def _run_interface(self, runtime):
img = nb.load(self.inputs.in_file)
ntotal = img.shape[-1] if img.dataobj.ndim == 4 else 1
t_mask = np.zeros((ntotal, ), dtype=bool)
if ntotal == 1:
self._results["t_mask"] = [True]
self._results["n_dummy"] = 1
return runtime
from nipype.algorithms.confounds import is_outlier
data = img.get_fdata(dtype="float32")[..., :self.inputs.n_volumes]
# Data can come with outliers showing very high numbers - preemptively prune
data = np.clip(
data,
a_min=0.0 if self.inputs.nonnegative else np.percentile(data, 0.2),
a_max=np.percentile(data, 99.8),
)
self._results["n_dummy"] = is_outlier(np.mean(data, axis=(0, 1, 2)))
start = 0
stop = self._results["n_dummy"]
if stop < 2:
stop = min(ntotal, self.inputs.n_volumes)
start = max(0, stop - self.inputs.zero_dummy_masked)
t_mask[start:stop] = True
self._results["t_mask"] = t_mask.tolist()
return runtime
def _get_vols_to_discard(img):
'''
Takes a nifti file, extracts the mean signal of the first 50 volumes and computes which are outliers.
is_outlier function: computes Modified Z-Scores (https://www.itl.nist.gov/div898/handbook/eda/section3/eda35h.htm) to determine which volumes are outliers.
'''
from nipype.algorithms.confounds import is_outlier
data_slice = sitk.GetArrayFromImage(img)[:50, :, :, :]
global_signal = data_slice.mean(axis=-1).mean(axis=-1).mean(axis=-1)
return is_outlier(global_signal)
def _get_series_len(bold_fname):
from nipype.algorithms.confounds import is_outlier
import nibabel as nb
import numpy as np
img = nb.load(bold_fname)
if len(img.shape) < 4:
return 1
data = img.get_fdata(dtype="float32")
# Data can come with outliers showing very high numbers - preemptively prune
data = np.clip(
data,
a_min=0.0,
a_max=np.percentile(data, 99.8),
)
outliers = is_outlier(np.mean(data, axis=(0, 1, 2)))
return img.shape[3] - outliers
def _run_interface(self, runtime):
in_nii = nb.load(self.inputs.in_file)
data_slice = in_nii.dataobj[:, :, :, :50]
global_signal = data_slice.mean(axis=0).mean(axis=0).mean(axis=0)
n_volumes_to_discard = is_outlier(global_signal)
out_ref_fname = os.path.abspath("ref_image.nii.gz")
if n_volumes_to_discard == 0:
if in_nii.shape[-1] > 40:
slice = data_slice[:, :, :, 20:40]
slice_fname = os.path.abspath("slice.nii.gz")
nb.Nifti1Image(slice, in_nii.affine,
in_nii.header).to_filename(slice_fname)
else:
slice_fname = self.inputs.in_file
if self.inputs.mc_method == "AFNI":
res = afni.Volreg(in_file=slice_fname,
args='-Fourier -twopass',
zpad=4,
outputtype='NIFTI_GZ').run()
elif self.inputs.mc_method == "FSL":
res = fsl.MCFLIRT(in_file=slice_fname,
ref_vol=0,
interpolation='sinc').run()
mc_slice_nii = nb.load(res.outputs.out_file)
median_image_data = np.median(mc_slice_nii.get_data(), axis=3)
nb.Nifti1Image(median_image_data, mc_slice_nii.affine,
mc_slice_nii.header).to_filename(out_ref_fname)
else:
median_image_data = np.median(
data_slice[:, :, :, :n_volumes_to_discard], axis=3)
nb.Nifti1Image(median_image_data, in_nii.affine,
in_nii.header).to_filename(out_ref_fname)
self._results = dict()
self._results["ref_image"] = out_ref_fname
self._results["n_volumes_to_discard"] = n_volumes_to_discard
return runtime
def _get_vols_to_discard(img):
from nipype.algorithms.confounds import is_outlier
data_slice = img.dataobj[:, :, :, :50]
global_signal = data_slice.mean(axis=0).mean(axis=0).mean(axis=0)
return is_outlier(global_signal)
def test_outliers(tmpdir):
np.random.seed(0)
in_data = np.random.randn(100)
in_data[0] += 10
assert is_outlier(in_data) == 1
def test_outliers(tmpdir):
np.random.seed(0)
in_data = np.random.randn(100)
in_data[0] += 10
assert is_outlier(in_data) == 1
def _get_vols_to_discard(img):
from nipype.algorithms.confounds import is_outlier
data_slice = img.dataobj[:, :, :, :50]
global_signal = data_slice.mean(axis=0).mean(axis=0).mean(axis=0)
return is_outlier(global_signal)
