def _extrapolate_scheme(self, scheme_name, runtime, fit_obj, mask_array, mask_img):
if scheme_name not in ("ABCD", "HCP"):
return
output_dwi_file = fname_presuffix(self.inputs.dwi_file,
suffix=scheme_name,
newpath=runtime.cwd, use_ext=True)
output_bval_file = fname_presuffix(self.inputs.dwi_file,
suffix='{}.bval'.format(scheme_name),
newpath=runtime.cwd, use_ext=False)
output_bvec_file = fname_presuffix(self.inputs.dwi_file,
suffix='{}.bvec'.format(scheme_name),
newpath=runtime.cwd, use_ext=False)
output_b_file = fname_presuffix(self.inputs.dwi_file,
suffix='{}.b'.format(scheme_name),
newpath=runtime.cwd, use_ext=False)
# Copy in the bval and bvecs
bval_file = pkgr('qsiprep', 'data/schemes/{}.bval'.format(scheme_name))
bvec_file = pkgr('qsiprep', 'data/schemes/{}.bvec'.format(scheme_name))
shutil.copyfile(bval_file, output_bval_file)
shutil.copyfile(bvec_file, output_bvec_file)
self._results['extrapolated_bvecs'] = bvec_file
self._results['extrapolated_bvals'] = bval_file
_convert_fsl_to_mrtrix(bval_file, bvec_file, output_b_file)
self._results['extrapolated_b'] = output_b_file
prediction_gtab = self._get_gtab(external_bvals=bval_file, external_bvecs=bvec_file)
prediction_shore = brainsuite_shore_basis(fit_obj.model.radial_order, fit_obj.model.zeta,
prediction_gtab, fit_obj.model.tau)
shore_array = fit_obj._shore_coef[mask_array]
output_data = np.zeros(mask_array.shape + (len(prediction_gtab.bvals),))
output_data[mask_array] = np.dot(shore_array, prediction_shore.T)
nb.Nifti1Image(output_data, mask_img.affine, mask_img.header).to_filename(output_dwi_file)
self._results['extrapolated_dwi'] = output_dwi_file
def _extrapolate_scheme(self, scheme_name, runtime, fit_obj, mask_img):
if scheme_name not in ("ABCD", "HCP"):
return
output_dwi_file = fname_presuffix(self.inputs.dwi_file,
suffix=scheme_name,
newpath=runtime.cwd, use_ext=True)
output_bval_file = fname_presuffix(self.inputs.dwi_file,
suffix='{}.bval'.format(scheme_name),
newpath=runtime.cwd, use_ext=False)
output_bvec_file = fname_presuffix(self.inputs.dwi_file,
suffix='{}.bvec'.format(scheme_name),
newpath=runtime.cwd, use_ext=False)
output_b_file = fname_presuffix(self.inputs.dwi_file,
suffix='{}.b'.format(scheme_name),
newpath=runtime.cwd, use_ext=False)
# Copy in the bval and bvecs
bval_file = pkgr('qsiprep', 'data/schemes/{}.bval'.format(scheme_name))
bvec_file = pkgr('qsiprep', 'data/schemes/{}.bvec'.format(scheme_name))
shutil.copyfile(bval_file, output_bval_file)
shutil.copyfile(bvec_file, output_bvec_file)
self._results['extrapolated_bvecs'] = bvec_file
self._results['extrapolated_bvals'] = bval_file
_convert_fsl_to_mrtrix(bval_file, bvec_file, output_b_file)
self._results['extrapolated_b'] = output_b_file
gtab_to_predict = self._get_gtab(external_bvals=bval_file, external_bvecs=bvec_file)
new_data = fit_obj.predict(gtab_to_predict, S0=1)
nb.Nifti1Image(new_data, mask_img.affine, mask_img.header).to_filename(output_dwi_file)
self._results['extrapolated_dwi'] = output_dwi_file
def init_anat_average_wf(
*,
bspline_fitting_distance=200,
longitudinal=False,
name="anat_average_wf",
num_maps=1,
omp_nthreads=None,
sloppy=False,
):
"""
Create an average from several images of the same modality.
Each image undergoes a clipping step, removing background noise and
high-intensity outliers, which is required by INU correction with the
N4 algorithm.
