Merge branch 'enh/tensor_mode'

Author: chrisgorgo

CHANGES

@@ -1,7 +1,8 @@
 Next release
 ============
 
-* ENH: New interfaces: nipy.Trim, fsl.GLM, fsl.SigLoss, spm.VBMSegment, fsl.InvWarp
+* ENH: New interfaces: nipy.Trim, fsl.GLM, fsl.SigLoss, spm.VBMSegment, fsl.InvWarp,
+       dipy.TensorMode
 * ENH: Allow control over terminal output for commandline interfaces
 * ENH: Added preliminary support for generating Python code from Workflows.
 * ENH: New workflows for dMRI and fMRI pre-processing: added motion artifact correction

nipype/interfaces/dipy/__init__.py

@@ -1 +1,2 @@
 from .tracks import TrackDensityMap
+from .tensors import TensorMode

nipype/interfaces/dipy/tensors.py

@@ -0,0 +1,114 @@
+# -*- coding: utf-8 -*-
+"""Change directory to provide relative paths for doctests
+   >>> import os
+   >>> filepath = os.path.dirname( os.path.realpath( __file__ ) )
+   >>> datadir = os.path.realpath(os.path.join(filepath, '../../testing/data'))
+   >>> os.chdir(datadir)
+"""
+
+from nipype.interfaces.base import (
+    TraitedSpec, BaseInterface, File)
+from nipype.utils.filemanip import split_filename
+import os.path as op
+import nibabel as nb
+import numpy as np
+from nipype.utils.misc import package_check
+import warnings
+
+from ... import logging
+iflogger = logging.getLogger('interface')
+
+try:
+    package_check('dipy', version='0.7.0')
+    import dipy.reconst.dti as dti
+    from dipy.core.gradients import GradientTable
+except Exception, e:
+    warnings.warn('dipy not installed')
+
+
+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')
+    out_filename = File(
+        genfile=True, desc='The output filename for the Tensor mode image')
+
+
+class TensorModeOutputSpec(TraitedSpec):
+    out_file = File(exists=True)
+
+
+class TensorMode(BaseInterface):
+    """
+    Creates a map of the mode of the diffusion tensors given a set of
+    diffusion-weighted images, as well as their associated b-values and
+    b-vectors. Fits the diffusion tensors and calculates tensor mode
+    with Dipy.
+
+    .. [1] Daniel B. Ennis and G. Kindlmann, "Orthogonal Tensor
+        Invariants and the Analysis of Diffusion Tensor Magnetic Resonance
+        Images", Magnetic Resonance in Medicine, vol. 55, no. 1, pp. 136-146,
+        2006.
+
+    Example
+    -------
+
+    >>> import nipype.interfaces.dipy as dipy
+    >>> mode = dipy.TensorMode()
+    >>> mode.inputs.in_file = 'diffusion.nii'
+    >>> mode.inputs.bvecs = 'bvecs'
+    >>> mode.inputs.bvals = 'bvals'
+    >>> mode.run()                                   # doctest: +SKIP
+    """
+    input_spec = TensorModeInputSpec
+    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
+
+        ## Write as a 3D Nifti image with the original affine
+        img = nb.Nifti1Image(mode_data, 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))
+        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 + '_mode.nii'