Collect and apply field maps

src/fmripost_aroma/data/io_spec.json

@@ -1,4 +1,5 @@
 {
+    "name": "aroma",
     "queries": {
         "raw": {
             "bold_raw": {
@@ -151,8 +152,8 @@
     },
     "entities": [
         {
-            "name": "datatype",
-            "pattern": "[/\\\\]+(anat|func)[/\\\\]+"
+            "name": "fmapid",
+            "pattern": "fmapid-([a-zA-Z0-9]+)"
         },
         {
             "name": "cohort",
@@ -172,7 +173,7 @@
             "pattern": "(?:^|_)thresh-([a-zA-Z0-9]+)"
         }
       ],
-    "patterns": [
+    "default_path_patterns": [
         "sub-{subject}[/ses-{session}]/{datatype<func>|func}/sub-{subject}[_ses-{session}]_task-{task}[_acq-{acquisition}][_ce-{ceagent}][_rec-{reconstruction}][_run-{run}][_echo-{echo}][_part-{part}][_space-{space}][_res-{res}][_desc-{desc}]_{suffix<bold|boldref|dseg|mask>}.{extension<nii|nii.gz|json>|nii.gz}",
         "sub-{subject}[/ses-{session}]/{datatype<func>|func}/sub-{subject}[_ses-{session}]_task-{task}[_acq-{acquisition}][_ce-{ceagent}][_rec-{reconstruction}][_run-{run}][_echo-{echo}][_part-{part}][_space-{space}][_res-{res}][_stat-{statistic}][_desc-{desc}]_{suffix<components|mixing>}.{extension<nii|nii.gz|json>|nii.gz}",
         "sub-{subject}[/ses-{session}]/{datatype<func>|func}/sub-{subject}[_ses-{session}]_task-{task}[_acq-{acquisition}][_ce-{ceagent}][_rec-{reconstruction}][_run-{run}][_echo-{echo}][_part-{part}][_space-{space}][_res-{res}][_stat-{statistic}][_desc-{desc}]_{suffix<components|mixing>}.{extension<tsv|json>|tsv}",

src/fmripost_aroma/interfaces/bids.py

@@ -31,4 +31,4 @@ class DerivativesDataSink(BaseDerivativesDataSink):
     _allowed_entities = set(config_entities)
     _config_entities = config_entities
     _config_entities_dict = merged_entities
-    _file_patterns = fmripost_aroma_spec['patterns']
+    _file_patterns = fmripost_aroma_spec['default_path_patterns']

src/fmripost_aroma/utils/bids.py

@@ -98,14 +98,21 @@ def collect_derivatives(
     if not entities:
         entities = {}
 
+    _spec = None
     if spec is None or patterns is None:
         _spec = json.loads(load_data.readable('io_spec.json').read_text())
 
         if spec is None:
             spec = _spec['queries']
 
         if patterns is None:
-            patterns = _spec['patterns']
+            patterns = _spec['default_path_patterns']
+
+        _spec.pop('queries')
+
+    config = ['bids', 'derivatives']
+    if _spec:
+        config = ['bids', 'derivatives', _spec]
 
     # Search for derivatives data
     derivs_cache = defaultdict(list, {})
@@ -114,7 +121,7 @@ def collect_derivatives(
         if isinstance(layout, Path):
             layout = BIDSLayout(
                 layout,
-                config=['bids', 'derivatives'],
+                config=config,
                 validate=False,
             )
 

src/fmripost_aroma/workflows/base.py

@@ -179,6 +179,7 @@
     if config.execution.derivatives:
         # Raw dataset + derivatives dataset
         config.loggers.workflow.info('Raw+derivatives workflow mode enabled')
+        # Just build a list of BOLD files right now
         subject_data = collect_derivatives(
             raw_dataset=config.execution.layout,
             derivatives_dataset=None,
@@ -191,6 +192,7 @@
     else:
         # Derivatives dataset only
         config.loggers.workflow.info('Derivatives-only workflow mode enabled')
+        # Just build a list of BOLD files right now
         subject_data = collect_derivatives(
             raw_dataset=None,
             derivatives_dataset=config.execution.layout,
@@ -304,14 +306,32 @@
 
     entities = extract_entities(bold_file)
 
