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ENH: Mask multi-echo data with reference mask instead of echo-specific masks #2349

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merged 8 commits into from
Sep 5, 2021
Merged

ENH: Mask multi-echo data with reference mask instead of echo-specific masks #2349

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7319c93
Add mask_file arg to T2SMap.
tsalo Dec 17, 2020
50d64f8
Add mask_file to init_bold_t2s_wf.
tsalo Dec 17, 2020
68539b9
Skull-strip only first echo and use that mask.
tsalo Dec 17, 2020
1254b91
Use mask from bold_sdc_wf instead of skullstrip_wf.
tsalo Dec 17, 2020
0e40835
Use bold_bold_trans_wf instead.
tsalo Dec 17, 2020
cd88ca6
Update fmriprep/workflows/bold/t2s.py
tsalo Dec 17, 2020
ac3474b
Merge branch 'master' into mask-to-t2smap
tsalo Apr 28, 2021
424276c
Merge branch 'nipreps:master' into mask-to-t2smap
tsalo Sep 3, 2021
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7 changes: 6 additions & 1 deletion fmriprep/interfaces/multiecho.py
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Original file line number Diff line number Diff line change
Expand Up @@ -56,9 +56,14 @@ class T2SMapInputSpec(CommandLineInputSpec):
mandatory=True,
minlen=3,
desc='echo times')
mask_file = File(argstr='--mask',
position=3,
mandatory=False,
desc='mask file',
exists=True)
fittype = traits.Enum('curvefit', 'loglin',
argstr='--fittype %s',
position=3,
position=4,
usedefault=True,
desc=('Desired fitting method: '
'"loglin" means that a linear model is fit '
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16 changes: 6 additions & 10 deletions fmriprep/workflows/bold/base.py
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Expand Up @@ -468,17 +468,14 @@ def init_func_preproc_wf(bold_file, has_fieldmap=False):

# MULTI-ECHO EPI DATA #############################################
if multiecho: # instantiate relevant interfaces, imports
from niworkflows.func.util import init_skullstrip_bold_wf
skullstrip_bold_wf = init_skullstrip_bold_wf(name='skullstrip_bold_wf')

split_opt_comb = bold_split.clone(name='split_opt_comb')

inputnode.inputs.bold_file = ref_file # Replace reference w first echo

join_echos = pe.JoinNode(
niu.IdentityInterface(fields=['bold_files', 'skullstripped_bold_files']),
niu.IdentityInterface(fields=['bold_files']),
joinsource=('meepi_echos' if run_stc is True else 'boldbuffer'),
joinfield=['bold_files', 'skullstripped_bold_files'],
joinfield=['bold_files'],
name='join_echos'
)

Expand Down Expand Up @@ -576,12 +573,11 @@ def init_func_preproc_wf(bold_file, has_fieldmap=False):
('outputnode.bold', 'bold_files')]),
(join_echos, final_boldref_wf, [
('bold_files', 'inputnode.bold_file')]),
(bold_bold_trans_wf, skullstrip_bold_wf, [
('outputnode.bold', 'inputnode.in_file')]),
(skullstrip_bold_wf, join_echos, [
('outputnode.skull_stripped_file', 'skullstripped_bold_files')]),
# use reference image mask used by bold_bold_trans_wf
(bold_bold_trans_wf, bold_t2s_wf, [
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I'd be careful to ensure that bold_bold_trans_wf does not depend on bold_t2s_wf. If this assumption is acceptable, then this seems the most reasonable solution to this problem.

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Is that because it would introduce a loop in the workflow? I agree that T2* mapping/optimal combination should happen after the native-space transforms are applied, mostly so that those transforms can be done all at once. There are solid reasons to do optimal combination after distortion correction, but I don't think that will have a very large effect on the data and I doubt that it's worth the extra effort since it would involve splitting up the transforms performed by bold_bold_trans_wf into two sets.

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Is that because it would introduce a loop in the workflow?

Yes, Nipype will not handle loops.

(('outputnode.bold_mask', pop_file), 'inputnode.bold_mask')]),
(join_echos, bold_t2s_wf, [
('skullstripped_bold_files', 'inputnode.bold_file')]),
('bold_files', 'inputnode.bold_file')]),
(bold_t2s_wf, bold_confounds_wf, [
('outputnode.bold', 'inputnode.bold')]),
(bold_t2s_wf, split_opt_comb, [
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7 changes: 5 additions & 2 deletions fmriprep/workflows/bold/t2s.py
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Expand Up @@ -69,6 +69,8 @@ def init_bold_t2s_wf(echo_times, mem_gb, omp_nthreads,
------
bold_file
list of individual echo files
bold_mask
a binary mask to apply to the BOLD files

Outputs
-------
Expand All @@ -90,7 +92,7 @@ def init_bold_t2s_wf(echo_times, mem_gb, omp_nthreads,
The optimally combined time series was carried forward as the *preprocessed BOLD*.
"""

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

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

Expand All @@ -99,7 +101,8 @@ def init_bold_t2s_wf(echo_times, mem_gb, omp_nthreads,
t2smap_node = pe.Node(T2SMap(echo_times=list(echo_times)), name='t2smap_node')

workflow.connect([
(inputnode, t2smap_node, [('bold_file', 'in_files')]),
(inputnode, t2smap_node, [('bold_file', 'in_files'),
('bold_mask', 'mask_file')]),
(t2smap_node, outputnode, [('optimal_comb', 'bold')]),
])

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