/
dti.py
1583 lines (1406 loc) · 51.7 KB
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dti.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 4.1.4.
"""
import os
import warnings
from ...utils.filemanip import fname_presuffix, split_filename, copyfile
from ..base import (
TraitedSpec,
isdefined,
File,
Directory,
InputMultiPath,
OutputMultiPath,
traits,
)
from .base import FSLCommand, FSLCommandInputSpec, Info
class DTIFitInputSpec(FSLCommandInputSpec):
dwi = File(
exists=True,
desc="diffusion weighted image data file",
argstr="-k %s",
position=0,
mandatory=True,
)
base_name = traits.Str(
"dtifit_",
desc=("base_name that all output files " "will start with"),
argstr="-o %s",
position=1,
usedefault=True,
)
mask = File(
exists=True,
desc="bet binary mask file",
argstr="-m %s",
position=2,
mandatory=True,
)
bvecs = File(
exists=True, desc="b vectors file", argstr="-r %s", position=3, mandatory=True
)
bvals = File(
exists=True, desc="b values file", argstr="-b %s", position=4, mandatory=True
)
min_z = traits.Int(argstr="-z %d", desc="min z")
max_z = traits.Int(argstr="-Z %d", desc="max z")
min_y = traits.Int(argstr="-y %d", desc="min y")
max_y = traits.Int(argstr="-Y %d", desc="max y")
min_x = traits.Int(argstr="-x %d", desc="min x")
max_x = traits.Int(argstr="-X %d", desc="max x")
save_tensor = traits.Bool(
desc="save the elements of the tensor", argstr="--save_tensor"
)
sse = traits.Bool(desc="output sum of squared errors", argstr="--sse")
cni = File(exists=True, desc="input counfound regressors", argstr="--cni=%s")
little_bit = traits.Bool(
desc="only process small area of brain", argstr="--littlebit"
)
gradnonlin = File(
exists=True, argstr="--gradnonlin=%s", desc="gradient non linearities"
)
class DTIFitOutputSpec(TraitedSpec):
V1 = File(exists=True, desc="path/name of file with the 1st eigenvector")
V2 = File(exists=True, desc="path/name of file with the 2nd eigenvector")
V3 = File(exists=True, desc="path/name of file with the 3rd eigenvector")
L1 = File(exists=True, desc="path/name of file with the 1st eigenvalue")
L2 = File(exists=True, desc="path/name of file with the 2nd eigenvalue")
L3 = File(exists=True, desc="path/name of file with the 3rd eigenvalue")
MD = File(exists=True, desc="path/name of file with the mean diffusivity")
FA = File(exists=True, desc="path/name of file with the fractional anisotropy")
MO = File(exists=True, desc="path/name of file with the mode of anisotropy")
S0 = File(
exists=True,
desc=(
"path/name of file with the raw T2 signal with no " "diffusion weighting"
),
)
tensor = File(exists=True, desc="path/name of file with the 4D tensor volume")
sse = File(exists=True, desc="path/name of file with the summed squared error")
class DTIFit(FSLCommand):
""" Use FSL dtifit command for fitting a diffusion tensor model at each
voxel
Example
-------
>>> from nipype.interfaces import fsl
>>> dti = fsl.DTIFit()
>>> dti.inputs.dwi = 'diffusion.nii'
>>> dti.inputs.bvecs = 'bvecs'
>>> dti.inputs.bvals = 'bvals'
>>> dti.inputs.base_name = 'TP'
>>> dti.inputs.mask = 'mask.nii'
>>> dti.cmdline
'dtifit -k diffusion.nii -o TP -m mask.nii -r bvecs -b bvals'
"""
_cmd = "dtifit"
input_spec = DTIFitInputSpec
output_spec = DTIFitOutputSpec
def _list_outputs(self):
keys_to_ignore = {"outputtype", "environ", "args"}
# Optional output: Map output name to input flag
opt_output = {"tensor": self.inputs.save_tensor, "sse": self.inputs.sse}
