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Merged
merged 14 commits into from
May 1, 2020
Merged

ENH: Remove t2s-coreg #2109

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04cf152
fix: remove bold_mask generation from t2s
emdupre Sep 29, 2019
267171e
sty: appease flake8, retrigger CI
emdupre Sep 30, 2019
40f1add
fix: pin nipype requirement on @effigies rec
emdupre Oct 2, 2019
6689b4e
Merge branch 'master' into fix/t2s-wf
effigies Oct 4, 2019
62b91f0
Merge remote-tracking branch 'origin' into fix/t2s-wf
emdupre Apr 29, 2020
d995645
Excise t2s-coreg.
tsalo Apr 29, 2020
4919d10
Remove mention of t2s-coreg from workflow documentation.
tsalo Apr 29, 2020
bf80c6d
Fix bold_sdc_wf output name.
tsalo Apr 29, 2020
a8e4813
Remove duplicate connection.
tsalo Apr 29, 2020
03b21da
Drop t2s-coreg from test.
tsalo Apr 29, 2020
ddedcd2
Add bold_bold_trans_wf --> bold_confounds_wf connection back in.
tsalo Apr 29, 2020
56efef6
Grab BOLD mask from bold_bold_trans_wf.
tsalo Apr 29, 2020
3a2f863
Remove unused output from init_bold_t2s_wf.
tsalo Apr 29, 2020
471fd9d
Address @mgxd's comment.
tsalo May 1, 2020
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2 changes: 1 addition & 1 deletion .circleci/config.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -779,7 +779,7 @@ jobs:
--config $PWD/nipype.cfg -w /tmp/${DATASET}/work \
/tmp/data/${DATASET} /tmp/${DATASET}/derivatives participant \
${FASTRACK_ARG} \
--fs-no-reconall --t2s-coreg --use-syn-sdc \
--fs-no-reconall --use-syn-sdc \
--dummy-scans 1 --sloppy --write-graph \
--output-spaces MNI152NLin2009cAsym \
--mem_mb 4096 --nthreads 2 -vv
Expand Down
5 changes: 1 addition & 4 deletions docs/workflows.rst
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Expand Up @@ -576,8 +576,5 @@ T2* Driven Coregistration
If multi-echo :abbr:`BOLD (blood-oxygen level-dependent)` data is supplied,
this workflow uses the `tedana`_ `T2* workflow`_ to generate an adaptive T2* map
and optimally weighted combination of all supplied single echo time series.
This optimaly combined time series is then carried forward for all subsequent
This optimally combined time series is then carried forward for all subsequent
preprocessing steps.
Optionally, if the ``--t2s-coreg`` flag is supplied, the T2* map is then used
in place of the :ref:`BOLD reference image <bold_ref>` to
:ref:`register the BOLD series to the T1w image <bold_reg>` of the same subject.
6 changes: 0 additions & 6 deletions fmriprep/cli/parser.py
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Expand Up @@ -142,12 +142,6 @@ def _bids_filter(value):
g_conf.add_argument(
'--longitudinal', action='store_true',
help='treat dataset as longitudinal - may increase runtime')
g_conf.add_argument(
'--t2s-coreg', action='store_true',
help='If provided with multi-echo BOLD dataset, create T2*-map and perform '
'T2*-driven coregistration. When multi-echo data is provided and this '
'option is not enabled, standard EPI-T1 coregistration is performed '
'using the middle echo.')
g_conf.add_argument(
'--output-spaces', nargs='*', action=OutputReferencesAction,
help="""\
Expand Down
52 changes: 14 additions & 38 deletions fmriprep/workflows/bold/base.py
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Expand Up @@ -209,17 +209,6 @@ def init_func_preproc_wf(bold_file):
'slicetiming' not in config.workflow.ignore and
(_get_series_len(ref_file) > 4 or "TooShort"))

# Check if MEEPI for T2* coregistration target
if config.workflow.t2s_coreg and not multiecho:
config.loggers.workflow.warning(
"No multiecho BOLD images found for T2* coregistration. "
"Using standard EPI-T1 coregistration.")
config.workflow.t2s_coreg = False

# By default, force-bbr for t2s_coreg unless user specifies otherwise
if config.workflow.t2s_coreg and config.workflow.use_bbr is None:
config.workflow.use_bbr = True

