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epi.py
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epi.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:
"""The fsl module provides classes for interfacing with the `FSL
<http://www.fmrib.ox.ac.uk/fsl/index.html>`_ command line tools. This
was written to work with FSL version 5.0.4.
"""
import os
import numpy as np
import nibabel as nb
import warnings
from ...utils.filemanip import split_filename, fname_presuffix
from ...utils import NUMPY_MMAP
from ..base import traits, TraitedSpec, InputMultiPath, File, isdefined
from .base import FSLCommand, FSLCommandInputSpec, Info
class PrepareFieldmapInputSpec(FSLCommandInputSpec):
scanner = traits.String(
"SIEMENS", argstr="%s", position=1, desc="must be SIEMENS", usedefault=True
)
in_phase = File(
exists=True,
argstr="%s",
position=2,
mandatory=True,
desc=(
"Phase difference map, in SIEMENS format range from " "0-4096 or 0-8192)"
),
)
in_magnitude = File(
exists=True,
argstr="%s",
position=3,
mandatory=True,
desc="Magnitude difference map, brain extracted",
)
delta_TE = traits.Float(
2.46,
usedefault=True,
mandatory=True,
argstr="%f",
position=-2,
desc=(
"echo time difference of the "
"fieldmap sequence in ms. (usually 2.46ms in"
" Siemens)"
),
)
nocheck = traits.Bool(
False,
position=-1,
argstr="--nocheck",
usedefault=True,
desc=("do not perform sanity checks for image " "size/range/dimensions"),
)
out_fieldmap = File(
argstr="%s", position=4, desc="output name for prepared fieldmap"
)
class PrepareFieldmapOutputSpec(TraitedSpec):
out_fieldmap = File(exists=True, desc="output name for prepared fieldmap")
class PrepareFieldmap(FSLCommand):
"""
Interface for the fsl_prepare_fieldmap script (FSL 5.0)
Prepares a fieldmap suitable for FEAT from SIEMENS data - saves output in
rad/s format (e.g. ```fsl_prepare_fieldmap SIEMENS
images_3_gre_field_mapping images_4_gre_field_mapping fmap_rads 2.65```).
Examples
--------
>>> from nipype.interfaces.fsl import PrepareFieldmap
>>> prepare = PrepareFieldmap()
>>> prepare.inputs.in_phase = "phase.nii"
>>> prepare.inputs.in_magnitude = "magnitude.nii"
>>> prepare.inputs.output_type = "NIFTI_GZ"
>>> prepare.cmdline # doctest: +ELLIPSIS
'fsl_prepare_fieldmap SIEMENS phase.nii magnitude.nii \
.../phase_fslprepared.nii.gz 2.460000'
>>> res = prepare.run() # doctest: +SKIP
"""
_cmd = "fsl_prepare_fieldmap"
input_spec = PrepareFieldmapInputSpec
output_spec = PrepareFieldmapOutputSpec
def _parse_inputs(self, skip=None):
if skip is None:
skip = []
if not isdefined(self.inputs.out_fieldmap):
self.inputs.out_fieldmap = self._gen_fname(
self.inputs.in_phase, suffix="_fslprepared"
)
if not isdefined(self.inputs.nocheck) or not self.inputs.nocheck:
skip += ["nocheck"]
return super(PrepareFieldmap, self)._parse_inputs(skip=skip)
def _list_outputs(self):
outputs = self.output_spec().get()
outputs["out_fieldmap"] = self.inputs.out_fieldmap
return outputs
def _run_interface(self, runtime):
runtime = super(PrepareFieldmap, self)._run_interface(runtime)
if runtime.returncode == 0:
out_file = self.inputs.out_fieldmap
im = nb.load(out_file, mmap=NUMPY_MMAP)
dumb_img = nb.Nifti1Image(np.zeros(im.shape), im.affine, im.header)
out_nii = nb.funcs.concat_images((im, dumb_img))
nb.save(out_nii, out_file)
return runtime
class TOPUPInputSpec(FSLCommandInputSpec):
in_file = File(
exists=True,
mandatory=True,
desc="name of 4D file with images",
