Merge pull request #1121 from arokem/dipy-tensor

NF: First pass at a dipy DTI interface.

Author: chrisgorgo

.gitignore

@@ -14,3 +14,4 @@
 .pydevproject
 .idea/
 /documentation.zip
+.DS_Store

nipype/interfaces/dipy/__init__.py

@@ -1,3 +1,3 @@
 from .tracks import TrackDensityMap
-from .tensors import TensorMode
+from .tensors import TensorMode, DTI
 from .preprocess import Resample, Denoise

nipype/interfaces/dipy/tensors.py

@@ -25,16 +25,116 @@
     have_dipy = False
 else:
     import dipy.reconst.dti as dti
-    from dipy.core.gradients import GradientTable
+    from dipy.core.gradients import gradient_table
+    from dipy.io.utils import nifti1_symmat
 
 
+def tensor_fitting(data, bvals, bvecs, mask_file=None):
+    """
+    Use dipy to fit DTI
+    
+    Parameters
+    ----------
+    in_file : str
+        Full path to a DWI data file.
+    bvals : str
+        Full path to a file containing gradient magnitude information (b-values).
+    bvecs : str
+        Full path to a file containing gradient direction information (b-vectors).
+    mask_file : str, optional
+        Full path to a file containing a binary mask. Defaults to use the entire volume.
+
+    Returns
+    -------
+    TensorFit object, affine
+    """
+    img = nb.load(in_file).get_data()
+    data = img.get_data()
+    affine = img.get_affine()
+    if mask_file is not None:
+        mask = nb.load(self.inputs.mask_file).get_data()
+    else:
+        mask=None
+
+    # Load information about the gradients:
+    gtab = grad.gradient_table(self.inputs.bvals, self.inputs.bvecs)
+        
+    # Fit it
+    tenmodel = dti.TensorModel(gtab)
+    return tenmodel.fit(data, mask), affine
+
+
+class DTIInputSpec(TraitedSpec):
+    in_file = File(exists=True, mandatory=True,
+                   desc='The input 4D diffusion-weighted image file')
+    bvecs = File(exists=True, mandatory=True,
+                 desc='The input b-vector text file')
+    bvals = File(exists=True, mandatory=True,
+                 desc='The input b-value text file')
+    mask_file = File(exists=True, mandatory=False,
+                 desc='An optional white matter mask')
+    out_filename = File(
+        genfile=True, desc='The output filename for the DTI parameters image')
+
+
+class DTIOutputSpec(TraitedSpec):
+    out_file = File(exists=True)
+
+
+class DTI(BaseInterface):
+    """
+    Calculates the diffusion tensor model parameters
+    
+    Example
+    -------
+
+    >>> import nipype.interfaces.dipy as dipy
+    >>> dti = dipy.DTI()
+    >>> dti.inputs.in_file = 'diffusion.nii'
+    >>> dti.inputs.bvecs = 'bvecs'
+    >>> dti.inputs.bvals = 'bvals'
+    >>> dti.run()                                   # doctest: +SKIP
+    """
+    input_spec = DTIInputSpec
+    output_spec = DTIOutputSpec
+
+    def _run_interface(self, runtime):
+        ten_fit, affine = tensor_fitting(self.inputs.in_file, 
+                                         self.inputs.bvals,
+                                         self.inputs.bvecs,
+                                         self.inputs.mask_file)
+        lower_triangular = tenfit.lower_triangular()
+        img = nifti1_symmat(lower_triangular, affine)
+        out_file = op.abspath(self._gen_outfilename())
+        nb.save(img, out_file)
+        iflogger.info('DTI parameters image saved as {i}'.format(i=out_file))
+        return runtime
+
+    def _list_outputs(self):
+        outputs = self._outputs().get()
+        outputs['out_file'] = op.abspath(self._gen_outfilename())
+        return outputs
+
+    def _gen_filename(self, name):
+        if name is 'out_filename':
+            return self._gen_outfilename()
+        else:
+            return None
+
+    def _gen_outfilename(self):
+        _, name, _ = split_filename(self.inputs.in_file)
+        return name + '_dti.nii'
+    
+
 class TensorModeInputSpec(TraitedSpec):
     in_file = File(exists=True, mandatory=True,
                    desc='The input 4D diffusion-weighted image file')
     bvecs = File(exists=True, mandatory=True,
                  desc='The input b-vector text file')
     bvals = File(exists=True, mandatory=True,
                  desc='The input b-value text file')
+    mask_file = File(exists=True, mandatory=False,
+                     desc='An optional white matter mask')
     out_filename = File(
         genfile=True, desc='The output filename for the Tensor mode image')
 
@@ -69,32 +169,11 @@ class TensorMode(BaseInterface):
     output_spec = TensorModeOutputSpec
 
     def _run_interface(self, runtime):
-        ## Load the 4D image files
-        img = nb.load(self.inputs.in_file)
-        data = img.get_data()
-        affine = img.get_affine()
-
-        ## Load the gradient strengths and directions
-        bvals = np.loadtxt(self.inputs.bvals)
-        gradients = np.loadtxt(self.inputs.bvecs).T
-
-        ## Place in Dipy's preferred format
-        gtab = GradientTable(gradients)
-        gtab.bvals = bvals
-
-        ## Mask the data so that tensors are not fit for
-        ## unnecessary voxels
-        mask = data[..., 0] > 50
-
-        ## Fit the tensors to the data
-        tenmodel = dti.TensorModel(gtab)
-        tenfit = tenmodel.fit(data, mask)
-
-        ## Calculate the mode of each voxel's tensor
-        mode_data = tenfit.mode
+        ten_fit = tensor_fitting(self.inputs.in_file, self.inputs.bvals, self.inputs.bvecs,
+                                 self.inputs.mask_file)
 
         ## Write as a 3D Nifti image with the original affine
-        img = nb.Nifti1Image(mode_data, affine)
+        img = nb.Nifti1Image(tenfit.mode, affine)
         out_file = op.abspath(self._gen_outfilename())
         nb.save(img, out_file)
         iflogger.info('Tensor mode image saved as {i}'.format(i=out_file))

nipype/interfaces/dipy/tests/test_auto_DTI.py

@@ -0,0 +1,31 @@
+# AUTO-GENERATED by tools/checkspecs.py - DO NOT EDIT
+from nipype.testing import assert_equal
+from nipype.interfaces.dipy.tensors import DTI
+
+def test_DTI_inputs():
+    input_map = dict(bvals=dict(mandatory=True,
+    ),
+    bvecs=dict(mandatory=True,
+    ),
+    in_file=dict(mandatory=True,
+    ),
+    mask_file=dict(mandatory=False,
+    ),
+    out_filename=dict(genfile=True,
+    ),
+    )
+    inputs = DTI.input_spec()
+
+    for key, metadata in input_map.items():
+        for metakey, value in metadata.items():
+            yield assert_equal, getattr(inputs.traits()[key], metakey), value
+
+def test_DTI_outputs():
+    output_map = dict(out_file=dict(),
+    )
+    outputs = DTI.output_spec()
+
+    for key, metadata in output_map.items():
+        for metakey, value in metadata.items():
+            yield assert_equal, getattr(outputs.traits()[key], metakey), value
+

nipype/interfaces/dipy/tests/test_auto_TensorMode.py

@@ -9,6 +9,8 @@ def test_TensorMode_inputs():
     ),
     in_file=dict(mandatory=True,
     ),
+    mask_file=dict(mandatory=False,
+    ),
     out_filename=dict(genfile=True,
     ),
     )