def load_vols(vols):
if isinstance(vols, list):
return [load_vol(vol) if isinstance(vol, basestring)
else vol for vol in vols]
elif isinstance(vols, basestring):
return nibabel.four_to_three(nibabel.load(vols))
elif is_niimg(vols):
return nibabel.four_to_three(vols)
else:
raise TypeError(type(vols))
def prep_data(self, nifti1, bval1, bvec1, nifti2, bval2, bvec2):
''' Load the reconstructed image files and generate the files that TOPUP needs. '''
ni1 = nb.load(nifti1)
ni2 = nb.load(nifti2)
phase_dim1 = ni1.get_header().get_dim_info()[1]
phase_dim2 = ni2.get_header().get_dim_info()[1]
bvals1 = np.loadtxt(bval1)
bvals2 = np.loadtxt(bval2)
bvecs1 = np.loadtxt(bvec1)
bvecs2 = np.loadtxt(bvec2)
nondwi1 = [im for i,im in enumerate(nb.four_to_three(ni1)) if bvals1[i]<10 and i<self.num_vols]
nondwi2 = [im for i,im in enumerate(nb.four_to_three(ni2)) if bvals2[i]<10 and i<self.num_vols]
b0 = nb.concat_images(nondwi1+nondwi2)
# Topup requires an even number of slices
if b0.shape[2]%2:
d = b0.get_data()
d = np.concatenate((d,np.zeros((d.shape[0],d.shape[1],1,d.shape[3]), dtype=d.dtype)),axis=2)
b0 = nb.Nifti1Image(d, b0.get_affine())
nb.save(b0, self.b0_file)
with open(self.acq_file, 'w') as f:
for i in xrange(len(nondwi1)):
row = ['0','0','0',str(self.readout_time1),'\n']
row[phase_dim1] = str(self.pe_dir1)
f.write(' '.join(row))
for i in xrange(len(nondwi2)):
row = ['0','0','0',str(self.readout_time2),'\n']
row[phase_dim2] = str(self.pe_dir2)
f.write(' '.join(row))
mux_ims1 = nb.four_to_three(ni1)[self.num_cal1:]
mux_ims2 = nb.four_to_three(ni2)[self.num_cal2:]
all_ims = nb.concat_images(mux_ims1 + mux_ims2)
if all_ims.shape[2]%2:
d = all_ims.get_data()
d = np.concatenate((d,np.zeros((d.shape[0],d.shape[1],1,d.shape[3]), dtype=d.dtype)),axis=2)
all_ims = nb.Nifti1Image(d, all_ims.get_affine())
nb.save(all_ims, self.dwi_base+'.nii.gz')
indices = ['1' for i in xrange(len(mux_ims1))] + [str(len(nondwi1)+1) for i in xrange(len(mux_ims2))]
with open(self.index_file, 'w') as f:
f.write(' '.join(indices))
bvals = np.concatenate((bvals1[self.num_cal1:],bvals2[self.num_cal2:]), axis=0)
bvecs = np.concatenate((bvecs1[:,self.num_cal1:],bvecs2[:,self.num_cal2:]), axis=1)
with open(self.bval_file, 'w') as f:
f.write(' '.join(['%0.1f' % value for value in bvals]))
with open(self.bvec_file, 'w') as f:
f.write(' '.join(['%0.4f' % value for value in bvecs[0,:]]) + '\n')
f.write(' '.join(['%0.4f' % value for value in bvecs[1,:]]) + '\n')
f.write(' '.join(['%0.4f' % value for value in bvecs[2,:]]) + '\n')
def test_save_vols():
# setup
n_scans = 10
output_dir = os.path.join(OUTPUT_DIR, inspect.stack()[0][3])
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# create 4D film
film = create_random_image(ndim=4, n_scans=n_scans)
threeD_vols = nibabel.four_to_three(film)
# save vols manually
film_filename = os.path.join(output_dir, "film.nii.gz")
threeD_vols_filenames = [os.path.join(output_dir, "fMETHODS-%06i" % i) for i in range(len(threeD_vols))]
# check saving seperate 3D vols
for stuff in [film, threeD_vols]:
if isinstance(stuff, list):
basenames = [os.path.basename(x) for x in threeD_vols_filenames]
else:
basenames = os.path.basename(film_filename)
for concat in [False, True]:
for bn in [None, basenames]:
saved_vols_filenames = save_vols(stuff, output_dir, ext=".nii.gz", concat=concat, basenames=bn)
if not concat and isinstance(stuff, list):
assert_true(isinstance(saved_vols_filenames, list))
assert_equal(len(saved_vols_filenames), n_scans)
if not bn is None:
assert_equal(os.path.basename(saved_vols_filenames[7]), "fMETHODS-000007.nii.gz")
else:
assert_true(isinstance(saved_vols_filenames, basestring))
assert_true(saved_vols_filenames.endswith(".nii.gz"), msg=saved_vols_filenames)
assert_true(is_4D(check_niimg_4d(saved_vols_filenames)))
def b0_average(in_dwi, in_bval, out_file=None):
"""
A function that averages the *b0* volumes from a DWI dataset.
.. warning:: *b0* should be already registered (head motion artifact should
be corrected).
"""
import numpy as np
import nibabel as nb
import os.path as op
if out_file is None:
fname, ext = op.splitext(op.basename(in_dwi))
if ext == ".gz":
fname, ext2 = op.splitext(fname)
ext = ext2 + ext
out_file = op.abspath("%s_avg_b0%s" % (fname, ext))
imgs = np.array(nb.four_to_three(nb.load(in_dwi)))
bval = np.loadtxt(in_bval)
b0s = [im.get_data().astype(np.float32)
for im in imgs[np.where(bval == 0)]]
b0 = np.average(np.array(b0s), axis=0)
hdr = imgs[0].get_header().copy()
hdr.set_data_shape(b0.shape)
hdr.set_xyzt_units('mm')
hdr.set_data_dtype(np.float32)
nb.Nifti1Image(b0, imgs[0].get_affine(), hdr).to_filename(out_file)
return out_file
def b0_average(in_dwi, in_bval, max_b=10.0, out_file=None):
"""
A function that averages the *b0* volumes from a DWI dataset.
As current dMRI data are being acquired with all b-values > 0.0,
the *lowb* volumes are selected by specifying the parameter max_b.
.. warning:: *b0* should be already registered (head motion artifact should
be corrected).
"""
import numpy as np
import nibabel as nb
import os.path as op
if out_file is None:
fname, ext = op.splitext(op.basename(in_dwi))
if ext == ".gz":
fname, ext2 = op.splitext(fname)
ext = ext2 + ext
out_file = op.abspath("%s_avg_b0%s" % (fname, ext))
imgs = np.array(nb.four_to_three(nb.load(in_dwi)))
bval = np.loadtxt(in_bval)
index = np.argwhere(bval <= max_b).flatten().tolist()
b0s = [im.get_data().astype(np.float32)
for im in imgs[index]]
b0 = np.average(np.array(b0s), axis=0)
hdr = imgs[0].get_header().copy()
hdr.set_data_shape(b0.shape)
hdr.set_xyzt_units('mm')
hdr.set_data_dtype(np.float32)
nb.Nifti1Image(b0, imgs[0].get_affine(), hdr).to_filename(out_file)
return out_file
def time_avg(in_file, index=[0], out_file=None):
"""
Average the input time-series, selecting the indices given in index
.. warning:: time steps should be already registered (corrected for
head motion artifacts).
"""
import numpy as np
import nibabel as nb
import os.path as op
if out_file is None:
fname, ext = op.splitext(op.basename(in_file))
if ext == ".gz":
fname, ext2 = op.splitext(fname)
ext = ext2 + ext
out_file = op.abspath("%s_baseline%s" % (fname, ext))
index = np.atleast_1d(index).tolist()
imgs = np.array(nb.four_to_three(nb.load(in_file)))[index]
if len(index) == 1:
data = imgs[0].get_data().astype(np.float32)
else:
data = np.average(np.array([im.get_data().astype(np.float32)
for im in imgs]), axis=0)
hdr = imgs[0].get_header().copy()
hdr.set_data_shape(data.shape)
hdr.set_xyzt_units('mm')
hdr.set_data_dtype(np.float32)
nb.Nifti1Image(data, imgs[0].get_affine(), hdr).to_filename(out_file)
return out_file
def test_load_4D_img():
# setup
output_dir = os.path.join(OUTPUT_DIR, inspect.stack()[0][3])
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# try loading from 4D niimg
film = create_random_image(n_scans=10)
loaded_4D_img = load_4D_img(film)
assert_true(is_niimg(loaded_4D_img))
assert_equal(loaded_4D_img.shape, film.shape)
# try loading from 4D image file
film = create_random_image(n_scans=10)
saved_img_filename = os.path.join(output_dir, "4D.nii.gz")
nibabel.save(film, saved_img_filename)
loaded_4D_img = load_4D_img(saved_img_filename)
assert_true(is_niimg(loaded_4D_img))
assert_equal(loaded_4D_img.shape, film.shape)
# try loading from list of 3D niimgs
film = create_random_image(n_scans=10)
loaded_4D_img = load_4D_img(nibabel.four_to_three(film))
assert_true(is_niimg(loaded_4D_img))
assert_equal(loaded_4D_img.shape, film.shape)
# try loading from list of 3D image files
film = create_random_image(n_scans=10)
saved_vols_filenames = save_vols(film,
output_dir,
ext='.nii.gz',
)
loaded_4D_img = load_4D_img(saved_vols_filenames)
assert_true(is_niimg(loaded_4D_img))
assert_equal(loaded_4D_img.shape, film.shape)
def _run_interface(self, runtime):
mask_imgs = [nb.load(fname) for fname in self.inputs.label_files]
if len(mask_imgs) == 1:
mask_imgs = nb.four_to_three(mask_imgs[0])
masks = [mask_img.get_data().astype(np.bool) for mask_img in mask_imgs]
n_masks = len(masks)
if n_masks != len(self.inputs.class_labels):
raise ValueError("Number of masks must match number of labels")
img = nb.load(self.inputs.in_file)
series = np.zeros((img.shape[3], n_masks))
data = img.get_data()
for j in range(n_masks):
series[:, j] = data[masks[j], :].mean(axis=0)
output = np.vstack((self.inputs.class_labels, series.astype(str)))
self._results['out_file'] = os.path.join(runtime.cwd,
self.inputs.out_file)
np.savetxt(
self._results['out_file'], output, fmt=b'%s', delimiter='\t')
return runtime
def read_niftis(file_lists):
"""
Read niftis.
Parameters
----------
file_lists: list of list of paths.
Each list of file paths is a unique class.
Returns
-------
data, labels: tuple of array-like and list
The data and corresponding labels
"""
data0 = load_image(file_lists[0][0]).get_data()
if len(data0.shape) == 3:
x, y, z = data0.shape
t = 1
elif len(data0.shape) == 4:
x, y, z, t = data0.shape
else:
raise ValueError("Cannot parse data with dimensions %r" % data0.shape)
dt = (sum(len(fl) for fl in file_lists)) * t
data = np.zeros((dt, x, y, z))
labels = [[i] * (len(fl) * t) for i, fl in enumerate(file_lists)]
labels = [item for sublist in labels for item in sublist]
for i, fl in enumerate(file_lists):
assert len([j for j in labels if j == i]) == len(fl) * t
flattened_list = [item for sublist in file_lists for item in sublist]
for i, f in enumerate(flattened_list):
logger.info("Loading subject from file: %s%s" % (f, '' * 30))
nifti = load_image(f)
subject_data = nifti.get_data()
if len(subject_data.shape) == 3:
data[i] = subject_data
elif len(subject_data.shape) == 4:
data[i * t: (i + 1) * t] = subject_data.transpose((3, 0, 1, 2))
else:
raise ValueError("Cannot parse subject data with dimensions %r"
% subject_data.shape)
logger.info("\rLoading subject from file: %s\n" % ('DONE' + " "*30))
if data.shape[0] != len(labels):
raise ValueError("Data and labels have different number of samples.")
