Merge remote-tracking branch 'upstream/master'

Author: Fabio Bernardoni

.zenodo.json

@@ -835,6 +835,11 @@
  "affiliation": "MIT, HMS",
  "name": "Ghosh, Satrajit",
  "orcid": "0000-0002-5312-6729"
+ },
+ {
+ "affiliation": "Charitè Universitätsmedizin Berlin, Germany",
+ "name": "Dell\'Orco, Andrea",
+ "orcid": "0000-0002-3964-8360"
  }
  ],
  "keywords": [

nipype/interfaces/ants/segmentation.py

@@ -268,7 +268,7 @@ class LaplacianThicknessInputSpec(ANTSCommandInputSpec):
  desc="gray matter segmentation image",
  position=2,
  )
- output_image = File(
+ output_image = traits.Str(
  desc="name of output file",
  argstr="%s",
  position=3,

nipype/interfaces/ants/tests/test_auto_LaplacianThickness.py

@@ -36,7 +36,6 @@ def test_LaplacianThickness_inputs():
  ),
  output_image=dict(
  argstr="%s",
- extensions=None,
  hash_files=False,
  keep_extension=True,
  name_source=["input_wm"],

nipype/interfaces/cat12/__init__.py

@@ -1,4 +1,4 @@
-from .preprocess import CAT12Segment
+from .preprocess import CAT12Segment, CAT12SANLMDenoising
 from .surface import (
  ExtractAdditionalSurfaceParameters,
  ExtractROIBasedSurfaceMeasures,

nipype/interfaces/cat12/preprocess.py

@@ -593,6 +593,155 @@ def _list_outputs(self):
  return outputs
 
 
+class CAT12SANLMDenoisingInputSpec(SPMCommandInputSpec):
+
+ in_files = InputMultiPath(
+ ImageFileSPM(exists=True),
+ field="data",
+ desc="Images for filtering.",
+ mandatory=True,
+ copyfile=False,
+ )
+
+ spm_type = traits.Enum(
+ "float32",
+ "uint16",
+ "uint8",
+ "same",
+ field="spm_type",
+ usedefault=True,
+ desc="Data type of the output images. 'same' matches the input image type.",
+ )
+
+ intlim = traits.Int(
+ field="intlim",
+ default_value=100,
+ usedefault=True,
+ desc="intensity limitation (default = 100)",
+ )
+
+ filename_prefix = traits.Str(
+ field="prefix",
+ default_value="sanlm_",
+ usedefault=True,
+ desc="Filename prefix. Specify the string to be prepended to the filenames of the filtered image file(s).",
+ )
+
+ filename_suffix = traits.Str(
+ field="suffix",
+ default_value="",
+ usedefault=True,
+ desc="Filename suffix. Specify the string to be appended to the filenames of the filtered image file(s).",
+ )
+
+ addnoise = traits.Float(
+ default_value=0.5,
+ usedefault=True,
+ field="addnoise",
+ desc="""Strength of additional noise in noise-free regions.
+ Add minimal amount of noise in regions without any noise to avoid image segmentation problems.
+ This parameter defines the strength of additional noise as percentage of the average signal intensity.""",
+ )
+
+ rician = traits.Bool(
+ True,
+ field="rician",
+ usedefault=True,
+ desc="""Rician noise
+ MRIs can have Gaussian or Rician distributed noise with uniform or nonuniform variance across the image.
+ If SNR is high enough (>3) noise can be well approximated by Gaussian noise in the foreground. However, for
+ SENSE reconstruction or DTI data a Rician distribution is expected. Please note that the Rician noise estimation
+ is sensitive for large signals in the neighbourhood and can lead to artefacts, e.g. cortex can be affected by
+ very high values in the scalp or in blood vessels.""",
+ )
+
+ replace_nan_and_inf = traits.Bool(
+ True,
+ field="replaceNANandINF",
+ usedefault=True,
+ desc="Replace NAN by 0, -INF by the minimum and INF by the maximum of the image.",
+ )
+
+ noisecorr_strength = traits.Enum(
+ "-Inf",
+ 2,
+ 4,
+ field="nlmfilter.optimized.NCstr",
+ usedefault=True,
+ desc="""Strength of Noise Corrections
+ Strength of the (sub-resolution) spatial adaptive non local means (SANLM) noise correction. Please note
+ that the filter strength is automatically estimated. Change this parameter only for specific conditions. The
+ "light" option applies half of the filter strength of the adaptive "medium" cases, whereas the "strong"
+ option uses the full filter strength, force sub-resolution filtering and applies an additional iteration.
+ Sub-resolution filtering is only used in case of high image resolution below 0.8 mm or in case of the
+ "strong" option. light = 2, medium = -Inf, strong = 4""",
+ )
+
+
+class CAT12SANLMDenoisingOutputSpec(TraitedSpec):
+
+ out_file = File(desc="out file")
+
+
+class CAT12SANLMDenoising(SPMCommand):
+ """
+ Spatially adaptive non-local means (SANLM) denoising filter
+
+ This function applies an spatial adaptive (sub-resolution) non-local means denoising filter
+ to the data. This filter will remove noise while preserving edges. The filter strength is
+ automatically estimated based on the standard deviation of the noise.
+
+ This filter is internally used in the segmentation procedure anyway. Thus, it is not
+ necessary (and not recommended) to apply the filter before segmentation.
+ ______________________________________________________________________
+ Christian Gaser, Robert Dahnke
+ Structural Brain Mapping Group (http://www.neuro.uni-jena.de)
+ Departments of Neurology and Psychiatry
+ Jena University Hospital
+ ______________________________________________________________________
+
+ Examples
+ --------
+ >>> from nipype.interfaces import cat12
+ >>> c = cat12.CAT12SANLMDenoising()
+ >>> c.inputs.in_files = 'anatomical.nii'
+ >>> c.run() # doctest: +SKIP
+ """
+
+ input_spec = CAT12SANLMDenoisingInputSpec
+ output_spec = CAT12SANLMDenoisingOutputSpec
+
+ def __init__(self, **inputs):
+ _local_version = SPMCommand().version
+ if _local_version and "12." in _local_version:
+ self._jobtype = "tools"
+ self._jobname = "cat.tools.sanlm"
+
+ SPMCommand.__init__(self, **inputs)
+
+ def _format_arg(self, opt, spec, val):
+ """Convert input to appropriate format for spm"""
+ if opt == "in_files":
+ if isinstance(val, list):
+ return scans_for_fnames(val)
+ else:
+ return scans_for_fname(val)
+ if opt == "spm_type":
+ type_map = {"same": 0, "uint8": 2, "uint16": 512, "float32": 16}
+ val = type_map[val]
+ return super(CAT12SANLMDenoising, self)._format_arg(opt, spec, val)
+
+ def _list_outputs(self):
+ outputs = self._outputs().get()
+ outputs["out_file"] = fname_presuffix(
+ self.inputs.in_files[0],
+ newpath=os.getcwd(),
+ prefix=self.inputs.filename_prefix,
+ suffix=self.inputs.filename_suffix,
+ )
+ return outputs
+
+
 class Cell2Str(Cell):
  def __str__(self):
  """Convert input to appropriate format for cat12"""

