Iterating over multiple pairs of PET and T1w files

.flake8

@@ -0,0 +1,9 @@
+[flake8]
+select = B,B9,C,D,DAR,E,F,N,RST,S,W
+ignore = E203,E501,RST201,RST203,RST301,W503
+max-line-length = 80
+max-complexity = 10
+docstring-convention = google
+per-file-ignores = tests/*:S101
+rst-roles = class,const,func,meth,mod,ref
+rst-directives = deprecated

.pre-commit-config.yaml

@@ -0,0 +1,30 @@
+repos:
+-   repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.4.0
+    hooks:
+    -   id: check-yaml
+    -   id: check-toml
+    -   id: check-added-large-files
+    -   id: end-of-file-fixer
+    -   id: trailing-whitespace
+-   repo: local
+    hooks:
+    -   id: black
+        name: black
+        entry: black
+        language: system
+        types: [python]
+        require_serial: true
+    -   id: flake8
+        name: flake8
+        entry: flake8
+        language: system
+        types: [python]
+        require_serial: true
+    -   id: isort
+        name: isort
+        entry: isort
+        require_serial: true
+        language: system
+        types_or: [cython, pyi, python]
+        args: ["--filter-files"]

README.md

@@ -1,18 +1,18 @@
 # petdeface
 A nipype implementation of an anatomical MR and PET defacing pipeline for BIDS datasets. This is a working prototype,
 in active development denoted by the 0.x.x version number. However, it is functional and can be used to deface PET and
-MR data as well as co-register the two modalities. Use is encouraged and feedback via Github issues or email to 
-[email protected] is more than welcome. As is often the case, this medical research software is constrained 
+MR data as well as co-register the two modalities. Use is encouraged and feedback via Github issues or email to
+[email protected] is more than welcome. As is often the case, this medical research software is constrained
 to testing on data that its developers have access to.
 
-This software can be installed via source or via pip from PyPi with `pip install petdeface` 
+This software can be installed via source or via pip from PyPi with `pip install petdeface`
 
 ## Requirements
 
 ### Non-Python Dependencies
 
 - FreeSurfer and MiDeFAce >= 7.3.2
-  - https://surfer.nmr.mgh.harvard.edu/ 
+  - https://surfer.nmr.mgh.harvard.edu/
   - https://surfer.nmr.mgh.harvard.edu/fswiki/MiDeFace
 
 ### Python Dependencies
@@ -25,9 +25,9 @@ This software can be installed via source or via pip from PyPi with `pip install
 
