fix: Calculate bold mask and dummy scans in transform-only runs (#3428)

This builds on #3427 to add a couple final things to the transform-only
workflow:

```
fmriprep $BIDS $OUT participant --derivatives fmriprep=$MINIMAL
```

BOLD masks were not getting generated from the boldref and dummy scans
were not being estimated.

This PR factors out the validation and dummy scan detection from
`init_raw_boldref_wf` and uses the full workflow in fit mode and the
subworkflow in transform mode.

Will rebase after #3427 is merged. Can compare now with:
c28a615...6c78e54

fmriprep/workflows/bold/fit.py

@@ -53,7 +53,7 @@
     init_ds_registration_wf,
     init_func_fit_reports_wf,
 )
-from .reference import init_raw_boldref_wf
+from .reference import init_raw_boldref_wf, init_validation_and_dummies_wf
 from .registration import init_bold_reg_wf
 from .stc import init_bold_stc_wf
 from .t2s import init_bold_t2s_wf
@@ -407,15 +407,18 @@ def init_bold_fit_wf(
         ])  # fmt:skip
     else:
         config.loggers.workflow.info('Found HMC boldref - skipping Stage 1')
-
-        validate_bold = pe.Node(ValidateImage(), name='validate_bold')
-        validate_bold.inputs.in_file = bold_file
-
         hmcref_buffer.inputs.boldref = precomputed['hmc_boldref']
 
+        validation_and_dummies_wf = init_validation_and_dummies_wf(bold_file=bold_file)
+
         workflow.connect([
-            (validate_bold, hmcref_buffer, [('out_file', 'bold_file')]),
-            (validate_bold, func_fit_reports_wf, [('out_report', 'inputnode.validation_report')]),
+            (validation_and_dummies_wf, hmcref_buffer, [
+                ('outputnode.bold_file', 'bold_file'),
+                ('outputnode.skip_vols', 'dummy_scans'),
+            ]),
+            (validation_and_dummies_wf, func_fit_reports_wf, [
+                ('outputnode.validation_report', 'inputnode.validation_report'),
+            ]),
             (hmcref_buffer, hmc_boldref_source_buffer, [('boldref', 'in_file')]),
         ])  # fmt:skip
 
@@ -591,6 +594,14 @@ def init_bold_fit_wf(
         config.loggers.workflow.info('Found coregistration reference - skipping Stage 3')
         regref_buffer.inputs.boldref = precomputed['coreg_boldref']
 
+        # TODO: Allow precomputed bold masks to be passed
+        # Also needs consideration for how it interacts above
+        skullstrip_precomp_ref_wf = init_skullstrip_bold_wf(name='skullstrip_precomp_ref_wf')
+        skullstrip_precomp_ref_wf.inputs.inputnode.in_file = precomputed['coreg_boldref']
+        workflow.connect([
+            (skullstrip_precomp_ref_wf, regref_buffer, [('outputnode.mask_file', 'boldmask')])
+        ])  # fmt:skip
+
     if not boldref2anat_xform:
         # calculate BOLD registration to T1w
         bold_reg_wf = init_bold_reg_wf(

fmriprep/workflows/bold/reference.py

@@ -80,7 +80,6 @@ def init_raw_boldref_wf(
         beginning of ``bold_file``
 
     """
-    from niworkflows.interfaces.bold import NonsteadyStatesDetector
     from niworkflows.interfaces.images import RobustAverage
 
     workflow = Workflow(name=name)
@@ -106,6 +105,96 @@ def init_raw_boldref_wf(
         name='outputnode',
     )
 