nipype/interfaces/cat12/tests/test_auto_CAT12SANLMDenoising.py

@@ -0,0 +1,72 @@
+# AUTO-GENERATED by tools/checkspecs.py - DO NOT EDIT
+from ..preprocess import CAT12SANLMDenoising
+
+
+def test_CAT12SANLMDenoising_inputs():
+ input_map = dict(
+ addnoise=dict(
+ field="addnoise",
+ usedefault=True,
+ ),
+ filename_prefix=dict(
+ field="prefix",
+ usedefault=True,
+ ),
+ filename_suffix=dict(
+ field="suffix",
+ usedefault=True,
+ ),
+ in_files=dict(
+ copyfile=False,
+ field="data",
+ mandatory=True,
+ ),
+ intlim=dict(
+ field="intlim",
+ usedefault=True,
+ ),
+ matlab_cmd=dict(),
+ mfile=dict(
+ usedefault=True,
+ ),
+ noisecorr_strength=dict(
+ field="nlmfilter.optimized.NCstr",
+ usedefault=True,
+ ),
+ paths=dict(),
+ replace_nan_and_inf=dict(
+ field="replaceNANandINF",
+ usedefault=True,
+ ),
+ rician=dict(
+ field="rician",
+ usedefault=True,
+ ),
+ spm_type=dict(
+ field="spm_type",
+ usedefault=True,
+ ),
+ use_mcr=dict(),
+ use_v8struct=dict(
+ min_ver="8",
+ usedefault=True,
+ ),
+ )
+ inputs = CAT12SANLMDenoising.input_spec()
+
+ for key, metadata in list(input_map.items()):
+ for metakey, value in list(metadata.items()):
+ assert getattr(inputs.traits()[key], metakey) == value
+
+
+def test_CAT12SANLMDenoising_outputs():
+ output_map = dict(
+ out_file=dict(
+ extensions=None,
+ ),
+ )
+ outputs = CAT12SANLMDenoising.output_spec()
+
+ for key, metadata in list(output_map.items()):
+ for metakey, value in list(metadata.items()):
+ assert getattr(outputs.traits()[key], metakey) == value