# Ignore optional output, whose corresponding input-flag is not defined
# or set to False
for output, input_flag in opt_output.items():
if isdefined(input_flag) and input_flag:
# this is wanted output, do not ignore
continue
keys_to_ignore.add(output)
outputs = self.output_spec().get()
for k in set(outputs.keys()) - keys_to_ignore:
outputs[k] = self._gen_fname(self.inputs.base_name, suffix="_" + k)
return outputs
class FSLXCommandInputSpec(FSLCommandInputSpec):
dwi = File(
exists=True,
argstr="--data=%s",
mandatory=True,
desc="diffusion weighted image data file",
)
mask = File(
exists=True,
argstr="--mask=%s",
mandatory=True,
desc="brain binary mask file (i.e. from BET)",
)
bvecs = File(
exists=True, argstr="--bvecs=%s", mandatory=True, desc="b vectors file"
)
bvals = File(exists=True, argstr="--bvals=%s", mandatory=True, desc="b values file")
logdir = Directory(".", argstr="--logdir=%s", usedefault=True)
n_fibres = traits.Range(
usedefault=True,
low=1,
value=2,
argstr="--nfibres=%d",
desc=("Maximum number of fibres to fit in each voxel"),
mandatory=True,
)
model = traits.Enum(
1,
2,
3,
argstr="--model=%d",
desc=(
"use monoexponential (1, default, required for "
"single-shell) or multiexponential (2, multi-"
"shell) model"
),
)
fudge = traits.Int(argstr="--fudge=%d", desc="ARD fudge factor")
n_jumps = traits.Int(
5000,
usedefault=True,
argstr="--njumps=%d",
desc="Num of jumps to be made by MCMC",
)
burn_in = traits.Range(
low=0,
value=0,
usedefault=True,
argstr="--burnin=%d",
desc=("Total num of jumps at start of MCMC to be " "discarded"),
)
burn_in_no_ard = traits.Range(
low=0,
value=0,
usedefault=True,
argstr="--burnin_noard=%d",
desc=("num of burnin jumps before the ard is" " imposed"),
)
sample_every = traits.Range(
low=0,
value=1,
usedefault=True,
argstr="--sampleevery=%d",
desc="Num of jumps for each sample (MCMC)",
)
update_proposal_every = traits.Range(
low=1,
value=40,
usedefault=True,
argstr="--updateproposalevery=%d",
desc=("Num of jumps for each update " "to the proposal density std " "(MCMC)"),
)
seed = traits.Int(
argstr="--seed=%d", desc="seed for pseudo random number generator"
)
_xor_inputs1 = ("no_ard", "all_ard")
no_ard = traits.Bool(
argstr="--noard", xor=_xor_inputs1, desc="Turn ARD off on all fibres"
)
all_ard = traits.Bool(
argstr="--allard", xor=_xor_inputs1, desc="Turn ARD on on all fibres"
)
_xor_inputs2 = ("no_spat", "non_linear", "cnlinear")
no_spat = traits.Bool(
argstr="--nospat",
xor=_xor_inputs2,
desc="Initialise with tensor, not spatially",
)
non_linear = traits.Bool(
argstr="--nonlinear", xor=_xor_inputs2, desc="Initialise with nonlinear fitting"
)
cnlinear = traits.Bool(
argstr="--cnonlinear",
xor=_xor_inputs2,
desc=("Initialise with constrained nonlinear " "fitting"),
)
rician = traits.Bool(argstr="--rician", desc=("use Rician noise modeling"))
_xor_inputs3 = ["f0_noard", "f0_ard"]
f0_noard = traits.Bool(
argstr="--f0",
xor=_xor_inputs3,
desc=(
"Noise floor model: add to the model an "
"unattenuated signal compartment f0"
),
)
f0_ard = traits.Bool(
argstr="--f0 --ardf0",
xor=_xor_inputs3 + ["all_ard"],
desc=(
"Noise floor model: add to the model an "
"unattenuated signal compartment f0"
),
)
force_dir = traits.Bool(
True,
argstr="--forcedir",
usedefault=True,
desc=(
"use the actual directory name given "
"(do not add + to make a new directory)"
),
)
class FSLXCommandOutputSpec(TraitedSpec):
dyads = OutputMultiPath(
File(exists=True), desc=("Mean of PDD distribution" " in vector form.")