Then INU correction is performed for each of the inputs and the range
of the image clipped again to fit within uint8.
Finally, each image is reoriented to have RAS+ data matrix and, if
more than one inputs, aligned and averaged with FreeSurfer's
``mri_robust_template``.
Parameters
----------
bspline_fitting_distance : :obj:`float`
Distance in mm between B-Spline control points for N4 INU estimation.
longitudinal : :obj:`bool`
Whether an unbiased middle point should be calculated.
name : :obj:`str`
This particular workflow's unique name (Nipype requirement).
num_maps : :obj:`int`
Then number of input 3D volumes to be averaged.
omp_nthreads : :obj:`int`
The number of threads for individual processes in this workflow.
sloppy : :obj:`bool`
Run in *sloppy* mode.
Inputs
------
in_files : :obj:`list`
A list of one or more input files. They can be 3D or 4D.
Outputs
-------
out_file : :obj:`str`
The output averaged reference file.
valid_list : :obj:`list`
A list of accepted/discarded volumes from the input list.
realign_xfms : :obj:`list`
List of rigid-body transformation matrices that bring every volume
into alignment with the average reference.
out_report : :obj:`str`
Path to a reportlet summarizing what happened in this workflow.
"""
from pkg_resources import resource_filename as pkgr
from nipype.interfaces.ants import N4BiasFieldCorrection
from nipype.interfaces.image import Reorient
from niworkflows.engine.workflows import LiterateWorkflow as Workflow
from niworkflows.interfaces.header import ValidateImage
from niworkflows.interfaces.nibabel import IntensityClip, SplitSeries
from niworkflows.interfaces.freesurfer import (
StructuralReference,
PatchedLTAConvert as LTAConvert,
)
from niworkflows.interfaces.images import TemplateDimensions, Conform
from niworkflows.interfaces.nitransforms import ConcatenateXFMs
from niworkflows.utils.misc import add_suffix
wf = Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=["in_files"]),
name="inputnode")
outputnode = pe.Node(
niu.IdentityInterface(
fields=["out_file", "valid_list", "realign_xfms", "out_report"]),
name="outputnode",
)
# 1. Validate each of the input images
validate = pe.MapNode(
ValidateImage(),
iterfield="in_file",
name="validate",
run_without_submitting=True,
)
# 2. Ensure we don't have two timepoints and implicitly squeeze image
split = pe.MapNode(SplitSeries(), iterfield="in_file", name="split")
# 3. INU correction of all independent volumes
clip_preinu = pe.MapNode(IntensityClip(p_min=50),
iterfield="in_file",
name="clip_preinu")
correct_inu = pe.MapNode(
N4BiasFieldCorrection(
dimension=3,
save_bias=False,
copy_header=True,
n_iterations=[50] * (5 - 2 * sloppy),
convergence_threshold=1e-7,
shrink_factor=4,
bspline_fitting_distance=bspline_fitting_distance,
),
iterfield="input_image",
n_procs=omp_nthreads,
name="correct_inu",
)
clip_postinu = pe.MapNode(IntensityClip(p_min=10.0, p_max=99.5),
iterfield="in_file",
name="clip_postinu")
# 4. Reorient T2w image(s) to RAS and resample to common voxel space
ref_dimensions = pe.Node(TemplateDimensions(), name="ref_dimensions")
conform = pe.MapNode(Conform(), iterfield="in_file", name="conform")
# fmt:off
wf.connect([
(inputnode, ref_dimensions, [("in_files", "t1w_list")]),
(inputnode, validate, [("in_files", "in_file")]),
(validate, split, [("out_file", "in_file")]),
(split, clip_preinu, [(("out_files", _flatten), "in_file")]),
(clip_preinu, correct_inu, [("out_file", "input_image")]),
(correct_inu, clip_postinu, [("output_image", "in_file")]),
(ref_dimensions, conform, [("t1w_valid_list", "in_file"),
("target_zooms", "target_zooms"),
("target_shape", "target_shape")]),
(ref_dimensions, outputnode, [("out_report", "out_report"),
("t1w_valid_list", "valid_list")]),
])
# fmt:on
# 5. Reorient template to RAS, if needed (mri_robust_template may set to LIA)
ensure_ras = pe.Node(Reorient(), name="ensure_ras")
if num_maps == 1:
get1st = pe.Node(niu.Select(index=[0]), name="get1st")
outputnode.inputs.realign_xfms = [
pkgr("smriprep", "data/itkIdentityTransform.txt")
]
# fmt:off
wf.connect([
(conform, get1st, [("out_file", "inlist")]),
(get1st, ensure_ras, [("out", "in_file")]),
(ensure_ras, outputnode, [("out_file", "out_file")]),
])
# fmt:on
return wf
from nipype.interfaces import freesurfer as fs
wf.__desc__ = f"""\
An anatomical reference-map was computed after registration of
{num_maps} images (after INU-correction) using
`mri_robust_template` [FreeSurfer {fs.Info().looseversion() or "<ver>"}, @fs_template].
