def register_atlas(uatlas_select,
atlas_select,
node_size,
basedir_path,
fa_path,
nodif_B0_mask,
anat_file,
wm_gm_int_in_dwi,
coords,
label_names,
gm_in_dwi,
vent_csf_in_dwi,
wm_in_dwi,
gtab_file,
dwi_file,
vox_size='2mm',
simple=False):
from pynets.registration import register
reg = register.DmriReg(basedir_path, fa_path, nodif_B0_mask, anat_file,
vox_size, simple)
if node_size is not None:
atlas_select = "%s%s%s" % (atlas_select, '_', node_size)
# Apply warps/coregister atlas to dwi
[dwi_aligned_atlas_wmgm_int, dwi_aligned_atlas,
aligned_atlas_t1mni] = reg.atlas2t1w2dwi_align(uatlas_select,
atlas_select)
return dwi_aligned_atlas_wmgm_int, dwi_aligned_atlas, aligned_atlas_t1mni, uatlas_select, atlas_select, coords, label_names, node_size, gm_in_dwi, vent_csf_in_dwi, wm_in_dwi, fa_path, gtab_file, nodif_B0_mask, dwi_file
def register_all(basedir_path,
fa_path,
nodif_B0_mask,
anat_file,
gtab_file,
dwi_file,
vox_size='2mm',
simple=False,
overwrite=False):
import os.path as op
from pynets.registration import register
reg = register.DmriReg(basedir_path, fa_path, nodif_B0_mask, anat_file,
vox_size, simple)
if (overwrite is True) or (op.isfile(reg.map_path) is False):
# Perform anatomical segmentation
reg.gen_tissue()
if (overwrite is True) or (op.isfile(reg.t1w2dwi) is False):
# Align t1w to dwi
reg.t1w2dwi_align()
if (overwrite is True) or (op.isfile(reg.wm_gm_int_in_dwi) is False):
# Align tissue
reg.tissue2dwi_align()
return reg.wm_gm_int_in_dwi, reg.wm_in_dwi, reg.gm_in_dwi, reg.vent_csf_in_dwi, reg.csf_mask_dwi, anat_file, nodif_B0_mask, fa_path, gtab_file, dwi_file
def register_atlas_dwi(uatlas, atlas, node_size, basedir_path, fa_path, B0_mask, anat_file, wm_gm_int_in_dwi, coords,
labels, gm_in_dwi, vent_csf_in_dwi, wm_in_dwi, gtab_file, dwi_file, vox_size='2mm',
simple=False):
"""
A Function to register an atlas to T1w-warped MNI-space, and restrict the atlas to grey-matter only.
Parameters
----------
uatlas : str
File path to atlas parcellation Nifti1Image in MNI template space.
atlas : str
Name of atlas parcellation used.
node_size : int
Spherical centroid node size in the case that coordinate-based centroids
are used as ROI's for tracking.
basedir_path : str
Path to directory to output direct-streamline normalized temp files and outputs.
fa_path : str
File path to FA Nifti1Image.
B0_mask : str
File path to B0 brain mask.
anat_file : str
Path to a skull-stripped anatomical Nifti1Image.
wm_gm_int_in_dwi : st
Path to wm-gm interface Nifti1Image file in native diffusion space.
coords : list
List of (x, y, z) tuples corresponding to the center-of-mass of each parcellation node.
labels : list
List of string labels corresponding to graph nodes.
gm_in_dwi : str
File path to grey-matter tissue segmentation Nifti1Image in native diffusion space.
vent_csf_in_dwi : str
File path to ventricular CSF tissue segmentation Nifti1Image in native diffusion space.
wm_in_dwi : str
File path to white-matter tissue segmentation Nifti1Image in native diffusion space.
gtab_file : str
File path to pickled DiPy gradient table object.
dwi_file : str
File path to diffusion weighted image.
vox_size : str
Voxel size in mm. (e.g. 2mm).
simple : bool
Indicates whether to use non-linear registration and BBR (True) or entirely linear methods (False).