# Build workflow
workflow = Workflow(name=wf_name)
workflow.__postdesc__ = """\
Expand Down Expand Up @@ -407,7 +396,6 @@ def init_func_preproc_wf(bold_file):
bold_t2s_wf = init_bold_t2s_wf(echo_times=tes,
mem_gb=mem_gb['resampled'],
omp_nthreads=omp_nthreads,
t2s_coreg=config.workflow.t2s_coreg,
name='bold_t2smap_wf')

workflow.connect([
Expand Down Expand Up @@ -464,10 +452,14 @@ def init_func_preproc_wf(bold_file):
('outputnode.ref_image_brain', 'inputnode.epi_brain'),
('outputnode.bold_mask', 'inputnode.epi_mask')]),
(bold_sdc_wf, bold_t1_trans_wf, [
('outputnode.out_warp', 'inputnode.fieldwarp')]),
('outputnode.out_warp', 'inputnode.fieldwarp'),
('outputnode.epi_mask', 'inputnode.ref_bold_mask'),
('outputnode.epi_brain', 'inputnode.ref_bold_brain')]),
(bold_sdc_wf, bold_bold_trans_wf, [
('outputnode.out_warp', 'inputnode.fieldwarp'),
('outputnode.epi_mask', 'inputnode.bold_mask')]),
(bold_sdc_wf, bold_reg_wf, [
('outputnode.epi_brain', 'inputnode.ref_bold_brain')]),
(bold_sdc_wf, summary, [('outputnode.method', 'distortion_correction')]),
# Connect bold_confounds_wf
(inputnode, bold_confounds_wf, [('t1w_tpms', 'inputnode.t1w_tpms'),
Expand All @@ -479,6 +471,9 @@ def init_func_preproc_wf(bold_file):
('outputnode.itk_t1_to_bold', 'inputnode.t1_bold_xform')]),
(bold_reference_wf, bold_confounds_wf, [
('outputnode.skip_vols', 'inputnode.skip_vols')]),
(bold_bold_trans_wf, bold_confounds_wf, [
('outputnode.bold_mask', 'inputnode.bold_mask'),
]),
(bold_confounds_wf, outputnode, [
('outputnode.confounds_file', 'confounds'),
]),
Expand All @@ -494,32 +489,13 @@ def init_func_preproc_wf(bold_file):
(outputnode, summary, [('confounds', 'confounds_file')]),
])

if not config.workflow.t2s_coreg:
workflow.connect([
(bold_sdc_wf, bold_reg_wf, [
('outputnode.epi_brain', 'inputnode.ref_bold_brain')]),
(bold_sdc_wf, bold_t1_trans_wf, [
('outputnode.epi_brain', 'inputnode.ref_bold_brain'),
('outputnode.epi_mask', 'inputnode.ref_bold_mask')]),
])
else:
workflow.connect([
# For t2s_coreg, replace EPI-to-T1w registration inputs
(bold_t2s_wf, bold_reg_wf, [
('outputnode.bold_ref_brain', 'inputnode.ref_bold_brain')]),
(bold_t2s_wf, bold_t1_trans_wf, [
('outputnode.bold_ref_brain', 'inputnode.ref_bold_brain'),
('outputnode.bold_mask', 'inputnode.ref_bold_mask')]),
])

# for standard EPI data, pass along correct file
if not multiecho:
workflow.connect([
(inputnode, func_derivatives_wf, [
('bold_file', 'inputnode.source_file')]),
(bold_bold_trans_wf, bold_confounds_wf, [
('outputnode.bold', 'inputnode.bold'),
('outputnode.bold_mask', 'inputnode.bold_mask')]),
('outputnode.bold', 'inputnode.bold')]),
(bold_split, bold_t1_trans_wf, [
('out_files', 'inputnode.bold_split')]),
])
Expand All @@ -531,8 +507,7 @@ def init_func_preproc_wf(bold_file):
(bold_bold_trans_wf, skullstrip_bold_wf, [
('outputnode.bold', 'inputnode.in_file')]),
(bold_t2s_wf, bold_confounds_wf, [
('outputnode.bold', 'inputnode.bold'),
('outputnode.bold_mask', 'inputnode.bold_mask')]),
('outputnode.bold', 'inputnode.bold')]),
(bold_t2s_wf, bold_t1_trans_wf, [
('outputnode.bold', 'inputnode.bold_split')]),
])
Expand Down Expand Up @@ -607,7 +582,7 @@ def init_func_preproc_wf(bold_file):
('outputnode.itk_bold_to_t1', 'transforms')]),
(bold_t1_trans_wf, boldmask_to_t1w, [
('outputnode.bold_mask_t1', 'reference_image')]),
(bold_bold_trans_wf if not multiecho else bold_t2s_wf, boldmask_to_t1w, [
(bold_bold_trans_wf, boldmask_to_t1w, [
('outputnode.bold_mask', 'input_image')]),
(boldmask_to_t1w, outputnode, [
('output_image', 'bold_mask_t1')]),
Expand Down Expand Up @@ -645,7 +620,7 @@ def init_func_preproc_wf(bold_file):
('outputnode.xforms', 'inputnode.hmc_xforms')]),
(bold_reg_wf, bold_std_trans_wf, [
('outputnode.itk_bold_to_t1', 'inputnode.itk_bold_to_t1')]),
(bold_bold_trans_wf if not multiecho else bold_t2s_wf, bold_std_trans_wf, [
(bold_bold_trans_wf, bold_std_trans_wf, [
('outputnode.bold_mask', 'inputnode.bold_mask')]),
(bold_sdc_wf, bold_std_trans_wf, [
('outputnode.out_warp', 'inputnode.fieldwarp')]),
Expand Down Expand Up @@ -817,7 +792,8 @@ def init_func_preproc_wf(bold_file):
(carpetplot_select_std, carpetplot_wf, [
('std2anat_xfm', 'inputnode.std2anat_xfm')]),
(bold_bold_trans_wf if not multiecho else bold_t2s_wf, carpetplot_wf, [
('outputnode.bold', 'inputnode.bold'),
('outputnode.bold', 'inputnode.bold')]),
(bold_bold_trans_wf, carpetplot_wf, [
('outputnode.bold_mask', 'inputnode.bold_mask')]),
(bold_reg_wf, carpetplot_wf, [
('outputnode.itk_t1_to_bold', 'inputnode.t1_bold_xform')]),
Expand Down
23 changes: 8 additions & 15 deletions fmriprep/workflows/bold/t2s.py
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Expand Up @@ -19,13 +19,12 @@