argstr="--imain=%s",
)
encoding_file = File(
exists=True,
mandatory=True,
xor=["encoding_direction"],
desc="name of text file with PE directions/times",
argstr="--datain=%s",
)
encoding_direction = traits.List(
traits.Enum("y", "x", "z", "x-", "y-", "z-"),
mandatory=True,
xor=["encoding_file"],
requires=["readout_times"],
argstr="--datain=%s",
desc=("encoding direction for automatic " "generation of encoding_file"),
)
readout_times = InputMultiPath(
traits.Float,
requires=["encoding_direction"],
xor=["encoding_file"],
mandatory=True,
desc=("readout times (dwell times by # " "phase-encode steps minus 1)"),
)
out_base = File(
desc=(
"base-name of output files (spline "
"coefficients (Hz) and movement parameters)"
),
name_source=["in_file"],
name_template="%s_base",
argstr="--out=%s",
hash_files=False,
)
out_field = File(
argstr="--fout=%s",
hash_files=False,
name_source=["in_file"],
name_template="%s_field",
desc="name of image file with field (Hz)",
)
out_warp_prefix = traits.Str(
"warpfield",
argstr="--dfout=%s",
hash_files=False,
desc="prefix for the warpfield images (in mm)",
usedefault=True,
)
out_mat_prefix = traits.Str(
"xfm",
argstr="--rbmout=%s",
hash_files=False,
desc="prefix for the realignment matrices",
usedefault=True,
)
out_jac_prefix = traits.Str(
"jac",
argstr="--jacout=%s",
hash_files=False,
desc="prefix for the warpfield images",
usedefault=True,
)
out_corrected = File(
argstr="--iout=%s",
hash_files=False,
name_source=["in_file"],
name_template="%s_corrected",
desc="name of 4D image file with unwarped images",
)
out_logfile = File(
argstr="--logout=%s",
desc="name of log-file",
name_source=["in_file"],
name_template="%s_topup.log",
keep_extension=True,
hash_files=False,
)
# TODO: the following traits admit values separated by commas, one value
# per registration level inside topup.
warp_res = traits.Float(
argstr="--warpres=%f",
desc=(
"(approximate) resolution (in mm) of warp "
"basis for the different sub-sampling levels"
),
)
subsamp = traits.Int(argstr="--subsamp=%d", desc="sub-sampling scheme")
fwhm = traits.Float(
argstr="--fwhm=%f", desc="FWHM (in mm) of gaussian smoothing kernel"
)
config = traits.String(
"b02b0.cnf",
argstr="--config=%s",
usedefault=True,
desc=("Name of config file specifying command line " "arguments"),
)
max_iter = traits.Int(argstr="--miter=%d", desc="max # of non-linear iterations")
reg_lambda = traits.Float(
argstr="--lambda=%0.f",
desc=(
"Weight of regularisation, default "
"depending on --ssqlambda and --regmod switches."
),
)
ssqlambda = traits.Enum(
1,
0,
argstr="--ssqlambda=%d",
desc=(
"Weight lambda by the current value of the "
"ssd. If used (=1), the effective weight of "
"regularisation term becomes higher for the "
"initial iterations, therefore initial steps"
" are a little smoother than they would "
"without weighting. This reduces the "
"risk of finding a local minimum."
),
)
regmod = traits.Enum(
"bending_energy",
"membrane_energy",
argstr="--regmod=%s",
desc=(
"Regularisation term implementation. Defaults "
"to bending_energy. Note that the two functions"
" have vastly different scales. The membrane "
"energy is based on the first derivatives and "
"the bending energy on the second derivatives. "
"The second derivatives will typically be much "
"smaller than the first derivatives, so input "
"lambda will have to be larger for "
"bending_energy to yield approximately the same"
" level of regularisation."