)
fsamples = OutputMultiPath(
File(exists=True), desc=("Samples from the " "distribution on f " "anisotropy")
)
mean_dsamples = File(exists=True, desc="Mean of distribution on diffusivity d")
mean_fsamples = OutputMultiPath(
File(exists=True), desc=("Mean of distribution on f " "anisotropy")
)
mean_S0samples = File(
exists=True, desc=("Mean of distribution on T2w" "baseline signal intensity S0")
)
mean_tausamples = File(
exists=True,
desc=("Mean of distribution on " "tau samples (only with rician " "noise)"),
)
phsamples = OutputMultiPath(File(exists=True), desc=("phi samples, per fiber"))
thsamples = OutputMultiPath(File(exists=True), desc=("theta samples, per fiber"))
class FSLXCommand(FSLCommand):
"""
Base support for ``xfibres`` and ``bedpostx``
"""
input_spec = FSLXCommandInputSpec
output_spec = FSLXCommandOutputSpec
def _run_interface(self, runtime):
self._out_dir = os.getcwd()
runtime = super(FSLXCommand, self)._run_interface(runtime)
if runtime.stderr:
self.raise_exception(runtime)
return runtime
def _list_outputs(self, out_dir=None):
outputs = self.output_spec().get()
n_fibres = self.inputs.n_fibres
if not out_dir:
if isdefined(self.inputs.logdir):
out_dir = os.path.abspath(self.inputs.logdir)
else:
out_dir = os.path.abspath("logdir")
multi_out = ["dyads", "fsamples", "mean_fsamples", "phsamples", "thsamples"]
single_out = ["mean_dsamples", "mean_S0samples"]
for k in single_out:
outputs[k] = self._gen_fname(k, cwd=out_dir)
if isdefined(self.inputs.rician) and self.inputs.rician:
outputs["mean_tausamples"] = self._gen_fname("mean_tausamples", cwd=out_dir)
for k in multi_out:
outputs[k] = []
for i in range(1, n_fibres + 1):
outputs["fsamples"].append(self._gen_fname("f%dsamples" % i, cwd=out_dir))
outputs["mean_fsamples"].append(
self._gen_fname("mean_f%dsamples" % i, cwd=out_dir)
)
for i in range(1, n_fibres + 1):
outputs["dyads"].append(self._gen_fname("dyads%d" % i, cwd=out_dir))
outputs["phsamples"].append(self._gen_fname("ph%dsamples" % i, cwd=out_dir))
outputs["thsamples"].append(self._gen_fname("th%dsamples" % i, cwd=out_dir))
return outputs
class BEDPOSTX5InputSpec(FSLXCommandInputSpec):
dwi = File(exists=True, desc="diffusion weighted image data file", mandatory=True)
mask = File(exists=True, desc="bet binary mask file", mandatory=True)
bvecs = File(exists=True, desc="b vectors file", mandatory=True)
bvals = File(exists=True, desc="b values file", mandatory=True)
logdir = Directory(argstr="--logdir=%s")
n_fibres = traits.Range(
usedefault=True,
low=1,
value=2,
argstr="-n %d",
desc=("Maximum number of fibres to fit in each voxel"),
mandatory=True,
)
model = traits.Enum(
1,
2,
3,
argstr="-model %d",
desc=(
"use monoexponential (1, default, required for "
"single-shell) or multiexponential (2, multi-"
"shell) model"
),
)
fudge = traits.Int(argstr="-w %d", desc="ARD fudge factor")
n_jumps = traits.Int(
5000, usedefault=True, argstr="-j %d", desc="Num of jumps to be made by MCMC"
)
burn_in = traits.Range(
low=0,
value=0,
usedefault=True,
argstr="-b %d",
desc=("Total num of jumps at start of MCMC to be " "discarded"),
)
sample_every = traits.Range(
low=0,
value=1,
usedefault=True,
argstr="-s %d",