+    # Attempt to extract the associated fmap ID
+    fmapid = None
+    all_fmapids = config.execution.layout.get_fmapids(
+        subject=entities['subject'],
+        session=entities.get('session', None),
+    )
+    if all_fmapids:
+        fmap_file = config.execution.layout.get_nearest(
+            bold_file,
+            to=all_fmapids,
+            suffix='xfm',
+            extension='.txt',
+            strict=False,
+            **{'from': 'boldref'},
+        )
+        if fmap_file:
+            fmapid = config.execution.layout.get_file(fmap_file).entities['to']
+
     functional_cache = defaultdict(list, {})
     if config.execution.derivatives:
         # Collect native-space derivatives and transforms
         functional_cache = collect_derivatives(
             raw_dataset=config.execution.layout,
             derivatives_dataset=None,
             entities=entities,
-            fieldmap_id=None,
+            fieldmap_id=fmapid,
             allow_multiple=False,
             spaces=None,
         )
@@ -322,7 +342,7 @@
                     raw_dataset=None,
                     derivatives_dataset=deriv_dir,
                     entities=entities,
-                    fieldmap_id=None,
+                    fieldmap_id=fmapid,
                     allow_multiple=False,
                     spaces=spaces,
                 ),
@@ -347,7 +367,7 @@
                 raw_dataset=None,
                 derivatives_dataset=config.execution.layout,
                 entities=entities,
-                fieldmap_id=None,
+                fieldmap_id=fmapid,
                 allow_multiple=False,
                 spaces=spaces,
             ),
@@ -371,30 +391,60 @@
             )
         skip_vols = get_nss(functional_cache['bold_confounds'])
 
+    inputnode = pe.Node(
+        niu.IdentityInterface(
+            fields=[
+                'bold_raw',
+                'bold_confounds',
+                'bold_mni152nlin6asym',
+                'motion_xfm',
+                'boldref2fmap_xfm',
+                'boldref2anat_xfm',
+                'anat2std_xfm',
+                'bold_ref_file',
+                'fmap',
+                'bold_mask_native',
+            ],
+        ),
+        name='inputnode',
+    )
+    inputnode.inputs.bold_raw = functional_cache['bold_raw']
+    inputnode.inputs.bold_confounds = functional_cache['bold_confounds']
+    inputnode.inputs.bold_mni152nlin6asym = functional_cache['bold_mni152nlin6asym']
+    inputnode.inputs.bold_mask_native = functional_cache['bold_mask_native']
+    # Transforms
+    inputnode.inputs.bold_hmc = functional_cache['bold_hmc']
+    inputnode.inputs.boldref2fmap = functional_cache['boldref2fmap']
+    inputnode.inputs.boldref2anat = functional_cache['boldref2anat']
+    inputnode.inputs.anat2mni152nlin6asym = functional_cache['anat2mni152nlin6asym']
+    # Field maps
+    inputnode.inputs.fmap = functional_cache['fmap']
+
     # Run ICA-AROMA
     ica_aroma_wf = init_ica_aroma_wf(bold_file=bold_file, metadata=bold_metadata, mem_gb=mem_gb)
-    ica_aroma_wf.inputs.inputnode.confounds = functional_cache['bold_confounds']
     ica_aroma_wf.inputs.inputnode.skip_vols = skip_vols
+    workflow.connect([(inputnode, ica_aroma_wf, [('bold_confounds', 'inputnode.confounds')])])
 
     mni6_buffer = pe.Node(niu.IdentityInterface(fields=['bold', 'bold_mask']), name='mni6_buffer')
 
     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.header import ValidateImage
         from templateflow.api import get as get_template
 
         from fmripost_aroma.interfaces.misc import ApplyTransforms
+        from fmripost_aroma.workflows.resample import init_bold_volumetric_resample_wf
 
         workflow.__desc__ += """\
 Raw BOLD series were resampled to MNI152NLin6Asym:res-2, for ICA-AROMA classification.
 """
 
         validate_bold = pe.Node(
-            ValidateImage(in_file=functional_cache['bold_raw']),
+            ValidateImage(),
             name='validate_bold',
         )
+        workflow.connect([(inputnode, validate_bold, [('bold_raw', 'in_file')])])
 
         stc_buffer = pe.Node(
             niu.IdentityInterface(fields=['bold_file']),
@@ -427,30 +477,27 @@
 
         bold_MNI6_wf = init_bold_volumetric_resample_wf(
             metadata=bold_metadata,
-            fieldmap_id=None,  # XXX: Ignoring the field map for now
+            fieldmap_id=fmapid,
             omp_nthreads=omp_nthreads,
             mem_gb=mem_gb,
             jacobian='fmap-jacobian' not in config.workflow.ignore,
             name='bold_MNI6_wf',
         )
-        bold_MNI6_wf.inputs.inputnode.motion_xfm = functional_cache['bold_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'
-        # 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
 