),
)
estmov = traits.Enum(1, 0, argstr="--estmov=%d", desc="estimate movements if set")
minmet = traits.Enum(
0,
1,
argstr="--minmet=%d",
desc=(
"Minimisation method 0=Levenberg-Marquardt, " "1=Scaled Conjugate Gradient"
),
)
splineorder = traits.Int(
argstr="--splineorder=%d",
desc=("order of spline, 2->Qadratic spline, " "3->Cubic spline"),
)
numprec = traits.Enum(
"double",
"float",
argstr="--numprec=%s",
desc=("Precision for representing Hessian, double " "or float."),
)
interp = traits.Enum(
"spline",
"linear",
argstr="--interp=%s",
desc="Image interpolation model, linear or spline.",
)
scale = traits.Enum(
0,
1,
argstr="--scale=%d",
desc=("If set (=1), the images are individually scaled" " to a common mean"),
)
regrid = traits.Enum(
1,
0,
argstr="--regrid=%d",
desc=("If set (=1), the calculations are done in a " "different grid"),
)
class TOPUPOutputSpec(TraitedSpec):
out_fieldcoef = File(exists=True, desc="file containing the field coefficients")
out_movpar = File(exists=True, desc="movpar.txt output file")
out_enc_file = File(desc="encoding directions file output for applytopup")
out_field = File(desc="name of image file with field (Hz)")
out_warps = traits.List(File(exists=True), desc="warpfield images")
out_jacs = traits.List(File(exists=True), desc="Jacobian images")
out_mats = traits.List(File(exists=True), desc="realignment matrices")
out_corrected = File(desc="name of 4D image file with unwarped images")
out_logfile = File(desc="name of log-file")
class TOPUP(FSLCommand):
"""
Interface for FSL topup, a tool for estimating and correcting
susceptibility induced distortions. See FSL documentation for
`reference <http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/TOPUP>`_,
`usage examples
<http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/topup/ExampleTopupFollowedByApplytopup>`_,
and `exemplary config files
<https://github.com/ahheckel/FSL-scripts/blob/master/rsc/fsl/fsl4/topup/b02b0.cnf>`_.
Examples
--------
>>> from nipype.interfaces.fsl import TOPUP
>>> topup = TOPUP()
>>> topup.inputs.in_file = "b0_b0rev.nii"
>>> topup.inputs.encoding_file = "topup_encoding.txt"
>>> topup.inputs.output_type = "NIFTI_GZ"
>>> topup.cmdline # doctest: +ELLIPSIS
'topup --config=b02b0.cnf --datain=topup_encoding.txt \
--imain=b0_b0rev.nii --out=b0_b0rev_base --iout=b0_b0rev_corrected.nii.gz \
--fout=b0_b0rev_field.nii.gz --jacout=jac --logout=b0_b0rev_topup.log \
--rbmout=xfm --dfout=warpfield'
>>> res = topup.run() # doctest: +SKIP
"""
_cmd = "topup"
input_spec = TOPUPInputSpec
output_spec = TOPUPOutputSpec
def _format_arg(self, name, trait_spec, value):
if name == "encoding_direction":
return trait_spec.argstr % self._generate_encfile()
if name == "out_base":
path, name, ext = split_filename(value)
if path != "":
if not os.path.exists(path):
raise ValueError("out_base path must exist if provided")
return super(TOPUP, self)._format_arg(name, trait_spec, value)
def _list_outputs(self):
outputs = super(TOPUP, self)._list_outputs()
del outputs["out_base"]
base_path = None
if isdefined(self.inputs.out_base):
base_path, base, _ = split_filename(self.inputs.out_base)
if base_path == "":
base_path = None
else:
base = split_filename(self.inputs.in_file)[1] + "_base"
outputs["out_fieldcoef"] = self._gen_fname(
base, suffix="_fieldcoef", cwd=base_path
)
outputs["out_movpar"] = self._gen_fname(
