FIX: Address some reliability issues of the functional masking workflow

.zenodo.json

@@ -1,3 +1,4 @@
+
 {
   "title": "NIWorkflows: NeuroImaging Workflows",
   "description": "<p>A collection of Nipype interfaces, patches and workflows for reuse in NIPreps (NeuroImaging PREProcesssing tools) such as fMRIPrep.</p>",
@@ -85,12 +86,6 @@
       "name": "de Hollander, Gilles",
       "orcid": "0000-0003-1988-5091",
       "type": "Researcher"
-    },
-    {
-      "affiliation": "Sagol School of Neuroscience, Tel Aviv University",
-      "name": "Ben-Zvi, Gal",
-      "orcid": "0000-0002-5655-9423",
-      "type": "Researcher"
     }
   ],
   "keywords": [

CHANGES.rst

@@ -1,59 +1,3 @@
-1.5.4 (April 08, 2022)
-======================
-Bug-fix release in the 1.5.x series.
-
-This change enables a bug-fix in sdcflows where images with slightly
-different affines were unable to be concatenated for use with TOPUP.
-
-* ENH: Add ``affine_tolerance`` flag to ``MergeSeries`` (#706)
-
-1.5.3 (April 05, 2022)
-======================
-
-A bugfix release incorporating changes from the 1.3.x and 1.4.x
-maintenance series.
-
-* FIX: Alternate query for template brain mask (#704)
-* FIX: Use copy function that does not preserve mtime when creating fsaverage directories (#703)
-* FIX: Test failures (#701)
-* ENH: Add optional session filter when collecting data (#678)
-* ENH: Specify path pattern for transformation files in dwi datatype (#699)
-* REF: Move BIDS skeleton generation into new ``testing`` module (#682)
-* MNT: Select magnitude images in collect_data for BIDS 1.5.0 (#594)
-* CI: Install package across Python versions and run pytest (#697)
-
-1.5.2 (March 23, 2022)
-======================
-A bugfix release ensuring compatibility with Python 3.7.
-
-* FIX: Add ``unlink()`` with ``missing_ok`` for Python 3.7 (#695)
-
-1.5.1 (March 11, 2022)
-======================
-A bugfix release addressing several issues and improving the carpet plot.
-
-* FIX: Set ``pixdim[4]`` to match ``RepetitionTime`` (#679)
-* FIX: Unlink destination files before saving derivative (#683)
-* FIX: Smarter decision to remap input segmentation to carpetplot (#691)
-* FIX: Error handling the segmentation in NIfTI extraction (#689)
-* ENH: Add a legend to carpet plots with more than one segment (#690)
-* ENH: Miscellaneous improvements to carpetplot (#688)
-* ENH: Refactor carpetplot reasigning responsibilities (#687)
-* ENH: Initiate a module of nipype interfaces for mathematical morphology (#651)
-* ENH: Reorder within-segment rows of carpetplot with hierarchical clustering (#686)
-* DOC: Fix doc builds in ``maint/1.3.x`` (#692)
-
-1.5.0 (February 07, 2022)
-=========================
-First release in the 1.5.x series.
-This release upgrades the Dockerfile to use FSL6, and includes some new interfaces.
-
-  * ENH: Additional nibabel interfaces (#680)
-  * ENH: Add utility function to generate sample BIDS directory (#677)
-  * ENH: Update to FSL6 (#675)
-  * FIX: Scipy docs path (#681)
-  * TEST: Drop excessively long interface equivalence tests (#674)
-
 1.4.8 (April 08, 2022)
 ======================
 Bug-fix release in the 1.4.x series.

Dockerfile

@@ -126,8 +126,8 @@ RUN curl -sSL "https://dl.dropbox.com/s/gwf51ykkk5bifyj/ants-Linux-centos6_x86_6
     | tar -xzC $ANTSPATH --strip-components 1
 ENV PATH="$ANTSPATH:$PATH"
 