 ## Usage
 ```bash
-usage: petdeface.py [-h] [--output_dir OUTPUT_DIR] [--anat_only] 
-       [--subject SUBJECT] [--session SESSION] [--docker] 
-       [--n_procs N_PROCS] [--skip_bids_validator] [--version] 
+usage: petdeface.py [-h] [--output_dir OUTPUT_DIR] [--anat_only]
+       [--subject SUBJECT] [--session SESSION] [--docker]
+       [--n_procs N_PROCS] [--skip_bids_validator] [--version]
        [--placement PLACEMENT] [--remove_existing] input_dir
 
 PetDeface
@@ -49,9 +49,9 @@ options:
   --skip_bids_validator
   --version, -v         show program's version number and exit
   --placement PLACEMENT, -p PLACEMENT
-                        Where to place the defaced images. Options are 
+                        Where to place the defaced images. Options are
                         'adjacent': next to the input_dir (default) in a folder appended with _defaced
-                        'inplace': defaces the dataset in place, e.g. replaces faced PET and T1w images 
+                        'inplace': defaces the dataset in place, e.g. replaces faced PET and T1w images
                         w/ defaced at input_dir
                         'derivatives': does all of the defacing within the derivatives folder in input_dir.
   --remove_existing, -r Remove existing output files in output_dir.
@@ -76,24 +76,24 @@ pip install petdeface-<X.X.X>-py3-none-any.whl # where X.X.X is the version numb
 
 ## Citations
 
-1. Dale A, Fischl B, Sereno MI. Cortical Surface-Based Analysis: I. Segmentation and Surface Reconstruction. 
+1. Dale A, Fischl B, Sereno MI. Cortical Surface-Based Analysis: I. Segmentation and Surface Reconstruction.
    Neuroimage. 1999;9(2):179–94. doi:10.1006/nimg.1998.0395.
 2. Fischl B. FreeSurfer. Neuroimage. 2012 Aug 15;62(2):774-81. doi: 10.1016/j.neuroimage.2012.01.021.
    Epub 2012 Jan 10. PMID: 22248573; PMCID: PMC3685476.
 3. Stefano Cerri, Douglas N. Greve, Andrew Hoopes, Henrik Lundell, Hartwig R. Siebner, Mark Mühlau, Koen Van Leemput,
    An open-source tool for longitudinal whole-brain and white matter lesion segmentation,
    NeuroImage: Clinical, Volume 38, 2023, 103354, ISSN 2213-1582, https://doi.org/10.1016/j.nicl.2023.103354.
    (https://www.sciencedirect.com/science/article/pii/S2213158223000438)
-4. Gorgolewski, Krzysztof J. ; Esteban, Oscar ; Burns, Christopher ; Ziegler, Erik ; Pinsard, Basile ; Madison, Cindee ; 
-   Waskom, Michael ; Ellis, David Gage ; Clark, Dav ; Dayan, Michael ; Manhães-Savio, Alexandre ; 
-   Notter, Michael Philipp ; Johnson, Hans ; Dewey, Blake E ; Halchenko, Yaroslav O. ; Hamalainen, Carlo ; 
+4. Gorgolewski, Krzysztof J. ; Esteban, Oscar ; Burns, Christopher ; Ziegler, Erik ; Pinsard, Basile ; Madison, Cindee ;
+   Waskom, Michael ; Ellis, David Gage ; Clark, Dav ; Dayan, Michael ; Manhães-Savio, Alexandre ;
+   Notter, Michael Philipp ; Johnson, Hans ; Dewey, Blake E ; Halchenko, Yaroslav O. ; Hamalainen, Carlo ;
    Keshavan, Anisha ; Clark, Daniel ; Huntenburg, Julia M. ; Hanke, Michael ; Nichols, B. Nolan ; Wassermann , Demian ;
-   Eshaghi, Arman ; Markiewicz, Christopher ; Varoquaux, Gael ; Acland, Benjamin ; Forbes, Jessica ; Rokem, Ariel ; 
-   Kong, Xiang-Zhen ; Gramfort, Alexandre ; Kleesiek, Jens ; Schaefer, Alexander ; Sikka, Sharad ; 
-   Perez-Guevara, Martin Felipe ; Glatard, Tristan ; Iqbal, Shariq ; Liu, Siqi ; Welch, David ; Sharp, Paul ; 
+   Eshaghi, Arman ; Markiewicz, Christopher ; Varoquaux, Gael ; Acland, Benjamin ; Forbes, Jessica ; Rokem, Ariel ;
+   Kong, Xiang-Zhen ; Gramfort, Alexandre ; Kleesiek, Jens ; Schaefer, Alexander ; Sikka, Sharad ;
+   Perez-Guevara, Martin Felipe ; Glatard, Tristan ; Iqbal, Shariq ; Liu, Siqi ; Welch, David ; Sharp, Paul ;
    Warner, Joshua ; Kastman, Erik ; Lampe, Leonie ; Perkins, L. Nathan ; Craddock, R. Cameron ; Küttner, René ;
-   Bielievtsov, Dmytro ; Geisler, Daniel ; Gerhard, Stephan ; Liem, Franziskus ; Linkersdörfer, Janosch ; 
