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[ENH] Add copy_header option to N4BiasFieldCorrection #2034

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Merged
merged 4 commits into from
May 26, 2017
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[ENH] Add copy_header option to N4BiasFieldCorrection #2034

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c1548a6
[ENH] Add copy_header option to N4BiasFieldCorrection
oesteban May 18, 2017
98af18e
@effigies suggestions
oesteban May 19, 2017
d973105
RF: Factor _copy_header, update bias image
effigies May 19, 2017
720809b
Merge pull request #4 from effigies/enh/N4-header
oesteban May 19, 2017
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20 changes: 20 additions & 0 deletions nipype/interfaces/ants/segmentation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -259,6 +259,9 @@ class N4BiasFieldCorrectionInputSpec(ANTSCommandInputSpec):
' to file.'), xor=['bias_image'])
bias_image = File(desc='Filename for the estimated bias.',
hash_files=False)
copy_header = traits.Bool(False, mandatory=True, usedefault=True,
desc='copy headers of the original image into the '
'output (corrected) file')


class N4BiasFieldCorrectionOutputSpec(TraitedSpec):
Expand Down Expand Up @@ -384,6 +387,23 @@ def _list_outputs(self):
self._gen_filename('bias_image'))
return outputs

def _run_interface(self, runtime, correct_return_codes=(0,)):
runtime = super(N4BiasFieldCorrection, self)._run_interface(
runtime, correct_return_codes)

if self.inputs.copy_header:
import nibabel as nb
refnii = nb.load(self.inputs.input_image)
hdr = refnii.header.copy()
out_file = self._gen_filename('output_image')
nii = nb.load(out_file)
hdr.set_data_dtype(nii.header.get_data_dtype())
nb.Nifti1Image(nii.get_data(), refnii.affine, hdr).to_filename(
out_file)
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Does N4BiasFieldCorrection always use NIfTI images, or are we making this assumption? To be more general, this could be:

if self.inputs.copy_header and runtime.returncode in correct_return_codes:
    import nibabel as nb
    in_img = nb.load(self.inputs.input_image)
    out_file = self._gen_filename('output_image')
    out_img = nb.load(out_file, mmap=False)
    new_img = out_img.__class__(out_img.get_data(), in_img.affine, in_img.header)
    new_img.set_data_dtype(out_img.get_data_dtype())
    new_img.to_filename(out_file)

A couple additional notes:

  • Should make sure we ran successfully before reading/overwriting the output
  • mmap=False on nb.load() - See Feature Request: Detect when writing to open mmap nibabel#492
  • No need to copy header (that's done in image creation)
  • I prefer to do get/set_data_dtype on the image itself, rather than the header, even though they're equivalent. Since an image contains a reference to the header and not the other way around, if some image class needs (in a future nibabel version) to modify the data portion of the image during this call, it can. Your call on this one.

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N4BiasFieldCorrection virtually accepts any of the file formats supported by ITK. So I guess it is worth checking the image type (Analyze or Nifti) before copying the header. @satra any thoughts?


return runtime



class CorticalThicknessInputSpec(ANTSCommandInputSpec):
dimension = traits.Enum(3, 2, argstr='-d %d', usedefault=True,
Expand Down
3 changes: 3 additions & 0 deletions nipype/interfaces/ants/tests/test_auto_N4BiasFieldCorrection.py
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Expand Up @@ -14,6 +14,9 @@ def test_N4BiasFieldCorrection_inputs():
),
convergence_threshold=dict(requires=['n_iterations'],
),
copy_header=dict(mandatory=True,
usedefault=True,
),
dimension=dict(argstr='-d %d',
usedefault=True,
),
Expand Down