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add all mrtrix interfaces for testing
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‎dmriprep/interfaces/mrtrix3.py

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#!/usr/bin/env python
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3-
from __future__ import (
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print_function,
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division,
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unicode_literals,
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absolute_import,
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)
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from __future__ import print_function, division, unicode_literals, absolute_import
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from nipype.interfaces.base import traits, TraitedSpec, File
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from nipype.interfaces.mrtrix3.base import MRTrix3BaseInputSpec, MRTrix3Base
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class MRResizeInputSpec(MRTrix3BaseInputSpec):
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class DWIDenoiseInputSpec(MRTrix3BaseInputSpec):
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in_file = File(
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exists=True, argstr="%s", position=-2, mandatory=True, desc="input DWI image"
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)
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mask = File(exists=True, argstr="-mask %s", position=1, desc="mask image")
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extent = traits.Tuple(
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(traits.Int, traits.Int, traits.Int),
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argstr="-extent %d,%d,%d",
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desc="set the window size of the denoising filter. (default = 5,5,5)",
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)
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noise = File(
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argstr="-noise %s",
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name_template="%s_noise",
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name_source="in_file",
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keep_extension=True,
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desc="the output noise map",
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)
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out_file = File(
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argstr="%s",
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position=-1,
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name_template="%s_denoised",
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name_source="in_file",
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keep_extension=True,
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desc="the output denoised DWI image",
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)
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class DWIDenoiseOutputSpec(TraitedSpec):
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noise = File(desc="the output noise map", exists=True)
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out_file = File(desc="the output denoised DWI image", exists=True)
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class DWIDenoise(MRTrix3Base):
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"""
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Denoise DWI data and estimate the noise level based on the optimal
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threshold for PCA.
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DWI data denoising and noise map estimation by exploiting data redundancy
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in the PCA domain using the prior knowledge that the eigenspectrum of
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random covariance matrices is described by the universal Marchenko Pastur
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distribution.
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Important note: image denoising must be performed as the first step of the
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image processing pipeline. The routine will fail if interpolation or
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smoothing has been applied to the data prior to denoising.
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Note that this function does not correct for non-Gaussian noise biases.
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For more information, see
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<https://mrtrix.readthedocs.io/en/latest/reference/commands/dwidenoise.html>
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Example
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-------
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>>> import nipype.interfaces.mrtrix3 as mrt
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>>> denoise = mrt.DWIDenoise()
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>>> denoise.inputs.in_file = 'dwi.mif'
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>>> denoise.inputs.mask = 'mask.mif'
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>>> denoise.inputs.noise = 'noise.mif'
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>>> denoise.cmdline # doctest: +ELLIPSIS
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'dwidenoise -mask mask.mif -noise noise.mif dwi.mif dwi_denoised.mif'
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>>> denoise.run() # doctest: +SKIP
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"""
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_cmd = "dwidenoise"
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input_spec = DWIDenoiseInputSpec
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output_spec = DWIDenoiseOutputSpec
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class MRDeGibbsInputSpec(MRTrix3BaseInputSpec):
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in_file = File(
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exists=True,
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exists=True, argstr="%s", position=-2, mandatory=True, desc="input DWI image"
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)
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axes = traits.ListInt(
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default_value=[0, 1],
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usedefault=True,
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sep=",",
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minlen=2,
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maxlen=2,
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argstr="-axes %s",
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desc="indicate the plane in which the data was acquired (axial = 0,1; "
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"coronal = 0,2; sagittal = 1,2",
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)
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nshifts = traits.Int(
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default_value=20,
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usedefault=True,
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argstr="-nshifts %d",
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desc="discretization of subpixel spacing (default = 20)",
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)
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minW = traits.Int(
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default_value=1,
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usedefault=True,
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argstr="-minW %d",
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desc="left border of window used for total variation (TV) computation "
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"(default = 1)",
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)
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maxW = traits.Int(
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default_value=3,
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usedefault=True,
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argstr="-maxW %d",
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desc="right border of window used for total variation (TV) computation "
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"(default = 3)",
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)
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out_file = File(
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name_template="%s_unr",
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name_source="in_file",
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keep_extension=True,
17116
argstr="%s",
18-
position=-2,
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position=-1,
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desc="the output unringed DWI image",
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)
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class MRDeGibbsOutputSpec(TraitedSpec):
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out_file = File(desc="the output unringed DWI image", exists=True)
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class MRDeGibbs(MRTrix3Base):
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"""
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Remove Gibbs ringing artifacts.
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This application attempts to remove Gibbs ringing artefacts from MRI images
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using the method of local subvoxel-shifts proposed by Kellner et al.
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This command is designed to run on data directly after it has been
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reconstructed by the scanner, before any interpolation of any kind has
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taken place. You should not run this command after any form of motion
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correction (e.g. not after dwipreproc). Similarly, if you intend running
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dwidenoise, you should run this command afterwards, since it has the
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potential to alter the noise structure, which would impact on dwidenoise's
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performance.
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Note that this method is designed to work on images acquired with full
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k-space coverage. Running this method on partial Fourier ('half-scan') data
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may lead to suboptimal and/or biased results, as noted in the original
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reference below. There is currently no means of dealing with this; users
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should exercise caution when using this method on partial Fourier data, and
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inspect its output for any obvious artefacts.
