def test_reshape_peaks_for_visualization():
data1 = np.random.randn(10, 5, 3).astype('float32')
data2 = np.random.randn(10, 2, 5, 3).astype('float32')
data3 = np.random.randn(10, 2, 12, 5, 3).astype('float32')
data1_reshape = reshape_peaks_for_visualization(data1)
data2_reshape = reshape_peaks_for_visualization(data2)
data3_reshape = reshape_peaks_for_visualization(data3)
assert_array_equal(data1_reshape.shape, (10, 15))
assert_array_equal(data2_reshape.shape, (10, 2, 15))
assert_array_equal(data3_reshape.shape, (10, 2, 12, 15))
assert_array_equal(data1_reshape.reshape(10, 5, 3), data1)
assert_array_equal(data2_reshape.reshape(10, 2, 5, 3), data2)
assert_array_equal(data3_reshape.reshape(10, 2, 12, 5, 3), data3)
def test_reshape_peaks_for_visualization():
data1 = np.random.randn(10, 5, 3).astype('float32')
data2 = np.random.randn(10, 2, 5, 3).astype('float32')
data3 = np.random.randn(10, 2, 12, 5, 3).astype('float32')
data1_reshape = reshape_peaks_for_visualization(data1)
data2_reshape = reshape_peaks_for_visualization(data2)
data3_reshape = reshape_peaks_for_visualization(data3)
assert_array_equal(data1_reshape.shape, (10, 15))
assert_array_equal(data2_reshape.shape, (10, 2, 15))
assert_array_equal(data3_reshape.shape, (10, 2, 12, 15))
assert_array_equal(data1_reshape.reshape(10, 5, 3), data1)
assert_array_equal(data2_reshape.reshape(10, 2, 5, 3), data2)
assert_array_equal(data3_reshape.reshape(10, 2, 12, 5, 3), data3)
def main():
parser = _build_arg_parser()
args = parser.parse_args()
if not args.not_all:
args.afd = args.afd or 'afd_max.nii.gz'
args.afd_total = args.afd_total or 'afd_total_sh0.nii.gz'
args.afd_sum = args.afd_sum or 'afd_sum.nii.gz'
args.nufo = args.nufo or 'nufo.nii.gz'
args.peaks = args.peaks or 'peaks.nii.gz'
arglist = [args.afd, args.afd_total, args.afd_sum, args.nufo, args.peaks]
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, [])
assert_outputs_exists(parser, args, arglist)
data, affine = load(args.input)
if args.mask is None:
mask = np.ones(data.shape[:-1])
else:
mask, affine2 = load(args.mask)
nufo_map, afd_map, afd_sum, peaks_dirs = get_maps(data, mask, args)
# Save result
if args.nufo:
save(nufo_map, affine, args.nufo)
if args.afd:
save(afd_map, affine, args.afd)
if args.afd_total:
# this is the analytical afd total
afd_tot = data[:, :, :, 0]
save(afd_tot, affine, args.afd_total)
if args.afd_sum:
save(afd_sum, affine, args.afd_sum)
if args.peaks:
nib.save(
nib.Nifti1Image(reshape_peaks_for_visualization(peaks_dirs),
affine), args.peaks)
if args.visu:
if nufo_map.max() > nufo_map.min():
nufo_map = (255 * (nufo_map - nufo_map.min()) /
(nufo_map.max() - nufo_map.min()))
if afd_map.max() > afd_map.min():
afd_map = (255 * (afd_map - afd_map.min()) /
(afd_map.max() - afd_map.min()))
save(nufo_map, affine, args.nufo, True)
save(afd_map, affine, args.afd, True)
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)
def main():
parser = _build_arg_parser()
args = parser.parse_args()
logging.basicConfig(level=logging.INFO)
if not args.not_all:
args.fodf = args.fodf or 'fodf.nii.gz'
args.peaks = args.peaks or 'peaks.nii.gz'
args.peak_indices = args.peak_indices or 'peak_indices.nii.gz'
arglist = [args.fodf, args.peaks, args.peak_indices]
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
# Check for FRF filename
base_odf_name, _ = split_name_with_nii(args.fodf)
frf_filename = base_odf_name + '_frf.txt'
if os.path.isfile(frf_filename) and not args.overwrite:
parser.error('Cannot save frf file, "{0}" already exists. '
'Use -f to overwrite.'.format(frf_filename))
vol = nib.load(args.input)
data = vol.get_data()
bvals, bvecs = read_bvals_bvecs(args.bvals, args.bvecs)
if args.mask_wm is not None:
wm_mask = nib.load(args.mask_wm).get_data().astype('bool')
else:
wm_mask = np.ones_like(data[..., 0], dtype=np.bool)
logging.info(
'No white matter mask specified! mask_data will be used instead, '
'if it has been supplied. \nBe *VERY* careful about the '
'estimation of the fiber response function for the CSD.')