+    # Simplify manually setting input image
+    if bold_file is not None:
+        inputnode.inputs.bold_file = bold_file
+
+    validation_and_dummies_wf = init_validation_and_dummies_wf()
+
+    gen_avg = pe.Node(RobustAverage(), name='gen_avg', mem_gb=1)
+
+    workflow.connect([
+        (inputnode, validation_and_dummies_wf, [
+            ('bold_file', 'inputnode.bold_file'),
+            ('dummy_scans', 'inputnode.dummy_scans'),
+        ]),
+        (validation_and_dummies_wf, gen_avg, [
+            ('outputnode.bold_file', 'in_file'),
+            ('outputnode.t_mask', 't_mask'),
+        ]),
+        (validation_and_dummies_wf, outputnode, [
+            ('outputnode.bold_file', 'bold_file'),
+            ('outputnode.skip_vols', 'skip_vols'),
+            ('outputnode.algo_dummy_scans', 'algo_dummy_scans'),
+            ('outputnode.validation_report', 'validation_report'),
+        ]),
+        (gen_avg, outputnode, [('out_file', 'boldref')]),
+    ])  # fmt:skip
+
+    return workflow
+
+
+def init_validation_and_dummies_wf(
+    bold_file=None,
+    name='validation_and_dummies_wf',
+):
+    """
+    Build a workflow that validates a BOLD image and detects non-steady-state volumes.
+
+    Workflow Graph
+        .. workflow::
+            :graph2use: orig
+            :simple_form: yes
+
+            from fmriprep.workflows.bold.reference import init_validation_and_dummies_wf
+            wf = init_validation_and_dummies_wf()
+
+    Parameters
+    ----------
+    bold_file : :obj:`str`
+        BOLD series NIfTI file
+    name : :obj:`str`
+        Name of workflow (default: ``validation_and_dummies_wf``)
+
+    Inputs
+    ------
+    bold_file : str
+        BOLD series NIfTI file
+    dummy_scans : int or None
+        Number of non-steady-state volumes specified by user at beginning of ``bold_file``
+
+    Outputs
+    -------
+    bold_file : str
+        Validated BOLD series NIfTI file
+    skip_vols : int
+        Number of non-steady-state volumes selected at beginning of ``bold_file``
+    algo_dummy_scans : int
+        Number of non-steady-state volumes agorithmically detected at
+        beginning of ``bold_file``
+
+    """
+    from niworkflows.interfaces.bold import NonsteadyStatesDetector
+
+    workflow = Workflow(name=name)
+
+    inputnode = pe.Node(
+        niu.IdentityInterface(fields=['bold_file', 'dummy_scans']),
+        name='inputnode',
+    )
+    outputnode = pe.Node(
+        niu.IdentityInterface(
+            fields=[
+                'bold_file',
+                'skip_vols',
+                'algo_dummy_scans',
+                't_mask',
+                'validation_report',
+            ]
+        ),
+        name='outputnode',
+    )
+
     # Simplify manually setting input image
     if bold_file is not None:
         inputnode.inputs.bold_file = bold_file
@@ -117,7 +206,6 @@ def init_raw_boldref_wf(
     )
 
     get_dummy = pe.Node(NonsteadyStatesDetector(), name='get_dummy')
-    gen_avg = pe.Node(RobustAverage(), name='gen_avg', mem_gb=1)
 
     calc_dummy_scans = pe.Node(
         niu.Function(function=pass_dummy_scans, output_names=['skip_vols_num']),
@@ -126,22 +214,20 @@ def init_raw_boldref_wf(
         mem_gb=DEFAULT_MEMORY_MIN_GB,
     )
 
-    # fmt: off
     workflow.connect([
         (inputnode, val_bold, [('bold_file', 'in_file')]),
-        (inputnode, get_dummy, [('bold_file', 'in_file')]),
-        (inputnode, calc_dummy_scans, [('dummy_scans', 'dummy_scans')]),
-        (val_bold, gen_avg, [('out_file', 'in_file')]),
-        (get_dummy, gen_avg, [('t_mask', 't_mask')]),
-        (get_dummy, calc_dummy_scans, [('n_dummy', 'algo_dummy_scans')]),
         (val_bold, outputnode, [
             ('out_file', 'bold_file'),
             ('out_report', 'validation_report'),
         ]),
+        (inputnode, get_dummy, [('bold_file', 'in_file')]),
+        (inputnode, calc_dummy_scans, [('dummy_scans', 'dummy_scans')]),
+        (get_dummy, calc_dummy_scans, [('n_dummy', 'algo_dummy_scans')]),
+        (get_dummy, outputnode, [
+            ('n_dummy', 'algo_dummy_scans'),
+            ('t_mask', 't_mask'),
+        ]),
         (calc_dummy_scans, outputnode, [('skip_vols_num', 'skip_vols')]),
-        (gen_avg, outputnode, [('out_file', 'boldref')]),
-        (get_dummy, outputnode, [('n_dummy', 'algo_dummy_scans')]),
-    ])
-    # fmt: on
+    ])  # fmt:skip
 
     return workflow