-# FSL 6.0.5.1
-RUN curl -sSL https://fsl.fmrib.ox.ac.uk/fsldownloads/fsl-6.0.5.1-centos7_64.tar.gz | tar zxv --no-same-owner -C /opt \
+# FSL 5.0.11
+RUN curl -sSL https://fsl.fmrib.ox.ac.uk/fsldownloads/fsl-5.0.11-centos6_64.tar.gz | tar zxv --no-same-owner -C /opt \
     --exclude='fsl/doc' \
     --exclude='fsl/refdoc' \
     --exclude='fsl/python/oxford_asl' \

docs/conf.py

@@ -238,9 +238,9 @@
     "nibabel": ("https://nipy.org/nibabel/", None),
     "nipype": ("https://nipype.readthedocs.io/en/latest/", None),
     "numpy": ("https://numpy.org/doc/stable/", None),
-    "pandas": ("https://pandas.pydata.org/pandas-docs/dev", None),
+    "pandas": ("http://pandas.pydata.org/pandas-docs/dev", None),
     "python": ("https://docs.python.org/3/", None),
-    "scipy": ("https://docs.scipy.org/doc/scipy-1.8.0/html-scipyorg/", None),
+    "scipy": ("https://docs.scipy.org/doc/scipy/reference", None),
     "smriprep": ("https://www.nipreps.org/smriprep/", None),
     "surfplot": ("https://surfplot.readthedocs.io/en/latest/", None),
     "templateflow": ("https://www.templateflow.org/python-client", None),

niworkflows/data/epi_atlasbased_brainmask.json

@@ -1,7 +1,6 @@
 {
     "winsorize_upper_quantile": 0.98,
     "winsorize_lower_quantile": 0.05,
-    "float": true,
     "metric": ["Mattes"],
     "metric_weight": [1],
     "radius_or_number_of_bins": [64],

niworkflows/data/nipreps.json

@@ -170,7 +170,6 @@
     "sub-{subject}[/ses-{session}]/{datatype<dwi>|dwi}/sub-{subject}[_ses-{session}][_acq-{acquisition}][_rec-{reconstruction}][_dir-{direction}][_run-{run}][_space-{space}][_cohort-{cohort}][_res-{resolution}][_desc-{desc}]_{suffix<dwi|epiref|lowb|dseg>}{extension<.json|.nii.gz|.nii>|.nii.gz}",
     "sub-{subject}[/ses-{session}]/{datatype<dwi>|dwi}/sub-{subject}[_ses-{session}][_acq-{acquisition}][_rec-{reconstruction}][_dir-{direction}][_run-{run}][_space-{space}][_cohort-{cohort}][_res-{resolution}]_desc-{desc}_{suffix<mask>}{extension<.json|.nii.gz|.nii>|.nii.gz}",
     "sub-{subject}[/ses-{session}]/{datatype<dwi>|dwi}/sub-{subject}[_ses-{session}][_acq-{acquisition}][_rec-{reconstruction}][_dir-{direction}][_run-{run}][_space-{space}][_cohort-{cohort}][_res-{resolution}][_desc-{desc}]_{suffix<dwi>}{extension<.tsv|.bval|.bvec>|.tsv}",
-    "sub-{subject}[/ses-{session}]/{datatype<dwi>|dwi}/sub-{subject}[_ses-{session}][_acq-{acquisition}][_rec-{reconstruction}][_dir-{direction}][_run-{run}]_from-{from}_to-{to}_mode-{mode<image|points>|image}_{suffix<xfm>|xfm}{extension<.txt|.h5>}",
     "sub-{subject}[/ses-{session}]/{datatype<fmap>|fmap}/sub-{subject}[_ses-{session}][_acq-{acquisition}][_dir-{direction}][_run-{run}][_part-{part}][_space-{space}][_cohort-{cohort}][_res-{resolution}][_fmapid-{fmapid}][_desc-{desc}]_{suffix<fieldmap>}{extension<.nii|.nii.gz|.json>|.nii.gz}",
     "sub-{subject}/{datatype<figures>}/sub-{subject}[_ses-{session}][_acq-{acquisition}][_ce-{ceagent}][_rec-{reconstruction}][_run-{run}][_space-{space}][_cohort-{cohort}][_desc-{desc}]_{suffix<T1w|T2w|T1rho|T1map|T2map|T2star|FLAIR|FLASH|PDmap|PD|PDT2|inplaneT[12]|angio|dseg|mask|dwi|epiref|T2starw|MTw|TSE>}{extension<.html|.svg>|.svg}",
     "sub-{subject}/{datatype<figures>}/sub-{subject}[_ses-{session}][_acq-{acquisition}][_ce-{ceagent}][_rec-{reconstruction}][_run-{run}][_space-{space}][_cohort-{cohort}][_fmapid-{fmapid}][_desc-{desc}]_{suffix<fieldmap>}{extension<.html|.svg>|.svg}",

niworkflows/func/util.py

@@ -363,6 +363,8 @@ def init_enhance_and_skullstrip_bold_wf(
         reportlet for the skull-stripping
 