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For more information, see
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<https://mrtrix.readthedocs.io/en/latest/reference/commands/mrdegibbs.html>
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Example
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-------
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>>> import nipype.interfaces.mrtrix3 as mrt
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>>> unring = mrt.MRDeGibbs()
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>>> unring.inputs.in_file = 'dwi.mif'
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>>> unring.cmdline
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'mrdegibbs -axes 0,1 -maxW 3 -minW 1 -nshifts 20 dwi.mif dwi_unr.mif'
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>>> unring.run() # doctest: +SKIP
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"""
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_cmd = "mrdegibbs"
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input_spec = MRDeGibbsInputSpec
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output_spec = MRDeGibbsOutputSpec
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class DWIBiasCorrectInputSpec(MRTrix3BaseInputSpec):
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in_file = File(
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exists=True, argstr="%s", position=-2, mandatory=True, desc="input DWI image"
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)
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in_mask = File(argstr="-mask %s", desc="input mask image for bias field estimation")
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use_ants = traits.Bool(
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argstr="-ants",
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mandatory=True,
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desc="use ANTS N4 to estimate the inhomogeneity field",
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xor=["use_fsl"],
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)
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use_fsl = traits.Bool(
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argstr="-fsl",
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mandatory=True,
20-
desc="input DWI image",
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desc="use FSL FAST to estimate the inhomogeneity field",
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xor=["use_ants"],
21183
)
22-
image_size = traits.List(
23-
traits.Int(),
24-
argstr="-size %s",
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bias = File(argstr="-bias %s", desc="bias field")
185+
out_file = File(
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name_template="%s_biascorr",
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name_source="in_file",
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keep_extension=True,
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argstr="%s",
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position=-1,
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desc="the output bias corrected DWI image",
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genfile=True,
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)
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class DWIBiasCorrectOutputSpec(TraitedSpec):
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bias = File(desc="the output bias field", exists=True)
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out_file = File(desc="the output bias corrected DWI image", exists=True)
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class DWIBiasCorrect(MRTrix3Base):
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"""
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Perform B1 field inhomogeneity correction for a DWI volume series.
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For more information, see
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<https://mrtrix.readthedocs.io/en/latest/reference/scripts/dwibiascorrect.html>
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Example
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-------
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>>> import nipype.interfaces.mrtrix3 as mrt
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>>> bias_correct = mrt.DWIBiasCorrect()
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>>> bias_correct.inputs.in_file = 'dwi.mif'
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>>> bias_correct.inputs.use_ants = True
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>>> bias_correct.cmdline
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'dwibiascorrect -ants dwi.mif dwi_biascorr.mif'
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>>> bias_correct.run() # doctest: +SKIP
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"""
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_cmd = "dwibiascorrect"
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input_spec = DWIBiasCorrectInputSpec
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output_spec = DWIBiasCorrectOutputSpec
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class MRResizeInputSpec(MRTrix3BaseInputSpec):
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in_file = File(
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exists=True, argstr="%s", position=-2, mandatory=True, desc="input DWI image"
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)
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image_size = traits.Tuple(
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(traits.Int, traits.Int, traits.Int),
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argstr="-size %d,%d,%d",
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mandatory=True,
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desc="define the new image size for the output image. This should be "
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"specified as a comma-separated list.",
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xor=["voxel_size", "scale_factor"],
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)
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voxel_size = traits.List(
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traits.Float(),
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argstr="-voxel %s",
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voxel_size = traits.Tuple(
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(traits.Float, traits.Float, traits.Float),
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argstr="-voxel %d,%d,%d",
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mandatory=True,
31241
desc="define the new voxel size for the output image. This can be "
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"specified either as a single value to be used for all "
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"dimensions, or as a comma-separated list of the size for each "
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"voxel dimension.",
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xor=["image_size", "scale_factor"],
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)
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scale_factor = traits.Float(
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argstr="-scale %s",
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scale_factor = traits.Tuple(
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(traits.Float, traits.Float, traits.Float),
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argstr="-scale %d,%d,%d",
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mandatory=True,
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desc="scale the image resolution by the supplied factor. This can be "
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"specified either as a single value to be used for all "
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"dimensions, or as a comma-separated list of scale factors for "
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"each dimension.",
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xor=["image_size", "voxel_size"],
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)
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interp = traits.Enum(
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"cubic",
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"nearest",
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"linear",
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"cubic",
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"sinc",
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default="cubic",
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usedefault=True,
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argstr="-interp %s",
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desc="set the interpolation method to use when resizing (choices: "
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"nearest, linear, cubic, sinc. Default: cubic).",
@@ -67,15 +281,18 @@ class MRResizeOutputSpec(TraitedSpec):
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class MRResize(MRTrix3Base):
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"""
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Resize an image by defining the new image resolution, voxel size or a scale factor
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Resize an image by defining the new image resolution, voxel size or a
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scale factor. If the image is 4D, then only the first 3 dimensions can be
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resized. Also, if the image is down-sampled, the appropriate smoothing is
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automatically applied using Gaussian smoothing.
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For more information, see
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<https://mrtrix.readthedocs.io/en/latest/reference/commands/mrresize.html>
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Example
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-------
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>>> import nipype.interfaces.mrtrix3 as mrt
76293
>>> resize = mrt.MRResize()
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>>> resize.inputs.in_file = 'dwi.mif'
78-
>>> resize.inputs.scal_factor = 2
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>>> resize.inputs.scale_factor = 2
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>>> resize.cmdline # doctest: +ELLIPSIS
80297
'mrresize -scale 2 dwi.mif dwi_resized.mif'
81298
>>> resize.run() # doctest: +SKIP

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