base, suffix="_movpar", ext=".txt", cwd=base_path
)
n_vols = nb.load(self.inputs.in_file).shape[-1]
ext = Info.output_type_to_ext(self.inputs.output_type)
fmt = os.path.abspath("{prefix}_{i:02d}{ext}").format
outputs["out_warps"] = [
fmt(prefix=self.inputs.out_warp_prefix, i=i, ext=ext)
for i in range(1, n_vols + 1)
]
outputs["out_jacs"] = [
fmt(prefix=self.inputs.out_jac_prefix, i=i, ext=ext)
for i in range(1, n_vols + 1)
]
outputs["out_mats"] = [
fmt(prefix=self.inputs.out_mat_prefix, i=i, ext=".mat")
for i in range(1, n_vols + 1)
]
if isdefined(self.inputs.encoding_direction):
outputs["out_enc_file"] = self._get_encfilename()
return outputs
def _get_encfilename(self):
out_file = os.path.join(
os.getcwd(), ("%s_encfile.txt" % split_filename(self.inputs.in_file)[1])
)
return out_file
def _generate_encfile(self):
"""Generate a topup compatible encoding file based on given directions
"""
out_file = self._get_encfilename()
durations = self.inputs.readout_times
if len(self.inputs.encoding_direction) != len(durations):
if len(self.inputs.readout_times) != 1:
raise ValueError(
(
"Readout time must be a float or match the"
"length of encoding directions"
)
)
durations = durations * len(self.inputs.encoding_direction)
lines = []
for idx, encdir in enumerate(self.inputs.encoding_direction):
direction = 1.0
if encdir.endswith("-"):
direction = -1.0
line = [
float(val[0] == encdir[0]) * direction for val in ["x", "y", "z"]
] + [durations[idx]]
lines.append(line)
np.savetxt(out_file, np.array(lines), fmt=b"%d %d %d %.8f")
return out_file
def _overload_extension(self, value, name=None):
if name == "out_base":
return value
return super(TOPUP, self)._overload_extension(value, name)
class ApplyTOPUPInputSpec(FSLCommandInputSpec):
in_files = InputMultiPath(
File(exists=True),
mandatory=True,
desc="name of file with images",
argstr="--imain=%s",
sep=",",
)
encoding_file = File(
exists=True,
mandatory=True,
desc="name of text file with PE directions/times",
argstr="--datain=%s",
)
in_index = traits.List(
traits.Int,
argstr="--inindex=%s",
sep=",",
desc="comma separated list of indices corresponding to --datain",
)
in_topup_fieldcoef = File(
exists=True,
argstr="--topup=%s",
copyfile=False,
requires=["in_topup_movpar"],
desc=("topup file containing the field " "coefficients"),
)
in_topup_movpar = File(
exists=True,
requires=["in_topup_fieldcoef"],
copyfile=False,
desc="topup movpar.txt file",
)
out_corrected = File(
desc="output (warped) image",
name_source=["in_files"],
name_template="%s_corrected",
argstr="--out=%s",
)
method = traits.Enum(
"jac",
"lsr",
argstr="--method=%s",
desc=("use jacobian modulation (jac) or least-squares" " resampling (lsr)"),
)
interp = traits.Enum(
"trilinear", "spline", argstr="--interp=%s", desc="interpolation method"
)
datatype = traits.Enum(
"char",
"short",
"int",
"float",
"double",
argstr="-d=%s",
desc="force output data type",
)
class ApplyTOPUPOutputSpec(TraitedSpec):
out_corrected = File(
exists=True, desc=("name of 4D image file with " "unwarped images")
)
class ApplyTOPUP(FSLCommand):
"""
Interface for FSL topup, a tool for estimating and correcting
susceptibility induced distortions.
`General reference
<http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/topup/ApplytopupUsersGuide>`_
and `use example
<http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/topup/ExampleTopupFollowedByApplytopup>`_.
