NF: First pass at a dipy DTI interface.

.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,
  ),
  )