data_in_wm = applymask(data, wm_mask)
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: {}'.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)
if args.mask is None:
mask = None
else:
mask = nib.load(args.mask).get_data().astype(np.bool)
# Raise warning for sh order if there is not enough DWIs
if data.shape[-1] < (args.sh_order + 1) * (args.sh_order + 2) / 2:
warnings.warn(
'We recommend having at least %s unique DWIs volumes, but you '
'currently have %s volumes. Try lowering the parameter --sh_order '
'in case of non convergence.',
(args.sh_order + 1) * (args.sh_order + 2) / 2), data.shape[-1]
fa_thresh = args.fa_thresh
# If threshold is too high, try lower until enough indices are found
# estimating a response function with fa < 0.5 does not make sense
nvox = 0
while nvox < 300 and fa_thresh > 0.5:
response, ratio, nvox = auto_response(gtab,
data_in_wm,
roi_center=args.roi_center,
roi_radius=args.roi_radius,
fa_thr=fa_thresh,
return_number_of_voxels=True)
logging.info('Number of indices is %s with threshold of %s', nvox,
fa_thresh)
fa_thresh -= 0.05
if fa_thresh <= 0:
raise ValueError(
'Could not find at least 300 voxels for estimating the frf!')
logging.info('Found %s valid voxels for frf estimation.', nvox)
response = list(response)
logging.info('Response function is %s', response)
if args.frf is not None:
l01 = np.array(literal_eval(args.frf), dtype=np.float64)
if not args.no_factor:
l01 *= 10**-4
response[0] = np.array([l01[0], l01[1], l01[1]])
ratio = l01[1] / l01[0]
logging.info("Eigenvalues for the frf of the input data are: %s",
response[0])
logging.info("Ratio for smallest to largest eigen value is %s", ratio)
np.savetxt(frf_filename, response[0])
if not args.frf_only:
reg_sphere = get_sphere('symmetric362')
peaks_sphere = get_sphere('symmetric724')
csd_model = ConstrainedSphericalDeconvModel(gtab,
response,
reg_sphere=reg_sphere,
sh_order=args.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,
return_sh=True,
sh_basis_type=args.basis,
sh_order=args.sh_order,
normalize_peaks=True,
parallel=parallel,
nbr_processes=nbr_processes)
if args.fodf:
nib.save(
nib.Nifti1Image(peaks_csd.shm_coeff.astype(np.float32),
vol.affine), args.fodf)
if args.peaks:
nib.save(
nib.Nifti1Image(reshape_peaks_for_visualization(peaks_csd),
vol.affine), args.peaks)
if args.peak_indices:
nib.save(nib.Nifti1Image(peaks_csd.peak_indices, vol.affine),
args.peak_indices)
sphere = get_sphere('symmetric362')
print "loading bval/bvec files"
bvals, bvecs = read_bvals_bvecs("tp3_data//bvals2000", "tp3_data//bvecs2000")
gtab = gradient_table(bvals, bvecs)
print "loading nifti files"
img = nib.load("tp3_data//dwi2000.nii.gz")
affine = img.get_affine()
data = img.get_data()
mask = nib.load("tp3_data//_binary_mask.nii.gz").get_data()
## Apply mask
data_in_wm = applymask(data, mask)
response, ratio = auto_response(gtab, data_in_wm)
# Computing ODF
print "computing fODF... please wait an hour"
csd_model = ConstrainedSphericalDeconvModel(gtab, response, reg_sphere=sphere)
peaks_csd = peaks_from_model(model=csd_model,
data=data,
sphere=sphere,
relative_peak_threshold=.25,
min_separation_angle=25,
mask=mask,
normalize_peaks=True,
parallel=True)
# Saving files
print "saving files"
nib.save(nib.Nifti1Image(peaks_csd.shm_coeff.astype(np.float32), affine), "tp3_data//_fodf.nii.gz")
nib.save(nib.Nifti1Image(reshape_peaks_for_visualization(peaks_csd), affine), "tp3_data//_fodfpeaks.nii.gz")
gtab = gradient_table(bvals, bvecs)
img = nib.load(dwi_filename)
affine = img.get_affine()
data = img.get_data()
mask = nib.load(mask_filename).get_data()
data_in_wm = applymask(data, mask)
response, ratio = auto_response(gtab, data_in_wm)
csd_model = ConstrainedSphericalDeconvModel(gtab, response, reg_sphere=sphere)
peaks_csd = peaks_from_model(model=csd_model,
data=data,
sphere=sphere,
relative_peak_threshold=.25,
min_separation_angle=25,
mask=mask,
normalize_peaks=True,
parallel=True)
nib.save(nib.Nifti1Image(peaks_csd.shm_coeff.astype(np.float32), affine), fODF_filename)
nib.save(nib.Nifti1Image(reshape_peaks_for_visualization(peaks_csd), affine), fODFpeaks_filename)