvals.min(), bvals.min())
if data.shape[-1] < (sh_order + 1) * (sh_order + 2) / 2:
logging.warning(
'We recommend having at least {} unique DWI volumes, but you '
'currently have {} volumes. Try lowering the parameter sh_order '
'in case of non convergence.'.format(
(sh_order + 1) * (sh_order + 2) / 2, data.shape[-1]))
# Checking bvals, bvecs values and loading gtab
if not is_normalized_bvecs(bvecs):
logging.warning('Your b-vectors do not seem normalized...')
bvecs = normalize_bvecs(bvecs)
gtab = gradient_table(bvals, bvecs, b0_threshold=b0_thr)
# Checking full_frf and separating it
if not full_frf.shape[0] == 4:
raise ValueError('FRF file did not contain 4 elements. '
'Invalid or deprecated FRF format')
frf = full_frf[0:3]
mean_b0_val = full_frf[3]
# Loading the sphere
reg_sphere = get_sphere('symmetric362')
# Computing CSD
csd_model = ConstrainedSphericalDeconvModel(
gtab, (frf, mean_b0_val),
reg_sphere=reg_sphere,
sh_order=sh_order)
# Computing CSD fit
csd_fit = fit_from_model(csd_model, data,
mask=mask, nbr_processes=args.nbr_processes)
# Saving results
shm_coeff = csd_fit.shm_coeff
if args.sh_basis == 'tournier07':
shm_coeff = convert_sh_basis(shm_coeff, reg_sphere, mask=mask,
nbr_processes=args.nbr_processes)
nib.save(nib.Nifti1Image(shm_coeff.astype(np.float32),
vol.affine), args.out_fODF)