RPT: Re-add summary and validation reports

Author: effigies

fmriprep/interfaces/reports.py

@@ -64,16 +64,11 @@
 \t\t\t<li>Repetition time (TR): {tr:.03g}s</li>
 \t\t\t<li>Phase-encoding (PE) direction: {pedir}</li>
 \t\t\t<li>{multiecho}</li>
-\t\t\t<li>Slice timing correction: {stc}</li>
 \t\t\t<li>Susceptibility distortion correction: {sdc}</li>
 \t\t\t<li>Registration: {registration}</li>
 \t\t\t<li>Non-steady-state volumes: {dummy_scan_desc}</li>
 \t\t</ul>
 \t\t</details>
-\t\t<details>
-\t\t\t<summary>Confounds collected</summary><br />
-\t\t\t<p>{confounds}.</p>
-\t\t</details>
 """
 
 ABOUT_TEMPLATE = """\t<ul>
@@ -193,10 +188,7 @@ def _generate_segment(self):
         )
 
 
-class FunctionalSummaryInputSpec(BaseInterfaceInputSpec):
-    slice_timing = traits.Enum(
-        False, True, 'TooShort', usedefault=True, desc='Slice timing correction used'
-    )
+class FunctionalSummaryInputSpec(TraitedSpec):
     distortion_correction = traits.Str(
         desc='Susceptibility distortion correction method', mandatory=True
     )
@@ -225,11 +217,10 @@ class FunctionalSummaryInputSpec(BaseInterfaceInputSpec):
         desc='Whether to initialize registration with the "header"'
         ' or by centering the volumes ("register")',
     )
-    confounds_file = File(exists=True, desc='Confounds file')
     tr = traits.Float(desc='Repetition time', mandatory=True)
     dummy_scans = traits.Either(traits.Int(), None, desc='number of dummy scans specified by user')
     algo_dummy_scans = traits.Int(desc='number of dummy scans determined by algorithm')
-    echo_idx = traits.List([], usedefault=True, desc="BIDS echo identifiers")
+    echo_idx = InputMultiObject(traits.Str, usedefault=True, desc="BIDS echo identifiers")
     orientation = traits.Str(mandatory=True, desc='Orientation of the voxel axes')
 
 
@@ -238,11 +229,6 @@ class FunctionalSummary(SummaryInterface):
 
     def _generate_segment(self):
         dof = self.inputs.registration_dof
-        stc = {
-            True: 'Applied',
-            False: 'Not applied',
-            'TooShort': 'Skipped (too few volumes)',
-        }[self.inputs.slice_timing]
         # #TODO: Add a note about registration_init below?
         reg = {
             'FSL': [
@@ -259,10 +245,6 @@ def _generate_segment(self):
 
         pedir = get_world_pedir(self.inputs.orientation, self.inputs.pe_direction)
 
-        if isdefined(self.inputs.confounds_file):
-            with open(self.inputs.confounds_file) as cfh:
-                conflist = cfh.readline().strip('\n').strip()
-
         dummy_scan_tmp = "{n_dum}"
         if self.inputs.dummy_scans == self.inputs.algo_dummy_scans:
             dummy_scan_msg = ' '.join(
@@ -290,10 +272,8 @@ def _generate_segment(self):
 
         return FUNCTIONAL_TEMPLATE.format(
             pedir=pedir,
-            stc=stc,
             sdc=self.inputs.distortion_correction,
             registration=reg,
-            confounds=re.sub(r'[\t ]+', ', ', conflist),
             tr=self.inputs.tr,
             dummy_scan_desc=dummy_scan_msg,
             multiecho=multiecho,

fmriprep/workflows/bold/fit.py

@@ -44,6 +44,7 @@
 from sdcflows.workflows.apply.registration import init_coeff2epi_wf
 
 from ... import config
+from ...interfaces.reports import FunctionalSummary
 from ...utils.bids import extract_entities
 
 # BOLD workflows
@@ -117,6 +118,11 @@ def init_bold_fit_wf(
     # This could become more complicated in the future
     bold_file = bold_files[0]
 
+    # Get metadata from BOLD file(s)
+    entities = extract_entities(bold_files)
+    metadata = layout.get_metadata(bold_file)
+    orientation = "".join(nb.aff2axcodes(nb.load(bold_file).affine))
+
     if os.path.isfile(bold_file):
         bold_tlen, mem_gb = _create_mem_gb(bold_file)
 
@@ -188,6 +194,22 @@ def init_bold_fit_wf(
         niu.IdentityInterface(fields=["boldref", "boldmask"]), name="regref_buffer"
     )
 
+    summary = pe.Node(
+        FunctionalSummary(
+            registration=("FSL", "FreeSurfer")[config.workflow.run_reconall],
+            registration_dof=config.workflow.bold2t1w_dof,
+            registration_init=config.workflow.bold2t1w_init,
+            pe_direction=metadata.get("PhaseEncodingDirection"),
+            echo_idx=entities.get("echo", []),
+            tr=metadata["RepetitionTime"],
+            orientation=orientation,
+        ),
+        name="summary",
+        mem_gb=config.DEFAULT_MEMORY_MIN_GB,
+        run_without_submitting=True,
+    )
+    summary.inputs.dummy_scans = config.workflow.dummy_scans
+
     func_fit_reports_wf = init_func_fit_reports_wf(
         # TODO: Enable sdc report even if we find coregref
         sdc_correction=not (have_coregref or fieldmap_id is None),
@@ -214,6 +236,7 @@ def init_bold_fit_wf(
             ("coreg_boldref", "inputnode.coreg_boldref"),
             ("boldref2anat_xfm", "inputnode.boldref2anat_xfm"),
         ]),
+        (summary, func_fit_reports_wf, [("out_report", "inputnode.summary_report")]),
     ])
     # fmt:on
 
