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utils.py
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utils.py
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# -*- coding: utf-8 -*-
# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
"""DTITK utility interfaces
DTI-TK developed by Gary Hui Zhang, gary.zhang@ucl.ac.uk
For additional help, visit http://dti-tk.sf.net
The high-dimensional tensor-based DTI registration algorithm
Zhang, H., Avants, B.B, Yushkevich, P.A., Woo, J.H., Wang, S., McCluskey, L.H.,
Elman, L.B., Melhem, E.R., Gee, J.C., High-dimensional spatial normalization of
diffusion tensor images improves the detection of white matter differences in
amyotrophic lateral sclerosis, IEEE Transactions on Medical Imaging,
26(11):1585-1597, November 2007. PMID: 18041273.
The original piecewise-affine tensor-based DTI registration algorithm at the
core of DTI-TK
Zhang, H., Yushkevich, P.A., Alexander, D.C., Gee, J.C., Deformable
registration of diffusion tensor MR images with explicit orientation
optimization, Medical Image Analysis, 10(5):764-785, October 2006. PMID:
16899392.
"""
from ..base import TraitedSpec, CommandLineInputSpec, File, traits, isdefined
from ...utils.filemanip import fname_presuffix
from .base import CommandLineDtitk, DTITKRenameMixin
import os
__docformat__ = "restructuredtext"
class TVAdjustVoxSpInputSpec(CommandLineInputSpec):
in_file = File(
desc="tensor volume to modify", exists=True, mandatory=True, argstr="-in %s"
)
out_file = File(
desc="output path",
argstr="-out %s",
name_source="in_file",
name_template="%s_avs",
keep_extension=True,
)
target_file = File(
desc="target volume to match", argstr="-target %s", xor=["voxel_size", "origin"]
)
voxel_size = traits.Tuple(
(traits.Float(), traits.Float(), traits.Float()),
desc="xyz voxel size (superseded by target)",
argstr="-vsize %g %g %g",
xor=["target_file"],
)
origin = traits.Tuple(
(traits.Float(), traits.Float(), traits.Float()),
desc="xyz origin (superseded by target)",
argstr="-origin %g %g %g",
xor=["target_file"],
)
class TVAdjustVoxSpOutputSpec(TraitedSpec):
out_file = File(exists=True)
class TVAdjustVoxSp(CommandLineDtitk):
"""
Adjusts the voxel space of a tensor volume.
Example
-------
>>> from nipype.interfaces import dtitk
>>> node = dtitk.TVAdjustVoxSp()
>>> node.inputs.in_file = 'im1.nii'
>>> node.inputs.target_file = 'im2.nii'
>>> node.cmdline
'TVAdjustVoxelspace -in im1.nii -out im1_avs.nii -target im2.nii'
>>> node.run() # doctest: +SKIP
"""
input_spec = TVAdjustVoxSpInputSpec
output_spec = TVAdjustVoxSpOutputSpec
_cmd = "TVAdjustVoxelspace"
class SVAdjustVoxSpInputSpec(CommandLineInputSpec):
in_file = File(
desc="scalar volume to modify", exists=True, mandatory=True, argstr="-in %s"
)
out_file = File(
desc="output path",
argstr="-out %s",
name_source="in_file",
name_template="%s_avs",
keep_extension=True,
)
target_file = File(
desc="target volume to match", argstr="-target %s", xor=["voxel_size", "origin"]
)
voxel_size = traits.Tuple(
(traits.Float(), traits.Float(), traits.Float()),
desc="xyz voxel size (superseded by target)",
argstr="-vsize %g %g %g",
xor=["target_file"],
)
origin = traits.Tuple(
(traits.Float(), traits.Float(), traits.Float()),
desc="xyz origin (superseded by target)",
argstr="-origin %g %g %g",
xor=["target_file"],
)
class SVAdjustVoxSpOutputSpec(TraitedSpec):
out_file = File(exists=True)
class SVAdjustVoxSp(CommandLineDtitk):
"""
Adjusts the voxel space of a scalar volume.