base_file = flattened_list[0]
# Use nibabel in case we need to convert from 4d to 3d
base = nib.load(base_file)
if len(base.shape) == 4:
base = nib.four_to_three(base)[0]
return data, labels, base
def _run_interface(self, runtime):
if not self._have_nipy:
raise RuntimeError('nipy is not installed')
from nipy.algorithms.registration.histogram_registration import HistogramRegistration
from nipy.algorithms.registration.affine import Affine
vol1_nii = nb.load(self.inputs.volume1)
vol2_nii = nb.load(self.inputs.volume2)
dims = vol1_nii.get_data().ndim
if dims == 3 or dims == 2:
vols1 = [vol1_nii]
vols2 = [vol2_nii]
if dims == 4:
vols1 = nb.four_to_three(vol1_nii)
vols2 = nb.four_to_three(vol2_nii)
if dims < 2 or dims > 4:
raise RuntimeError('Image dimensions not supported (detected %dD file)' % dims)
if isdefined(self.inputs.mask1):
mask1 = nb.load(self.inputs.mask1).get_data() == 1
else:
mask1 = None
if isdefined(self.inputs.mask2):
mask2 = nb.load(self.inputs.mask2).get_data() == 1
else:
mask2 = None
self._similarity = []
for ts1, ts2 in zip(vols1, vols2):
histreg = HistogramRegistration(from_img=ts1,
to_img=ts2,
similarity=self.inputs.metric,
from_mask=mask1,
to_mask=mask2)
self._similarity.append(histreg.eval(Affine()))
return runtime
def test_load_specific_vol():
# setup
output_dir = os.path.join(OUTPUT_DIR, inspect.stack()[0][3])
if not os.path.exists(output_dir):
os.makedirs(output_dir)
n_scans = 23
# create 4D film
film = create_random_image(ndim=4, n_scans=n_scans)
# test loading vol from nibabel image object
for t in xrange(n_scans):
_vol, _n_scans = load_specific_vol(film, t)
assert_equal(_n_scans, n_scans)
assert_true(isinstance(_vol, type(film)))
assert_equal(_vol.shape, film.shape[:-1])
numpy.testing.assert_array_equal(_vol.get_data(),
film.get_data()[..., t])
# test loading vol from a single 4D filename
for ext in IMAGE_EXTENSIONS:
for film_filename_type in ['str', 'list']:
if film_filename_type == 'str':
# save film as single filename with extension ext
film_filename = os.path.join(output_dir, "4D%s" % ext)
nibabel.save(film, film_filename)
else:
# save film as multiple filenames (3D vols), with ext extension
vols = nibabel.four_to_three(film)
film_filename = []
for t, vol in zip(xrange(n_scans), vols):
vol_filename = os.path.join(output_dir,
"vol_%i%s" % (t, ext))
nibabel.save(vol, vol_filename)
film_filename.append(vol_filename)
# test loading proper
for t in xrange(n_scans):
# note that .img loads as Nifti1Pair, not Nifti1Image
vol_type = nibabel.Nifti1Pair if ext == '.img' else \
nibabel.Nifti1Image
# load specific 3D vol from 4D film by filename
_vol, _n_scans = load_specific_vol(film_filename, t)
assert_equal(_n_scans, n_scans)
assert_true(isinstance(_vol, vol_type))
assert_equal(_vol.shape, film.shape[:-1])
numpy.testing.assert_array_equal(_vol.get_data(),
film.get_data()[..., t])
def test_do_3Dto4D_merge():
# setup
n_scans = 10
output_dir = os.path.join(OUTPUT_DIR, inspect.stack()[0][3])
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# create 4D film
film = create_random_image(ndim=4, n_scans=n_scans)
threeD_vols = nibabel.four_to_three(film)
_film = do_3Dto4D_merge(threeD_vols)
assert_equal(_film.shape, film.shape)
save_vols(threeD_vols, output_dir, ext='.nii.gz')
def split_warp_volumes_fn(in_file):
from nipype import logging
from nipype.utils.filemanip import split_filename
import nibabel as nb
import os.path as op
iflogger = logging.getLogger('interface')
iflogger.info(in_file)
path, name, ext = split_filename(in_file)
image = nb.load(in_file)
x_img, y_img, z_img = nb.four_to_three(image)
x = op.abspath(name + '_x' + ".nii.gz")
y = op.abspath(name + '_y' + ".nii.gz")
z = op.abspath(name + '_z' + ".nii.gz")
nb.save(x_img, x)
nb.save(y_img, y)
nb.save(z_img, z)
return x, y, z
def _run_interface(self, runtime):
from dipy.reconst.csdeconv import ConstrainedSphericalDeconvModel
from dipy.data import get_sphere
# import marshal as pickle
import pickle as pickle
import gzip
img = nb.load(self.inputs.in_file)
imref = nb.four_to_three(img)[0]
affine = img.get_affine()
if isdefined(self.inputs.in_mask):
msk = nb.load(self.inputs.in_mask).get_data()
else:
msk = np.ones(imref.get_shape())
data = img.get_data().astype(np.float32)
hdr = imref.get_header().copy()
gtab = self._get_gradient_table()
resp_file = np.loadtxt(self.inputs.response)
response = (np.array(resp_file[0:3]), resp_file[-1])
ratio = response[0][1] / response[0][0]
if abs(ratio - 0.2) > 0.1:
IFLOGGER.warn(('Estimated response is not prolate enough. '
'Ratio=%0.3f.') % ratio)
csd_model = ConstrainedSphericalDeconvModel(
gtab, response, sh_order=self.inputs.sh_order)
IFLOGGER.info('Fitting CSD model')
csd_fit = csd_model.fit(data, msk)
f = gzip.open(self._gen_filename('csdmodel', ext='.pklz'), 'wb')
pickle.dump(csd_model, f, -1)
f.close()
if self.inputs.save_fods:
sphere = get_sphere('symmetric724')
fods = csd_fit.odf(sphere)
nb.Nifti1Image(fods.astype(np.float32), img.get_affine(),
None).to_filename(self._gen_filename('fods'))
return runtime
def _run_interface(self, runtime):
from dipy.reconst.csdeconv import ConstrainedSphericalDeconvModel
from dipy.data import get_sphere
# import marshal as pickle
import cPickle as pickle
import gzip
img = nb.load(self.inputs.in_file)
imref = nb.four_to_three(img)[0]
affine = img.get_affine()
if isdefined(self.inputs.in_mask):
msk = nb.load(self.inputs.in_mask).get_data()
else:
msk = np.ones(imref.get_shape())
data = img.get_data().astype(np.float32)
hdr = imref.get_header().copy()
gtab = self._get_gradient_table()
resp_file = np.loadtxt(self.inputs.response)
response = (np.array(resp_file[0:3]), resp_file[-1])
ratio = response[0][1] / response[0][0]
if abs(ratio - 0.2) > 0.1:
IFLOGGER.warn(('Estimated response is not prolate enough. '
'Ratio=%0.3f.') % ratio)
csd_model = ConstrainedSphericalDeconvModel(
gtab, response, sh_order=self.inputs.sh_order)
IFLOGGER.info('Fitting CSD model')
csd_fit = csd_model.fit(data, msk)
f = gzip.open(self._gen_filename('csdmodel', ext='.pklz'), 'wb')
pickle.dump(csd_model, f, -1)
f.close()
if self.inputs.save_fods:
sphere = get_sphere('symmetric724')
fods = csd_fit.odf(sphere)
nb.Nifti1Image(fods.astype(np.float32), img.get_affine(),
None).to_filename(self._gen_filename('fods'))
return runtime
def smooth_image(img, fwhm, **kwargs):
"""Function wrapper LinearFilter class. Spatially smoothens img with
kernel of size fwhm.
Parameters
----------
img: ``ni.Nifti1Image``
image to be smoothen
fwhm: 1D array like of size as big as there are spatial dimensions
in the image
FWHM of smoothing kernel
**kwargs: dict-like
key-word arguments passed to LinearFilter constructor.
Returns
-------
Smoothened image, same type and size as the input img.
"""
if isinstance(img, basestring):
img = ni.load(img)
elif isinstance(img, tuple):
assert len(img) == 2
return smooth_image(ni.Nifti1Image(img[0], img[1]), fwhm, **kwargs)
elif isinstance(img, list):
return [smooth_image(x, fwhm, **kwargs) for x in img]
else:
assert is_niimg(img)
if len(img.shape) == 4:
return ni.concat_images(
[smooth_image(vol, fwhm, **kwargs)
for vol in ni.four_to_three(img)])
else:
assert len(img.shape) == 3
smoothing_kernel = LinearFilter(
img.get_affine(),
img.shape,
fwhm=fwhm,
**kwargs)
return ni.Nifti1Image(smoothing_kernel.smooth(img.get_data(),
clean=True),
img.get_affine())
def test_save_vols():
# setup
n_scans = 10
output_dir = os.path.join(OUTPUT_DIR, inspect.stack()[0][3])
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# create 4D film
film = create_random_image(ndim=4, n_scans=n_scans)
threeD_vols = nibabel.four_to_three(film)
# save vols manually
film_filename = os.path.join(output_dir, 'film.nii.gz')
threeD_vols_filenames = [os.path.join(output_dir, 'fMETHODS-%06i' % i)
for i in range(len(threeD_vols))]
# check saving seperate 3D vols
for stuff in [film, threeD_vols]:
if isinstance(stuff, list):
basenames = [os.path.basename(x)
for x in threeD_vols_filenames]
else:
basenames = os.path.basename(film_filename)
for concat in [False, True]:
for bn in [None, basenames]:
saved_vols_filenames = save_vols(stuff,
output_dir,
ext='.nii.gz',
concat=concat,
basenames=bn
)
if not concat and isinstance(stuff, list):
assert_true(isinstance(
saved_vols_filenames, list))
assert_equal(len(saved_vols_filenames),
n_scans)
if not bn is None:
assert_equal(os.path.basename(saved_vols_filenames[7]),
'fMETHODS-000007.nii.gz')
else:
assert_true(isinstance(saved_vols_filenames, basestring))
assert_true(saved_vols_filenames.endswith('.nii.gz'),
msg=saved_vols_filenames)
assert_true(is_4D(check_niimg_4d(
saved_vols_filenames)))
def _run_interface(self, runtime):
img = nb.load(self.inputs.in_file)
mask_imgs = [nb.load(fname) for fname in self.inputs.label_files]
if len(mask_imgs) == 1 and len(mask_imgs[0].shape) == 4:
mask_imgs = nb.four_to_three(mask_imgs[0])
# This check assumes all input masks have same dimensions
if img.shape[:3] != mask_imgs[0].shape[:3]:
raise NotImplementedError(
"Input image and mask should be of "
"same dimensions before running SignalExtraction")
# Load the mask.
# If mask is a list, each mask is treated as its own ROI/parcel
# If mask is a 3D, each integer is treated as its own ROI/parcel
if len(mask_imgs) > 1:
masks = [
mask_img.get_data() >= self.inputs.prob_thres
for mask_img in mask_imgs
]
else:
labelsmap = mask_imgs[0].get_data()
labels = np.unique(labelsmap)
labels = labels[labels != 0]
masks = [labelsmap == l for l in labels]
if len(masks) != len(self.inputs.class_labels):
raise ValueError("Number of masks must match number of labels")
series = np.zeros((img.shape[3], len(masks)))
metadata = {}
data = img.get_data()
for j, (mask, label) in enumerate(zip(masks,
self.inputs.class_labels)):
series[:, j] = data[mask, :].mean(axis=0)
metadata[label] = {'Method': 'Mean'}
output = np.vstack((self.inputs.class_labels, series.astype(str)))
self._results['out_file'] = os.path.join(runtime.cwd,
self.inputs.out_file)
self._results['metadata'] = metadata
np.savetxt(self._results['out_file'],
output,
fmt=b'%s',
delimiter='\t')
return runtime
def smooth_image(img, fwhm, **kwargs):
"""Function wrapper LinearFilter class. Spatially smoothens img with
kernel of size fwhm.
Parameters
----------
img: ``ni.Nifti1Image``
image to be smoothen
fwhm: 1D array like of size as big as there are spatial dimensions
in the image
FWHM of smoothing kernel
**kwargs: dict-like
key-word arguments passed to LinearFilter constructor.
Returns
-------
Smoothened image, same type and size as the input img.
"""
if isinstance(img, basestring):
img = ni.load(img)
elif isinstance(img, tuple):
assert len(img) == 2
return smooth_image(ni.Nifti1Image(img[0], img[1]), fwhm, **kwargs)
elif isinstance(img, list):
return [smooth_image(x, fwhm, **kwargs) for x in img]
else:
assert is_niimg(img)
if len(img.shape) == 4:
return ni.concat_images([
smooth_image(vol, fwhm, **kwargs) for vol in ni.four_to_three(img)
])
else:
assert len(img.shape) == 3
smoothing_kernel = LinearFilter(img.get_affine(),
img.shape,
fwhm=fwhm,
**kwargs)
return ni.Nifti1Image(
smoothing_kernel.smooth(img.get_data(), clean=True),
img.get_affine())
def _split_epi_lists(in_files, pe_dir, max_trs=50):
"""
Split input EPIs and generate an output list of PEs.
Inputs
------
in_files : list of ``BIDSFile``s
The EPI images that will be pooled into field estimation.
pe_dir : str
The phase-encoding direction of the IntendedFor EPI scan.
max_trs : int
Index of frame after which all volumes will be discarded
from the input EPI images.
"""
from os import path as op
import nibabel as nb
matched_pe = []
opposed_pe = []
for i, (epi_path, epi_pe) in enumerate(in_files):
if epi_pe[0] == pe_dir[0]:
img = nb.load(epi_path)
if len(img.shape) == 3:
splitnii = [img]
else:
splitnii = nb.four_to_three(img.slicer[:, :, :, :max_trs])
for j, nii in enumerate(splitnii):
out_name = op.abspath('dir-%s_tstep-%03d_pe-%03d.nii.gz' %
(epi_pe, i, j))
nii.to_filename(out_name)
if epi_pe == pe_dir:
matched_pe.append(out_name)
else:
opposed_pe.append(out_name)
if matched_pe:
return [opposed_pe, matched_pe]
return [opposed_pe]
def load_specific_vol(vols, t, strict=False):
"""
Utility function for loading specific volume on demand.