-   Margulies, Daniel S. ; Andberg, Sami Kristian ; Stadler, Jörg ; Steele, Christopher John ; Broderick, William ; 
-   Cooper, Gavin ; Floren, Andrew ; Huang, Lijie ; Gonzalez, Ivan ; McNamee, Daniel ; Papadopoulos Orfanos, Dimitri ; 
-   Pellman, John ; Triplett, William ; Ghosh, Satrajit (2016). Nipype: a flexible, lightweight and extensible 
+   Bielievtsov, Dmytro ; Geisler, Daniel ; Gerhard, Stephan ; Liem, Franziskus ; Linkersdörfer, Janosch ;
+   Margulies, Daniel S. ; Andberg, Sami Kristian ; Stadler, Jörg ; Steele, Christopher John ; Broderick, William ;
+   Cooper, Gavin ; Floren, Andrew ; Huang, Lijie ; Gonzalez, Ivan ; McNamee, Daniel ; Papadopoulos Orfanos, Dimitri ;
+   Pellman, John ; Triplett, William ; Ghosh, Satrajit (2016). Nipype: a flexible, lightweight and extensible
    neuroimaging data processing framework in Python. 0.12.0-rc1. Zenodo. 10.5281/zenodo.50186

petdeface/mideface.py

@@ -1,30 +1,26 @@
-import os
-from bids import BIDSLayout
-from nipype.interfaces.base import (
-    TraitedSpec,
-    CommandLineInputSpec,
-    CommandLine,
-    File,
-    Directory,
-    traits,
-)
+from nipype.interfaces.base import CommandLine
+from nipype.interfaces.base import CommandLineInputSpec
+from nipype.interfaces.base import Directory
+from nipype.interfaces.base import File
+from nipype.interfaces.base import TraitedSpec
+from nipype.interfaces.base import traits
+
 
 class MidefaceInputSpec(CommandLineInputSpec):
-    in_file = File(
-        desc="Volume to deface",
-        exists=True,
-        argstr="--i %s",
-    )
+    in_file = File(desc="Volume to deface", exists=True, argstr="--i %s")
     out_file = File(
         desc="Defaced input",
         argstr="--o %s",
-        name_source=["in_file"],
+        name_source="in_file",
         name_template="%s_defaced",
-        output_name="out_file",
+        keep_extension=True,
     )
     out_facemask = File(
         desc="Facemask",
         argstr="--facemask %s",
+        name_source="in_file",
+        name_template="%s_facemask",
+        keep_extension=True,
     )
     odir = Directory(
         desc="Output directory (turns on PostHeadSurf)",
@@ -62,7 +58,8 @@ class MidefaceInputSpec(CommandLineInputSpec):
         argstr="--xmask-synthseg %d",
     )
     fill_const = traits.Tuple(
-        traits.Float, traits.Float,
+        traits.Float,
+        traits.Float,
         desc="Fill constants",
         argstr="--fill-const %f %f",
     )
@@ -137,28 +134,59 @@ class MidefaceInputSpec(CommandLineInputSpec):
         desc="Set Xvfb display number for taking pics",
         argstr="--display %d",
     )
-    apply = traits.Str(
-        desc="Apply midface output to a second volume",
-        argstr="--apply %s",
-    )
     check = traits.Tuple(
-        File, File,
+        File,
+        File,
         desc="Check whether a volume has been defaced",
         argstr="--check %s %s",
     )
 
+
 class MidefaceOutputSpec(TraitedSpec):
-    out_file = File(desc="Defaced input")
-    out_facemask = File(desc="Facemask")
+    out_file = File(desc="Defaced input", exists=True)
+    out_facemask = File(desc="Facemask", exists=True)
+
 
 class Mideface(CommandLine):
     _cmd = "mideface"
     input_spec = MidefaceInputSpec
     output_spec = MidefaceOutputSpec
 
-    def _list_outputs(self):
-        outputs = self.output_spec().get()
-        outputs["out_file"] = os.path.abspath(self.inputs.out_file)
-        outputs["out_facemask"] = os.path.abspath(self.inputs.out_facemask)
-        return outputs
-
+
+class ApplyMidefaceInputSpec(CommandLineInputSpec):
+    in_file = File(
+        desc="Volume to deface",
+        exists=True,
+        position=1,
+        argstr="%s",
+    )
+    facemask = File(
+        desc="Facemask",
+        exists=True,
+        position=2,
+        argstr="%s",
+    )
+    lta_file = File(