Examples
--------
>>> from nipype.interfaces.fsl import ApplyTOPUP
>>> applytopup = ApplyTOPUP()
>>> applytopup.inputs.in_files = ["epi.nii", "epi_rev.nii"]
>>> applytopup.inputs.encoding_file = "topup_encoding.txt"
>>> applytopup.inputs.in_topup_fieldcoef = "topup_fieldcoef.nii.gz"
>>> applytopup.inputs.in_topup_movpar = "topup_movpar.txt"
>>> applytopup.inputs.output_type = "NIFTI_GZ"
>>> applytopup.cmdline # doctest: +ELLIPSIS
'applytopup --datain=topup_encoding.txt --imain=epi.nii,epi_rev.nii \
--inindex=1,2 --topup=topup --out=epi_corrected.nii.gz'
>>> res = applytopup.run() # doctest: +SKIP
"""
_cmd = "applytopup"
input_spec = ApplyTOPUPInputSpec
output_spec = ApplyTOPUPOutputSpec
def _parse_inputs(self, skip=None):
if skip is None:
skip = []
# If not defined, assume index are the first N entries in the
# parameters file, for N input images.
if not isdefined(self.inputs.in_index):
self.inputs.in_index = list(range(1, len(self.inputs.in_files) + 1))
return super(ApplyTOPUP, self)._parse_inputs(skip=skip)
def _format_arg(self, name, spec, value):
if name == "in_topup_fieldcoef":
return spec.argstr % value.split("_fieldcoef")[0]
return super(ApplyTOPUP, self)._format_arg(name, spec, value)
class EddyInputSpec(FSLCommandInputSpec):
in_file = File(
exists=True,
mandatory=True,
argstr="--imain=%s",
desc="File containing all the images to estimate distortions for",
)
in_mask = File(
exists=True, mandatory=True, argstr="--mask=%s", desc="Mask to indicate brain"
)
in_index = File(
exists=True,
mandatory=True,
argstr="--index=%s",
desc="File containing indices for all volumes in --imain "
"into --acqp and --topup",
)
in_acqp = File(
exists=True,
mandatory=True,
argstr="--acqp=%s",
desc="File containing acquisition parameters",
)
in_bvec = File(
exists=True,
mandatory=True,
argstr="--bvecs=%s",
desc="File containing the b-vectors for all volumes in --imain",
)
in_bval = File(
exists=True,
mandatory=True,
argstr="--bvals=%s",
desc="File containing the b-values for all volumes in --imain",
)
out_base = traits.Str(
default_value="eddy_corrected",
usedefault=True,
argstr="--out=%s",
desc="Basename for output image",
)
session = File(
exists=True,
argstr="--session=%s",
desc="File containing session indices for all volumes in --imain",
)
in_topup_fieldcoef = File(
exists=True,
argstr="--topup=%s",
requires=["in_topup_movpar"],
desc="Topup results file containing the field coefficients",
)
in_topup_movpar = File(
exists=True,
requires=["in_topup_fieldcoef"],
desc="Topup results file containing the movement parameters (movpar.txt)",
)
field = File(
exists=True,
argstr="--field=%s",
desc="Non-topup derived fieldmap scaled in Hz",
)
field_mat = File(
exists=True,
argstr="--field_mat=%s",
desc="Matrix specifying the relative positions of the fieldmap, "
"--field, and the first volume of the input file, --imain",
)
flm = traits.Enum(
"quadratic",
"linear",
"cubic",
usedefault=True,
argstr="--flm=%s",
desc="First level EC model",
)
slm = traits.Enum(
"none",
"linear",
"quadratic",
usedefault=True,
argstr="--slm=%s",
desc="Second level EC model",
)
fep = traits.Bool(
False, argstr="--fep", desc="Fill empty planes in x- or y-directions"
)
initrand = traits.Bool(
False,
argstr="--initrand",
desc="Resets rand for when selecting voxels",
min_ver="5.0.10",
)
interp = traits.Enum(
"spline",
"trilinear",
usedefault=True,
argstr="--interp=%s",
desc="Interpolation model for estimation step",
)
nvoxhp = traits.Int(
default_value=1000,
usedefault=True,
argstr="--nvoxhp=%s",