Parameters
----------
vols: string(s) or nibabel image object(s)
input volumes or single 4D film
t: int
index of requested volume in the film
"""
assert t >= 0
if isinstance(vols, list):
n_scans = len(vols)
vol = load_vol(vols[t])
elif is_niimg(vols) or isinstance(vols, basestring):
_vols = nibabel.load(vols) if isinstance(vols, basestring) else vols
if len(_vols.shape) != 4:
if strict:
raise ValueError(
"Expecting 4D image, got %iD" % len(_vols.shape))
else:
return _vols, 1
n_scans = _vols.shape[-1]
vol = nibabel.four_to_three(_vols)[t]
else: # unhandled type
raise TypeError(
("vols must be string, image object, or list of such; "
"got %s" % type(vols)))
# delete trivial dimension
if len(vol.shape) == 4:
vol = nibabel.Nifti1Image(vol.get_data()[..., ..., ..., 0],
vol.get_affine())
assert is_niimg(vol)
assert is_3D(vol)
return vol, n_scans
def normalization_2_common_space(self):
print('normalization')
for (input_nii_file,output_nii_file) in zip(self.input_nii, self.normalized_nii):
print('run %s' % input_nii_file)
print('generate %s' % output_nii_file)
if os.path.exists(output_nii_file): continue
dir_name = os.path.dirname(output_nii_file)
if not os.path.exists(dir_name): os.makedirs(dir_name)
mni_nii_file = self.get_mni152_nii_file(input_nii_file)
nib_img = nib.load(input_nii_file)
if len(nib_img.shape) > 3:
nib_3d_imgs = nib.four_to_three(nib_img)
else:
nib_3d_imgs = [nib_img]
dyn_3d_nib_files = []
dyn_3d_nib_in_root = tempfile.mkdtemp()
for nib_3d_img in nib_3d_imgs:
nib_3d_img_file = os.path.join(dyn_3d_nib_in_root, uuid.uuid4().__str__() + '.nii.gz')
nib_3d_img.to_filename(nib_3d_img_file)
dyn_3d_nib_files.append(nib_3d_img_file)
dyn_3d_nib_out_root = tempfile.mkdtemp()
dyn_3d_nib_out_files = []
for dyn_3d_nib_file in dyn_3d_nib_files:
dyn_3d_nib_out_file = os.path.join(dyn_3d_nib_out_root, uuid.uuid4().__str__() + '.nii.gz')
devnull = open(os.devnull, 'w')
subprocess.call(['flirt',
'-in', dyn_3d_nib_file,
'-out', dyn_3d_nib_out_file,
'-ref', mni_nii_file,
'-bins', '256',
'-dof', '12'],stdout=devnull, stderr=subprocess.STDOUT)
dyn_3d_nib_out_files.append(dyn_3d_nib_out_file)
nib_3d_imgs = []
for dyn_3d_nib_out_file in dyn_3d_nib_out_files:
nib_3d_imgs.append(nib.load(dyn_3d_nib_out_file))
if len(nib_3d_imgs) > 1:
nib_4d_img = nib.concat_images(nib_3d_imgs)
nib_4d_img.to_filename(output_nii_file)
else:
nib_3d_imgs[0].to_filename(output_nii_file)
if os.path.isdir(dyn_3d_nib_in_root): shutil.rmtree(dyn_3d_nib_in_root)
if os.path.isdir(dyn_3d_nib_out_root): shutil.rmtree(dyn_3d_nib_out_root)
return
def _run_interface(self, runtime):
in_file = self.inputs.in_file
img = nb.load(in_file)
extra_dims = tuple(dim for dim in img.shape[3:] if dim > 1) or (1, )
if len(extra_dims) != 1:
raise ValueError(
f"Invalid shape {'x'.join(str(s) for s in img.shape)}")
img = img.__class__(img.dataobj.reshape(img.shape[:3] + extra_dims),
img.affine, img.header)
self._results["out_files"] = []
for i, img_3d in enumerate(nb.four_to_three(img)):
out_file = fname_presuffix(in_file,
suffix=f"_idx-{i:03}",
newpath=runtime.cwd)
img_3d.to_filename(out_file)
self._results["out_files"].append(out_file)
return runtime
def test_transform():
# setup
output_dir = os.path.join(OUTPUT_DIR, inspect.stack()[0][3])
if not os.path.exists(output_dir):
os.makedirs(output_dir)
film = nibabel.Nifti1Image(np.random.rand(11, 13, 17, 19),
np.eye(4))
threeD_vols = nibabel.four_to_three(film)
# filenames
film_filename = os.path.join(output_dir, 'film.nii.gz')
threeD_vols_filenames = [os.path.join(output_dir, 'fMETHODS-%06i' % i)
for i in xrange(len(threeD_vols))]
for stuff in [film, threeD_vols]:
for as_files in [False, True]:
if as_files:
if isinstance(stuff, list):
basenames = [os.path.basename(x)
for x in threeD_vols_filenames]
else:
basenames = os.path.basename(film_filename)
stuff = save_vols(stuff, output_dir, basenames=basenames)
fmristc = fMRISTC().fit(raw_data=stuff)
output = fmristc.transform(output_dir=output_dir)
# test output type, shape, etc.
if isinstance(stuff, list):
nose.tools.assert_true(isinstance(
output, list))
nose.tools.assert_equal(len(output),
film.shape[-1])
if as_files:
nose.tools.assert_equal(os.path.basename(output[7]),
'afMETHODS-000007.nii.gz')
else:
if as_files:
nose.tools.assert_equal(os.path.basename(output),
'afilm.nii.gz')
def estimate_motion(nifti_image):
# BEGIN STDOUT SUPRESSION
actualstdout = sys.stdout
sys.stdout = open(os.devnull,'w')
# We want to use the middle time point as the reference. But the algorithm does't allow that, so fake it.
ref_vol = nifti_image.shape[3]/2 + 1
ims = nb.four_to_three(nifti_image)
reg = Realign4d(nb.concat_images([ims[ref_vol]] + ims)) # in the next release, we'll need to add tr=1.
reg.estimate(loops=3) # default: loops=5
aligned = reg.resample(0)[:,:,:,1:]
sys.stdout = actualstdout
# END STDOUT SUPRESSION
abs_disp = []
rel_disp = []
transrot = []
prev_T = None
# skip the first one, since it's the reference volume
for T in reg._transforms[0][1:]:
# get the full affine for this volume by pre-multiplying by the reference affine
#mc_affine = np.dot(ni.get_affine(), T.as_affine())
transrot.append(T.translation.tolist()+T.rotation.tolist())
# Compute the mean displacement
# See http://www.fmrib.ox.ac.uk/analysis/techrep/tr99mj1/tr99mj1/node5.html
# radius of the spherical head assumption (in mm):
R = 80.
# The center of the volume. Assume 0,0,0 in world coordinates.
# Note: it might be better to use the center of mass of the brain mask.
xc = np.matrix((0,0,0)).T
T_error = T.as_affine() - np.eye(4)
A = np.matrix(T_error[0:3,0:3])
t = np.matrix(T_error[0:3,3]).T
abs_disp.append(np.sqrt( R**2. / 5 * np.trace(A.T * A) + (t + A*xc).T * (t + A*xc) ).item())
if prev_T!=None:
T_error = T.as_affine() - prev_T.as_affine() # - np.eye(4)
A = np.matrix(T_error[0:3,0:3])
t = np.matrix(T_error[0:3,3]).T
rel_disp.append(np.sqrt( R**2. / 5 * np.trace(A.T * A) + (t + A*xc).T * (t + A*xc) ).item())
else:
rel_disp.append(0.0)
prev_T = T
return aligned,np.array(abs_disp),np.array(rel_disp),np.array(transrot)
def load_specific_vol(vols, t, strict=False):
"""
Utility function for loading specific volume on demand.
Parameters
----------
vols: string(s) or nibabel image object(s)
input volumes or single 4D film
t: int
index of requested volume in the film
"""
assert t >= 0
if isinstance(vols, list):
n_scans = len(vols)
vol = load_vol(vols[t])
elif is_niimg(vols) or isinstance(vols, basestring):
_vols = nibabel.load(vols) if isinstance(vols, basestring) else vols
if len(_vols.shape) != 4:
if strict:
raise ValueError("Expecting 4D image, got %iD" %
len(_vols.shape))
else:
return _vols, 1
n_scans = _vols.shape[-1]
vol = nibabel.four_to_three(_vols)[t]
else: # unhandled type
raise TypeError(("vols must be string, image object, or list of such; "
"got %s" % type(vols)))
# delete trivial dimension
if len(vol.shape) == 4:
vol = nibabel.Nifti1Image(vol.get_data()[..., ..., ..., 0],
vol.get_affine())
assert is_niimg(vol)
assert is_3D(vol)
return vol, n_scans
def _load_mixed_gambles(zmap_imgs):
"""Ravel zmaps (one per subject) along time axis, resulting,
in a n_subjects * n_trials 3D niimgs and, and then make
gain vector y of same length.
"""
X = []
y = []
mask = []
for zmap_img in zmap_imgs:
# load subject data
this_X = zmap_img.get_data()
affine = zmap_img.get_affine()
finite_mask = np.all(np.isfinite(this_X), axis=-1)
this_mask = np.logical_and(np.all(this_X != 0, axis=-1),
finite_mask)
this_y = np.array([np.arange(1, 9)] * 6).ravel()
# gain levels
if len(this_y) != this_X.shape[-1]:
raise RuntimeError("%s: Expecting %i volumes, got %i!" % (
zmap_img, len(this_y), this_X.shape[-1]))
# standardize subject data
this_X -= this_X.mean(axis=-1)[..., np.newaxis]
std = this_X.std(axis=-1)
std[std == 0] = 1
this_X /= std[..., np.newaxis]
# commit subject data
X.append(this_X)
y.extend(this_y)
mask.append(this_mask)
y = np.array(y)
X = np.concatenate(X, axis=-1)
mask = np.sum(mask, axis=0) > .5 * len(zmap_imgs)
mask = np.logical_and(mask, np.all(np.isfinite(X), axis=-1))
X = X[mask, :].T
tmp = np.zeros(list(mask.shape) + [len(X)])
tmp[mask, :] = X.T
mask_img = nibabel.Nifti1Image(mask.astype(np.int), affine)
X = nibabel.four_to_three(nibabel.Nifti1Image(tmp, affine))
return X, y, mask_img
def load_craddock_2011(number_of_clusters: int, similarity_measure: str = 't', algorithm='2level') -> nibabel.Nifti1Image:
"""
:param number_of_clusters: See the list of cluster sizes above or in volume_cluster_number.csv
:param similarity_measure: t, s, or random (temporal, spatial, random)
:param algorithm: 2level, mean, none
:return:
"""
if not ["t", "s", "random"].__contains__(similarity_measure):
raise RuntimeError("%s is not a valid similarity measure for the craddock atlases. Please use 't', 's', 'random'" % similarity_measure)
if not ["2level", "mean", None].__contains__(algorithm):
raise RuntimeError(
"%s is not a valid algorithm type for the craddock atlases. Please use '2level', 'mean', or None" % similarity_measure)
if not CRADDOCK_CLUSTER_SIZES.__contains__(number_of_clusters):
raise RuntimeError("%d is not a valid cluster size for the craddock atlases. Please use one of %s" % (number_of_clusters, str(CRADDOCK_CLUSTER_SIZES)))
algorithm = algorithm + '_' if algorithm is not None else ''
dataset_path = os.path.join(__get_data_folder_path(), 'craddock_2011', '%scorr05_%sall.nii.gz' % (similarity_measure, algorithm))
dataset_img = nibabel.load(dataset_path)
return nibabel.four_to_three(dataset_img)[CRADDOCK_CLUSTER_SIZES.index(number_of_clusters)]
def _flatten_split_merge(in_files):
if isinstance(in_files, str):
in_files = [in_files]
nfiles = len(in_files)
all_nii = []
for fname in in_files:
nii = nb.squeeze_image(nb.load(fname))
if nii.get_data().ndim > 3:
all_nii += nb.four_to_three(nii)
else:
all_nii.append(nii)
if len(all_nii) == 1:
LOGGER.warning('File %s cannot be split', all_nii[0])
return in_files[0], in_files
if len(all_nii) == nfiles:
flat_split = in_files
else:
splitname = fname_presuffix(in_files[0],
suffix='_split%04d',
newpath=os.getcwd())
flat_split = []
for i, nii in enumerate(all_nii):
flat_split.append(splitname % i)
nii.to_filename(flat_split[-1])
# Only one 4D file was supplied
if nfiles == 1:
merged = in_files[0]
else:
# More that one in_files - need merge
merged = fname_presuffix(in_files[0],
suffix='_merged',
newpath=os.getcwd())
nb.concat_images(all_nii).to_filename(merged)
return merged, flat_split
def _make_sd(func_filenames=None,
anat_filename=None,
ext=".nii.gz",
n_sessions=1,
make_sess_dirs=False,
func_ndim=4,
unique_func_names=False,
output_dir="/tmp/titi"):
if not func_filenames is None:
n_sessions = len(func_filenames)
func = [create_random_image(ndim=func_ndim) for _ in range(n_sessions)]
anat = create_random_image(ndim=3)
if anat_filename is None:
anat_filename = '%s/anat%s' % (DATA_DIR, ext)
_save_img(anat, anat_filename)
if not func_filenames is None:
for sess_func, filename in zip(func, func_filenames):
if isinstance(filename, str):
_save_img(sess_func, filename)
else:
vols = nibabel.four_to_three(sess_func)
for x, y in zip(vols, filename):
assert isinstance(y, str), type(y)
_save_img(x, y)
else:
func_filenames = []
for sess in range(n_sessions):
sess_dir = DATA_DIR if not make_sess_dirs else os.path.join(
DATA_DIR, "session%i" % sess)
if not os.path.exists(sess_dir):