+        desc="Registration matrix lta file",
+        exists=True,
+        position=3,
+        argstr="%s",
+    )
+    out_file = File(
+        desc="Defaced input",
+        position=4,
+        argstr="%s",
+        name_source="in_file",
+        name_template="%s_defaced",
+        keep_extension=True,
+    )
+
+
+class ApplyMidefaceOutputSpec(TraitedSpec):
+    out_file = File(desc="Defaced input", exists=True)
+
+
+class ApplyMideface(CommandLine):
+    _cmd = "mideface --apply"
+    input_spec = ApplyMidefaceInputSpec
+    output_spec = ApplyMidefaceOutputSpec

petdeface/pet.py

@@ -1,42 +1,59 @@
-def create_weighted_average_pet(pet_file, bids_dir):
-
-    import json
-    from niworkflows.interfaces.bids import ReadSidecarJSON
-    import nibabel as nib
-    import numpy as np
-    import os
-    from pathlib import Path
-
-    """
-    Create a time-weighted average of dynamic PET data using mid-frames
-
-    Arguments
-    ---------
-    pet_file: string
-        path to input dynamic PET volume
-    bids_dir: string
-        path to BIDS directory containing the PET file
-    """     
-
-    img = nib.load(pet_file)        
-    data = img.get_fdata()
-
-    meta = ReadSidecarJSON(in_file = pet_file, 
-                           bids_dir = bids_dir, 
-                           bids_validate = False).run()
-
-    frames_start = np.array(meta.outputs.out_dict['FrameTimesStart'])
-    frames_duration = np.array(meta.outputs.out_dict['FrameDuration'])
-
-    frames = range(data.shape[-1])
-
-    new_pth = os.getcwd()
-
-    mid_frames = frames_start + frames_duration/2
-    wavg = np.trapz(data[..., frames], dx=np.diff(mid_frames[frames]), axis=3)/np.sum(mid_frames)
-
-    out_name = Path(pet_file.replace('_pet.', '_desc-wavg_pet.')).name
-    out_file = os.path.join(new_pth, out_name)    
-    nib.save(nib.Nifti1Image(wavg, img.affine), out_file)
-
-    return out_file
+import os
+
+import nibabel as nib
+import numpy as np
+from nipype.interfaces.base import BaseInterface
+from nipype.interfaces.base import BaseInterfaceInputSpec
+from nipype.interfaces.base import File
+from nipype.interfaces.base import TraitedSpec
+from nipype.utils.filemanip import split_filename
+from niworkflows.interfaces.bids import ReadSidecarJSON
+
+
+class WeightedAverageInputSpec(BaseInterfaceInputSpec):
+    pet_file = File(exists=True, desc="Dynamic PET", mandatory=True)
+
+
+class WeightedAverageOutputSpec(TraitedSpec):
+    out_file = File(exists=True, desc="Time-weighted average of dynamic PET")
+
+
+class WeightedAverage(BaseInterface):
+    """Create a time-weighted average of dynamic PET data using mid-frames."""
+
+    input_spec = WeightedAverageInputSpec
+    output_spec = WeightedAverageOutputSpec
+
+    def _run_interface(self, runtime):
+        pet_file = self.inputs.pet_file
+        bids_dir = os.path.dirname(pet_file)
+
+        img = nib.load(pet_file)
+        data = img.get_fdata()
+
+        meta = ReadSidecarJSON(
+            in_file=pet_file, bids_dir=bids_dir, bids_validate=False
+        ).run()
+
+        frames_start = np.array(meta.outputs.out_dict["FrameTimesStart"])
+        frames_duration = np.array(meta.outputs.out_dict["FrameDuration"])
+
+        mid_frames = frames_start + frames_duration / 2
+        wavg = np.trapz(data, x=mid_frames) / (mid_frames[-1] - mid_frames[0])
+
+        _, base, ext = split_filename(pet_file)
+        out_name = base.replace("_pet", "_desc-wavg_pet")
+        out_file = out_name + ext
+        nib.save(nib.Nifti1Image(wavg, img.affine), out_file)
+
+    def _list_outputs(self):
+        outputs = self._outputs().get()
+        pet_file = self.inputs.pet_file
+        _, base, ext = split_filename(pet_file)
+
+        out_name = base.replace("_pet", "_desc-wavg_pet")
+        out_file = os.path.abspath(out_name + ext)
+
+        outputs["out_file"] = out_file
+
+        return outputs