Example
-------
>>> from nipype.interfaces import dtitk
>>> node = dtitk.SVAdjustVoxSp()
>>> node.inputs.in_file = 'im1.nii'
>>> node.inputs.target_file = 'im2.nii'
>>> node.cmdline
'SVAdjustVoxelspace -in im1.nii -out im1_avs.nii -target im2.nii'
>>> node.run() # doctest: +SKIP
"""
input_spec = SVAdjustVoxSpInputSpec
output_spec = SVAdjustVoxSpOutputSpec
_cmd = "SVAdjustVoxelspace"
class TVResampleInputSpec(CommandLineInputSpec):
in_file = File(
desc="tensor volume to resample", exists=True, mandatory=True, argstr="-in %s"
)
out_file = File(
desc="output path",
name_source="in_file",
name_template="%s_resampled",
keep_extension=True,
argstr="-out %s",
)
target_file = File(
desc="specs read from the target volume",
argstr="-target %s",
xor=["array_size", "voxel_size", "origin"],
)
align = traits.Enum(
"center",
"origin",
argstr="-align %s",
desc="how to align output volume to input volume",
)
interpolation = traits.Enum(
"LEI", "EI", argstr="-interp %s", desc="Log Euclidean Euclidean Interpolation"
)
array_size = traits.Tuple(
(traits.Int(), traits.Int(), traits.Int()),
desc="resampled array size",
xor=["target_file"],
argstr="-size %d %d %d",
)
voxel_size = traits.Tuple(
(traits.Float(), traits.Float(), traits.Float()),
desc="resampled voxel size",
xor=["target_file"],
argstr="-vsize %g %g %g",
)
origin = traits.Tuple(
(traits.Float(), traits.Float(), traits.Float()),
desc="xyz origin",
xor=["target_file"],
argstr="-origin %g %g %g",
)
class TVResampleOutputSpec(TraitedSpec):
out_file = File(exists=True)
class TVResample(CommandLineDtitk):
"""
Resamples a tensor volume.
Example
-------
>>> from nipype.interfaces import dtitk
>>> node = dtitk.TVResample()
>>> node.inputs.in_file = 'im1.nii'
>>> node.inputs.target_file = 'im2.nii'
>>> node.cmdline
'TVResample -in im1.nii -out im1_resampled.nii -target im2.nii'
>>> node.run() # doctest: +SKIP
"""
input_spec = TVResampleInputSpec
output_spec = TVResampleOutputSpec
_cmd = "TVResample"
class SVResampleInputSpec(CommandLineInputSpec):
in_file = File(
desc="image to resample", exists=True, mandatory=True, argstr="-in %s"
)
out_file = File(
desc="output path",
name_source="in_file",
name_template="%s_resampled",
keep_extension=True,
argstr="-out %s",
)
target_file = File(
desc="specs read from the target volume",
argstr="-target %s",
xor=["array_size", "voxel_size", "origin"],
)
align = traits.Enum(
"center",
"origin",
argstr="-align %s",
desc="how to align output volume to input volume",
)
array_size = traits.Tuple(
(traits.Int(), traits.Int(), traits.Int()),
desc="resampled array size",
xor=["target_file"],
argstr="-size %d %d %d",
)
voxel_size = traits.Tuple(
(traits.Float(), traits.Float(), traits.Float()),
desc="resampled voxel size",
xor=["target_file"],
argstr="-vsize %g %g %g",
)
origin = traits.Tuple(
(traits.Float(), traits.Float(), traits.Float()),
desc="xyz origin",
xor=["target_file"],
argstr="-origin %g %g %g",
)
class SVResampleOutputSpec(TraitedSpec):
out_file = File(exists=True)
class SVResample(CommandLineDtitk):
"""
Resamples a scalar volume.