desc="# of voxels used to estimate the hyperparameters",
)
fudge_factor = traits.Float(
default_value=10.0,
usedefault=True,
argstr="--ff=%s",
desc="Fudge factor for hyperparameter error variance",
)
dont_sep_offs_move = traits.Bool(
False,
argstr="--dont_sep_offs_move",
desc="Do NOT attempt to separate field offset from subject movement",
)
dont_peas = traits.Bool(
False,
argstr="--dont_peas",
desc="Do NOT perform a post-eddy alignment of shells",
)
fwhm = traits.Float(
desc="FWHM for conditioning filter when estimating the parameters",
argstr="--fwhm=%s",
)
niter = traits.Int(
5, usedefault=True, argstr="--niter=%s", desc="Number of iterations"
)
method = traits.Enum(
"jac",
"lsr",
usedefault=True,
argstr="--resamp=%s",
desc="Final resampling method (jacobian/least squares)",
)
repol = traits.Bool(
False, argstr="--repol", desc="Detect and replace outlier slices"
)
outlier_nstd = traits.Int(
argstr="--ol_nstd",
desc="Number of std off to qualify as outlier",
requires=["repol"],
min_ver="5.0.10",
)
outlier_nvox = traits.Int(
argstr="--ol_nvox",
desc="Min # of voxels in a slice for inclusion in outlier detection",
requires=["repol"],
min_ver="5.0.10",
)
outlier_type = traits.Enum(
"sw",
"gw",
"both",
argstr="--ol_type",
desc="Type of outliers, slicewise (sw), groupwise (gw) or both (both)",
requires=["repol"],
min_ver="5.0.10",
)
outlier_pos = traits.Bool(
False,
argstr="--ol_pos",
desc="Consider both positive and negative outliers if set",
requires=["repol"],
min_ver="5.0.10",
)
outlier_sqr = traits.Bool(
False,
argstr="--ol_sqr",
desc="Consider outliers among sums-of-squared differences if set",
requires=["repol"],
min_ver="5.0.10",
)
multiband_factor = traits.Int(
argstr="--mb=%s", desc="Multi-band factor", min_ver="5.0.10"
)
multiband_offset = traits.Enum(
0,
1,
-1,
argstr="--mb_offs=%d",
desc="Multi-band offset (-1 if bottom slice removed, 1 if top slice removed",
requires=["multiband_factor"],
min_ver="5.0.10",
)
mporder = traits.Int(
argstr="--mporder=%s",
desc="Order of slice-to-vol movement model",
requires=["use_cuda"],
min_ver="5.0.11",
)
slice2vol_niter = traits.Int(
argstr="--s2v_niter=%d",
desc="Number of iterations for slice-to-vol",
requires=["mporder"],
min_ver="5.0.11",
)
slice2vol_lambda = traits.Int(
argstr="--s2v_lambda=%d",
desc="Regularisation weight for slice-to-vol movement (reasonable range 1-10)",
requires=["mporder"],
min_ver="5.0.11",
)
slice2vol_interp = traits.Enum(
"trilinear",
"spline",
argstr="--s2v_interp=%s",
desc="Slice-to-vol interpolation model for estimation step",
requires=["mporder"],
min_ver="5.0.11",
)
slice_order = traits.File(
exists=True,
argstr="--slspec=%s",
desc="Name of text file completely specifying slice/group acquisition",
requires=["mporder"],
xor=["json"],
min_ver="5.0.11",
)
json = traits.File(
exists=True,
argstr="--json=%s",
desc="Name of .json text file with information about slice timing",
requires=["mporder"],
xor=["slice_order"],
min_ver="6.0.1",
)
estimate_move_by_susceptibility = traits.Bool(
False,
argstr="--estimate_move_by_susceptibility",
desc="Estimate how susceptibility field changes with subject movement",
min_ver="6.0.1",
)
mbs_niter = traits.Int(
argstr="--mbs_niter=%s",
desc="Number of iterations for MBS estimation",
requires=["estimate_move_by_susceptibility"],
min_ver="6.0.1",
)
mbs_lambda = traits.Int(
argstr="--mbs_lambda=%s",
desc="Weighting of regularisation for MBS estimation",
requires=["estimate_move_by_susceptibility"],
min_ver="6.0.1",
)
mbs_ksp = traits.Int(
argstr="--mbs_ksp=%smm",
desc="Knot-spacing for MBS field estimation",