# pylint: disable=R0914
def init_bold_t2s_wf(echo_times, mem_gb, omp_nthreads,
t2s_coreg=False, name='bold_t2s_wf'):
name='bold_t2s_wf'):
"""
Combine multiple echos of :abbr:`ME-EPI (multi-echo echo-planar imaging)`.

This workflow wraps the `tedana`_ `T2* workflow`_ to optimally
combine multiple echos and derive a T2* map for optional use as a
coregistration target.
combine multiple echos and derive a T2* map.
The following steps are performed:

#. :abbr:`HMC (head motion correction)` on individual echo files.
Expand All @@ -43,8 +42,6 @@ def init_bold_t2s_wf(echo_times, mem_gb, omp_nthreads,
Size of BOLD file in GB
omp_nthreads : :obj:`int`
Maximum number of threads an individual process may use
t2s_coreg : :obj:`bool`
Use the calculated T2*-map for T2*-driven coregistration
name : :obj:`str`
Name of workflow (default: ``bold_t2s_wf``)

Expand All @@ -64,7 +61,6 @@ def init_bold_t2s_wf(echo_times, mem_gb, omp_nthreads,

"""
from niworkflows.engine.workflows import LiterateWorkflow as Workflow
from niworkflows.func.util import init_skullstrip_bold_wf

workflow = Workflow(name=name)
workflow.__desc__ = """\
Expand All @@ -74,26 +70,23 @@ def init_bold_t2s_wf(echo_times, mem_gb, omp_nthreads,
used to fit the model.
The calculated T2* map was then used to optimally combine preprocessed BOLD across
echoes following the method described in [@posse_t2s].
The optimally combined time series was carried forward as the *preprocessed BOLD*{}.
""".format('' if not t2s_coreg else ', and the T2* map was also retained as the BOLD reference')
The optimally combined time series was carried forward as the *preprocessed BOLD*.
"""

inputnode = pe.Node(niu.IdentityInterface(fields=['bold_file']), name='inputnode')

outputnode = pe.Node(niu.IdentityInterface(fields=['bold', 'bold_mask', 'bold_ref_brain']),
outputnode = pe.Node(niu.IdentityInterface(fields=['bold', 'bold_ref_brain']),
name='outputnode')

LOGGER.log(25, 'Generating T2* map and optimally combined ME-EPI time series.')

t2smap_node = pe.Node(T2SMap(echo_times=echo_times), name='t2smap_node')
skullstrip_t2smap_wf = init_skullstrip_bold_wf(name='skullstrip_t2smap_wf')

workflow.connect([
(inputnode, t2smap_node, [('bold_file', 'in_files')]),
(t2smap_node, outputnode, [('optimal_comb', 'bold')]),
(t2smap_node, skullstrip_t2smap_wf, [('t2star_map', 'inputnode.in_file')]),
(skullstrip_t2smap_wf, outputnode, [
('outputnode.mask_file', 'bold_mask'),
('outputnode.skull_stripped_file', 'bold_ref_brain')]),
(t2smap_node, outputnode, [
('optimal_comb', 'bold'),
('t2star_map', 'bold_ref_brain')]),
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@effigies effigies Apr 29, 2020 •
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This workflow is now inputnode -> t2smap_node -> outputnode, so the workflow itself can be replaced in the calling function by t2smap_node. Unless it's useful to keep this space open to expand the workflow in the future?

Also, bold_ref_brain does not seem to be being used anywhere.

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@tsalo tsalo Apr 29, 2020

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I don't know if implementing it would require that bold_t2s_wf remain a workflow, but in #1010 (comment) I'm proposing a way to incorporate tedana's component classification into fMRIPrep. It might be worth it to keep the workflow around until #1010 is resolved at least.

I can remove bold_ref_brain now.

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@effigies effigies Apr 29, 2020

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Sure. Let's keep this change minimal and we can reconsider architecture later. I realized there's also the workflow description that would need to be moved out of this separate module.

])

return workflow