"""
conform_xfm = pe.MapNode(
LTAConvert(in_lta="identity.nofile", out_lta=True),
iterfield=["source_file", "target_file"],
name="conform_xfm",
)
# 6. StructuralReference is fs.RobustTemplate if > 1 volume, copying otherwise
merge = pe.Node(
StructuralReference(
auto_detect_sensitivity=True,
initial_timepoint=1, # For deterministic behavior
intensity_scaling=True, # 7-DOF (rigid + intensity)
subsample_threshold=200,
fixed_timepoint=not longitudinal,
no_iteration=not longitudinal,
transform_outputs=True,
),
mem_gb=2 * num_maps - 1,
name="merge",
)
# 7. Final intensity equalization/conformation
clip_final = pe.Node(IntensityClip(p_min=2.0, p_max=99.9),
name="clip_final")
merge_xfm = pe.MapNode(
niu.Merge(2),
name="merge_xfm",
iterfield=["in1", "in2"],
run_without_submitting=True,
)
concat_xfms = pe.MapNode(
ConcatenateXFMs(inverse=True),
name="concat_xfms",
iterfield=["in_xfms"],
run_without_submitting=True,
)
def _set_threads(in_list, maximum):
return min(len(in_list), maximum)
# fmt:off
wf.connect([
(ref_dimensions, conform_xfm, [("t1w_valid_list", "source_file")]),
(conform, conform_xfm, [("out_file", "target_file")]),
(conform, merge,
[("out_file", "in_files"),
(("out_file", _set_threads, omp_nthreads), "num_threads"),
(("out_file", add_suffix, "_template"), "out_file")]),
(merge, ensure_ras, [("out_file", "in_file")]),
# Combine orientation and template transforms
(conform_xfm, merge_xfm, [("out_lta", "in1")]),
(merge, merge_xfm, [("transform_outputs", "in2")]),
(merge_xfm, concat_xfms, [("out", "in_xfms")]),
# Output
(ensure_ras, clip_final, [("out_file", "in_file")]),
(clip_final, outputnode, [("out_file", "out_file")]),
(concat_xfms, outputnode, [("out_xfm", "realign_xfms")]),
])
# fmt:on
return wf
def init_anat_template_wf(longitudinal,
omp_nthreads,
num_t1w,
name='anat_template_wf'):
"""
Generate a canonically-oriented, structural average from all input T1w images.
Workflow Graph
.. workflow::
:graph2use: orig
:simple_form: yes
from smriprep.workflows.anatomical import init_anat_template_wf
wf = init_anat_template_wf(
longitudinal=False, omp_nthreads=1, num_t1w=1)
Parameters
----------
longitudinal : bool
Create unbiased structural average, regardless of number of inputs
(may increase runtime)
omp_nthreads : int
Maximum number of threads an individual process may use
num_t1w : int
Number of T1w images
name : str, optional
Workflow name (default: anat_template_wf)
Inputs
------
t1w
List of T1-weighted structural images
Outputs
-------
t1w_ref
Structural reference averaging input T1w images, defining the T1w space.
t1w_realign_xfm
List of affine transforms to realign input T1w images
out_report
Conformation report
"""
workflow = Workflow(name=name)
if num_t1w > 1:
workflow.__desc__ = """\
A T1w-reference map was computed after registration of
{num_t1w} T1w images (after INU-correction) using
`mri_robust_template` [FreeSurfer {fs_ver}, @fs_template].
