[ENH]: Add functions to mrtrix3 interface

.zenodo.json

@@ -891,16 +891,6 @@
       "name": "Mihai, Paul Glad",
       "orcid": "0000-0001-5715-6442"
     },
-    {
-      "affiliation": "Department of Psychology, Stanford University",
-      "name": "Gorgolewski, Krzysztof J.",
-      "orcid": "0000-0003-3321-7583"
-    },
-    {
-      "affiliation": "MIT, HMS",
-      "name": "Ghosh, Satrajit",
-      "orcid": "0000-0002-5312-6729"
-    },
     {
       "affiliation": "University of Tübingen and MPI for Biological Cybernertics",
       "name": "Bannert, Michael M.",
@@ -909,13 +899,27 @@
     {
       "affiliation": "Research Centre Juelich",
       "name": "Wu, Jianxiao",
-      "orcid": "0000-0002-4866-272X",
+      "orcid": "0000-0002-4866-272X"
+    },
+    {
+      "affiliation": "Department of Neurology, BG-University Hospital Bergmannsheil Bochum, Germany",
+      "name": "Butry, Lionel"
     },
     {
       "affiliation": "Lund University",
       "name": "Anijärv, Toomas Erik",
-      "orcid": "0000-0002-3650-4230",
+      "orcid": "0000-0002-3650-4230"
+    },
+    {
+      "affiliation": "Department of Psychology, Stanford University",
+      "name": "Gorgolewski, Krzysztof J.",
+      "orcid": "0000-0003-3321-7583"
     },
+    {
+      "affiliation": "MIT, HMS",
+      "name": "Ghosh, Satrajit",
+      "orcid": "0000-0002-5312-6729"
+    }
   ],
   "keywords": [
     "neuroimaging",

nipype/interfaces/mrtrix3/__init__.py

@@ -30,4 +30,7 @@
     SHConv,
     TensorMetrics,
     TransformFSLConvert,
+    MaskFilter,
+    MTNormalise,
+    Generate5tt2gmwmi,
 )

