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@tsalo
tsalo committed Nov 9, 2023
1 parent 0511e13 commit 168b247
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4 changes: 4 additions & 0 deletions aslprep/workflows/asl/registration.py
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Original file line number Diff line number Diff line change
Expand Up @@ -178,6 +178,10 @@ def init_asl_t1_trans_wf(
Other files (e.g., CBF images) are coregistered to T1w space outside of this workflow.
One element that keeps me from just using fMRIPrep's ``init_bold_t1_trans_wf`` is that
a new reference image is created within the workflow, and ASLPrep and fMRIPrep use different
strategies to create reference images.
Workflow Graph
.. workflow::
:graph2use: orig
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111 changes: 81 additions & 30 deletions aslprep/workflows/asl/resampling.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,15 +3,10 @@
"""Workflows for resampling data."""
from nipype.interfaces import utility as niu
from nipype.pipeline import engine as pe
from niworkflows.engine.workflows import LiterateWorkflow as Workflow
from niworkflows.interfaces.itk import MultiApplyTransforms
from niworkflows.interfaces.nibabel import GenerateSamplingReference
from niworkflows.interfaces.nilearn import Merge
from niworkflows.interfaces.utility import KeySelect
from niworkflows.utils.spaces import format_reference

from aslprep.config import DEFAULT_MEMORY_MIN_GB
from aslprep.interfaces.ants import ApplyTransforms
from aslprep.interfaces.fsl import Split
from aslprep.utils.misc import (
_aslist,
Expand All @@ -21,7 +16,6 @@
_split_spec,
)
from aslprep.utils.spaces import SpatialReferences
from aslprep.workflows.asl.util import init_asl_reference_wf


def init_asl_std_trans_wf(
Expand All @@ -33,11 +27,15 @@ def init_asl_std_trans_wf(
scorescrub: bool,
basil: bool,
generate_reference: bool,
use_compression: bool = True,
name: str = "asl_std_trans_wf",
use_compression: bool = True,
):
"""Sample ASL into standard space with a single-step resampling of the original ASL series.
One element that keeps me from just using fMRIPrep's ``init_bold_std_trans_wf`` is that
a new reference image is created within the workflow, and ASLPrep and fMRIPrep use different
strategies to create reference images.
.. important::
This workflow provides two outputnodes.
One output node (with name ``poutputnode``) will be parameterized in a Nipype sense
Expand Down Expand Up @@ -99,7 +97,7 @@ def init_asl_std_trans_wf(
a :abbr:`DFM (displacements field map)` in ITK format
hmc_xforms
List of affine transforms aligning each volume to ``ref_image`` in ITK format
aslref_to_anat_xfm
itk_bold_to_t1
Affine transform from ``ref_asl_brain`` to T1 space (ITK format)
name_source
ASL series NIfTI file
Expand All @@ -122,6 +120,17 @@ def init_asl_std_trans_wf(
Template identifiers synchronized correspondingly to previously
described outputs.
"""
from fmriprep.interfaces.maths import Clip
from niworkflows.engine.workflows import LiterateWorkflow as Workflow
from niworkflows.interfaces.fixes import FixHeaderApplyTransforms as ApplyTransforms
from niworkflows.interfaces.itk import MultiApplyTransforms
from niworkflows.interfaces.nibabel import GenerateSamplingReference
from niworkflows.interfaces.nilearn import Merge
from niworkflows.interfaces.utility import KeySelect
from niworkflows.utils.spaces import format_reference

from aslprep.workflows.asl.util import init_asl_reference_wf

workflow = Workflow(name=name)
std_vol_references = [
(s.fullname, s.spec) for s in spaces.references if s.standard and s.dim == 3
Expand All @@ -131,7 +140,7 @@ def init_asl_std_trans_wf(
niu.IdentityInterface(
fields=[
"name_source",
"aslcontext",
"aslcontext", # not in the fMRIPrep version
"asl_split",
"asl_mask",
"asl_aseg",
Expand All @@ -140,7 +149,7 @@ def init_asl_std_trans_wf(
# Transforms
"hmc_xforms", # may be "identity"
"fieldwarp", # may be "identity"
"aslref_to_anat_xfm",
"itk_bold_to_t1",
"anat2std_xfm",
# CBF outputs
"mean_cbf",
Expand Down Expand Up @@ -175,15 +184,13 @@ def init_asl_std_trans_wf(
run_without_submitting=True,
name="split_target",
)

workflow.connect([(iterablesource, split_target, [("std_target", "in_target")])])

select_std = pe.Node(
KeySelect(fields=["anat2std_xfm"]),
name="select_std",
run_without_submitting=True,
)

# fmt:off
workflow.connect([
(inputnode, select_std, [
Expand All @@ -199,14 +206,13 @@ def init_asl_std_trans_wf(
name="select_tpl",
run_without_submitting=True,
)

workflow.connect([(iterablesource, select_tpl, [("std_target", "template")])])

gen_ref = pe.Node(
GenerateSamplingReference(),
name="gen_ref",
mem_gb=0.3,
) # 256x256x256 * 64 / 8 ~ 150MB)
mem_gb=0.3, # 256x256x256 * 64 / 8 ~ 150MB)
)