""".format(num_t1w=num_t1w, fs_ver=fs.Info().looseversion() or '<ver>')
inputnode = pe.Node(niu.IdentityInterface(fields=['t1w']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(
fields=['t1w_ref', 't1w_valid_list', 't1w_realign_xfm', 'out_report']),
name='outputnode')
# 0. Reorient T1w image(s) to RAS and resample to common voxel space
t1w_ref_dimensions = pe.Node(TemplateDimensions(),
name='t1w_ref_dimensions')
t1w_conform = pe.MapNode(Conform(),
iterfield='in_file',
name='t1w_conform')
workflow.connect([
(inputnode, t1w_ref_dimensions, [('t1w', 't1w_list')]),
(t1w_ref_dimensions, t1w_conform, [('t1w_valid_list', 'in_file'),
('target_zooms', 'target_zooms'),
('target_shape', 'target_shape')]),
(t1w_ref_dimensions, outputnode, [('out_report', 'out_report'),
('t1w_valid_list', 't1w_valid_list')
]),
])
if num_t1w == 1:
get1st = pe.Node(niu.Select(index=[0]), name='get1st')
outputnode.inputs.t1w_realign_xfm = [
pkgr('smriprep', 'data/itkIdentityTransform.txt')
]
workflow.connect([
(t1w_conform, get1st, [('out_file', 'inlist')]),
(get1st, outputnode, [('out', 't1w_ref')]),
])
return workflow
t1w_conform_xfm = pe.MapNode(LTAConvert(in_lta='identity.nofile',
out_lta=True),
iterfield=['source_file', 'target_file'],
name='t1w_conform_xfm')
# 1. Template (only if several T1w images)
# 1a. Correct for bias field: the bias field is an additive factor
# in log-transformed intensity units. Therefore, it is not a linear
# combination of fields and N4 fails with merged images.
# 1b. Align and merge if several T1w images are provided
n4_correct = pe.MapNode(N4BiasFieldCorrection(dimension=3,
copy_header=True),
iterfield='input_image',
name='n4_correct',
n_procs=1) # n_procs=1 for reproducibility
# StructuralReference is fs.RobustTemplate if > 1 volume, copying otherwise
t1w_merge = pe.Node(
StructuralReference(
auto_detect_sensitivity=True,
initial_timepoint=1, # For deterministic behavior
intensity_scaling=True, # 7-DOF (rigid + intensity)
subsample_threshold=200,
fixed_timepoint=not longitudinal,
no_iteration=not longitudinal,
transform_outputs=True,
),
mem_gb=2 * num_t1w - 1,
name='t1w_merge')
# 2. Reorient template to RAS, if needed (mri_robust_template may set to LIA)
t1w_reorient = pe.Node(image.Reorient(), name='t1w_reorient')
concat_affines = pe.MapNode(ConcatenateLTA(out_type='RAS2RAS',
invert_out=True),
iterfield=['in_lta1', 'in_lta2'],
name='concat_affines')
lta_to_itk = pe.MapNode(LTAConvert(out_itk=True),
iterfield=['in_lta'],
name='lta_to_itk')
def _set_threads(in_list, maximum):
return min(len(in_list), maximum)
workflow.connect([
(t1w_ref_dimensions, t1w_conform_xfm, [('t1w_valid_list',
'source_file')]),
(t1w_conform, t1w_conform_xfm, [('out_file', 'target_file')]),
(t1w_conform, n4_correct, [('out_file', 'input_image')]),
(t1w_conform, t1w_merge,
[(('out_file', _set_threads, omp_nthreads), 'num_threads'),
(('out_file', add_suffix, '_template'), 'out_file')]),
(n4_correct, t1w_merge, [('output_image', 'in_files')]),
(t1w_merge, t1w_reorient, [('out_file', 'in_file')]),
# Combine orientation and template transforms
(t1w_conform_xfm, concat_affines, [('out_lta', 'in_lta1')]),