os.makedirs(sess_dir)
func_filename = '%s/func%s%s' % (sess_dir, "_sess_%i_" % sess if (
n_sessions > 1 and unique_func_names) else "", ext)
_save_img(func[sess], func_filename)
func_filenames.append(func_filename)
sd = SubjectData(anat=anat_filename,
func=func_filenames,
output_dir=output_dir)
return sd
def preprocess_fmri_fullbrain(experiment_data):
# prepare the full-brain fMRI activation plots
# http://nilearn.github.io/plotting/index.html
fmri_data = nibabel.load(experiment_data['nifti_path'])
fmri_data_slices = nibabel.four_to_three(fmri_data)
for i in range(MAXIMUM_EPI_PLOTS):
output_file_cond = os.path.join(
os.path.dirname(__file__), '..', '..', 'temp', 'fmri',
experiment_data['participant'] + '_fMRIfull_' + str(i) + '.png')
# TODO make the cut slice configurable directly from the UI
if OVERWRITE_EPI_PLOTS or not os.path.exists(output_file_cond):
plotting.plot_img(fmri_data_slices[i],
output_file=output_file_cond,
title='Scan ' + str(i),
cut_coords=(config.FMRI_CUT_SLICE_X,
config.FMRI_CUT_SLICE_Y,
config.FMRI_CUT_SLICE_Z),
annotate=True,
draw_cross=True,
black_bg=True,
cmap=plt.cm.nipy_spectral)
def _flatten_split_merge(in_files):
from builtins import bytes, str
if isinstance(in_files, (bytes, str)):
in_files = [in_files]
nfiles = len(in_files)
all_nii = []
for fname in in_files:
nii = nb.squeeze_image(nb.load(fname))
if nii.get_data().ndim > 3:
all_nii += nb.four_to_three(nii)
else:
all_nii.append(nii)
if len(all_nii) == 1:
LOGGER.warn('File %s cannot be split', all_nii[0])
return in_files[0], in_files
if len(all_nii) == nfiles:
flat_split = in_files
else:
splitname = genfname(in_files[0], suffix='split%04d')
flat_split = []
for i, nii in enumerate(all_nii):
flat_split.append(splitname % i)
nii.to_filename(flat_split[-1])
# Only one 4D file was supplied
if nfiles == 1:
merged = in_files[0]
else:
# More that one in_files - need merge
merged = genfname(in_files[0], suffix='merged')
nb.concat_images(all_nii).to_filename(merged)
return merged, flat_split
def normalization_intensity(self):
for (input_nii_file, output_nii_file) in zip(self.smoothed_nii, self.intensity_norm_nii):
print('run intensity normalization %s' % input_nii_file)
print('generate %s' % output_nii_file)
if os.path.exists(output_nii_file): continue
dir_name = os.path.dirname(output_nii_file)
if not os.path.exists(dir_name): os.makedirs(dir_name)
nib_img = nib.load(input_nii_file)
if len(nib_img.shape) > 3:
nib_3d_imgs = nib.four_to_three(nib_img)
else:
nib_3d_imgs = [nib_img]
dyn_3d_nib_files = []
dyn_3d_nib_in_root = tempfile.mkdtemp()
for nib_3d_img in nib_3d_imgs:
nib_3d_img_file = os.path.join(dyn_3d_nib_in_root, uuid.uuid4().__str__() + '.nii.gz')
nib_3d_img.to_filename(nib_3d_img_file)
dyn_3d_nib_files.append(nib_3d_img_file)
dyn_3d_nib_out_root = tempfile.mkdtemp()
dyn_3d_nib_out_files = []
for dyn_3d_nib_file in dyn_3d_nib_files:
dyn_3d_nib_out_file = os.path.join(dyn_3d_nib_out_root, uuid.uuid4().__str__() + '.nii.gz')
nib_3d_img = nib.load(dyn_3d_nib_file)
np_3d_img = nib_3d_img.get_data()
np_3d_img_norm = np_3d_img / np.mean(np_3d_img)
nib_3d_img_norm = nib.Nifti1Image(np_3d_img_norm, nib_3d_img.affine)
nib_3d_img_norm.to_filename(dyn_3d_nib_out_file)
dyn_3d_nib_out_files.append(dyn_3d_nib_out_file)
nib_3d_imgs = []
for dyn_3d_nib_out_file in dyn_3d_nib_out_files:
nib_3d_imgs.append(nib.load(dyn_3d_nib_out_file))
if len(nib_3d_imgs) > 1:
nib_4d_img = nib.concat_images(nib_3d_imgs)
nib_4d_img.to_filename(output_nii_file)
else:
nib_3d_imgs[0].to_filename(output_nii_file)
if os.path.isdir(dyn_3d_nib_in_root): shutil.rmtree(dyn_3d_nib_in_root)
if os.path.isdir(dyn_3d_nib_out_root): shutil.rmtree(dyn_3d_nib_out_root)
return
def remove_bad_volumes(dwi, bvec_file, bval_file, base_name, thresh):
import numpy as np
import nibabel as nb
import os.path as op
from nipype.utils.filemanip import split_filename
dwi_4D = nb.load(dwi)
dwi_files = nb.four_to_three(dwi_4D)
bvecs = np.transpose(np.loadtxt(bvec_file))
bvals = np.transpose(np.loadtxt(bval_file))
bad_indices = np.where(np.abs(bvecs[:,0]) >= thresh)[0]
n_removed = len(bad_indices)
dwi_files = [i for j, i in enumerate(dwi_files) if j not in bad_indices]
bvecs = [i for j, i in enumerate(bvecs) if j not in bad_indices]
bvals = [i for j, i in enumerate(bvals) if j not in bad_indices]
corr_dwi = nb.concat_images(dwi_files)
corr_bvecs = np.transpose(bvecs)
corr_bvals = np.transpose(bvals)
assert(len(dwi_files) == len(bvecs) == len(bvals))
_, name, _ = split_filename(dwi)
out_dwi = op.abspath(base_name + "%f_vib.nii.gz" % thresh)
_, bvec_name, _ = split_filename(bvec_file)
out_bvecs = op.abspath(base_name + "%f_vib.bvec" % thresh)
_, bval_name, _ = split_filename(bval_file)
out_bvals = op.abspath(base_name + "%f_vib.bval" % thresh)
nb.save(corr_dwi, out_dwi)
np.savetxt(out_bvecs, corr_bvecs)
np.savetxt(out_bvals, corr_bvals)
print("%d volumes were removed at threshold %f" % (n_removed, thresh))
return out_dwi, out_bvecs, out_bvals, n_removed
def remove_bad_volumes(dwi, bvec_file, bval_file, thresh=0.8):
import numpy as np
import nibabel as nb
import os.path as op
from nipype.utils.filemanip import split_filename
dwi_4D = nb.load(dwi)
dwi_files = nb.four_to_three(dwi_4D)
bvecs = np.transpose(np.loadtxt(bvec_file))
bvals = np.transpose(np.loadtxt(bval_file))
bad_indices = np.where(np.abs(bvecs[:,0]) >= thresh)[0]
n_removed = len(bad_indices)
dwi_files = [i for j, i in enumerate(dwi_files) if j not in bad_indices]
bvecs = [i for j, i in enumerate(bvecs) if j not in bad_indices]
bvals = [i for j, i in enumerate(bvals) if j not in bad_indices]
corr_dwi = nb.concat_images(dwi_files)
corr_bvecs = np.transpose(bvecs)
corr_bvals = np.transpose(bvals)
assert(len(dwi_files) == len(bvecs) == len(bvals))
_, name, _ = split_filename(dwi)
out_dwi = op.abspath(name + "_vib.nii.gz")
_, bvec_name, _ = split_filename(bvec_file)
out_bvecs = op.abspath(bvec_name + "_vib.bvec")
_, bval_name, _ = split_filename(bval_file)
out_bvals = op.abspath(bval_name + "_vib.bval")
nb.save(corr_dwi, out_dwi)
np.savetxt(out_bvecs, corr_bvecs)
np.savetxt(out_bvals, corr_bvals)
print("%d volumes were removed at threshold %f" % (n_removed, thresh))
return out_dwi, out_bvecs, out_bvals, n_removed
def smooth_gaussian(self):
gaussian_filter = self.gaussian_filter
for (input_nii_file, output_nii_file) in zip(self.normalized_nii,self.smoothed_nii):
print('run gaussian smooth %s' % (input_nii_file))
print('generate %s' % output_nii_file)
if os.path.exists(output_nii_file): continue
dir_name = os.path.dirname(output_nii_file)
if not os.path.exists(dir_name): os.makedirs(dir_name)
nib_img = nib.load(input_nii_file)
if len(nib_img.shape) > 3:
nib_3d_imgs = nib.four_to_three(nib_img)
else:
nib_3d_imgs = [nib_img]
dyn_3d_nib_files = []
dyn_3d_nib_in_root = tempfile.mkdtemp()
for nib_3d_img in nib_3d_imgs:
nib_3d_img_file = os.path.join(dyn_3d_nib_in_root, uuid.uuid4().__str__() + '.nii.gz')
nib_3d_img.to_filename(nib_3d_img_file)
dyn_3d_nib_files.append(nib_3d_img_file)
dyn_3d_nib_out_root = tempfile.mkdtemp()
dyn_3d_nib_out_files = []
for dyn_3d_nib_file in dyn_3d_nib_files:
dyn_3d_nib_out_file = os.path.join(dyn_3d_nib_out_root, uuid.uuid4().__str__() + '.nii.gz')
sitk_image_0 = sitk.ReadImage(dyn_3d_nib_file)
sitk_image_1 = sitk.SmoothingRecursiveGaussian(sitk_image_0, gaussian_filter)
sitk.WriteImage(sitk_image_1, dyn_3d_nib_out_file)
dyn_3d_nib_out_files.append(dyn_3d_nib_out_file)
nib_3d_imgs = []
for dyn_3d_nib_out_file in dyn_3d_nib_out_files:
nib_3d_imgs.append(nib.load(dyn_3d_nib_out_file))
if len(nib_3d_imgs) > 1:
nib_4d_img = nib.concat_images(nib_3d_imgs)
nib_4d_img.to_filename(output_nii_file)
else:
nib_3d_imgs[0].to_filename(output_nii_file)
if isdir(dyn_3d_nib_in_root): shutil.rmtree(dyn_3d_nib_in_root)
if isdir(dyn_3d_nib_out_root): shutil.rmtree(dyn_3d_nib_out_root)
return
def test_transform():
# setup
output_dir = os.path.join(OUTPUT_DIR, inspect.stack()[0][3])
if not os.path.exists(output_dir):
os.makedirs(output_dir)
film = nibabel.Nifti1Image(np.random.rand(11, 13, 17, 19), np.eye(4))
threeD_vols = nibabel.four_to_three(film)
# filenames
film_filename = os.path.join(output_dir, 'film.nii.gz')
threeD_vols_filenames = [
os.path.join(output_dir, 'fMETHODS-%06i.nii' % i)
for i in range(len(threeD_vols))
]
for stuff in [film, threeD_vols]:
for as_files in [False, True]:
if as_files:
if isinstance(stuff, list):
basenames = [
os.path.basename(x) for x in threeD_vols_filenames
]
else:
basenames = os.path.basename(film_filename)
stuff = save_vols(stuff, output_dir, basenames=basenames)
fmristc = fMRISTC().fit(raw_data=stuff)
output = fmristc.transform(output_dir=output_dir)
# test output type, shape, etc.
if isinstance(stuff, list):
assert isinstance(output, list)
assert len(output) == film.shape[-1]
if as_files:
assert os.path.basename(
output[7]) == 'afMETHODS-000007.nii'
else:
if as_files:
assert os.path.basename(output) == 'afilm.nii.gz'
def __init__(self, archivePath, **entities):
"""
Args:
archivePath: Absolute path of the BIDS archive.
entities: BIDS entities (subject, session, task, run, suffix, datatype) that
define the particular subject/run of the data to stream
"""
self.bidsArchive = BidsArchive(archivePath)
# TODO - when we have BidsRun
# self.bidsRun = self.bidsArchive.getBidsRun(**entities)
images = self.bidsArchive.getImages(**entities)
if len(images) == 0:
raise ValidationError('No matching images found')
if len(images) > 1:
raise ValidationError('More than one match, please give more specific subject/session/task/run')
self.bidsImage = images[0]
self.niftiImage = self.bidsImage.get_image()
self.filename = self.niftiImage.get_filename()
self.imgVolumes = nib.four_to_three(self.niftiImage)
self.metadata = self.bidsArchive.getSidecarMetadata(self.filename, includeEntities=True)
self.metadata.pop('extension')
self.numVolumes = len(self.imgVolumes)
self.nextVol = 0
def _make_sd(func_filenames=None, anat_filename=None, ext=".nii.gz",
n_sessions=1, make_sess_dirs=False,
unique_func_names=False, output_dir="/tmp/titi"):
if not func_filenames is None:
n_sessions = len(func_filenames)
func = [create_random_image(ndim=4) for _ in xrange(n_sessions)]
anat = create_random_image(ndim=3)
if anat_filename is None:
anat_filename = '%s/anat%s' % (DATA_DIR, ext)
_save_img(anat, anat_filename)
if not func_filenames is None:
for sess_func, filename in zip(func, func_filenames):
if isinstance(filename, basestring):
_save_img(sess_func, filename)
else:
vols = nibabel.four_to_three(sess_func)
for x, y in zip(vols, filename):
assert isinstance(y, basestring), type(y)
_save_img(x, y)
else:
func_filenames = []
for sess in xrange(n_sessions):
sess_dir = DATA_DIR if not make_sess_dirs else os.path.join(
DATA_DIR, "session%i" % sess)
if not os.path.exists(sess_dir):
os.makedirs(sess_dir)
func_filename = '%s/func%s%s' % (
sess_dir, "_sess_%i_" % sess if (
n_sessions > 1 and unique_func_names) else "", ext)
_save_img(func[sess], func_filename)
func_filenames.append(func_filename)
sd = SubjectData(anat=anat_filename,
func=func_filenames,
output_dir=output_dir)
return sd
def b0_average(in_file, out_file=None):
"""
Average the b0 volumes.
Args:
in_file (str): The b0 volumes already registered.
out_file (optional[str]): Name of the file.
Returns:
The mean of the b0 volumes.
Warnings:
The b0 volumes must be registered.