+        # Pass transforms and warped fieldmap to bold_MNI6_wf
         workflow.connect([
+            (inputnode, bold_MNI6_wf, [
+                ('bold_hmc', 'inputnode.motion_xfm'),
+                ('boldref2fmap', 'inputnode.boldref2fmap_xfm'),
+                ('boldref2anat', 'inputnode.boldref2anat_xfm'),
+                ('anat2mni152nlin6asym', 'inputnode.anat2std_xfm'),
+                # use mask as boldref?
+                ('bold_mask_native', 'inputnode.bold_ref_file'),
+            ]),
             # Resample BOLD to MNI152NLin6Asym, may duplicate bold_std_wf above
-            # XXX: Ignoring the field map for now
-            # (inputnode, bold_MNI6_wf, [
-            #     ('fmap_ref', 'inputnode.fmap_ref'),
-            #     ('fmap_coeff', 'inputnode.fmap_coeff'),
-            #     ('fmap_id', 'inputnode.fmap_id'),
-            # ]),
             (stc_buffer, bold_MNI6_wf, [('bold_file', 'inputnode.bold_file')]),
             (bold_MNI6_wf, mni6_buffer, [('outputnode.bold_file', 'bold')]),
         ])  # fmt:skip
@@ -459,7 +506,6 @@
         mask_to_mni6 = pe.Node(
             ApplyTransforms(
                 interpolation='GenericLabel',
-                input_image=functional_cache['bold_mask_native'],
                 reference_image=mni6_mask,
                 transforms=[
                     functional_cache['anat2mni152nlin6asym'],
@@ -468,15 +514,22 @@
             ),
             name='mask_to_mni6',
         )
-        workflow.connect([(mask_to_mni6, mni6_buffer, [('output_image', 'bold_mask')])])
+        workflow.connect([
+            (inputnode, mask_to_mni6, [('bold_mask_native', 'input_image')]),
+            (mask_to_mni6, mni6_buffer, [('output_image', 'bold_mask')]),
+        ])  # fmt:skip
 
     elif 'bold_mni152nlin6asym' in functional_cache:
         workflow.__desc__ += """\
 Preprocessed BOLD series in MNI152NLin6Asym:res-2 space were collected for ICA-AROMA
 classification.
 """
-        mni6_buffer.inputs.bold = functional_cache['bold_mni152nlin6asym']
-        mni6_buffer.inputs.bold_mask = functional_cache['bold_mask_mni152nlin6asym']
+        workflow.connect([
+            (inputnode, mni6_buffer, [
+                ('bold_mni152nlin6asym', 'bold'),
+                ('bold_mask_native', 'bold_mask'),
+            ]),
+        ])  # fmt:skip
 
     else:
         raise ValueError('No valid BOLD series found for ICA-AROMA classification.')
@@ -491,19 +544,23 @@
     # Generate reportlets
     func_fit_reports_wf = init_func_fit_reports_wf(output_dir=config.execution.output_dir)
     func_fit_reports_wf.inputs.inputnode.source_file = bold_file
-    func_fit_reports_wf.inputs.inputnode.anat2std_xfm = functional_cache['anat2mni152nlin6asym']
-    func_fit_reports_wf.inputs.inputnode.anat_dseg = functional_cache['anat_dseg']
-    workflow.connect([(mni6_buffer, func_fit_reports_wf, [('bold', 'inputnode.bold_mni6')])])
+    workflow.connect([
+        (inputnode, func_fit_reports_wf, [
+            ('anat2mni152nlin6asym', 'inputnode.anat2std_xfm'),
+            ('anat_dseg', 'inputnode.anat_dseg'),
+        ]),
+        (mni6_buffer, func_fit_reports_wf, [('bold', 'inputnode.bold_mni6')]),
+    ])  # fmt:skip
 
     if config.workflow.denoise_method:
         # Now denoise the output-space BOLD data using ICA-AROMA
         denoise_wf = init_denoise_wf(bold_file=bold_file, metadata=bold_metadata)
         denoise_wf.inputs.inputnode.skip_vols = skip_vols
         denoise_wf.inputs.inputnode.space = 'MNI152NLin6Asym'
         denoise_wf.inputs.inputnode.res = '2'
-        denoise_wf.inputs.inputnode.confounds_file = functional_cache['bold_confounds']
 
         workflow.connect([
+            (inputnode, denoise_wf, [('bold_confounds', 'inputnode.confounds_file')]),
             (mni6_buffer, denoise_wf, [
                 ('bold', 'inputnode.bold_file'),
                 ('bold_mask', 'inputnode.bold_mask'),