(t1w_merge, concat_affines, [('transform_outputs', 'in_lta2')]),
(concat_affines, lta_to_itk, [('out_file', 'in_lta')]),
# Output
(t1w_reorient, outputnode, [('out_file', 't1w_ref')]),
(lta_to_itk, outputnode, [('out_itk', 't1w_realign_xfm')]),
])
return workflow
def _run_interface(self, runtime):
out_path = _find(self.inputs.source_file, self.images)
if out_path is None:
for k in self.images.keys():
if k in self.inputs.in_file[0]:
out_path = k
if "anat" in self.inputs.in_file[0]:
out_path = out_path + "/" + "T1w"
if out_path is None:
out_path = ""
_, ext = _splitext(self.inputs.in_file[0])
compress = ext == '.nii'
if compress:
ext = '.nii.gz'
base_directory = runtime.cwd
if isdefined(self.inputs.base_directory):
base_directory = os.path.abspath(self.inputs.base_directory)
if base_directory == self.fmriprep_output_dir:
# don't copy file
return runtime
elif base_directory == self.fmriprep_reportlets_dir:
# write to json
work_dir = os.path.dirname(base_directory)
json_id = "%s.%s" % (self.node_id, self.inputs.suffix)
json_id = re.sub(r'func_preproc_[^.]*', "func_preproc_wf", json_id)
json_data = {"id": json_id}
out_path = os.path.join(out_path, "qualitycheck")
os.makedirs(os.path.join(self.output_dir, out_path), exist_ok=True)
touch_fname = os.path.join(out_path, json_data["id"] + ext)
touch_path = os.path.join(self.output_dir, touch_fname)
if not os.path.isfile(touch_path):
for i, fname in enumerate(self.inputs.in_file):
copy(fname, touch_path)
# with open(fname, "r") as f:
# json_data["html"] += f.read()
json_data["fname"] = os.path.join(
os.path.basename(self.output_dir), touch_fname)
with fasteners.InterProcessLock(
os.path.join(work_dir, "qc.lock")):
json_file = os.path.join(work_dir, "qc.json")
with open(json_file, "ab+") as f:
f.seek(0, 2)
if f.tell() == 0:
f.write(json.dumps([json_data]).encode())
else:
f.seek(-1, 2)
f.truncate()
f.write(','.encode())
f.write(json.dumps(json_data).encode())
f.write(']'.encode())
Path(touch_path).touch()
html_path = os.path.join(os.path.dirname(self.output_dir),
"index.html")
if not os.path.isfile(html_path):
copy(pkgr('pipeline', 'index.html'), html_path)
else:
# copy file to out_path
out_path = os.path.join(base_directory, out_path)
os.makedirs(out_path, exist_ok=True)
formatstr = '{suffix}{ext}'
if len(self.inputs.in_file) > 1 and not isdefined(
self.inputs.extra_values):
formatstr = '{suffix}{i:04d}{ext}'
for i, fname in enumerate(self.inputs.in_file):
out_file = formatstr.format(suffix=self.inputs.suffix,
i=i,
ext=ext)
if isdefined(self.inputs.extra_values):
out_file = out_file.format(
extra_value=self.inputs.extra_values[i])
out_file = os.path.join(out_path, out_file)
self._results['out_file'].append(out_file)
if compress:
with open(fname, 'rb') as f_in:
with gzip.open(out_file, 'wb') as f_out:
copyfileobj(f_in, f_out)
else:
copy(fname, out_file)
return runtime
def init_dwi_recon_workflow(dwi_files,
workflow_spec,
output_dir,
reportlets_dir,
has_transform,
omp_nthreads,
name="recon_wf"):
"""Convert a workflow spec into a nipype workflow.