desc="Num of jumps for each sample (MCMC)",
)
out_dir = Directory(
"bedpostx",
mandatory=True,
desc="output directory",
usedefault=True,
position=1,
argstr="%s",
)
gradnonlin = traits.Bool(
False, argstr="-g", desc=("consider gradient nonlinearities, " "default off")
)
grad_dev = File(exists=True, desc="grad_dev file, if gradnonlin, -g is True")
use_gpu = traits.Bool(False, desc="Use the GPU version of bedpostx")
class BEDPOSTX5OutputSpec(TraitedSpec):
mean_dsamples = File(exists=True, desc="Mean of distribution on diffusivity d")
mean_fsamples = OutputMultiPath(
File(exists=True), desc=("Mean of distribution on f " "anisotropy")
)
mean_S0samples = File(
exists=True, desc=("Mean of distribution on T2w" "baseline signal intensity S0")
)
mean_phsamples = OutputMultiPath(
File(exists=True), desc="Mean of distribution on phi"
)
mean_thsamples = OutputMultiPath(
File(exists=True), desc="Mean of distribution on theta"
)
merged_thsamples = OutputMultiPath(
File(exists=True), desc=("Samples from the distribution " "on theta")
)
merged_phsamples = OutputMultiPath(
File(exists=True), desc=("Samples from the distribution " "on phi")
)
merged_fsamples = OutputMultiPath(
File(exists=True),
desc=("Samples from the distribution on " "anisotropic volume fraction"),
)
dyads = OutputMultiPath(
File(exists=True), desc="Mean of PDD distribution in vector form."
)
dyads_dispersion = OutputMultiPath(File(exists=True), desc=("Dispersion"))
class BEDPOSTX5(FSLXCommand):
"""
BEDPOSTX stands for Bayesian Estimation of Diffusion Parameters Obtained
using Sampling Techniques. The X stands for modelling Crossing Fibres.
bedpostx runs Markov Chain Monte Carlo sampling to build up distributions
on diffusion parameters at each voxel. It creates all the files necessary
for running probabilistic tractography. For an overview of the modelling
carried out within bedpostx see this `technical report
<http://www.fmrib.ox.ac.uk/analysis/techrep/tr03tb1/tr03tb1/index.html>`_.
.. note:: Consider using
:func:`niflow.nipype1.workflows.fsl.dmri.create_bedpostx_pipeline` instead.
Example
-------
>>> from nipype.interfaces import fsl
>>> bedp = fsl.BEDPOSTX5(bvecs='bvecs', bvals='bvals', dwi='diffusion.nii',
... mask='mask.nii', n_fibres=1)
>>> bedp.cmdline
'bedpostx bedpostx -b 0 --burnin_noard=0 --forcedir -n 1 -j 5000 \
-s 1 --updateproposalevery=40'
"""
_cmd = "bedpostx"
_default_cmd = _cmd
input_spec = BEDPOSTX5InputSpec
output_spec = BEDPOSTX5OutputSpec
_can_resume = True
def __init__(self, **inputs):
super(BEDPOSTX5, self).__init__(**inputs)
self.inputs.on_trait_change(self._cuda_update, "use_gpu")
def _cuda_update(self):
if isdefined(self.inputs.use_gpu) and self.inputs.use_gpu:
self._cmd = "bedpostx_gpu"
else:
self._cmd = self._default_cmd
def _run_interface(self, runtime):
subjectdir = os.path.abspath(self.inputs.out_dir)
if not os.path.exists(subjectdir):
os.makedirs(subjectdir)
_, _, ext = split_filename(self.inputs.mask)
copyfile(self.inputs.mask, os.path.join(subjectdir, "nodif_brain_mask" + ext))
_, _, ext = split_filename(self.inputs.dwi)
copyfile(self.inputs.dwi, os.path.join(subjectdir, "data" + ext))
copyfile(self.inputs.bvals, os.path.join(subjectdir, "bvals"))
