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Apply transforms to raw data when MNI6 derivatives aren't available #58

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merged 12 commits into from
Sep 3, 2024
+458 −150
Merged

Apply transforms to raw data when MNI6 derivatives aren't available #58

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6fe4756
Get it running.
tsalo Aug 29, 2024
07afbd2
Remove unused workflow.
tsalo Aug 29, 2024
2f1dc8b
Add run_stc back in.
tsalo Aug 29, 2024
81671f3
Merge remote-tracking branch 'upstream/main' into minimal-resampling
tsalo Aug 29, 2024
f7f10da
Update test_base.py
tsalo Aug 29, 2024
a45ff25
Add boilerplate about transforms.
tsalo Aug 31, 2024
cdaab8c
Update base.py
tsalo Aug 31, 2024
849103f
Add issue templates.
tsalo Sep 1, 2024
e689043
Use the STCed data.
tsalo Sep 1, 2024
cffd29c
Add normalization report
tsalo Sep 2, 2024
138a918
Get normalization figure working.
tsalo Sep 2, 2024
0754dbb
Pin nitransforms.
tsalo Sep 3, 2024
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Update test_base.py
@tsalo
tsalo committed Aug 29, 2024
commit f7f10da6e92829aa3b071a25e6e172996fb51e01
1 change: 1 addition & 0 deletions src/fmripost_aroma/tests/test_base.py
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Original file line number Diff line number Diff line change
@@ -20,6 +20,7 @@ def test_init_ica_aroma_wf(tmp_path_factory):
wf = init_ica_aroma_wf(
bold_file='sub-01_task-rest_bold.nii.gz',
metadata={'RepetitionTime': 2.0},
mem_gb={'resampled': 1},
)
assert wf.name == 'aroma_task_rest_wf'

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src/fmripost_aroma/workflows/base.py
def init_single_run_wf(bold_file):
"""Set up a single-run workflow for fMRIPost-AROMA."""
from fmriprep.utils.misc import estimate_bold_mem_usage

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from nipype.interfaces import utility as niu
from niworkflows.engine.workflows import LiterateWorkflow as Workflow
from fmripost_aroma.workflows.aroma import init_denoise_wf, init_ica_aroma_wf
spaces = config.workflow.spaces
omp_nthreads = config.nipype.omp_nthreads

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workflow = Workflow(name=_get_wf_name(bold_file, 'single_run'))
bold_metadata = config.execution.layout.get_metadata(bold_file)
mem_gb = estimate_bold_mem_usage(bold_file)[1]
ica_aroma_wf = init_ica_aroma_wf(bold_file=bold_file, metadata=bold_metadata, mem_gb=mem_gb)

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entities = extract_entities(bold_file)
functional_cache = defaultdict(list, {})
if config.execution.derivatives:
# Collect native-space derivatives and transforms
functional_cache = collect_derivatives(

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raw_dataset=config.execution.layout,
derivatives_dataset=None,
entities=entities,
if ('bold_mni152nlin6asym' not in functional_cache) and ('bold_raw' in functional_cache):
# Resample to MNI152NLin6Asym:res-2, for ICA-AROMA classification
from fmriprep.workflows.bold.apply import init_bold_volumetric_resample_wf
from fmriprep.workflows.bold.stc import init_bold_stc_wf
from niworkflows.interfaces.fixes import FixHeaderApplyTransforms as ApplyTransforms
from niworkflows.interfaces.header import ValidateImage
from templateflow.api import get as get_template

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validate_bold = pe.Node(

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ValidateImage(in_file=functional_cache['bold_raw']),
name='validate_bold',
)
stc_buffer = pe.Node(

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niu.IdentityInterface(fields=['bold_file']),
name='stc_buffer',
)
run_stc = ('SliceTiming' in bold_metadata) and 'slicetiming' not in config.workflow.ignore

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if run_stc:
bold_stc_wf = init_bold_stc_wf(

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mem_gb=mem_gb,
metadata=bold_metadata,
name='resample_stc_wf',
)
# TODO: Grab skip-vols from confounds file
bold_stc_wf.inputs.inputnode.skip_vols = config.workflow.dummy_scans or 0
workflow.connect([

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(validate_bold, bold_stc_wf, [('out_file', 'inputnode.bold_file')]),
(bold_stc_wf, stc_buffer, [('outputnode.stc_file', 'bold_file')]),
]) # fmt:skip
else:
workflow.connect([(validate_bold, stc_buffer, [('out_file', 'bold_file')])])

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mni6_mask = str(

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get_template(
'MNI152NLin6Asym',
resolution=2,
)
)
bold_MNI6_wf = init_bold_volumetric_resample_wf(

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metadata=bold_metadata,
fieldmap_id=None, # XXX: Ignoring the field map for now
omp_nthreads=omp_nthreads,
jacobian='fmap-jacobian' not in config.workflow.ignore,
name='bold_MNI6_wf',
)
bold_MNI6_wf.inputs.inputnode.bold_file = functional_cache['bold_raw']
bold_MNI6_wf.inputs.inputnode.motion_xfm = functional_cache['hmc']
bold_MNI6_wf.inputs.inputnode.boldref2fmap_xfm = functional_cache['boldref2fmap']
bold_MNI6_wf.inputs.inputnode.boldref2anat_xfm = functional_cache['boldref2anat']
bold_MNI6_wf.inputs.inputnode.anat2std_xfm = functional_cache['anat2mni152nlin6asym']
bold_MNI6_wf.inputs.inputnode.resolution = '02'

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# use mask as boldref?
bold_MNI6_wf.inputs.inputnode.bold_ref_file = functional_cache['bold_mask_native']
bold_MNI6_wf.inputs.inputnode.target_mask = mni6_mask
bold_MNI6_wf.inputs.inputnode.target_ref_file = mni6_mask

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workflow.connect([
# Resample BOLD to MNI152NLin6Asym, may duplicate bold_std_wf above
(bold_MNI6_wf, mni6_buffer, [('outputnode.bold_file', 'bold_mni152nlin6asym')]),
]) # fmt:skip
mask_to_mni6 = pe.Node(

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ApplyTransforms(
interpolation='MultiLabel',
input_image=functional_cache['bold_mask_native'],
),
name='mask_to_mni6',
)
workflow.connect([

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(mask_to_mni6, mni6_buffer, [('output_image', 'bold_mask_mni152nlin6asym')]),
]) # fmt:skip