"""
import os.path as op
import nibabel as nb
import numpy as np
if out_file is None:
fname, ext = op.splitext(op.basename(in_file))
if ext == ".gz":
fname, ext2 = op.splitext(fname)
ext = ext2 + ext
out_file = op.abspath("%s_avg_b0%s" % (fname, ext))
imgs = np.array(nb.four_to_three(nb.load(in_file)))
b0s = [im.get_data().astype(np.float32) for im in imgs]
b0 = np.average(np.array(b0s), axis=0)
hdr = imgs[0].get_header().copy()
hdr.set_data_shape(b0.shape)
hdr.set_xyzt_units("mm")
hdr.set_data_dtype(np.float32)
nb.Nifti1Image(b0, imgs[0].get_affine(), hdr).to_filename(out_file)
return out_file
def _run_interface(self, runtime):
nii_list = []
for f in self.inputs.in_files:
filenii = nb.squeeze_image(nb.load(f))
ndim = filenii.dataobj.ndim
if ndim == 3:
nii_list.append(filenii)
continue
elif self.inputs.allow_4D and ndim == 4:
nii_list += nb.four_to_three(filenii)
continue
else:
raise ValueError(
"Input image has an incorrect number of dimensions"
f" ({ndim}).")
img_4d = nb.concat_images(nii_list)
out_file = fname_presuffix(self.inputs.in_files[0],
suffix="_merged",
newpath=runtime.cwd)
img_4d.to_filename(out_file)
self._results["out_file"] = out_file
return runtime
def time_avg(in_file, index=[0], out_file=None):
"""
Average the input time-series, selecting the indices given in index
.. warning:: time steps should be already registered (corrected for
head motion artifacts).
"""
import numpy as np
import nibabel as nb
import os.path as op
from nipype.utils import NUMPY_MMAP
if out_file is None:
fname, ext = op.splitext(op.basename(in_file))
if ext == ".gz":
fname, ext2 = op.splitext(fname)
ext = ext2 + ext
out_file = op.abspath("%s_baseline%s" % (fname, ext))
index = np.atleast_1d(index).tolist()
imgs = np.array(nb.four_to_three(nb.load(in_file, mmap=NUMPY_MMAP)))[index]
if len(index) == 1:
data = imgs[0].get_data().astype(np.float32)
else:
data = np.average(np.array(
[im.get_data().astype(np.float32) for im in imgs]),
axis=0)
hdr = imgs[0].header.copy()
hdr.set_data_shape(data.shape)
hdr.set_xyzt_units('mm')
hdr.set_data_dtype(np.float32)
nb.Nifti1Image(data, imgs[0].affine, hdr).to_filename(out_file)
return out_file
def b0_average(in_dwi, in_bval, max_b=10.0, out_file=None):
"""
A function that averages the *b0* volumes from a DWI dataset.
As current dMRI data are being acquired with all b-values > 0.0,
the *lowb* volumes are selected by specifying the parameter max_b.
.. warning:: *b0* should be already registered (head motion artifact should
be corrected).
"""
import numpy as np
import nibabel as nb
import os.path as op
from nipype.utils import NUMPY_MMAP
if out_file is None:
fname, ext = op.splitext(op.basename(in_dwi))
if ext == ".gz":
fname, ext2 = op.splitext(fname)
ext = ext2 + ext
out_file = op.abspath("%s_avg_b0%s" % (fname, ext))
imgs = np.array(nb.four_to_three(nb.load(in_dwi, mmap=NUMPY_MMAP)))
bval = np.loadtxt(in_bval)
index = np.argwhere(bval <= max_b).flatten().tolist()
b0s = [im.get_data().astype(np.float32)
for im in imgs[index]]
b0 = np.average(np.array(b0s), axis=0)
hdr = imgs[0].header.copy()
hdr.set_data_shape(b0.shape)
hdr.set_xyzt_units('mm')
hdr.set_data_dtype(np.float32)
nb.Nifti1Image(b0, imgs[0].affine, hdr).to_filename(out_file)
return out_file
def test_load_4D_img():
# setup
output_dir = os.path.join(OUTPUT_DIR, inspect.stack()[0][3])
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# try loading from 4D niimg
film = create_random_image(n_scans=10)
loaded_4D_img = load_4D_img(film)
assert_true(is_niimg(loaded_4D_img))
assert_equal(loaded_4D_img.shape, film.shape)
# try loading from 4D image file
film = create_random_image(n_scans=10)
saved_img_filename = os.path.join(output_dir, "4D.nii.gz")
nibabel.save(film, saved_img_filename)
loaded_4D_img = load_4D_img(saved_img_filename)
assert_true(is_niimg(loaded_4D_img))
assert_equal(loaded_4D_img.shape, film.shape)
# try loading from list of 3D niimgs
film = create_random_image(n_scans=10)
loaded_4D_img = load_4D_img(nibabel.four_to_three(film))
assert_true(is_niimg(loaded_4D_img))
assert_equal(loaded_4D_img.shape, film.shape)
# try loading from list of 3D image files
film = create_random_image(n_scans=10)
saved_vols_filenames = save_vols(
film,
output_dir,
ext='.nii.gz',
)
loaded_4D_img = load_4D_img(saved_vols_filenames)
assert_true(is_niimg(loaded_4D_img))
assert_equal(loaded_4D_img.shape, film.shape)
def _run_interface(self, runtime):
iflogger.info(
'Segmentation image: {img}'.format(img=self.inputs.segmentation_file))
rois = get_roi_list(self.inputs.segmentation_file)
number_of_nodes = len(rois)
iflogger.info('Found {roi} unique region values'.format(roi=len(rois)))
if len(self.inputs.in_files) > 1:
iflogger.info('Multiple input images detected')
iflogger.info(len(self.inputs.in_files))
in_files = self.inputs.in_files
try:
# VERY tempermental solution to sorting the functional images.
# RELIES ON xyz-01_out.nii from recent resampling.
in_files = sorted(in_files, key=lambda x:
int(x.split("_")[-2].split("-")[-2]))
except IndexError:
# Tempermental solution to sorting the functional images.
# RELIES ON xyz_out.nii from recent resampling.
in_files = sorted(in_files, key=lambda x:
int(x.split("_")[-2]))
elif isdefined(self.inputs.in_file4d):
iflogger.info('Single four-dimensional image selected')
in_file4d = nb.load(self.inputs.in_file4d)
in_files = nb.four_to_three(in_file4d)
else:
iflogger.info('Single functional image provided')
in_files = self.inputs.in_files
rois = get_roi_list(self.inputs.segmentation_file)
fMRI_timecourse, _, _, _, _ = get_timecourse_by_region(
in_files, self.inputs.segmentation_file, rois)
timecourse_at_each_node = fMRI_timecourse.T
time_course_image = nb.load(self.inputs.time_course_file)
iflogger.info(np.shape(timecourse_at_each_node))
number_of_components = 30
number_of_images = 198
time_course_data = time_course_image.get_data()
onerow = np.ones(number_of_images)
time_course_per_IC = np.vstack((onerow.T, time_course_data.T)).T
iflogger.info(np.shape(time_course_per_IC))
y = timecourse_at_each_node
X = time_course_per_IC
n = number_of_images
XTX_inv = np.linalg.inv(np.dot(X.T, X))
N = number_of_components + 1
contrast = np.concatenate(
(np.zeros((1, number_of_components)), np.eye(number_of_components))).T
a = np.empty((number_of_nodes, N))
residues = np.empty((number_of_nodes, 1))
rank = np.empty((number_of_nodes, 1))
singularvalues = np.empty((number_of_nodes, N))
resids = np.empty((number_of_nodes, number_of_images))
error_variance = np.empty((number_of_nodes, 1))
t_values = np.empty((number_of_nodes, number_of_components))
beta_value = np.empty((number_of_components, 1))
for node_idx, node in enumerate(rois):
a[node_idx], residues[node_idx], rank[node_idx], singularvalues[
node_idx] = np.linalg.lstsq(X, y[:, node_idx])
resids[node_idx, :] = y[:, node_idx] - \
np.dot(X, a[node_idx]) # e = y - Xa;
error_variance[node_idx] = np.var(resids[node_idx])
for IC in range(0, number_of_components):
t_values[node_idx, IC] = np.dot(contrast[IC], a[node_idx]) / \
np.sqrt(
error_variance[node_idx] * np.dot(np.dot(contrast[IC], XTX_inv), contrast[IC, :].T))
beta_value[IC] = np.dot(contrast[IC], a[node_idx])
t_value_dict = {}
t_value_dict['t_value_per_node'] = t_values
t_value_dict['timecourse_at_each_node'] = y
t_value_dict['timecourse_per_IC'] = X
t_value_dict['number_of_images'] = n
t_value_dict['a'] = a
t_value_dict['contrast'] = contrast
t_value_dict['residuals'] = resids
t_value_dict['error_variance'] = error_variance
out_file = op.abspath(self.inputs.out_t_value_threshold_file)
iflogger.info(
'Saving T-values per node, per IC, as {file}'.format(file=out_file))
sio.savemat(out_file, t_value_dict)
return runtime
def nonlinfit_fn(dwi, bvecs, bvals, base_name):
import nibabel as nb
import numpy as np
import os.path as op
import dipy.reconst.dti as dti
from dipy.core.gradients import GradientTable
dwi_img = nb.load(dwi)
dwi_data = dwi_img.get_data()
dwi_affine = dwi_img.get_affine()
from dipy.segment.mask import median_otsu
b0_mask, mask = median_otsu(dwi_data, 2, 4)
# Mask the data so that tensors are not fit for
# unnecessary voxels
mask_img = nb.Nifti1Image(mask.astype(np.float32), dwi_affine)
b0_imgs = nb.Nifti1Image(b0_mask.astype(np.float32), dwi_affine)
b0_img = nb.four_to_three(b0_imgs)[0]
out_mask_name = op.abspath(base_name + '_binary_mask.nii.gz')
out_b0_name = op.abspath(base_name + '_b0_mask.nii.gz')
nb.save(mask_img, out_mask_name)
nb.save(b0_img, out_b0_name)
# Load the gradient strengths and directions
bvals = np.loadtxt(bvals)
gradients = np.loadtxt(bvecs)
# Dipy wants Nx3 arrays
if gradients.shape[0] == 3:
gradients = gradients.T
assert(gradients.shape[1] == 3)
# Place in Dipy's preferred format
gtab = GradientTable(gradients)
gtab.bvals = bvals
# Fit the tensors to the data
tenmodel = dti.TensorModel(gtab, fit_method="NLLS")
tenfit = tenmodel.fit(dwi_data, mask)
# Calculate the fit, fa, and md of each voxel's tensor
tensor_data = tenfit.lower_triangular()
print('Computing anisotropy measures (FA, MD, RGB)')
from dipy.reconst.dti import fractional_anisotropy, color_fa
evals = tenfit.evals.astype(np.float32)
FA = fractional_anisotropy(np.abs(evals))
FA = np.clip(FA, 0, 1)
MD = dti.mean_diffusivity(np.abs(evals))
norm = dti.norm(tenfit.quadratic_form)
RGB = color_fa(FA, tenfit.evecs)
evecs = tenfit.evecs.astype(np.float32)
mode = tenfit.mode.astype(np.float32)
mode = np.nan_to_num(mode)
# Write tensor as a 4D Nifti image with the original affine
tensor_fit_img = nb.Nifti1Image(tensor_data.astype(np.float32), dwi_affine)
mode_img = nb.Nifti1Image(mode.astype(np.float32), dwi_affine)
norm_img = nb.Nifti1Image(norm.astype(np.float32), dwi_affine)
FA_img = nb.Nifti1Image(FA.astype(np.float32), dwi_affine)
evecs_img = nb.Nifti1Image(evecs, dwi_affine)
evals_img = nb.Nifti1Image(evals, dwi_affine)
rgb_img = nb.Nifti1Image(np.array(255 * RGB, 'uint8'), dwi_affine)
MD_img = nb.Nifti1Image(MD.astype(np.float32), dwi_affine)
out_tensor_file = op.abspath(base_name + "_tensor.nii.gz")
out_mode_file = op.abspath(base_name + "_mode.nii.gz")
out_fa_file = op.abspath(base_name + "_fa.nii.gz")
out_norm_file = op.abspath(base_name + "_norm.nii.gz")
out_evals_file = op.abspath(base_name + "_evals.nii.gz")
out_evecs_file = op.abspath(base_name + "_evecs.nii.gz")
out_rgb_fa_file = op.abspath(base_name + "_rgb_fa.nii.gz")
out_md_file = op.abspath(base_name + "_md.nii.gz")
nb.save(rgb_img, out_rgb_fa_file)
nb.save(norm_img, out_norm_file)
nb.save(mode_img, out_mode_file)
nb.save(tensor_fit_img, out_tensor_file)
nb.save(evecs_img, out_evecs_file)
nb.save(evals_img, out_evals_file)
nb.save(FA_img, out_fa_file)
nb.save(MD_img, out_md_file)
print('Tensor fit image saved as {i}'.format(i=out_tensor_file))
print('FA image saved as {i}'.format(i=out_fa_file))
print('MD image saved as {i}'.format(i=out_md_file))
return out_tensor_file, out_fa_file, out_md_file, \
out_evecs_file, out_evals_file, out_rgb_fa_file, out_norm_file, \
out_mode_file, out_mask_name, out_b0_name
def _run_interface(self, runtime):
from dipy.core.gradients import GradientTable
from dipy.reconst.dti import fractional_anisotropy, mean_diffusivity
from dipy.reconst.csdeconv import recursive_response, auto_response
img = nb.load(self.inputs.in_file)
imref = nb.four_to_three(img)[0]
affine = img.affine
if isdefined(self.inputs.in_mask):
msk = nb.load(self.inputs.in_mask).get_data()
msk[msk > 0] = 1
msk[msk < 0] = 0
else:
msk = np.ones(imref.shape)
data = img.get_data().astype(np.float32)
gtab = self._get_gradient_table()
evals = np.nan_to_num(nb.load(self.inputs.in_evals).get_data())
FA = np.nan_to_num(fractional_anisotropy(evals)) * msk
indices = np.where(FA > self.inputs.fa_thresh)
S0s = data[indices][:, np.nonzero(gtab.b0s_mask)[0]]
S0 = np.mean(S0s)
if self.inputs.auto:
response, ratio = auto_response(gtab,
data,
roi_radius=self.inputs.roi_radius,
fa_thr=self.inputs.fa_thresh)
response = response[0].tolist() + [S0]
elif self.inputs.recursive:
MD = np.nan_to_num(mean_diffusivity(evals)) * msk
indices = np.logical_or(FA >= 0.4,
(np.logical_and(FA >= 0.15, MD >= 0.0011)))
data = nb.load(self.inputs.in_file).get_data()
response = recursive_response(gtab,
data,
mask=indices,
sh_order=8,
peak_thr=0.01,
init_fa=0.08,
init_trace=0.0021,
iter=8,
convergence=0.001,
parallel=True)
ratio = abs(response[1] / response[0])
else:
lambdas = evals[indices]
l01 = np.sort(np.mean(lambdas, axis=0))
response = np.array([l01[-1], l01[-2], l01[-2], S0])
ratio = abs(response[1] / response[0])
if ratio > 0.25:
IFLOGGER.warn(
'Estimated response is not prolate enough. '
'Ratio=%0.3f.', ratio)
elif ratio < 1.e-5 or np.any(np.isnan(response)):
response = np.array([1.8e-3, 3.6e-4, 3.6e-4, S0])
IFLOGGER.warn(
'Estimated response is not valid, using a default one')
else:
IFLOGGER.info('Estimated response: %s', str(response[:3]))
np.savetxt(op.abspath(self.inputs.response), response)
wm_mask = np.zeros_like(FA)
wm_mask[indices] = 1
nb.Nifti1Image(wm_mask.astype(np.uint8), affine,
None).to_filename(op.abspath(self.inputs.out_mask))
return runtime
def _run_interface(self, runtime):
in_files = self.inputs.in_files
if not isinstance(in_files, list):
in_files = [self.inputs.in_files]
if self.inputs.to_ras:
in_files = [reorient(inf, newpath=runtime.cwd) for inf in in_files]
run_hmc = self.inputs.hmc and len(in_files) > 1
nii_list = []
# Remove one-sized extra dimensions
for i, f in enumerate(in_files):
filenii = nb.load(f)
filenii = nb.squeeze_image(filenii)
if len(filenii.shape) == 5:
raise RuntimeError("Input image (%s) is 5D." % f)
if filenii.dataobj.ndim == 4:
nii_list += nb.four_to_three(filenii)
else:
nii_list.append(filenii)
if len(nii_list) > 1:
filenii = nb.concat_images(nii_list)
else:
filenii = nii_list[0]
merged_fname = fname_presuffix(self.inputs.in_files[0],
suffix="_merged",
newpath=runtime.cwd)
filenii.to_filename(merged_fname)
self._results["out_file"] = merged_fname
self._results["out_avg"] = merged_fname
if filenii.dataobj.ndim < 4:
# TODO: generate identity out_mats and zero-filled out_movpar
return runtime
if run_hmc:
mcflirt = fsl.MCFLIRT(
cost="normcorr",
save_mats=True,
save_plots=True,
ref_vol=0,
in_file=merged_fname,
)
mcres = mcflirt.run()
filenii = nb.load(mcres.outputs.out_file)
self._results["out_file"] = mcres.outputs.out_file
self._results["out_mats"] = mcres.outputs.mat_file
self._results["out_movpar"] = mcres.outputs.par_file
hmcdata = filenii.get_fdata(dtype="float32")
if self.inputs.grand_mean_scaling:
if not isdefined(self.inputs.in_mask):
mean = np.median(hmcdata, axis=-1)
thres = np.percentile(mean, 25)
mask = mean > thres
else:
mask = nb.load(
self.inputs.in_mask).get_fdata(dtype="float32") > 0.5
nimgs = hmcdata.shape[-1]
means = np.median(hmcdata[mask[..., np.newaxis]].reshape(
(-1, nimgs)).T,
axis=-1)
max_mean = means.max()
for i in range(nimgs):
hmcdata[..., i] *= max_mean / means[i]
hmcdata = hmcdata.mean(axis=3)
if self.inputs.zero_based_avg:
hmcdata -= hmcdata.min()
self._results["out_avg"] = fname_presuffix(self.inputs.in_files[0],
suffix="_avg",
newpath=runtime.cwd)
nb.Nifti1Image(hmcdata, filenii.affine,
filenii.header).to_filename(self._results["out_avg"])
return runtime
def _run_interface(self, runtime):
from dipy.reconst.peaks import peaks_from_model
from dipy.tracking.eudx import EuDX
from dipy.data import get_sphere
# import marshal as pickle
import pickle as pickle
import gzip
if (not (isdefined(self.inputs.in_model)
or isdefined(self.inputs.in_peaks))):
raise RuntimeError(('At least one of in_model or in_peaks should '
'be supplied'))
img = nb.load(self.inputs.in_file)
imref = nb.four_to_three(img)[0]
affine = img.affine
data = img.get_data().astype(np.float32)
hdr = imref.header.copy()
hdr.set_data_dtype(np.float32)
hdr['data_type'] = 16
sphere = get_sphere('symmetric724')
self._save_peaks = False
if isdefined(self.inputs.in_peaks):
IFLOGGER.info('Peaks file found, skipping ODF peaks search...')
f = gzip.open(self.inputs.in_peaks, 'rb')
peaks = pickle.load(f)
f.close()
else:
self._save_peaks = True
IFLOGGER.info('Loading model and computing ODF peaks')
f = gzip.open(self.inputs.in_model, 'rb')
odf_model = pickle.load(f)
f.close()
peaks = peaks_from_model(
model=odf_model,
data=data,
sphere=sphere,
relative_peak_threshold=self.inputs.peak_threshold,
min_separation_angle=self.inputs.min_angle,
parallel=self.inputs.multiprocess)
f = gzip.open(self._gen_filename('peaks', ext='.pklz'), 'wb')
pickle.dump(peaks, f, -1)
f.close()
hdr.set_data_shape(peaks.gfa.shape)
nb.Nifti1Image(peaks.gfa.astype(np.float32), affine,
hdr).to_filename(self._gen_filename('gfa'))
IFLOGGER.info('Performing tractography')
if isdefined(self.inputs.tracking_mask):
msk = nb.load(self.inputs.tracking_mask).get_data()
msk[msk > 0] = 1
msk[msk < 0] = 0
else:
msk = np.ones(imref.shape)
gfa = peaks.gfa * msk
seeds = self.inputs.num_seeds
if isdefined(self.inputs.seed_coord):
seeds = np.loadtxt(self.inputs.seed_coord)
elif isdefined(self.inputs.seed_mask):
seedmsk = nb.load(self.inputs.seed_mask).get_data()
assert (seedmsk.shape == data.shape[:3])
seedmsk[seedmsk > 0] = 1
seedmsk[seedmsk < 1] = 0
seedps = np.array(np.where(seedmsk == 1), dtype=np.float32).T
vseeds = seedps.shape[0]
nsperv = (seeds // vseeds) + 1
IFLOGGER.info(
'Seed mask is provided (%d voxels inside '
'mask), computing seeds (%d seeds/voxel).', vseeds, nsperv)
if nsperv > 1:
IFLOGGER.info('Needed %d seeds per selected voxel (total %d).',
nsperv, vseeds)
seedps = np.vstack(np.array([seedps] * nsperv))
voxcoord = seedps + np.random.uniform(-1, 1, size=seedps.shape)
nseeds = voxcoord.shape[0]
seeds = affine.dot(
np.vstack((voxcoord.T, np.ones((1, nseeds)))))[:3, :].T
if self.inputs.save_seeds:
np.savetxt(self._gen_filename('seeds', ext='.txt'), seeds)
if isdefined(self.inputs.tracking_mask):
tmask = msk
a_low = 0.1
else:
tmask = gfa
a_low = self.inputs.gfa_thresh
eu = EuDX(tmask,
peaks.peak_indices[..., 0],
seeds=seeds,
affine=affine,
odf_vertices=sphere.vertices,
a_low=a_low)
ss_mm = [np.array(s) for s in eu]
trkfilev = nb.trackvis.TrackvisFile([(s, None, None) for s in ss_mm],
points_space='rasmm',
affine=np.eye(4))
trkfilev.to_file(self._gen_filename('tracked', ext='.trk'))
return runtime
def _run_interface(self, runtime):
# Determine the parameters
experiment = self.inputs.experiment
subject, session, run = self.inputs.run
self._results["run_tuple"] = self.inputs.run
self._results["subject"] = subject
self._results["session"] = session
self._results["run"] = run
# Spec out paths to the input files
keys = dict(subject=subject, experiment=experiment,
session=session, run=run)
sb_fname = op.join(self.inputs.data_dir, subject, "func",
self.inputs.sb_template.format(**keys))
ts_fname = op.join(self.inputs.data_dir, subject, "func",
self.inputs.ts_template.format(**keys))
# Load the input images in canonical orientation
sb_img = nib.as_closest_canonical(nib.load(sb_fname))
ts_img = nib.as_closest_canonical(nib.load(ts_fname))
# Convert image datatypes to float
sb_img.set_data_dtype(np.float32)
ts_img.set_data_dtype(np.float32)
# Optionally crop the first n frames of the timeseries
if self.inputs.crop_frames > 0:
ts_data = ts_img.get_data()
ts_data = ts_data[..., self.inputs.crop_frames:]
ts_img = nib.Nifti1Image(ts_data, ts_img.affine, ts_img.header)
# Write out the new images
self.write_image("sb_file", "sb.nii.gz", sb_img)
self.write_image("ts_file", "ts.nii.gz", ts_img)
# Write out each frame of the timeseries
os.mkdir("frames")
ts_frames = []
ts_frame_imgs = nib.four_to_three(ts_img)
for i, frame_img in enumerate(ts_frame_imgs):
frame_fname = op.abspath("frames/frame{:04d}.nii.gz".format(i))
ts_frames.append(frame_fname)
frame_img.to_filename(frame_fname)
self._results["ts_frames"] = ts_frames
# Make a GIF movie of the raw timeseries
out_plot = self.define_output("ts_plot", "raw.gif")
self.write_time_series_gif(runtime, ts_img, out_plot)
# Load files from the template directory
template_path = op.join(self.inputs.proc_dir, subject, "template")
results = dict(
reg_file=op.join(template_path, "anat2func.mat"),
seg_file=op.join(template_path, "seg.nii.gz"),
anat_file=op.join(template_path, "anat.nii.gz"),
mask_file=op.join(template_path, "mask.nii.gz"),
)
self._results.update(results)
return runtime
def _save_2_pet_suv_bqml(self,
series_dicom_root,
sub_root,
sub_name='sub-001',
func_name='pet',
task_name='rest'):
# find udcm2bids tags and calc convert factor
func_root = os.path.join(sub_root, func_name)
bqml_nii_file = os.path.join(
func_root, sub_name + '_task-' + task_name + '_PET-BQML.nii.gz')
suvbw_nii_file = bqml_nii_file.replace('_PET-BQML', '_PET-SUVbw')
suvbw_json_file = suvbw_nii_file.replace('.nii.gz', '.json')
if os.path.exists(suvbw_nii_file): return
suv_factor = []
acqdatetime = []
dicom_files = glob(os.path.join(series_dicom_root, '*.dcm'))
for i, file in enumerate(dicom_files):
ds = pydicom.read_file(file, stop_before_pixels=False)
dt = str(ds.AcquisitionDateTime)[:14]
if dt not in acqdatetime:
acqdatetime.append(dt)
suv_factor.append(self._calc_uih_pet_suvbw_factor(ds))
# convert to bids
if not os.path.isdir(func_root): os.mkdir(func_root)
# check whether exist or not
if not os.path.exists(bqml_nii_file):
devnull = open(os.devnull, 'w')
subprocess.call([
'dcm2niix', '-b', 'y', '-z', 'y', '-f', sub_name + '_task-' +
task_name + '_PET-BQML', '-o', func_root, series_dicom_root
],
stdout=devnull,
stderr=subprocess.STDOUT)
# convert bqml to suv_bw with suv_factor
nib_img_bqml = nib.load(bqml_nii_file)
if len(suv_factor) > 1:
nib_3d_imgs_bqml = nib.four_to_three(nib_img_bqml)
np_3d_imgs_bqml = [
nib_3d_img_bqml.get_data()
for nib_3d_img_bqml in nib_3d_imgs_bqml
]
affines = [
nib_3d_img_bqml.affine for nib_3d_img_bqml in nib_3d_imgs_bqml
]
np_3d_imgs_suvbw = [
np_3d_img * c_factor
for np_3d_img, c_factor in zip(np_3d_imgs_bqml, suv_factor)
]
nib_3d_imgs_suvbw = [
nib.Nifti1Image(new_np_3d_img, affine)
for new_np_3d_img, affine in zip(np_3d_imgs_suvbw, affines)
]
nib_img_suvbw = nib.concat_images(nib_3d_imgs_suvbw)
nib_img_suvbw.to_filename(suvbw_nii_file)
else:
nib_3d_img_bqml = nib_img_bqml
np_3d_img_bqml = nib_3d_img_bqml.get_data()
affine = nib_3d_img_bqml.affine
np_3d_img_suvbw = np_3d_img_bqml * suv_factor[0]
nib_3d_img_suvbw = nib.Nifti1Image(np_3d_img_suvbw, affine)
nib_3d_img_suvbw.to_filename(suvbw_nii_file)
with open(suvbw_json_file, 'wt', encoding='utf-8') as f_json:
json.dump(
{
'suvbw_factor': suv_factor,
'acquisition_time': acqdatetime
},
f_json,
indent=4)
return
def _run_interface(self, runtime):
# Load the 4D raw fieldmap image and select first frame
raw_img_frames = nib.load(self.inputs.raw_file)
raw_img = nib.four_to_three(raw_img_frames)[0]
affine, header = raw_img.affine, raw_img.header
# Write out the raw image to serve as a registration target
self.write_image("raw_file", "raw.nii.gz", raw_img)
# Load the 4D jacobian image
jac_img_frames = nib.concat_images(self.inputs.jacobian_files)
jac_data = jac_img_frames.get_data()
# Load the 4D corrected fieldmap image
corr_img_frames = nib.load(self.inputs.corrected_file)
corr_data = corr_img_frames.get_data()
# Jacobian modulate the corrected image
corr_mod_data = corr_data * jac_data
corr_img_frames = nib.Nifti1Image(corr_mod_data, affine, header)
# Average the corrected image over the final dimension and write
corr_mod_data = corr_mod_data.mean(axis=-1)
self.write_image("corrected_file", "func.nii.gz",
corr_mod_data, affine, header)
# Save the jacobian images using the raw geometry
self.write_image("jacobian_file", "jacobian.nii.gz",
jac_data, affine, header)