"""
atlas_names = workflow_spec.get('atlases', [])
space = workflow_spec['space']
workflow = Workflow(name=name)
scans_iter = pe.Node(niu.IdentityInterface(fields=['dwi_file']),
name='scans_iter')
scans_iter.iterables = ("dwi_file", dwi_files)
inputnode = pe.Node(niu.IdentityInterface(fields=input_fields +
['dwi_file']),
name='inputnode')
qsiprep_preprocessed_dwi_data = pe.Node(
QsiReconIngress(), name="qsiprep_preprocessed_dwi_data")
# For doctests
if not workflow_spec['name'] == 'fake':
scans_iter.inputs.dwi_file = dwi_files
# Connect the collected diffusion data (gradients, etc) to the inputnode
workflow.connect([(scans_iter, qsiprep_preprocessed_dwi_data,
([('dwi_file', 'dwi_file')])),
(qsiprep_preprocessed_dwi_data, inputnode,
[(trait, trait) for trait in qsiprep_output_names])])
# Resample all atlases to dwi_file's resolution
get_atlases = pe.Node(GetConnectivityAtlases(atlas_names=atlas_names,
space=space),
name='get_atlases',
run_without_submitting=True)
# Resample ROI targets to DWI resolution for ODF plotting
crossing_rois_file = pkgr('qsiprep', 'data/crossing_rois.nii.gz')
odf_rois = pe.Node(ants.ApplyTransforms(interpolation="MultiLabel",
dimension=3),
name="odf_rois")
odf_rois.inputs.input_image = crossing_rois_file
if has_transform and space == "T1w":
workflow.connect(inputnode, 't1_2_mni_reverse_transform', odf_rois,
'transforms')
elif space == 'template':
odf_rois.inputs.transforms = ['identity']
else:
LOGGER.warning("Unable to transform ODF ROIs to dwi data. "
"No ODF reports will be created.")
odf_rois = pe.Node(niu.IdentityInterface(fields=['output_image']),
name='odf_rois')
workflow.connect(scans_iter, 'dwi_file', odf_rois, 'reference_image')
# Save the atlases
if len(atlas_names) > 0:
if space == "T1w":
if not has_transform:
LOGGER.critical(
"No reverse transform found, unable to move atlases"
" into DWI space")
workflow.connect([(inputnode, get_atlases, [
('t1_2_mni_reverse_transform', 'forward_transform')
])])
for atlas in workflow_spec['atlases']:
workflow.connect([
(get_atlases,
pe.Node(ReconDerivativesDataSink(space=space,
desc=atlas,
suffix="atlas",
compress=True),
name='ds_atlases_' + atlas,
run_without_submitting=True),
[(('atlas_configs', _get_resampled, atlas,
'dwi_resolution_file'), 'in_file')]),
(get_atlases,
pe.Node(ReconDerivativesDataSink(space=space,
desc=atlas,
suffix="atlas",
extension=".mif.gz",
compress=True),
name='ds_atlas_mifs_' + atlas,
run_without_submitting=True),
[(('atlas_configs', _get_resampled, atlas,
'dwi_resolution_mif'), 'in_file')]),
(get_atlases,
pe.Node(ReconDerivativesDataSink(space=space,
desc=atlas,
extension=".txt",
suffix="mrtrixLUT"),
name='ds_atlas_mrtrix_lut_' + atlas,
run_without_submitting=True),
[(('atlas_configs', _get_resampled, atlas, 'mrtrix_lut'),
'in_file')]),
(get_atlases,
pe.Node(ReconDerivativesDataSink(space=space,
desc=atlas,
extension=".txt",
suffix="origLUT"),
name='ds_atlas_orig_lut_' + atlas,
run_without_submitting=True),
[(('atlas_configs', _get_resampled, atlas, 'orig_lut'),
'in_file')]),
])
workflow.connect(inputnode, "dwi_file", get_atlases, "reference_image")
# Read nodes from workflow spec, make sure we can implement them
nodes_to_add = []
for node_spec in workflow_spec['nodes']:
if not node_spec['name']:
raise Exception("Node has no name [{}]".format(node_spec))
new_node = workflow_from_spec(omp_nthreads, has_transform
or space == 'template', node_spec)
if new_node is None:
raise Exception("Unable to create a node for %s" % node_spec)
nodes_to_add.append(new_node)
workflow.add_nodes(nodes_to_add)
_check_repeats(workflow.list_node_names())
# Now that all nodes are in the workflow, connect them
for node_spec in workflow_spec['nodes']:
# get the nipype node object
node_name = node_spec['name']
node = workflow.get_node(node_name)
if node_spec.get('input', 'qsiprep') == 'qsiprep':
# directly connect all the qsiprep outputs to every node
workflow.connect([(inputnode, node,
_as_connections(input_fields,
dest_prefix='inputnode.'))])