Default is True.
Returns
-------
dwi_aligned_atlas_t1mni_wmgm_int : str
File path to atlas parcellation Nifti1Image in T1w-MNI warped native diffusion space, restricted only to wm-gm
interface.
dwi_aligned_atlas : str
File path to atlas parcellation Nifti1Image in native diffusion space.
aligned_atlas_t1mni : str
File path to atlas parcellation Nifti1Image in T1w-warped MNI space.
uatlas : str
File path to atlas parcellation Nifti1Image in MNI template space.
atlas : str
Name of atlas parcellation used.
coords : list
List of (x, y, z) tuples corresponding to the center-of-mass of each parcellation node.
labels : list
List of string labels corresponding to graph nodes.
node_size : int
Spherical centroid node size in the case that coordinate-based centroids
are used as ROI's for tracking.
gm_in_dwi : str
File path to grey-matter tissue segmentation Nifti1Image in native diffusion space.
vent_csf_in_dwi : str
File path to ventricular CSF tissue segmentation Nifti1Image in native diffusion space.
wm_in_dwi : str
File path to white-matter tissue segmentation Nifti1Image in native diffusion space.
fa_path : str
File path to FA Nifti1Image.
gtab_file : str
File path to pickled DiPy gradient table object.
B0_mask : str
File path to B0 brain mask.
dwi_file : str
File path to diffusion weighted image.
"""
from pynets.registration import register
reg = register.DmriReg(basedir_path, fa_path, B0_mask, anat_file, vox_size, simple)
if node_size is not None:
atlas = "%s%s%s" % (atlas, '_', node_size)
# Apply warps/coregister atlas to dwi
[dwi_aligned_atlas_wmgm_int, dwi_aligned_atlas, aligned_atlas_t1mni] = reg.atlas2t1w2dwi_align(uatlas, atlas)
return dwi_aligned_atlas_wmgm_int, dwi_aligned_atlas, aligned_atlas_t1mni, uatlas, atlas, coords, labels, node_size, gm_in_dwi, vent_csf_in_dwi, wm_in_dwi, fa_path, gtab_file, B0_mask, dwi_file
def register_all_dwi(basedir_path, fa_path, B0_mask, anat_file, gtab_file, dwi_file, vox_size='2mm', simple=False,
overwrite=False):
"""
A Function to register an atlas to T1w-warped MNI-space, and restrict the atlas to grey-matter only.
Parameters
----------
basedir_path : str
Path to directory to output direct-streamline normalized temp files and outputs.
fa_path : str
File path to FA Nifti1Image.
B0_mask : str
File path to B0 brain mask.
anat_file : str
Path to a skull-stripped anatomical Nifti1Image.
gtab_file : str
File path to pickled DiPy gradient table object.
dwi_file : str
File path to diffusion weighted image.
vox_size : str
Voxel size in mm. (e.g. 2mm).
simple : bool
Indicates whether to use non-linear registration and BBR (True) or entirely linear methods (False).
Default is True.
overwrite : bool
Indicates whether to overwrite existing registration files. Default is False.
Returns
-------
wm_gm_int_in_dwi : st
Path to wm-gm interface Nifti1Image file in native diffusion space.
wm_in_dwi : str
File path to white-matter tissue segmentation Nifti1Image in native diffusion space.
gm_in_dwi : str
File path to grey-matter tissue segmentation Nifti1Image in native diffusion space.
vent_csf_in_dwi : str
File path to ventricular CSF tissue segmentation Nifti1Image in native diffusion space.
csf_mask_dwi : str
File path to CSF tissue segmentation Nifti1Image in native diffusion space.
anat_file : str
Path to a skull-stripped anatomical Nifti1Image.
B0_mask : str
File path to B0 brain mask.
fa_path : str
File path to FA Nifti1Image.
gtab_file : str
File path to pickled DiPy gradient table object.
dwi_file : str
File path to diffusion weighted image.