# Select the first warpfield image
# We combine the two fieldmap images so that the first one has
# a phase encoding that matches the time series data.
# Also note that when the fieldmap images have multiple frames,
# the warps corresponding to those frames are identical.
warp_file = self.inputs.warp_files[0]
# Load in the the warp file and save out with the correct affine
# (topup doesn't save the header geometry correctly for some reason)
warp_data = nib.load(warp_file).get_data()
self.write_image("warp_file", "warp.nii.gz", warp_data, affine, header)
# Select the warp along the phase encode direction
# Note: we elsewhere currently require phase encoding to be AP or PA
# so because the input node transforms the fieldmap to canonical
# orientation this will work. But in the future we might want to ne
# more flexible with what we accept and will need to change this.
warp_data_y = warp_data[..., 1]
# Write out a mask to exclude voxels with large distortions/dropout
mask_data = (np.abs(warp_data_y) < 4).astype(np.int)
self.write_image("mask_file", "warp_mask.nii.gz",
mask_data, affine, header)
# Generate a QC image of the warpfield
m = Mosaic(raw_img, warp_data_y)
m.plot_overlay("coolwarm", vmin=-6, vmax=6, alpha=.75)
self.write_visualization("warp_plot", "warp.png", m)
# Generate a QC gif of the unwarping performance
self.generate_unwarp_gif(runtime, raw_img_frames, corr_img_frames)
return runtime
def save_vols(vols, output_dir, basenames=None, affine=None,
concat=False, prefix='', ext=None):
"""
Saves a single 4D image or a couple of 3D vols unto disk.
vols: single 4D nibabel image object, or list of 3D nibabel image objects
volumes, of ndarray
volumes to be saved
output_dir: string
existing filename, destination directory
basenames: string or list of string, optional (default None)
basename(s) for output image(s)
affine: 2D array of shape (4, 4)
affine matrix for the output images
concat: bool, optional (default False)
concatenate all vols into a single film
prefix: string, optional (default '')
prefix to be prepended to output file basenames
ext: string, optional (default ".nii.gz")
file extension for output images
Returns
-------
string of list of strings, dependending on whether vols is list or
not, and on whether concat is set or not
the output image filename(s)
"""
def _nifti_or_ndarray_to_nifti(x):
if is_niimg(x):
if not affine is None:
raise ValueError(
("vol is of type %s; not expecting `affine` parameter."
) % type(x))
else:
return x
if affine is None:
raise ValueError(
"vol is of type ndarray; you need to specifiy `affine`")
else:
return nibabel.Nifti1Image(x, affine)
if not basenames is None:
basenames = get_basenames(basenames, ext=ext)
# sanitize output_dir
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# vols are ndarray ?
if isinstance(vols, np.ndarray):
vols = _nifti_or_ndarray_to_nifti(vols)
# concat vols to single 4D film ?
if concat:
if isinstance(vols, list):
vols = nibabel.concat_images([_nifti_or_ndarray_to_nifti(vol)
for vol in vols],
check_affines=False
)
if not basenames is None:
if not isinstance(basenames, basestring):
basenames = basenames[0]
else:
if not basenames is None:
if not isinstance(basenames, basestring):
raise RuntimeError(
("concat=True specified but basenames is of type %s "
"instead of string") % type(basenames))
if not isinstance(vols, list):
if basenames is None:
basenames = get_basenames("vols", ext=ext)
if not isinstance(basenames, basestring):
vols = nibabel.four_to_three(vols)
filenames = []
for vol, basename in zip(vols, basenames):
assert isinstance(basename, basestring)
filename = os.path.join(output_dir, "%s%s" % (
prefix, basename))
nibabel.save(vol, filename)
filenames.append(filename)
else:
filenames = os.path.join(output_dir, "%s%s" % (
prefix, basenames))
nibabel.save(vols, filenames)
return filenames
else:
n_vols = len(vols)
filenames = []
if basenames is None:
if prefix:
prefix = prefix + "_"
else:
if isinstance(basenames, basestring):
basenames = ["vol%i_%s" % (t, basenames)
for t in xrange(len(vols))]
else:
assert len(set(basenames)) == len(vols), basenames
for t, vol in zip(xrange(n_vols), vols):
if isinstance(vol, np.ndarray):
if affine is None:
raise ValueError(
("vols is of type ndarray; you need to specifiy"
" `affine`"))
else:
vol = nibabel.Nifti1Image(vol, affine)
# save realigned vol unto disk
if basenames is None:
if ext is None:
ext = ".nii.gz"
output_filename = os.path.join(output_dir,
get_basename("%svol_%i" % (
prefix, t), ext=ext))
else:
basename = basenames if isinstance(
basenames, basestring) else basenames[t]
output_filename = os.path.join(output_dir,
get_basenames("%s%s" % (
prefix, basename), ext=ext))
vol = load_vol(vol) if not is_niimg(vol) else vol
nibabel.save(vol, output_filename)
# update rvols and filenames
filenames.append(output_filename)
return filenames
def _run_interface(self, runtime):
from dipy.reconst.peaks import peaks_from_model
from dipy.tracking.eudx import EuDX
from dipy.data import get_sphere
# import marshal as pickle
import pickle as pickle
import gzip
if (not (isdefined(self.inputs.in_model)
or isdefined(self.inputs.in_peaks))):
raise RuntimeError(('At least one of in_model or in_peaks should '
'be supplied'))
img = nb.load(self.inputs.in_file)
imref = nb.four_to_three(img)[0]
affine = img.affine
data = img.get_data().astype(np.float32)
hdr = imref.header.copy()
hdr.set_data_dtype(np.float32)
hdr['data_type'] = 16
sphere = get_sphere('symmetric724')
self._save_peaks = False
if isdefined(self.inputs.in_peaks):
IFLOGGER.info('Peaks file found, skipping ODF peaks search...')
f = gzip.open(self.inputs.in_peaks, 'rb')
peaks = pickle.load(f)
f.close()
else:
self._save_peaks = True
IFLOGGER.info('Loading model and computing ODF peaks')
f = gzip.open(self.inputs.in_model, 'rb')
odf_model = pickle.load(f)
f.close()
peaks = peaks_from_model(
model=odf_model,
data=data,
sphere=sphere,
relative_peak_threshold=self.inputs.peak_threshold,
min_separation_angle=self.inputs.min_angle,
parallel=self.inputs.multiprocess)
f = gzip.open(self._gen_filename('peaks', ext='.pklz'), 'wb')
pickle.dump(peaks, f, -1)
f.close()
hdr.set_data_shape(peaks.gfa.shape)
nb.Nifti1Image(peaks.gfa.astype(np.float32), affine, hdr).to_filename(
self._gen_filename('gfa'))
IFLOGGER.info('Performing tractography')
if isdefined(self.inputs.tracking_mask):
msk = nb.load(self.inputs.tracking_mask).get_data()
msk[msk > 0] = 1
msk[msk < 0] = 0
else:
msk = np.ones(imref.shape)
gfa = peaks.gfa * msk
seeds = self.inputs.num_seeds
if isdefined(self.inputs.seed_coord):
seeds = np.loadtxt(self.inputs.seed_coord)
elif isdefined(self.inputs.seed_mask):
seedmsk = nb.load(self.inputs.seed_mask).get_data()
assert (seedmsk.shape == data.shape[:3])
seedmsk[seedmsk > 0] = 1
seedmsk[seedmsk < 1] = 0
seedps = np.array(np.where(seedmsk == 1), dtype=np.float32).T
vseeds = seedps.shape[0]
nsperv = (seeds // vseeds) + 1
IFLOGGER.info('Seed mask is provided (%d voxels inside '
'mask), computing seeds (%d seeds/voxel).', vseeds,
nsperv)
if nsperv > 1:
IFLOGGER.info('Needed %d seeds per selected voxel (total %d).',
nsperv, vseeds)
seedps = np.vstack(np.array([seedps] * nsperv))
voxcoord = seedps + np.random.uniform(-1, 1, size=seedps.shape)
nseeds = voxcoord.shape[0]
seeds = affine.dot(
np.vstack((voxcoord.T, np.ones((1, nseeds)))))[:3, :].T
if self.inputs.save_seeds:
np.savetxt(self._gen_filename('seeds', ext='.txt'), seeds)
if isdefined(self.inputs.tracking_mask):
tmask = msk
a_low = 0.1
else:
tmask = gfa
a_low = self.inputs.gfa_thresh
eu = EuDX(
tmask,
peaks.peak_indices[..., 0],
seeds=seeds,
affine=affine,
odf_vertices=sphere.vertices,
a_low=a_low)
ss_mm = [np.array(s) for s in eu]
trkfilev = nb.trackvis.TrackvisFile(
[(s, None, None) for s in ss_mm],
points_space='rasmm',
affine=np.eye(4))
trkfilev.to_file(self._gen_filename('tracked', ext='.trk'))
return runtime
print('')
print("Shape of permutation data:")
for s in range(nsubj):
print(dat[s].shape)
print('')
end = timer()
print("Data loaded: {seconds:.4f} seconds".format(seconds=end-start))
middle = timer()
if args.mask:
try:
mask_img = nib.four_to_three(nib.load(args.mask))[0]
except ValueError:
mask_img = nib.load(args.mask)
mask_data = mask_img.get_data()
# If it is a probability map of tissue types, this says treat all voxels
# that have a probability 0.2 or higher of being grey matter.
# 0.2 is based on John Ashburner's VBM tutorial (VBMclass15.pdf) and Kurth,
# Gaser, and Luders (2015, Nature Protocols)
print("Masking reference data ...")
try:
mask = (mask_data > 0.2) * automask
except:
print("Dimension mismatch between mask and data! Resampling the mask and trying again ...")
rmask = nib.processing.resample_from_to(mask_img, reference_img)
mask_data = rmask.get_data()
def _run_interface(self, runtime):
if len(self.inputs.in_files) > 1:
iflogger.info('Multiple input images detected')
iflogger.info(len(self.inputs.in_files))
in_files = self.inputs.in_files
elif isdefined(self.inputs.in_file4d):
iflogger.info('Single four-dimensional image selected')
in_file4d = nb.load(self.inputs.in_file4d)
in_files = nb.four_to_three(in_file4d)
else:
iflogger.info('Single functional image provided')
in_files = self.inputs.in_files
if len(in_files) == 1:
iflogger.error(
"Only one functional image was input. Pearson's correlation coefficient can not be calculated")
raise ValueError
else:
rois = get_roi_list(self.inputs.segmentation_file)
fMRI_timecourse = get_timecourse_by_region(
in_files, self.inputs.segmentation_file, rois)
timecourse_at_each_node = fMRI_timecourse.T
iflogger.info(np.shape(timecourse_at_each_node))
iflogger.info(
'Structural Network: {s}'.format(s=self.inputs.structural_network))
structural_network = nx.read_gpickle(self.inputs.structural_network)
rois = structural_network.nodes()
#rois = get_roi_list(self.inputs.segmentation_file)
number_of_nodes = len(rois)
iflogger.info('Found {roi} unique region values'.format(roi=len(rois)))
newntwk = structural_network.copy()
newntwk = remove_all_edges(newntwk)
simple_correlation_matrix = np.zeros(
(number_of_nodes, number_of_nodes))
iflogger.info('Drawing edges...')