Example
-------
>>> from nipype.interfaces import dtitk
>>> node = dtitk.SVResample()
>>> node.inputs.in_file = 'im1.nii'
>>> node.inputs.target_file = 'im2.nii'
>>> node.cmdline
'SVResample -in im1.nii -out im1_resampled.nii -target im2.nii'
>>> node.run() # doctest: +SKIP
"""
input_spec = SVResampleInputSpec
output_spec = SVResampleOutputSpec
_cmd = "SVResample"
class TVtoolInputSpec(CommandLineInputSpec):
in_file = File(
desc="scalar volume to resample", exists=True, argstr="-in %s", mandatory=True
)
"""NOTE: there are a lot more options here; not implementing all of them"""
in_flag = traits.Enum("fa", "tr", "ad", "rd", "pd", "rgb", argstr="-%s", desc="")
out_file = File(argstr="-out %s", genfile=True)
class TVtoolOutputSpec(TraitedSpec):
out_file = File()
class TVtool(CommandLineDtitk):
"""
Calculates a tensor metric volume from a tensor volume.
Example
-------
>>> from nipype.interfaces import dtitk
>>> node = dtitk.TVtool()
>>> node.inputs.in_file = 'im1.nii'
>>> node.inputs.in_flag = 'fa'
>>> node.cmdline
'TVtool -in im1.nii -fa -out im1_fa.nii'
>>> node.run() # doctest: +SKIP
"""
input_spec = TVtoolInputSpec
output_spec = TVtoolOutputSpec
_cmd = "TVtool"
def _list_outputs(self):
outputs = self._outputs().get()
out_file = self.inputs.out_file
if not isdefined(out_file):
out_file = self._gen_filename("out_file")
outputs["out_file"] = os.path.abspath(out_file)
return outputs
def _gen_filename(self, name):
if name != "out_file":
return
return fname_presuffix(
os.path.basename(self.inputs.in_file), suffix="_" + self.inputs.in_flag
)
"""Note: SVTool not implemented at this time"""
class BinThreshInputSpec(CommandLineInputSpec):
in_file = File(
desc="Image to threshold/binarize",
exists=True,
position=0,
argstr="%s",
mandatory=True,
)
out_file = File(
desc="output path",
position=1,
argstr="%s",
keep_extension=True,
name_source="in_file",
name_template="%s_thrbin",
)
lower_bound = traits.Float(
0.01,
usedefault=True,
position=2,
argstr="%g",
mandatory=True,
desc="lower bound of binarization range",
)
upper_bound = traits.Float(
100,
usedefault=True,
position=3,
argstr="%g",
mandatory=True,
desc="upper bound of binarization range",
)
inside_value = traits.Float(
1,
position=4,
argstr="%g",
usedefault=True,
mandatory=True,
desc="value for voxels in " "binarization range",
)
outside_value = traits.Float(
0,
position=5,
argstr="%g",
usedefault=True,
mandatory=True,
desc="value for voxels" "outside of binarization range",
)
class BinThreshOutputSpec(TraitedSpec):
out_file = File(exists=True)
class BinThresh(CommandLineDtitk):
"""
Binarizes an image.
Example
-------
>>> from nipype.interfaces import dtitk
>>> node = dtitk.BinThresh()
>>> node.inputs.in_file = 'im1.nii'
>>> node.inputs.lower_bound = 0
>>> node.inputs.upper_bound = 100
>>> node.inputs.inside_value = 1
>>> node.inputs.outside_value = 0
>>> node.cmdline
'BinaryThresholdImageFilter im1.nii im1_thrbin.nii 0 100 1 0'
>>> node.run() # doctest: +SKIP
"""
input_spec = BinThreshInputSpec
output_spec = BinThreshOutputSpec
_cmd = "BinaryThresholdImageFilter"
class BinThreshTask(DTITKRenameMixin, BinThresh):
pass
class SVAdjustVoxSpTask(DTITKRenameMixin, SVAdjustVoxSp):
pass
class SVResampleTask(DTITKRenameMixin, SVResample):
pass
class TVAdjustOriginTask(DTITKRenameMixin, TVAdjustVoxSp):
pass
class TVAdjustVoxSpTask(DTITKRenameMixin, TVAdjustVoxSp):
pass
class TVResampleTask(DTITKRenameMixin, TVResample):
pass
class TVtoolTask(DTITKRenameMixin, TVtool):
pass