     """
+    from niworkflows.interfaces.nibabel import ApplyMask, BinaryDilation
+
     workflow = Workflow(name=name)
     inputnode = pe.Node(
         niu.IdentityInterface(fields=["in_file", "pre_mask"]), name="inputnode"
@@ -374,20 +376,6 @@ def init_enhance_and_skullstrip_bold_wf(
         name="outputnode",
     )
 
-    # Dilate pre_mask
-    pre_dilate = pe.Node(
-        fsl.DilateImage(
-            operation="max",
-            kernel_shape="sphere",
-            kernel_size=3.0,
-            internal_datatype="char",
-        ),
-        name="pre_mask_dilate",
-    )
-
-    # Ensure mask's header matches reference's
-    check_hdr = pe.Node(MatchHeader(), name="check_hdr", run_without_submitting=True)
-
     # Run N4 normally, force num_threads=1 for stability (images are small, no need for >1)
     n4_correct = pe.Node(
         N4BiasFieldCorrection(
@@ -403,16 +391,8 @@ def init_enhance_and_skullstrip_bold_wf(
     skullstrip_first_pass = pe.Node(
         fsl.BET(frac=0.2, mask=True), name="skullstrip_first_pass"
     )
-    bet_dilate = pe.Node(
-        fsl.DilateImage(
-            operation="max",
-            kernel_shape="sphere",
-            kernel_size=6.0,
-            internal_datatype="char",
-        ),
-        name="skullstrip_first_dilate",
-    )
-    bet_mask = pe.Node(fsl.ApplyMask(), name="skullstrip_first_mask")
+    first_dilate = pe.Node(BinaryDilation(radius=6), name="first_dilate")
+    first_mask = pe.Node(ApplyMask(), name="first_mask")
 
     # Use AFNI's unifize for T2 constrast & fix header
     unifize = pe.Node(
@@ -438,11 +418,10 @@ def init_enhance_and_skullstrip_bold_wf(
     combine_masks = pe.Node(fsl.BinaryMaths(operation="mul"), name="combine_masks")
 
     # Compute masked brain
-    apply_mask = pe.Node(fsl.ApplyMask(), name="apply_mask")
+    apply_mask = pe.Node(ApplyMask(), name="apply_mask")
 
     if not pre_mask:
         from nipype.interfaces.ants.utils import AI
-        from ..interfaces.nibabel import Binarize
 
         bold_template = get_template(
             "MNI152NLin2009cAsym", resolution=2, desc="fMRIPrep", suffix="boldref"
@@ -482,8 +461,7 @@ def init_enhance_and_skullstrip_bold_wf(
         norm.inputs.fixed_image = str(bold_template)
         map_brainmask = pe.Node(
             ApplyTransforms(
-                interpolation="BSpline",
-                float=True,
+                interpolation="Linear",
                 # Use the higher resolution and probseg for numerical stability in rounding
                 input_image=str(
                     get_template(
@@ -496,10 +474,12 @@ def init_enhance_and_skullstrip_bold_wf(
             ),
             name="map_brainmask",
         )
-        binarize_mask = pe.Node(Binarize(thresh_low=brainmask_thresh), name="binarize_mask")
+        # Ensure mask's header matches reference's
+        check_hdr = pe.Node(MatchHeader(), name="check_hdr", run_without_submitting=True)
 