# for from_conn, to_conn in default_connections:
# workflow.connect(inputnode, from_conn, node, 'inputnode.' + to_conn)
# _check_repeats(workflow.list_node_names())
# connect the outputs from the upstream node to this node
else:
upstream_node = workflow.get_node(node_spec['input'])
upstream_outputnode_name = node_spec['input'] + '.outputnode'
upstream_outputnode = workflow.get_node(upstream_outputnode_name)
upstream_outputs = set(upstream_outputnode.outputs.get().keys())
downstream_inputnode_name = node_name + ".inputnode"
downstream_inputnode = workflow.get_node(downstream_inputnode_name)
downstream_inputs = set(downstream_inputnode.outputs.get().keys())
connect_from_upstream = upstream_outputs.intersection(
downstream_inputs)
connect_from_qsiprep = default_input_set - connect_from_upstream
# LOGGER.info("connecting %s from %s to %s", connect_from_qsiprep,
# inputnode, node)
workflow.connect([(inputnode, node,
_as_connections(connect_from_qsiprep,
dest_prefix='inputnode.'))])
# for qp_connection in connect_from_qsiprep:
# workflow.connect(inputnode, qp_connection, node, 'inputnode.' + qp_connection)
_check_repeats(workflow.list_node_names())
# LOGGER.info("connecting %s from %s to %s", connect_from_upstream,
# upstream_outputnode_name, downstream_inputnode_name)
workflow.connect([(upstream_node, node,
_as_connections(connect_from_upstream,
src_prefix='outputnode.',
dest_prefix='inputnode.'))])
# for upstream_connection in connect_from_upstream:
# workflow.connect(upstream_node, "outputnode." + upstream_connection,
# node, 'inputnode.' + upstream_connection)
_check_repeats(workflow.list_node_names())
# If it's a connectivity calculation, send it the atlas configs
if node_spec['action'] == 'connectivity':
workflow.connect([(get_atlases, node, [
('atlas_configs', 'inputnode.atlas_configs')
])])
_check_repeats(workflow.list_node_names())
# Send the ODF rois to reconstruction nodes
if node_spec['action'] == 'csd' or 'reconstruction' in node_spec[
'action']:
workflow.connect([(odf_rois, node, [('output_image',
'inputnode.odf_rois')])])
_check_repeats(workflow.list_node_names())
# Fill-in datasinks and reportlet datasinks seen so far
for node in workflow.list_node_names():
node_suffix = node.split('.')[-1]
if node_suffix.startswith('ds_'):
workflow.connect(scans_iter, 'dwi_file', workflow.get_node(node),
'source_file')
workflow.get_node(node).inputs.space = space
if "report" in node_suffix:
workflow.get_node(node).inputs.base_directory = reportlets_dir
else:
workflow.get_node(node).inputs.base_directory = output_dir
return workflow
def get_dsi_studio_ODF_geometry(odf_key):
mat_path = pkgr('qsiprep', 'data/odfs.mat')
m = loadmat(mat_path)
odf_vertices = m[odf_key + "_vertices"].T
odf_faces = m[odf_key + "_faces"].T
return odf_vertices, odf_faces
def init_t2w_template_wf(longitudinal,
omp_nthreads,
num_t2w,
name="anat_t2w_template_wf"):
"""
Adapts :py:func:`~smriprep.workflows.anatomical.init_anat_template_wf` for T2w image reference
"""
from pkg_resources import resource_filename as pkgr
from nipype.interfaces import freesurfer as fs, image, ants
from niworkflows.engine.workflows import LiterateWorkflow as Workflow
from niworkflows.interfaces.freesurfer import (
StructuralReference,
PatchedLTAConvert as LTAConvert,
)
from niworkflows.interfaces.images import TemplateDimensions, Conform, ValidateImage
from niworkflows.interfaces.nitransforms import ConcatenateXFMs
from niworkflows.utils.misc import add_suffix
