Add normalization report

src/fmripost_aroma/data/io_spec.json

@@ -78,6 +78,18 @@
                 "extension": [
                     ".tsv"
                 ]
+            },
+            "anat_dseg": {
+                "datatype": "anat",
+                "space": null,
+                "res": null,
+                "den": null,
+                "desc": null,
+                "suffix": "dseg",
+                "extension": [
+                    ".nii.gz",
+                    ".nii"
+                ]
             }
         },
         "transforms": {

src/fmripost_aroma/workflows/base.py

@@ -285,6 +285,7 @@ def init_single_run_wf(bold_file):
 
     from fmripost_aroma.utils.bids import collect_derivatives, extract_entities
     from fmripost_aroma.workflows.aroma import init_denoise_wf, init_ica_aroma_wf
+    from fmripost_aroma.workflows.outputs import init_func_fit_reports_wf
 
     spaces = config.workflow.spaces
     omp_nthreads = config.nipype.omp_nthreads
@@ -294,7 +295,6 @@ def init_single_run_wf(bold_file):
 
     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)
 
     entities = extract_entities(bold_file)
 
@@ -365,6 +365,8 @@ def init_single_run_wf(bold_file):
             )
         skip_vols = get_nss(functional_cache['confounds'])
 
+    # 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['confounds']
     ica_aroma_wf.inputs.inputnode.skip_vols = skip_vols
 
@@ -479,6 +481,13 @@ def init_single_run_wf(bold_file):
         ]),
     ])  # fmt:skip
 
+    # 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')])])
+
     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)

src/fmripost_aroma/workflows/outputs.py

@@ -0,0 +1,135 @@
+# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
+# vi: set ft=python sts=4 ts=4 sw=4 et:
+#
+# Copyright The NiPreps Developers <[email protected]>
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# We support and encourage derived works from this project, please read
+# about our expectations at
+#
+#     https://www.nipreps.org/community/licensing/
+#
+"""Writing out derivative files."""
+
+from __future__ import annotations
+
+from fmriprep.utils.bids import dismiss_echo
+from nipype.interfaces import utility as niu
+from nipype.pipeline import engine as pe
+from niworkflows.interfaces.fixes import FixHeaderApplyTransforms as ApplyTransforms
+from niworkflows.utils.images import dseg_label
+
+from fmripost_aroma.config import DEFAULT_MEMORY_MIN_GB
+from fmripost_aroma.interfaces.bids import DerivativesDataSink
+
+
+def init_func_fit_reports_wf(
+    *,
+    output_dir: str,
+    name='func_fit_reports_wf',
+) -> pe.Workflow:
+    """Set up a battery of datasinks to store reports in the right location.
+
+    Parameters
+    ----------
+    freesurfer : :obj:`bool`
+        FreeSurfer was enabled
+    output_dir : :obj:`str`
+        Directory in which to save derivatives
+    name : :obj:`str`
+        Workflow name (default: func_fit_reports_wf)
+
+    Inputs
+    ------
+    source_file
+        Input BOLD images
+    """
+    from nireports.interfaces.reporting.base import (
+        SimpleBeforeAfterRPT as SimpleBeforeAfter,
+    )
+
+    from fmripost_aroma.interfaces.nilearn import MeanImage
+
+    workflow = pe.Workflow(name=name)
+
+    inputfields = [
+        'source_file',
+        'bold_mni6',
+        'anat_dseg',
+        'anat2std_xfm',
+    ]
+    inputnode = pe.Node(niu.IdentityInterface(fields=inputfields), name='inputnode')
+
+    # Average the BOLD image over time
+    calculate_mean_bold = pe.Node(
+        MeanImage(),
+        name='calculate_mean_bold',
+        mem_gb=1,
+    )
+    workflow.connect([(inputnode, calculate_mean_bold, [('bold_mni6', 'bold_file')])])
+
+    # Warp the tissue segmentation to MNI
+    dseg_to_mni6 = pe.Node(
+        ApplyTransforms(interpolation='MultiLabel'),
+        name='dseg_to_mni6',
+        mem_gb=1,
+    )
+    workflow.connect([
+        (inputnode, dseg_to_mni6, [
+            ('anat_dseg', 'input_image'),
+            ('anat2std_xfm', 'transforms'),
+            ('bold_mni6', 'reference_image'),
+        ]),
+    ])  # fmt:skip
+
+    mni6_wm = pe.Node(
+        niu.Function(function=dseg_label),
+        name='mni6_wm',
+        mem_gb=DEFAULT_MEMORY_MIN_GB,
+    )
+    mni6_wm.inputs.label = 2  # BIDS default is WM=2
+    workflow.connect([(dseg_to_mni6, mni6_wm, [('output_image', 'in_file')])])
+
+    # EPI-MNI registration
+    epi_mni_report = pe.Node(
+        SimpleBeforeAfter(
+            before_label='T1w',
+            after_label='EPI',
+            dismiss_affine=True,
+        ),
+        name='epi_mni_report',
+        mem_gb=0.1,
+    )
+    workflow.connect([
+        (inputnode, epi_mni_report, [('coreg_boldref', 'after')]),
+        (calculate_mean_bold, epi_mni_report, [('out_file', 'before')]),
+        (mni6_wm, epi_mni_report, [('output_image', 'wm_seg')]),
+    ])  # fmt:skip
+
+    ds_epi_mni_report = pe.Node(
+        DerivativesDataSink(
+            base_directory=output_dir,
+            desc='coreg',
+            suffix='bold',
+            datatype='figures',
+            dismiss_entities=dismiss_echo(),
+        ),
+        name='ds_epi_mni_report',
+    )
+    workflow.connect([
+        (inputnode, ds_epi_mni_report, [('source_file', 'source_file')]),
+        (epi_mni_report, ds_epi_mni_report, [('out_report', 'in_file')]),
+    ])  # fmt:skip
+
+    return workflow