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[ENH] add align_epi_anat.py interface
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‎nipype/interfaces/afni/__init__.py

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"""
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from .base import Info
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from .preprocess import (Allineate, Automask, AutoTcorrelate,
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AutoTLRC,
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from .preprocess import (AlignEpiAnatPy,Allineate, Automask,
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AutoTcorrelate,AutoTLRC,
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Bandpass, BlurInMask, BlurToFWHM,
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ClipLevel, DegreeCentrality, Despike,
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Detrend, ECM, Fim, Fourier, Hist, LFCD,

‎nipype/interfaces/afni/preprocess.py

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import os
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import os.path as op
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from distutils import spawn
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from ...utils.filemanip import (load_json, save_json, split_filename)
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from ..base import (
@@ -46,6 +47,167 @@ class CentralityInputSpec(AFNICommandInputSpec):
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desc='Mask the dataset to target brain-only voxels',
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argstr='-automask')
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class AlignEpiAnatPyInputSpec(CommandLineInputSpec):
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outputtype = traits.Enum('AFNI', list(Info.ftypes.keys()),
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desc='AFNI output filetype')
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py27_path = File(
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desc='Path to Python 2.7 executable for running afni python scripts',
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argstr='%s '+spawn.find_executable('align_epi_anat.py'),
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exists=True,
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#default='/opt/miniconda/envs/py27/bin/python',
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mandatory=True,
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position=0
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)
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in_file = File(
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desc='EPI dataset to align',
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argstr='-epi %s',
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mandatory=True,
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exists=True,
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copyfile=False)
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anat = File(
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desc='name of structural dataset',
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argstr='-anat %s',
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mandatory=True,
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exists=True,
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copyfile=False)
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epi_base = traits.Str(
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desc='the epi base used in alignment'
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'should be one of (0/mean/median/max/subbrick#)',
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mandatory=True,
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argstr='-epi_base %s')
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anat2epi = traits.Bool(
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default = True,
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desc='align anatomical to EPI dataset (default)',
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argstr='-anat2epi')
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epi2anat = traits.Bool(
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desc='align EPI to anatomical dataset',
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argstr='-epi2anat')
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save_skullstrip = traits.Bool(
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desc='save skull-stripped (not aligned)',
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argstr='-save_skullstrip')
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suffix = traits.Str(
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desc='append suffix to the original anat/epi dataset to use'
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'in the resulting dataset names (default is "_al")',
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argstr='-suffix %s')
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epi_strip = traits.Enum(('3dSkullStrip','3dAutomask','None'),
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desc='method to mask brain in EPI data'
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'should be one of[3dSkullStrip]/3dAutomask/None)',
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argstr='-epi_strip %s')
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volreg = traits.Enum(('on','off'),
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desc='do volume registration on EPI dataset before alignment'
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'should be \'on\' or \'off\', defaults to \'on\'',
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default='on',
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argstr='-volreg %s')
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tshift = traits.Enum(('on','off'),
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desc='do time shifting of EPI dataset before alignment'
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'should be \'on\' or \'off\', defaults to \'on\'',
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default='on',
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argstr='-tshift %s')
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class AlignEpiAnatPyOutputSpec(TraitedSpec):
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anat_al_orig = File(
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desc="A version of the anatomy that is aligned to the EPI")
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epi_al_orig = File(
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desc="A version of the EPI dataset aligned to the anatomy")
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epi_tlrc_al = File(
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desc="A version of the EPI dataset aligned to a standard template")
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anat_al_mat = File(
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desc="matrix to align anatomy to the EPI")
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epi_al_mat = File(
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desc="matrix to align EPI to anatomy")
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epi_vr_al_mat = File(
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desc="matrix to volume register EPI")
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epi_reg_al_mat = File(
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desc="matrix to volume register and align epi to anatomy")
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epi_al_tlrc_mat = File(
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desc="matrix to volume register and align epi"
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"to anatomy and put into standard space")
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epi_vr_motion = File(
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desc="motion parameters from EPI time-series"