wf = Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=["t2w"]),
name="inputnode")
outputnode = pe.Node(
niu.IdentityInterface(fields=[
"t2w_ref", "t2w_valid_list", "t2_realign_xfm", "out_report"
]),
name="outputnode",
)
# 0. Reorient T2w image(s) to RAS and resample to common voxel space
t2w_ref_dimensions = pe.Node(TemplateDimensions(),
name='t2w_ref_dimensions')
t2w_conform = pe.MapNode(Conform(),
iterfield='in_file',
name='t2w_conform')
wf.connect([
(inputnode, t2w_ref_dimensions, [('t2w', 't1w_list')]),
(t2w_ref_dimensions, t2w_conform, [('t1w_valid_list', 'in_file'),
('target_zooms', 'target_zooms'),
('target_shape', 'target_shape')]),
(t2w_ref_dimensions, outputnode, [('out_report', 'out_report'),
('t1w_valid_list', 't2w_valid_list')
]),
])
if num_t2w == 1:
get1st = pe.Node(niu.Select(index=[0]), name='get1st')
outputnode.inputs.t2w_realign_xfm = [
pkgr('smriprep', 'data/itkIdentityTransform.txt')
]
wf.connect([
(t2w_conform, get1st, [('out_file', 'inlist')]),
(get1st, outputnode, [('out', 't2w_ref')]),
])
return wf
wf.__desc__ = f"""\
A T2w-reference map was computed after registration of
{num_t2w} T2w images (after INU-correction) using
`mri_robust_template` [FreeSurfer {fs.Info().looseversion() or "<ver>"}, @fs_template].
"""
t2w_conform_xfm = pe.MapNode(LTAConvert(in_lta='identity.nofile',
out_lta=True),
iterfield=['source_file', 'target_file'],
name='t2w_conform_xfm')
# 1a. Correct for bias field: the bias field is an additive factor
# in log-transformed intensity units. Therefore, it is not a linear
# combination of fields and N4 fails with merged images.
# 1b. Align and merge if several T1w images are provided
n4_correct = pe.MapNode(ants.N4BiasFieldCorrection(dimension=3,
copy_header=True),
iterfield='input_image',
name='n4_correct',
n_procs=1) # n_procs=1 for reproducibility
# StructuralReference is fs.RobustTemplate if > 1 volume, copying otherwise
t2w_merge = pe.Node(
StructuralReference(
auto_detect_sensitivity=True,
initial_timepoint=1, # For deterministic behavior
intensity_scaling=True, # 7-DOF (rigid + intensity)
subsample_threshold=200,
fixed_timepoint=not longitudinal,
no_iteration=not longitudinal,
transform_outputs=True,
),
mem_gb=2 * num_t2w - 1,
name='t2w_merge')
# 2. Reorient template to RAS, if needed (mri_robust_template may set to LIA)
t2w_reorient = pe.Node(image.Reorient(), name='t2w_reorient')
merge_xfm = pe.MapNode(niu.Merge(2),
name='merge_xfm',
iterfield=['in1', 'in2'],
run_without_submitting=True)
concat_xfms = pe.MapNode(ConcatenateXFMs(inverse=True),
name="concat_xfms",
iterfield=['in_xfms'],
run_without_submitting=True)
def _set_threads(in_list, maximum):
return min(len(in_list), maximum)
wf.connect([
(t2w_ref_dimensions, t2w_conform_xfm, [('t1w_valid_list',
'source_file')]),
(t2w_conform, t2w_conform_xfm, [('out_file', 'target_file')]),
(t2w_conform, n4_correct, [('out_file', 'input_image')]),
(t2w_conform, t2w_merge,
[(('out_file', _set_threads, omp_nthreads), 'num_threads'),
(('out_file', add_suffix, '_template'), 'out_file')]),
(n4_correct, t2w_merge, [('output_image', 'in_files')]),
(t2w_merge, t2w_reorient, [('out_file', 'in_file')]),
# Combine orientation and template transforms
(t2w_conform_xfm, merge_xfm, [('out_lta', 'in1')]),
(t2w_merge, merge_xfm, [('transform_outputs', 'in2')]),
(merge_xfm, concat_xfms, [('out', 'in_xfms')]),
# Output
(t2w_reorient, outputnode, [('out_file', 't2w_ref')]),
(concat_xfms, outputnode, [('out_xfm', 't2w_realign_xfm')]),
])
return wf
