ENH: Adds interfaces for MRtrix utils shconv and sh2amp

.zenodo.json

@@ -711,6 +711,11 @@
     {
       "name": "Marina, Ana"
     },
+    {
+      "affiliation": "University of Sydney",
+      "name": "Close, Thomas",
+      "orcid": "0000-0002-4160-2134"
+    },
     {
       "name": "Davison, Andrew"
     },

nipype/interfaces/mrtrix3/__init__.py

@@ -13,6 +13,8 @@
     MRConvert,
     MRResize,
     DWIExtract,
+    SHConv,
+    SH2Amp
 )
 from .preprocess import (
     ResponseSD,

nipype/interfaces/mrtrix3/reconst.py

@@ -4,7 +4,8 @@
 
 import os.path as op
 
-from ..base import traits, TraitedSpec, File, Undefined, InputMultiObject
+from ..base import (
+    traits, TraitedSpec, File, Undefined, InputMultiObject, isdefined)
 from .base import MRTrix3BaseInputSpec, MRTrix3Base
 
 
@@ -50,10 +51,16 @@ class FitTensorInputSpec(MRTrix3BaseInputSpec):
             "only applies to the non-linear methods"
         ),
     )
+    predicted_signal = File(
+        argstr='-predicted_signal %s',
+        desc=(
+            "specify a file to contain the predicted signal from the tensor "
+            "fits. This can be used to calculate the residual signal"))
 
 
 class FitTensorOutputSpec(TraitedSpec):
     out_file = File(exists=True, desc="the output DTI file")
+    predicted_signal = File(desc="Predicted signal from fitted tensors")
 
 
 class FitTensor(MRTrix3Base):
@@ -81,6 +88,9 @@ class FitTensor(MRTrix3Base):
     def _list_outputs(self):
         outputs = self.output_spec().get()
         outputs["out_file"] = op.abspath(self.inputs.out_file)
+        if isdefined(self.inputs.predicted_signal):
+            outputs['predicted_signal'] = op.abspath(
+                self.inputs.predicted_signal)
         return outputs
 
 
@@ -144,12 +154,18 @@ class EstimateFODInputSpec(MRTrix3BaseInputSpec):
             "[ az el ] pairs for the directions."
         ),
     )
+    predicted_signal = File(
+        argstr='-predicted_signal %s',
+        desc=(
+            "specify a file to contain the predicted signal from the FOD "
+            "estimates. This can be used to calculate the residual signal"))
 
 
 class EstimateFODOutputSpec(TraitedSpec):
     wm_odf = File(argstr="%s", desc="output WM ODF")
     gm_odf = File(argstr="%s", desc="output GM ODF")
     csf_odf = File(argstr="%s", desc="output CSF ODF")
+    predicted_signal = File(desc="output predicted signal")
 
 
 class EstimateFOD(MRTrix3Base):
@@ -187,6 +203,9 @@ def _list_outputs(self):
             outputs["gm_odf"] = op.abspath(self.inputs.gm_odf)
         if self.inputs.csf_odf != Undefined:
             outputs["csf_odf"] = op.abspath(self.inputs.csf_odf)
+        if self.inputs.predicted_signal != Undefined:
+            outputs["predicted_signal"] = op.abspath(
+                self.inputs.predicted_signal)
         return outputs
 
 

nipype/interfaces/mrtrix3/utils.py

@@ -765,3 +765,132 @@ class MRResize(MRTrix3Base):
     _cmd = "mrresize"
     input_spec = MRResizeInputSpec
     output_spec = MRResizeOutputSpec
+
+
+class SHConvInputSpec(CommandLineInputSpec):
+
+    in_file = File(
+        exists=True,
+        argstr="%s",
+        mandatory=True,
+        position=-3,
+        desc="input ODF image",
+    )
+
+    # General options
+    response = File(
+        exists=True,
+        mandatory=True,
+        argstr="%s",
+        position=-2,
+        desc=("The response function"),
+    )
+
+    out_file = File(
+        "sh.mif",
+        argstr="%s",
+        mandatory=True,
+        position=-1,
+        usedefault=True,
+        desc="the output spherical harmonics",
+    )
+
+
+class SHConvOutputSpec(TraitedSpec):
+    out_file = File(exists=True,
+                    desc="the output convoluted spherical harmonics file")
+
+
+class SHConv(CommandLine):
+    """
+    Convolve spherical harmonics with a tissue response function. Useful for
+    checking residuals of ODF estimates.
+
+
+    Example
+    -------
+
+    >>> import nipype.interfaces.mrtrix3 as mrt
+    >>> sh = mrt.SHConv()
+    >>> sh.inputs.in_file = 'odf.mif'
+    >>> sh.inputs.response = 'response.txt'
+    >>> sh.cmdline                               # doctest: +ELLIPSIS
+    'shconv odf.mif response.txt sh.mif'
+    >>> sh.run()                                 # doctest: +SKIP
+    """
+
+    _cmd = "shconv"
+    input_spec = SHConvInputSpec
+    output_spec = SHConvOutputSpec
+
+    def _list_outputs(self):
+        outputs = self.output_spec().get()
+        outputs["out_file"] = op.abspath(self.inputs.out_file)
+        return outputs
+
+
+
+class SH2AmpInputSpec(CommandLineInputSpec):
+    in_file = File(
+        exists=True,
+        argstr="%s",
+        mandatory=True,
+        position=-3,
+        desc="input ODF image",
+    )
+
+    # General options
+    directions = File(
+        exists=True,
+        mandatory=True,
+        argstr="%s",
+        position=-2,
+        desc=("The gradient directions along which to sample the spherical "
+              "harmonics MRtrix format"),
+    )
+
+    out_file = File(
+        "amp.mif",
+        argstr="%s",
+        mandatory=True,
+        position=-1,
+        usedefault=True,
+        desc="the output spherical harmonics",
+    )
+
+    nonnegative = traits.Bool(
+        argstr='-nonnegative',
+        desc="cap all negative amplitudes to zero")
+
+
+class SH2AmpOutputSpec(TraitedSpec):
+    out_file = File(exists=True,
+                    desc="the output convoluted spherical harmonics file")
+
+
+class SH2Amp(CommandLine):
+    """
+    Sample spherical harmonics on a set of gradient orientations.  Useful for
+    checking residuals of ODF estimates.
+
+
+    Example
+    -------
+
+    >>> import nipype.interfaces.mrtrix3 as mrt
+    >>> sh = mrt.SH2Amp()
+    >>> sh.inputs.in_file = 'sh.mif'
+    >>> sh.inputs.directions = 'grads.txt'
+    >>> sh.cmdline                               # doctest: +ELLIPSIS
+    'sh2amp sh.mif grads.txt amp.mif'
+    >>> sh.run()                                 # doctest: +SKIP
+    """
+
+    _cmd = "sh2amp"
+    input_spec = SH2AmpInputSpec
+    output_spec = SH2AmpOutputSpec
+
+    def _list_outputs(self):
+        outputs = self.output_spec().get()
+        outputs["out_file"] = op.abspath(self.inputs.out_file)
+        return outputs