         # fmt: off
         workflow.connect([
+            (inputnode, check_hdr, [("in_file", "reference")]),
             (inputnode, init_aff, [("in_file", "moving_image")]),
             (inputnode, map_brainmask, [("in_file", "reference_image")]),
             (inputnode, norm, [("in_file", "moving_image")]),
@@ -508,37 +488,34 @@ def init_enhance_and_skullstrip_bold_wf(
                 ("reverse_invert_flags", "invert_transform_flags"),
                 ("reverse_transforms", "transforms"),
             ]),
-            (map_brainmask, binarize_mask, [("output_image", "in_file")]),
-            (binarize_mask, pre_dilate, [("out_mask", "in_file")]),
+            (map_brainmask, check_hdr, [("output_image", "in_file")]),
+            (check_hdr, n4_correct, [("out_file", "weight_image")]),
         ])
         # fmt: on
     else:
         # fmt: off
         workflow.connect([
-            (inputnode, pre_dilate, [("pre_mask", "in_file")]),
+            (inputnode, n4_correct, [("pre_mask", "weight_image")]),
         ])
         # fmt: on
 
     # fmt: off
     workflow.connect([
-        (inputnode, check_hdr, [("in_file", "reference")]),
-        (pre_dilate, check_hdr, [("out_file", "in_file")]),
-        (check_hdr, n4_correct, [("out_file", "mask_image")]),
         (inputnode, n4_correct, [("in_file", "input_image")]),
         (inputnode, fixhdr_unifize, [("in_file", "hdr_file")]),
         (inputnode, fixhdr_skullstrip2, [("in_file", "hdr_file")]),
         (n4_correct, skullstrip_first_pass, [("output_image", "in_file")]),
-        (skullstrip_first_pass, bet_dilate, [("mask_file", "in_file")]),
-        (bet_dilate, bet_mask, [("out_file", "mask_file")]),
-        (skullstrip_first_pass, bet_mask, [("out_file", "in_file")]),
-        (bet_mask, unifize, [("out_file", "in_file")]),
+        (skullstrip_first_pass, first_dilate, [("mask_file", "in_file")]),
+        (first_dilate, first_mask, [("out_file", "in_mask")]),
+        (skullstrip_first_pass, first_mask, [("out_file", "in_file")]),
+        (first_mask, unifize, [("out_file", "in_file")]),
         (unifize, fixhdr_unifize, [("out_file", "in_file")]),
         (fixhdr_unifize, skullstrip_second_pass, [("out_file", "in_file")]),
         (skullstrip_first_pass, combine_masks, [("mask_file", "in_file")]),
         (skullstrip_second_pass, fixhdr_skullstrip2, [("out_file", "in_file")]),
         (fixhdr_skullstrip2, combine_masks, [("out_file", "operand_file")]),
         (fixhdr_unifize, apply_mask, [("out_file", "in_file")]),
-        (combine_masks, apply_mask, [("out_file", "mask_file")]),
+        (combine_masks, apply_mask, [("out_file", "in_mask")]),
         (combine_masks, outputnode, [("out_file", "mask_file")]),
         (apply_mask, outputnode, [("out_file", "skull_stripped_file")]),
         (n4_correct, outputnode, [("output_image", "bias_corrected_file")]),
@@ -580,6 +557,8 @@ def init_skullstrip_bold_wf(name="skullstrip_bold_wf"):
         reportlet for the skull-stripping
 
     """
+    from niworkflows.interfaces.nibabel import ApplyMask
+
     workflow = Workflow(name=name)
     inputnode = pe.Node(niu.IdentityInterface(fields=["in_file"]), name="inputnode")
     outputnode = pe.Node(
@@ -595,7 +574,7 @@ def init_skullstrip_bold_wf(name="skullstrip_bold_wf"):
         afni.Automask(dilate=1, outputtype="NIFTI_GZ"), name="skullstrip_second_pass"
     )
     combine_masks = pe.Node(fsl.BinaryMaths(operation="mul"), name="combine_masks")
-    apply_mask = pe.Node(fsl.ApplyMask(), name="apply_mask")
+    apply_mask = pe.Node(ApplyMask(), name="apply_mask")
     mask_reportlet = pe.Node(SimpleShowMaskRPT(), name="mask_reportlet")
 