requires=["estimate_move_by_susceptibility"],
min_ver="6.0.1",
)
num_threads = traits.Int(
1, usedefault=True, nohash=True, desc="Number of openmp threads to use"
)
is_shelled = traits.Bool(
False,
argstr="--data_is_shelled",
desc="Override internal check to ensure that date are acquired "
"on a set of b-value shells",
)
use_cuda = traits.Bool(False, desc="Run eddy using cuda gpu")
cnr_maps = traits.Bool(
False, desc="Output CNR-Maps", argstr="--cnr_maps", min_ver="5.0.10"
)
residuals = traits.Bool(
False, desc="Output Residuals", argstr="--residuals", min_ver="5.0.10"
)
class EddyOutputSpec(TraitedSpec):
out_corrected = File(
exists=True, desc="4D image file containing all the corrected volumes"
)
out_parameter = File(
exists=True,
desc="Text file with parameters defining the field and movement for each scan",
)
out_rotated_bvecs = File(
exists=True, desc="File containing rotated b-values for all volumes"
)
out_movement_rms = File(
exists=True, desc="Summary of the 'total movement' in each volume"
)
out_restricted_movement_rms = File(
exists=True,
desc="Summary of the 'total movement' in each volume "
"disregarding translation in the PE direction",
)
out_shell_alignment_parameters = File(
exists=True,
desc="Text file containing rigid body movement parameters "
"between the different shells as estimated by a "
"post-hoc mutual information based registration",
)
out_shell_pe_translation_parameters = File(
exists=True,
desc="Text file containing translation along the PE-direction "
"between the different shells as estimated by a "
"post-hoc mutual information based registration",
)
out_shell_pe_translation_parameters = File(
exists=True,
desc="Text file containing translation along the PE-direction "
"between the different shells as estimated by a "
"post-hoc mutual information based registration",
)
out_outlier_map = File(
exists=True,
desc="Matrix where rows represent volumes and columns represent "
'slices. "0" indicates that scan-slice is not an outlier '
'and "1" indicates that it is',
)
out_outlier_n_stdev_map = File(
exists=True,
desc="Matrix where rows represent volumes and columns represent "
"slices. Values indicate number of standard deviations off the "
"mean difference between observation and prediction is",
)
out_outlier_n_sqr_stdev_map = File(
exists=True,
desc="Matrix where rows represent volumes and columns represent "
"slices. Values indicate number of standard deivations off the "
"square root of the mean squared difference between observation "
"and prediction is",
)
out_outlier_report = File(
exists=True,
desc="Text file with a plain language report on what "
"outlier slices eddy has found",
)
out_outlier_free = File(
exists=True,
desc="4D image file not corrected for susceptibility or eddy-"
"current distortions or subject movement but with outlier "
"slices replaced",
)
out_movement_over_time = File(
exists=True,
desc="Text file containing translations (mm) and rotations "
"(radians) for each excitation",
)
out_cnr_maps = File(exists=True, desc="path/name of file with the cnr_maps")
out_residuals = File(exists=True, desc="path/name of file with the residuals")
class Eddy(FSLCommand):
"""
Interface for FSL eddy, a tool for estimating and correcting eddy
currents induced distortions. `User guide
<https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/eddy/UsersGuide>`__ and
`more info regarding acqp file
<https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/eddy/Faq#How_do_I_know_what_to_put_into_my_--acqp_file.3F>`_.