for idx_i, roi_i in enumerate(rois):
#iflogger.info('ROI:{i}, T-value: {t}'.format(i=roi_i, t=t_i))
for idx_j, roi_j in enumerate(rois):
#iflogger.info('...ROI:{j}, T-value: {t}'.format(j=roi_j, t=t_j))
if idx_j > idx_i:
simple_correlation_matrix[idx_i, idx_j] = pearsonr(
fMRI_timecourse[idx_i], fMRI_timecourse[idx_j])[0]
elif roi_i == roi_j:
simple_correlation_matrix[idx_i, idx_j] = 0.5
simple_correlation_matrix = simple_correlation_matrix + \
simple_correlation_matrix.T
stats = {'correlation': simple_correlation_matrix}
newntwk = add_edge_data(simple_correlation_matrix, newntwk)
path, name, ext = split_filename(self.inputs.out_network_file)
if not ext == '.pck':
ext = '.pck'
out_network_file = op.abspath(name + ext)
iflogger.info(
'Saving simple correlation network as {out}'.format(out=out_network_file))
nx.write_gpickle(newntwk, out_network_file)
path, name, ext = split_filename(self.inputs.out_stats_file)
if not ext == '.mat':
ext = '.mat'
out_stats_file = op.abspath(name + ext)
iflogger.info(
'Saving image statistics as {stats}'.format(stats=out_stats_file))
sio.savemat(out_stats_file, stats)
return runtime
def _run_interface(self, runtime):
if isdefined(self.inputs.lookup_table):
LUT_dict = get_names(self.inputs.lookup_table)
if len(self.inputs.in_files) > 1:
iflogger.info('Multiple input images detected')
iflogger.info(len(self.inputs.in_files))
in_files = self.inputs.in_files
elif isdefined(self.inputs.in_file4d):
iflogger.info('Single four-dimensional image selected')
in_files = nb.four_to_three(self.inputs.in_file4d)
else:
iflogger.info('Single functional image provided')
in_files = self.inputs.in_files
per_file_stats = {}
seg_file = nb.load(self.inputs.segmentation_file)
segmentationdata = seg_file.get_data()
if isdefined(self.inputs.resolution_network_file):
try:
gp = nx.read_gpickle(self.inputs.resolution_network_file)
except:
gp = nx.read_graphml(self.inputs.resolution_network_file)
nodedict = gp.node[gp.nodes()[0]]
if not nodedict.has_key('dn_position'):
iflogger.info("Creating node positions from segmentation")
G = nx.Graph()
for u, d in gp.nodes_iter(data=True):
iflogger.info('Node ID {id}'.format(id=int(u)))
G.add_node(int(u), d)
xyz = tuple(
np.mean(np.where(np.flipud(segmentationdata) == int(d["dn_correspondence_id"])), axis=1))
G.node[int(u)]['dn_position'] = xyz
ntwkname = op.abspath('nodepositions.pck')
nx.write_gpickle(G, ntwkname)
else:
ntwkname = self.inputs.resolution_network_file
rois = get_roi_list(self.inputs.segmentation_file)
roi_mean_tc, roi_max_tc, roi_min_tc, roi_std_tc, voxels = get_timecourse_by_region(
in_files, self.inputs.segmentation_file, rois)
stats = {}
stats['func_max'] = roi_max_tc
stats['func_mean'] = roi_mean_tc
stats['func_min'] = roi_min_tc
stats['func_stdev'] = roi_std_tc
stats['number_of_voxels'] = voxels
stats['rois'] = rois
if isdefined(self.inputs.lookup_table):
stats['roi_names'] = []
for x in rois:
try:
stats['roi_names'].append(LUT_dict[x])
except KeyError:
stats['roi_names'].append("Unknown_ROI_" + str(x))
global all_ntwks
all_ntwks = list()
for in_file_idx, in_file in enumerate(in_files):
ntwks = list()
per_file_stats = {}
per_file_stats['func_max'] = roi_max_tc[:, in_file_idx]
per_file_stats['func_mean'] = roi_mean_tc[:, in_file_idx]
per_file_stats['func_min'] = roi_min_tc[:, in_file_idx]
per_file_stats['func_stdev'] = roi_std_tc[:, in_file_idx]
per_file_stats['rois'] = rois
if isdefined(self.inputs.lookup_table):
per_file_stats['roi_names'] = [LUT_dict[x] for x in rois]
#per_file_stats['number_of_voxels'] = voxels[in_file_idx]
for key in per_file_stats.keys():
iflogger.info(key)
iflogger.info(np.shape(per_file_stats[key]))
try:
nwtk = nx.read_gpickle(ntwkname)
newntwk = add_node_data(per_file_stats[key], nwtk)
except:
iflogger.error(key)
iflogger.error(np.shape(per_file_stats[key]))
iflogger.error(
"Double-check the subjects' freesurfer directory.")
raise Exception(
"There may not be enough regions in the segmentation file!")
name = '{k}_{i}'.format(k=key, i=str(in_file_idx))
out_file = op.abspath(name + '.pck')
nx.write_gpickle(newntwk, out_file)
ntwks.append(out_file)
all_ntwks.extend(ntwks)
else:
rois = get_roi_list(self.inputs.segmentation_file)
roi_mean_tc, roi_max_tc, roi_min_tc, roi_std_tc, voxels = get_timecourse_by_region(
in_files, self.inputs.segmentation_file, rois)
stats = {}
stats['func_max'] = roi_max_tc
stats['func_mean'] = roi_mean_tc
stats['func_min'] = roi_min_tc
stats['func_stdev'] = roi_std_tc
stats['number_of_voxels'] = voxels
stats['rois'] = rois
if isdefined(self.inputs.lookup_table):
stats['roi_names'] = []
for x in rois:
try:
stats['roi_names'].append(LUT_dict[x])
except KeyError:
stats['roi_names'].append("Unknown_ROI_" + str(x))
if isdefined(self.inputs.subject_id):
stats['subject_id'] = self.inputs.subject_id
out_stats_file = op.abspath(self.inputs.out_stats_file)
iflogger.info(
'Saving image statistics as {stats}'.format(stats=out_stats_file))
sio.savemat(out_stats_file, stats)
return runtime
def save_vols(vols,
output_dir,
basenames=None,
affine=None,
concat=False,
prefix='',
ext=None):
"""
Saves a single 4D image or a couple of 3D vols unto disk.
vols: single 4D nibabel image object, or list of 3D nibabel image objects
volumes, of ndarray
volumes to be saved
output_dir: string
existing filename, destination directory
basenames: string or list of string, optional (default None)
basename(s) for output image(s)
affine: 2D array of shape (4, 4)
affine matrix for the output images
concat: bool, optional (default False)
concatenate all vols into a single film
prefix: string, optional (default '')
prefix to be prepended to output file basenames
ext: string, optional (default ".nii.gz")
file extension for output images
Returns
-------
string of list of strings, dependending on whether vols is list or
not, and on whether concat is set or not
the output image filename(s)
"""
if ext is None:
ext = ".nii.gz"
def _nifti_or_ndarray_to_nifti(x):
if is_niimg(x):
if affine is not None:
raise ValueError(
("vol is of type %s; not expecting `affine` parameter.") %
type(x))
else:
return x
if affine is None:
raise ValueError(
"vol is of type ndarray; you need to specifiy `affine`")
else:
return nibabel.Nifti1Image(x, affine)
if basenames is not None:
basenames = get_basenames(basenames, ext=ext)
# sanitize output_dir
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# vols are ndarray ?
if isinstance(vols, np.ndarray):
vols = _nifti_or_ndarray_to_nifti(vols)
# concat vols to single 4D film ?
if concat:
if isinstance(vols, list):
vols = nibabel.concat_images(
[_nifti_or_ndarray_to_nifti(vol) for vol in vols],
check_affines=False)
if basenames is not None:
if not isinstance(basenames, _basestring):
basenames = basenames[0]
else:
if basenames is not None:
if not isinstance(basenames, _basestring):
raise RuntimeError(
("concat=True specified but basenames is of type %s "
"instead of string") % type(basenames))
if not isinstance(vols, list):
if basenames is None:
basenames = get_basenames("vols", ext=ext)
if not isinstance(basenames, _basestring):
vols = nibabel.four_to_three(vols)
filenames = []
for vol, basename in zip(vols, basenames):
if not isinstance(basename, _basestring):
raise RuntimeError
filename = os.path.join(output_dir,
"%s%s" % (prefix, basename))
nibabel.save(vol, filename)
filenames.append(filename)
else:
filenames = os.path.join(output_dir, "%s%s" % (prefix, basenames))
nibabel.save(vols, filenames)
return filenames
else:
n_vols = len(vols)
filenames = []
if basenames is None:
if prefix:
prefix = prefix + "_"
else:
if isinstance(basenames, _basestring):
basenames = [
"vol%i_%s" % (t, basenames) for t in range(len(vols))
]
else:
if len(set(basenames)) != len(vols):
raise RuntimeError
for t, vol in zip(range(n_vols), vols):
if isinstance(vol, np.ndarray):
if affine is None:
raise ValueError(
("vols is of type ndarray; you need to specifiy"
" `affine`"))
else:
vol = nibabel.Nifti1Image(vol, affine)
# save realigned vol unto disk
if basenames is None:
output_filename = os.path.join(
output_dir, get_basename("%svol_%i" % (prefix, t),
ext=ext))
else:
basename = basenames if isinstance(
basenames, _basestring) else basenames[t]
output_filename = os.path.join(
output_dir,
get_basenames("%s%s" % (prefix, basename), ext=ext))
vol = check_niimg(vol)
nibabel.save(vol, output_filename)
# update rvols and filenames
filenames.append(output_filename)
return filenames
def _run_interface(self, runtime):
data_dir = op.abspath('./denoise/components')
if not os.path.exists(data_dir):
os.makedirs(data_dir)
in_files = self.inputs.in_files
if len(self.inputs.in_files) > 1:
print 'Multiple ({n}) input images detected! Copying to {d}...'.format(n=len(self.inputs.in_files), d=data_dir)
for in_file in self.inputs.in_files:
path, name, ext = split_filename(in_file)
shutil.copyfile(in_file, op.join(data_dir, name) + ext)
if ext == '.img':
shutil.copyfile(op.join(path, name) + '.hdr',
op.join(data_dir, name) + '.hdr')
elif ext == '.hdr':
shutil.copyfile(op.join(path, name) + '.img',
op.join(data_dir, name) + '.img')
print 'Copied!'
in_files = self.inputs.in_files
elif isdefined(self.inputs.in_file4d):
print 'Single four-dimensional image selected. Splitting and copying to {d}'.format(d=data_dir)
in_files = nb.four_to_three(self.inputs.in_file4d)
for in_file in in_files:
path, name, ext = split_filename(in_file)
shutil.copyfile(in_file, op.join(data_dir, name) + ext)
print 'Copied!'
else:
print 'Single functional image provided. Ending...'
in_files = self.inputs.in_files
nComponents = len(in_files)
path, name, ext = split_filename(self.inputs.time_course_image)
shutil.copyfile(self.inputs.time_course_image,
op.join(data_dir, name) + ext)
if ext == '.img':
shutil.copyfile(op.join(path, name) + '.hdr',
op.join(data_dir, name) + '.hdr')
elif ext == '.hdr':
shutil.copyfile(op.join(path, name) + '.img',
op.join(data_dir, name) + '.img')
data_dir = op.abspath('./denoise')
path, name, ext = split_filename(self.inputs.ica_mask_image)
shutil.copyfile(self.inputs.ica_mask_image,
op.join(data_dir, name) + ext)
if ext == '.img':
shutil.copyfile(op.join(path, name) + '.hdr',
op.join(data_dir, name) + '.hdr')
elif ext == '.hdr':
shutil.copyfile(op.join(path, name) + '.img',
op.join(data_dir, name) + '.img')
mask_file = op.join(data_dir, name)
repetition_time = self.inputs.repetition_time
neuronal_image = op.abspath(self.inputs.out_neuronal_image)
non_neuronal_image = op.abspath(self.inputs.out_non_neuronal_image)
coma_rest_lib_path = op.abspath(self.inputs.coma_rest_lib_path)
d = dict(
data_dir=data_dir, mask_name=mask_file, nComponents=nComponents, Tr=repetition_time,
nameNeuronal=neuronal_image, nameNonNeuronal=non_neuronal_image, coma_rest_lib_path=coma_rest_lib_path)
script = Template("""
restlib_path = '$coma_rest_lib_path';
setup_restlib_paths(restlib_path)
dataDir = '$data_dir';
maskName = '$mask_name';
nCompo = $nComponents;
Tr = $Tr;
nameNeuronalData = '$nameNeuronal';
nameNonNeuronalData = '$nameNonNeuronal';
denoiseImage(dataDir,maskName,nCompo,Tr,nameNeuronalData,nameNonNeuronalData, restlib_path);
""").substitute(d)
result = MatlabCommand(script=script, mfile=True,
prescript=[''], postscript=[''])
r = result.run()
print 'Neuronal component image saved as {n}'.format(n=neuronal_image)
print 'Non-neuronal component image saved as {n}'.format(n=non_neuronal_image)
return runtime
def _run_interface(self, runtime):
from dipy.core.gradients import GradientTable
from dipy.reconst.dti import fractional_anisotropy, mean_diffusivity
from dipy.reconst.csdeconv import recursive_response, auto_response
img = nb.load(self.inputs.in_file)
imref = nb.four_to_three(img)[0]
affine = img.affine
if isdefined(self.inputs.in_mask):
msk = nb.load(self.inputs.in_mask).get_data()
msk[msk > 0] = 1
msk[msk < 0] = 0
else:
msk = np.ones(imref.shape)
data = img.get_data().astype(np.float32)
gtab = self._get_gradient_table()
evals = np.nan_to_num(nb.load(self.inputs.in_evals).get_data())
FA = np.nan_to_num(fractional_anisotropy(evals)) * msk
indices = np.where(FA > self.inputs.fa_thresh)
S0s = data[indices][:, np.nonzero(gtab.b0s_mask)[0]]
S0 = np.mean(S0s)
if self.inputs.auto:
response, ratio = auto_response(gtab, data,
roi_radius=self.inputs.roi_radius,
fa_thr=self.inputs.fa_thresh)
response = response[0].tolist() + [S0]
elif self.inputs.recursive:
MD = np.nan_to_num(mean_diffusivity(evals)) * msk
indices = np.logical_or(
FA >= 0.4, (np.logical_and(FA >= 0.15, MD >= 0.0011)))
data = nb.load(self.inputs.in_file).get_data()
response = recursive_response(gtab, data, mask=indices, sh_order=8,
peak_thr=0.01, init_fa=0.08,
init_trace=0.0021, iter=8,
convergence=0.001,
parallel=True)
ratio = abs(response[1] / response[0])
else:
lambdas = evals[indices]
l01 = np.sort(np.mean(lambdas, axis=0))
response = np.array([l01[-1], l01[-2], l01[-2], S0])
ratio = abs(response[1] / response[0])
if ratio > 0.25:
IFLOGGER.warn('Estimated response is not prolate enough. '
'Ratio=%0.3f.', ratio)
elif ratio < 1.e-5 or np.any(np.isnan(response)):
response = np.array([1.8e-3, 3.6e-4, 3.6e-4, S0])
IFLOGGER.warn('Estimated response is not valid, using a default one')
else:
IFLOGGER.info('Estimated response: %s', str(response[:3]))
np.savetxt(op.abspath(self.inputs.response), response)
wm_mask = np.zeros_like(FA)
wm_mask[indices] = 1
nb.Nifti1Image(
wm_mask.astype(np.uint8), affine,
None).to_filename(op.abspath(self.inputs.out_mask))
return runtime