# fmt:off
workflow.connect([
Expand All @@ -216,30 +222,30 @@ def init_asl_std_trans_wf(
])
# fmt:on

mask_merge_tfms = pe.Node(
niu.Merge(2),
name="mask_merge_tfms",
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB,
mask_std_tfm = pe.Node(
ApplyTransforms(interpolation="MultiLabel"),
name="mask_std_tfm",
mem_gb=1,
)

# fmt:off
workflow.connect([
(inputnode, mask_merge_tfms, [(("aslref_to_anat_xfm", _aslist), "in2")]),
(select_std, mask_merge_tfms, [("anat2std_xfm", "in1")]),
(inputnode, mask_std_tfm, [("asl_mask", "input_image")]),
(gen_ref, mask_std_tfm, [("out_file", "reference_image")]),
])
# fmt:on

mask_std_tfm = pe.Node(
ApplyTransforms(interpolation="MultiLabel"),
name="mask_std_tfm",
mem_gb=1,
mask_merge_tfms = pe.Node(
niu.Merge(2),
name="mask_merge_tfms",
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB,
)

# fmt:off
workflow.connect([
(inputnode, mask_std_tfm, [("asl_mask", "input_image")]),
(gen_ref, mask_std_tfm, [("out_file", "reference_image")]),
(inputnode, mask_merge_tfms, [(("itk_bold_to_t1", _aslist), "in2")]),
(select_std, mask_merge_tfms, [("anat2std_xfm", "in1")]),
(mask_merge_tfms, mask_std_tfm, [("out", "transforms")]),
])
# fmt:on
Expand All @@ -255,7 +261,7 @@ def init_asl_std_trans_wf(
# fmt:off
workflow.connect([
(inputnode, merge_xforms, [
(("aslref_to_anat_xfm", _aslist), "in2"),
(("itk_bold_to_t1", _aslist), "in2"),
("fieldwarp", "in3"), # may be "identity"
("hmc_xforms", "in4"), # may be "identity"
]),
Expand All @@ -278,6 +284,15 @@ def init_asl_std_trans_wf(
])
# fmt:on

# Interpolation can occasionally produce below-zero values as an artifact
threshold = pe.MapNode(
Clip(minimum=0),
name="threshold",
iterfield=["in_file"],
mem_gb=DEFAULT_MEMORY_MIN_GB,
)
workflow.connect([(asl_to_std_transform, threshold, [("out_files", "in_file")])])

# NOTE: Not in GE workflow.
# The GE workflow doesn't apply HMC, so it accepts a 4D ASL file that doesn't need to be
# re-merged back to 4D like the non-GE 3D files.
Expand All @@ -290,7 +305,7 @@ def init_asl_std_trans_wf(
# fmt:off
workflow.connect([
(inputnode, merge_3d_to_4d, [("name_source", "header_source")]),
(asl_to_std_transform, merge_3d_to_4d, [("out_files", "in_files")]),
(threshold, merge_3d_to_4d, [("out_file", "in_files")]),
])
# fmt:on

Expand Down Expand Up @@ -347,6 +362,8 @@ def init_asl_std_trans_wf(

output_names = [f"{input_}_std" for input_ in inputs_to_warp]
output_names += ["asl_std", "aslref_std", "asl_mask_std", "spatial_reference", "template"]
if freesurfer:
output_names.extend(["asl_aseg_std", "asl_aparc_std"])

poutputnode = pe.Node(niu.IdentityInterface(fields=output_names), name="poutputnode")

Expand All @@ -361,6 +378,36 @@ def init_asl_std_trans_wf(
])
# fmt:on

if freesurfer:
# Sample the parcellation files to functional space
aseg_std_tfm = pe.Node(
ApplyTransforms(interpolation="MultiLabel"),
name="aseg_std_tfm",
mem_gb=1,
)
# fmt:off
workflow.connect([
(inputnode, aseg_std_tfm, [("asl_aseg", "input_image")]),
(select_std, aseg_std_tfm, [("anat2std_xfm", "transforms")]),
(gen_ref, aseg_std_tfm, [("out_file", "reference_image")]),
(aseg_std_tfm, poutputnode, [("output_image", "asl_aseg_std")]),
])
# fmt:on

aparc_std_tfm = pe.Node(
ApplyTransforms(interpolation="MultiLabel"),
name="aparc_std_tfm",
mem_gb=1,
)
# fmt:off
workflow.connect([
(inputnode, aparc_std_tfm, [("asl_aparc", "input_image")]),
(select_std, aparc_std_tfm, [("anat2std_xfm", "transforms")]),
(gen_ref, aparc_std_tfm, [("out_file", "reference_image")]),
(aparc_std_tfm, poutputnode, [("output_image", "asl_aparc_std")]),
])
# fmt:on

inputs_4d = ["cbf_ts", "cbf_ts_score"]
for input_name in inputs_to_warp:
kwargs = {}
Expand Down Expand Up @@ -467,6 +514,10 @@ def init_asl_preproc_trans_wf(
Same as ``aslref``, but once the brain mask has been applied
"""
from niworkflows.engine.workflows import LiterateWorkflow as Workflow

from aslprep.workflows.asl.util import init_asl_reference_wf

workflow = Workflow(name=name)
# workflow.__desc__ = """\
# The ASL timeseries were resampled onto their original,
Expand Down

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