@@ -240,6 +263,10 @@ def init_bold_fit_wf(
             (hmc_boldref_wf, hmcref_buffer, [("outputnode.bold_file", "bold_file")]),
             (hmc_boldref_wf, ds_hmc_boldref_wf, [("outputnode.boldref", "inputnode.boldref")]),
             (ds_hmc_boldref_wf, hmcref_buffer, [("outputnode.boldref", "boldref")]),
+            (hmc_boldref_wf, summary, [("outputnode.algo_dummy_scans", "algo_dummy_scans")]),
+            (hmc_boldref_wf, func_fit_reports_wf, [
+                ("outputnode.validation_report", "inputnode.validation_report"),
+            ]),
         ])
         # fmt:on
     else:
@@ -248,9 +275,15 @@ def init_bold_fit_wf(
         validate_bold = pe.Node(ValidateImage(), name="validate_bold")
         validate_bold.inputs.in_file = bold_file
 
-        workflow.connect(validate_bold, "out_file", hmcref_buffer, "bold_file")
         hmcref_buffer.inputs.boldref = precomputed["hmc_boldref"]
 
+        # fmt:off
+        workflow.connect([
+            (validate_bold, hmcref_buffer, [("out_file", "bold_file")]),
+            (validate_bold, func_fit_reports_wf, [("out_report", "inputnode.validation_report")]),
+        ])
+        # fmt:on
+
     # Stage 2: Estimate head motion
     if not hmc_xforms:
         config.loggers.workflow.info("Stage 2: Adding motion correction workflow")
@@ -381,6 +414,7 @@ def init_bold_fit_wf(
                     ('outputnode.corrected_mask', 'boldmask'),
                 ]),
                 (fmap_select, func_fit_reports_wf, [("fmap_ref", "inputnode.fmap_ref")]),
+                (fmap_select, summary, [("sdc_method", "distortion_correction")]),
                 (fmapreg_buffer, func_fit_reports_wf, [
                     ("boldref2fmap_xfm", "inputnode.boldref2fmap_xfm"),
                 ]),
@@ -441,6 +475,7 @@ def init_bold_fit_wf(
             (regref_buffer, ds_boldreg_wf, [("boldref", "inputnode.source_files")]),
             (bold_reg_wf, ds_boldreg_wf, [("outputnode.itk_bold_to_t1", "inputnode.xform")]),
             (ds_boldreg_wf, outputnode, [("outputnode.xform", "boldref2anat_xfm")]),
+            (bold_reg_wf, summary, [("outputnode.fallback", "fallback")]),
         ])
         # fmt:on
     else:

fmriprep/workflows/bold/outputs.py

@@ -208,9 +208,36 @@ def init_func_fit_reports_wf(
         # May be missing
         "subject_id",
         "subjects_dir",
+        # Report snippets
+        "summary_report",
+        "validation_report",
     ]
     inputnode = pe.Node(niu.IdentityInterface(fields=inputfields), name="inputnode")
 
+    ds_summary = pe.Node(
+        DerivativesDataSink(
+            base_directory=output_dir,
+            desc="summary",
+            datatype="figures",
+            dismiss_entities=("echo",),
+        ),
+        name="ds_report_summary",
+        run_without_submitting=True,
+        mem_gb=config.DEFAULT_MEMORY_MIN_GB,
+    )
+
+    ds_validation = pe.Node(
+        DerivativesDataSink(
+            base_directory=output_dir,
+            desc="validation",
+            datatype="figures",
+            dismiss_entities=("echo",),
+        ),
+        name="ds_report_validation",
+        run_without_submitting=True,
+        mem_gb=config.DEFAULT_MEMORY_MIN_GB,
+    )
+
     # Resample anatomical references into BOLD space for plotting
     t1w_boldref = pe.Node(
         ApplyTransforms(
@@ -244,6 +271,14 @@ def init_func_fit_reports_wf(
 
     # fmt:off
     workflow.connect([
+        (inputnode, ds_summary, [
+            ('source_file', 'source_file'),
+            ('summary_report', 'in_file'),
+        ]),
+        (inputnode, ds_validation, [
+            ('source_file', 'source_file'),
+            ('validation_report', 'in_file'),
+        ]),
         (inputnode, t1w_boldref, [
             ('t1w_preproc', 'input_image'),
             ('coreg_boldref', 'reference_image'),

fmriprep/workflows/bold/reference.py

@@ -94,7 +94,15 @@ def init_raw_boldref_wf(
         name="inputnode",
     )
     outputnode = pe.Node(
-        niu.IdentityInterface(fields=["bold_file", "boldref", "skip_vols", "algo_dummy_scans"]),
+        niu.IdentityInterface(
+            fields=[
+                "bold_file",
+                "boldref",
+                "skip_vols",
+                "algo_dummy_scans",
+                "validation_report",
+            ]
+        ),
         name="outputnode",
     )
 
@@ -126,7 +134,10 @@ def init_raw_boldref_wf(
         (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")]),
+        (val_bold, outputnode, [
+            ("out_file", "bold_file"),
+            ("out_report", "validation_report"),
+        ]),
         (calc_dummy_scans, outputnode, [("skip_vols_num", "skip_vols")]),
         (gen_avg, outputnode, [("out_file", "boldref")]),
         (get_dummy, outputnode, [("n_dummy", "algo_dummy_scans")]),