     # fmt: off
@@ -607,7 +586,7 @@ def init_skullstrip_bold_wf(name="skullstrip_bold_wf"):
         (combine_masks, outputnode, [("out_file", "mask_file")]),
         # Masked file
         (inputnode, apply_mask, [("in_file", "in_file")]),
-        (combine_masks, apply_mask, [("out_file", "mask_file")]),
+        (combine_masks, apply_mask, [("out_file", "in_mask")]),
         (apply_mask, outputnode, [("out_file", "skull_stripped_file")]),
         # Reportlet
         (inputnode, mask_reportlet, [("in_file", "background_file")]),

niworkflows/interfaces/bids.py

@@ -126,7 +126,7 @@ class BIDSInfo(SimpleInterface):
 
     >>> bids_info = BIDSInfo(bids_dir=str(datadir / 'ds054'), bids_validate=False)
     >>> bids_info.inputs.in_file = '''\
-sub-01/func/ses-retest/sub-01_ses-retest_task-covertverbgeneration_rec-MB_acq-AP_run-01_bold.nii.gz'''
+sub-01/func/ses-retest/sub-01_ses-retest_task-covertverbgeneration_rec-MB_acq-AP_run-1_bold.nii.gz'''
     >>> res = bids_info.run()
     >>> res.outputs
     <BLANKLINE>
@@ -416,12 +416,12 @@ class DerivativesDataSink(SimpleInterface):
     >>> dsink.inputs.desc = 'preproc'
     >>> res = dsink.run()
     >>> res.outputs.out_file  # doctest: +ELLIPSIS
-    '.../niworkflows/sub-02/ses-noanat/func/sub-02_ses-noanat_task-rest_run-01_\
+    '.../niworkflows/sub-02/ses-noanat/func/sub-02_ses-noanat_task-rest_run-1_\
 desc-preproc_bold.nii'
 
     >>> bids_dir = tmpdir / 'bidsroot' / 'sub-02' / 'ses-noanat' / 'func'
     >>> bids_dir.mkdir(parents=True, exist_ok=True)
-    >>> tricky_source = bids_dir / 'sub-02_ses-noanat_task-rest_run-01_bold.nii.gz'
+    >>> tricky_source = bids_dir / 'sub-02_ses-noanat_task-rest_run-1_bold.nii.gz'
     >>> tricky_source.open('w').close()
     >>> dsink = DerivativesDataSink(base_directory=str(tmpdir), check_hdr=False)
     >>> dsink.inputs.in_file = str(tmpfile)
@@ -430,7 +430,7 @@ class DerivativesDataSink(SimpleInterface):
     >>> dsink.inputs.RepetitionTime = 0.75
     >>> res = dsink.run()
     >>> res.outputs.out_meta  # doctest: +ELLIPSIS
-    '.../niworkflows/sub-02/ses-noanat/func/sub-02_ses-noanat_task-rest_run-01_\
+    '.../niworkflows/sub-02/ses-noanat/func/sub-02_ses-noanat_task-rest_run-1_\
 desc-preproc_bold.json'
 
     >>> Path(res.outputs.out_meta).read_text().splitlines()[1]
@@ -450,7 +450,7 @@ class DerivativesDataSink(SimpleInterface):
     >>> dsink.inputs.RepetitionTime = 0.75
     >>> res = dsink.run()
     >>> res.outputs.out_meta  # doctest: +ELLIPSIS
-    '.../niworkflows/sub-02/ses-noanat/func/sub-02_ses-noanat_task-rest_run-01_\
+    '.../niworkflows/sub-02/ses-noanat/func/sub-02_ses-noanat_task-rest_run-1_\
 space-MNI152NLin6Asym_res-01_desc-preproc_bold.json'
 
     >>> lines = Path(res.outputs.out_meta).read_text().splitlines()
@@ -475,7 +475,7 @@ class DerivativesDataSink(SimpleInterface):
     >>> dsink.inputs.meta_dict = {'RepetitionTime': 1.75, 'SkullStripped': False, 'Z': 'val'}
     >>> res = dsink.run()
     >>> res.outputs.out_meta  # doctest: +ELLIPSIS
-    '.../niworkflows/sub-02/ses-noanat/func/sub-02_ses-noanat_task-rest_run-01_\
+    '.../niworkflows/sub-02/ses-noanat/func/sub-02_ses-noanat_task-rest_run-1_\
 space-MNI152NLin6Asym_desc-preproc_bold.json'
 
     >>> lines = Path(res.outputs.out_meta).read_text().splitlines()