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"registration (tsh included in name if slice"
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"timing correction is also included).")
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skullstrip = File(
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desc="skull-stripped (not aligned) volume")
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class AlignEpiAnatPy(AFNICommand):
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"""align EPI to anatomical datasets or vice versa
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This Python script computes the alignment between two datasets, typically
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an EPI and an anatomical structural dataset, and applies the resulting
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transformation to one or the other to bring them into alignment.
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This script computes the transforms needed to align EPI and
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anatomical datasets using a cost function designed for this purpose. The
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script combines multiple transformations, thereby minimizing the amount of
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interpolation applied to the data.
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Basic Usage:
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align_epi_anat.py -anat anat+orig -epi epi+orig -epi_base 5
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The user must provide EPI and anatomical datasets and specify the EPI
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sub-brick to use as a base in the alignment.
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Internally, the script always aligns the anatomical to the EPI dataset,
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and the resulting transformation is saved to a 1D file.
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As a user option, the inverse of this transformation may be applied to the
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EPI dataset in order to align it to the anatomical data instead.
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This program generates several kinds of output in the form of datasets
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and transformation matrices which can be applied to other datasets if
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needed. Time-series volume registration, oblique data transformations and
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Talairach (standard template) transformations will be combined as needed
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and requested (with options to turn on and off each of the steps) in
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order to create the aligned datasets.
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For complete details, see the `align_epi_anat.py' Documentation.
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<https://afni.nimh.nih.gov/pub/dist/doc/program_help/align_epi_anat.py.html>`_
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Examples
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========
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>>> from nipype.interfaces import afni
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>>> al_ea = afni.AlignEpiAnatPy()
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>>> al_ea.inputs.py27_path = "/opt/miniconda/envs/py27/bin/python"
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>>> al_ea.inputs.anat = "structural.nii"
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>>> al_ea.inputs.in_file = "functional.nii"
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>>> al_ea.inputs.epi_base = '0'
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>>> al_ea.inputs.epi_strip = '3dAutomask'
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>>> al_ea.inputs.volreg = 'off'
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>>> al_ea.inputs.tshift = 'off'
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>>> al_ea.inputs.save_skullstrip = True
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>>> al_ea.cmdline # doctest: +ALLOW_UNICODE
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'echo "" && /opt/miniconda/envs/py27/bin/python /root/abin/align_epi_anat.py -anat structural.nii -epi_base 0 -epi_strip 3dAutomask -epi functional.nii -save_skullstrip -tshift off -volreg off'
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>>> res = allineate.run() # doctest: +SKIP
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"""
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_cmd = 'echo "" && '
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input_spec = AlignEpiAnatPyInputSpec
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output_spec = AlignEpiAnatPyOutputSpec
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def _list_outputs(self):
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outputs = self.output_spec().get()
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anat_prefix = ''.join(self._gen_fname(self.inputs.anat).split('+')[:-1])
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epi_prefix = ''.join(self._gen_fname(self.inputs.in_file).split('+')[:-1])
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ext = '.HEAD'
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matext='.1D'
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if not isdefined(self.inputs.suffix):
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suffix = '_al'
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else:
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suffix = '_'+self.inputs.suffix
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if self.inputs.anat2epi:
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outputs['anat_al_orig'] = os.path.abspath(self._gen_fname(anat_prefix, suffix=suffix+'+orig')+ext)
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outputs['anat_al_mat'] = os.path.abspath(self._gen_fname(anat_prefix, suffix=suffix+'_mat.aff12')+matext)
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if self.inputs.epi2anat:
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outputs['epi_al_orig'] = os.path.abspath(self._gen_fname(epi_prefix, suffix=suffix+'+orig')+ext)
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outputs['epi_al_mat'] = os.path.abspath(self._gen_fname(epi_prefix, suffix=suffix+'_mat.aff12')+matext)
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if self.inputs.volreg == 'on':
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outputs['epi_vr_al_mat'] = os.path.abspath(self._gen_fname(epi_prefix, suffix='_vr'+suffix+'_mat.aff12')+matext)
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if self.inputs.tshift == 'on':
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outputs['epi_vr_motion'] = os.path.abspath(self._gen_fname(epi_prefix, suffix='tsh_vr_motion')+matext)
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elif self.inputs.tshift == 'off':
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outputs['epi_vr_motion'] = os.path.abspath(self._gen_fname(epi_prefix, suffix='vr_motion')+matext)
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if self.inputs.volreg == 'on' and self.inputs.epi2anat:
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outputs['epi_reg_al_mat']= os.path.abspath(self._gen_fname(epi_prefix, suffix='_reg'+suffix+'_mat.aff12')+matext)
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if self.inputs.save_skullstrip:
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outputs.skullstrip = os.path.abspath(self._gen_fname(anat_prefix, suffix='_ns'+'+orig')+ext)
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return outputs
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class AllineateInputSpec(AFNICommandInputSpec):
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in_file = File(

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