nipype/interfaces/mrtrix3/utils.py

@@ -1185,3 +1185,197 @@
         outputs = self.output_spec().get()
         outputs["out_file"] = op.abspath(self.inputs.out_file)
         return outputs
+
+class MaskFilterInputSpec(CommandLineInputSpec):
+    in_file = File(
+        exists=True,
+        mandatory=True,
+        argstr="%s",
+        position=-3,
+        desc="Input mask",
+    )
+    filter = traits.Str(
+        mandatory=True, 
+        argstr="%s",
+        position=-2,
+        desc="Filter to perform (e.g. dilate, erode)"
+    )
+    out_file = File(
+        name_source=["input_image"],
+        mandatory=True,
+        argstr="%s",
+        position=-1,
+        desc="Output mask"
+    )
+    npass = traits.Int(
+        argstr="-npass %d",
+        position=1,
+        desc="Number of passes"
+    )
+
+class MaskFilterOutputSpec(TraitedSpec):
+    out_file = File(exists=True, desc="the filtered output mask")
+
+class MaskFilter(CommandLine):
+    """
+    Perform filtering operations on 3D / 4D mask images. 
+    Only supports dilate / erode filters at the moment.
+    For more information see: https://mrtrix.readthedocs.io/en/latest/reference/commands/maskfilter.html
+
+
+    Example
+    -------
+
+    >>> import nipype.interfaces.mrtrix3 as mrt
+    >>> mf = mrt.MaskFilter()
+    >>> mf.inputs.in_file = 'mask.mif'
+    >>> mf.inputs.filter = 'dilate'
+    >>> mf.inputs.npass = 2
+    >>> mf.out_file = 'mask_filtered.mif'
+    >>> mf.cmdline
+    'maskfilter -npass 2 mask.mif dilate mask_filtered.mif'
+    >>> mf.run()
+    """
+
+    _cmd = "maskfilter"
+    input_spec = MaskFilterInputSpec
+    output_spec = MaskFilterOutputSpec
+
+    def _list_outputs(self):
+        outputs = self.output_spec().get()
+        outputs["out_file"] = op.abspath(self.inputs.out_file)
+        return outputs
+
+class MTNormaliseInputSpec(MRTrix3BaseInputSpec):
+    wm_fod = File(
+        argstr="%s",
+        exists=True,
+        position=1,
+        desc="input fod of white matter tissue compartment"
+    )
+    out_file_wm = File(
+        argstr="%s",
+        position=2,
+        desc="output file of white matter tissue compartment"
+    )
+    gm_fod = File(
+        argstr="%s",
+        exists=True,
+        position=3,
+        desc="input fod of grey matter tissue compartment"
+    )
+    out_file_gm = File(
+        argstr="%s",
+        position=4,
+        desc="output file of grey matter tissue compartment"
+    )
+    csf_fod = File(
+        argstr="%s",
+        exists=True,
+        position=5,
+        desc="input fod of CSF tissue compartment"
+    )
+    out_file_csf = File(
+        argstr="%s",
+        position=6,
+        desc="output file of CSF tissue compartment 3"
+    )
+    mask = File(
+        argstr="-mask %s",
+        exists=True,
+        position=-1,
+        desc="input brain mask"
+    )
+
+class MTNormaliseOutputSpec(TraitedSpec):
+    out_file_wm = File(exists=True, desc="the normalized white matter fod")
+    out_file_gm = File(exists=True, desc="the normalized grey matter fod")
+    out_file_csf = File(exists=True, desc="the normalized csf fod")
+
+
+class MTNormalise(CommandLine):
+    """
+    Multi-tissue informed log-domain intensity normalisation
+
+
+    Example
+    -------
+
+    >>> import nipype.interfaces.mrtrix3 as mrt
+    >>> mtn = mrt.MTnormalise()
+    >>> mtn.inputs.fod_wm = 'wmfod.mif'
+    >>> mtn.inputs.fod_gm = 'gmfod.mif'
+    >>> mtn.inputs.fod_csf = 'csffod.mif'
+    >>> mtn.inputs.out_file_wm = 'wmfod_norm.mif'
+    >>> mtn.inputs.out_file_gm = 'gmfod_norm.mif'
+    >>> mtn.inputs.out_file_csf = 'csffod_norm.mif'
+    >>> mtn.inputs.mask = 'mask.mif'
+    >>> mtn.cmdline                      
+    'mtnormalise wmfod.mif wmfod_norm.mif gmfod.mif gmfod_norm.mif csffod.mif csffod_norm.mif -mask mask.mif'
+    >>> mtn.run()                                 
+    """
+
+    _cmd = "mtnormalise"
+    input_spec = MTNormaliseInputSpec
+    output_spec = MTNormaliseOutputSpec
+
+    def _list_outputs(self):
+        outputs = self.output_spec().get()
+        outputs["out_file_wm"] = op.abspath(self.inputs.out_file_wm)
+        outputs["out_file_gm"] = op.abspath(self.inputs.out_file_gm)
+        outputs["out_file_csf"] = op.abspath(self.inputs.out_file_csf)
+        return outputs
+
+
+class Generate5tt2gmwmiInputSpec(MRTrix3BaseInputSpec):
+    in_file = File(
+        exists=True,
+        argstr="%s",
+        mandatory=True,
+        position=-2,
+        desc="the input 5TT segmented anatomical image",
+       )
+    mask_out = File(
+        "mask_gmwmi.mif",
+        argstr="%s",
+        mandatory=True,
+        position=-1,
+        desc="the output mask image",
+        )
+    mask_in = File(
+        argstr="-mask_in %s",
+        position=-3,
+        desc="filter an imput mask image according to those voxels that lie upon the grey matter - white matter boundary",
+        )
+
+
+class Generate5tt2gmwmiOutputSpec(TraitedSpec):
+    mask_out = File(exists=True, desc="the output mask file")
+
+
+class Generate5tt2gmwmi(CommandLine):
+    """
+    Generate a mask image appropriate for seeding streamlines on 
+    the grey matter-white matter interface
+
+
+    Example
+    -------
+
+    >>> import nipype.interfaces.mrtrix3 as mrt
+    >>> gmwmi = mrt.Generate5TT2GMWMI()
+    >>> gmwmi.inputs.in_file = '5tt_in.mif'
+    >>> gmwmi.inputs.mask_out = 'mask_gmwmi.mif'
+    >>> gmwmi.cmdline                               
+    '5tt2gmwmi 5tt_in.mif mask_gmwmi.mif'
+    >>> gmwmi.run()                             
+    """
+
+    _cmd = "5tt2gmwmi"
+    input_spec = Generate5tt2gmwmiInputSpec
+    output_spec = Generate5tt2gmwmiOutputSpec
+
+    def _list_outputs(self):
+        outputs = self.output_spec().get()
+        outputs["mask_out"] = op.abspath(self.inputs.mask_out)
+        return outputs