Examples
--------
>>> from nipype.interfaces.fsl import Eddy
Running eddy on a CPU using OpenMP:
>>> eddy = Eddy()
>>> eddy.inputs.in_file = 'epi.nii'
>>> eddy.inputs.in_mask = 'epi_mask.nii'
>>> eddy.inputs.in_index = 'epi_index.txt'
>>> eddy.inputs.in_acqp = 'epi_acqp.txt'
>>> eddy.inputs.in_bvec = 'bvecs.scheme'
>>> eddy.inputs.in_bval = 'bvals.scheme'
>>> eddy.cmdline # doctest: +ELLIPSIS
'eddy_openmp --flm=quadratic --ff=10.0 \
--acqp=epi_acqp.txt --bvals=bvals.scheme --bvecs=bvecs.scheme \
--imain=epi.nii --index=epi_index.txt --mask=epi_mask.nii \
--interp=spline --resamp=jac --niter=5 --nvoxhp=1000 \
--out=.../eddy_corrected --slm=none'
Running eddy on an Nvidia GPU using cuda:
>>> eddy.inputs.use_cuda = True
>>> eddy.cmdline # doctest: +ELLIPSIS
'eddy_cuda --flm=quadratic --ff=10.0 \
--acqp=epi_acqp.txt --bvals=bvals.scheme --bvecs=bvecs.scheme \
--imain=epi.nii --index=epi_index.txt --mask=epi_mask.nii \
--interp=spline --resamp=jac --niter=5 --nvoxhp=1000 \
--out=.../eddy_corrected --slm=none'
Running eddy with slice-to-volume motion correction:
>>> eddy.inputs.mporder = 6
>>> eddy.inputs.slice2vol_niter = 5
>>> eddy.inputs.slice2vol_lambda = 1
>>> eddy.inputs.slice2vol_interp = 'trilinear'
>>> eddy.inputs.slice_order = 'epi_slspec.txt'
>>> eddy.cmdline # doctest: +ELLIPSIS
'eddy_cuda --flm=quadratic --ff=10.0 \
--acqp=epi_acqp.txt --bvals=bvals.scheme --bvecs=bvecs.scheme \
--imain=epi.nii --index=epi_index.txt --mask=epi_mask.nii \
--interp=spline --resamp=jac --mporder=6 --niter=5 --nvoxhp=1000 \
--out=.../eddy_corrected --s2v_interp=trilinear --s2v_lambda=1 \
--s2v_niter=5 --slspec=epi_slspec.txt --slm=none'
>>> res = eddy.run() # doctest: +SKIP
"""
_cmd = "eddy_openmp"
input_spec = EddyInputSpec
output_spec = EddyOutputSpec
_num_threads = 1
def __init__(self, **inputs):
super(Eddy, self).__init__(**inputs)
self.inputs.on_trait_change(self._num_threads_update, "num_threads")
if not isdefined(self.inputs.num_threads):
self.inputs.num_threads = self._num_threads
else:
self._num_threads_update()
self.inputs.on_trait_change(self._use_cuda, "use_cuda")
if isdefined(self.inputs.use_cuda):
self._use_cuda()
def _num_threads_update(self):
self._num_threads = self.inputs.num_threads
if not isdefined(self.inputs.num_threads):
if "OMP_NUM_THREADS" in self.inputs.environ:
del self.inputs.environ["OMP_NUM_THREADS"]
else:
self.inputs.environ["OMP_NUM_THREADS"] = str(self.inputs.num_threads)
def _use_cuda(self):
self._cmd = "eddy_cuda" if self.inputs.use_cuda else "eddy_openmp"
def _run_interface(self, runtime):
# If 'eddy_openmp' is missing, use 'eddy'
FSLDIR = os.getenv("FSLDIR", "")
cmd = self._cmd
if all(
(
FSLDIR != "",
cmd == "eddy_openmp",
not os.path.exists(os.path.join(FSLDIR, "bin", cmd)),
)
):
self._cmd = "eddy"
runtime = super(Eddy, self)._run_interface(runtime)
# Restore command to avoid side-effects
self._cmd = cmd