"""
import os.path as op
from pynets.registration import register
reg = register.DmriReg(basedir_path, fa_path, B0_mask, anat_file, vox_size, simple)
if (overwrite is True) or (op.isfile(reg.map_path) is False):
# Perform anatomical segmentation
reg.gen_tissue()
if (overwrite is True) or (op.isfile(reg.t1w2dwi) is False):
# Align t1w to dwi
reg.t1w2dwi_align()
if (overwrite is True) or (op.isfile(reg.wm_gm_int_in_dwi) is False):
# Align tissue
reg.tissue2dwi_align()
return reg.wm_gm_int_in_dwi, reg.wm_in_dwi, reg.gm_in_dwi, reg.vent_csf_in_dwi, reg.csf_mask_dwi, anat_file, B0_mask, fa_path, gtab_file, dwi_file
def _run_interface(self, runtime):
import gc
import os.path as op
from pynets.registration import register
from nipype.utils.filemanip import fname_presuffix, copyfile
anat_file_tmp_path = fname_presuffix(self.inputs.anat_file, suffix='_tmp', newpath=runtime.cwd)
copyfile(self.inputs.anat_file, anat_file_tmp_path, copy=True, use_hardlink=False)
fa_tmp_path = fname_presuffix(self.inputs.fa_path, suffix='_tmp', newpath=runtime.cwd)
copyfile(self.inputs.fa_path, fa_tmp_path, copy=True, use_hardlink=False)
ap_tmp_path = fname_presuffix(self.inputs.ap_path, suffix='_tmp', newpath=runtime.cwd)
copyfile(self.inputs.ap_path, ap_tmp_path, copy=True, use_hardlink=False)
B0_mask_tmp_path = fname_presuffix(self.inputs.B0_mask, suffix='_tmp', newpath=runtime.cwd)
copyfile(self.inputs.B0_mask, B0_mask_tmp_path, copy=True, use_hardlink=False)
reg = register.DmriReg(basedir_path=runtime.cwd,
fa_path=fa_tmp_path,
ap_path=ap_tmp_path,
B0_mask=B0_mask_tmp_path,
anat_file=anat_file_tmp_path,
mask=self.inputs.mask,
vox_size=self.inputs.vox_size,
simple=self.inputs.simple)
if (self.inputs.overwrite is True) or (op.isfile(reg.map_path) is False):
# Perform anatomical segmentation
reg.gen_tissue()
if (self.inputs.overwrite is True) or (op.isfile(reg.t1w2dwi) is False):
# Align t1w to dwi
reg.t1w2dwi_align()
if (self.inputs.overwrite is True) or (op.isfile(reg.wm_gm_int_in_dwi) is False):
# Align tissue
reg.tissue2dwi_align()
if self.inputs.waymask is not None:
if (self.inputs.overwrite is True) or (op.isfile(reg.waymask_in_dwi) is False):
# Align waymask
reg.waymask2dwi_align(self.inputs.waymask)
else:
reg.waymask_in_dwi = None
self._results['wm_in_dwi'] = reg.wm_in_dwi
self._results['gm_in_dwi'] = reg.gm_in_dwi
self._results['vent_csf_in_dwi'] = reg.vent_csf_in_dwi
self._results['csf_mask_dwi'] = reg.csf_mask_dwi
self._results['anat_file'] = self.inputs.anat_file
self._results['t1w2dwi'] = reg.t1w2dwi
self._results['B0_mask'] = self.inputs.B0_mask
self._results['ap_path'] = self.inputs.ap_path
self._results['gtab_file'] = self.inputs.gtab_file
self._results['dwi_file'] = self.inputs.dwi_file
self._results['waymask_in_dwi'] = reg.waymask_in_dwi
self._results['basedir_path'] = runtime.cwd
gc.collect()
return runtime