copyfile(self.inputs.bvecs, os.path.join(subjectdir, "bvecs"))
if isdefined(self.inputs.grad_dev):
_, _, ext = split_filename(self.inputs.grad_dev)
copyfile(self.inputs.grad_dev, os.path.join(subjectdir, "grad_dev" + ext))
retval = super(BEDPOSTX5, self)._run_interface(runtime)
self._out_dir = subjectdir + ".bedpostX"
return retval
def _list_outputs(self):
outputs = self.output_spec().get()
n_fibres = self.inputs.n_fibres
multi_out = [
"merged_thsamples",
"merged_fsamples",
"merged_phsamples",
"mean_phsamples",
"mean_thsamples",
"mean_fsamples",
"dyads_dispersion",
"dyads",
]
single_out = ["mean_dsamples", "mean_S0samples"]
for k in single_out:
outputs[k] = self._gen_fname(k, cwd=self._out_dir)
for k in multi_out:
outputs[k] = []
for i in range(1, n_fibres + 1):
outputs["merged_thsamples"].append(
self._gen_fname("merged_th%dsamples" % i, cwd=self._out_dir)
)
outputs["merged_fsamples"].append(
self._gen_fname("merged_f%dsamples" % i, cwd=self._out_dir)
)
outputs["merged_phsamples"].append(
self._gen_fname("merged_ph%dsamples" % i, cwd=self._out_dir)
)
outputs["mean_thsamples"].append(
self._gen_fname("mean_th%dsamples" % i, cwd=self._out_dir)
)
outputs["mean_phsamples"].append(
self._gen_fname("mean_ph%dsamples" % i, cwd=self._out_dir)
)
outputs["mean_fsamples"].append(
self._gen_fname("mean_f%dsamples" % i, cwd=self._out_dir)
)
outputs["dyads"].append(self._gen_fname("dyads%d" % i, cwd=self._out_dir))
outputs["dyads_dispersion"].append(
self._gen_fname("dyads%d_dispersion" % i, cwd=self._out_dir)
)
return outputs
class XFibres5InputSpec(FSLXCommandInputSpec):
gradnonlin = File(
exists=True,
argstr="--gradnonlin=%s",
desc="gradient file corresponding to slice",
)
class XFibres5(FSLXCommand):
"""
Perform model parameters estimation for local (voxelwise) diffusion
parameters
"""
_cmd = "xfibres"
input_spec = XFibres5InputSpec
output_spec = FSLXCommandOutputSpec
XFibres = XFibres5
BEDPOSTX = BEDPOSTX5
class ProbTrackXBaseInputSpec(FSLCommandInputSpec):
thsamples = InputMultiPath(File(exists=True), mandatory=True)
phsamples = InputMultiPath(File(exists=True), mandatory=True)
fsamples = InputMultiPath(File(exists=True), mandatory=True)
samples_base_name = traits.Str(
"merged",
desc=("the rootname/base_name for samples " "files"),
argstr="--samples=%s",
usedefault=True,
)
mask = File(
exists=True,
desc="bet binary mask file in diffusion space",
argstr="-m %s",
mandatory=True,
)
seed = traits.Either(
File(exists=True),
traits.List(File(exists=True)),
traits.List(traits.List(traits.Int(), minlen=3, maxlen=3)),
desc=("seed volume(s), or voxel(s) or freesurfer " "label file"),
argstr="--seed=%s",
mandatory=True,
)
target_masks = InputMultiPath(
File(exits=True),
desc=("list of target masks - required for " "seeds_to_targets classification"),
argstr="--targetmasks=%s",
)
waypoints = File(
exists=True,
desc=(
"waypoint mask or ascii list of waypoint masks - "
"only keep paths going through ALL the masks"
),
argstr="--waypoints=%s",
)
network = traits.Bool(
desc=(
"activate network mode - only keep paths "
"going through at least one seed mask "
"(required if multiple seed masks)"
),
argstr="--network",
)
seed_ref = File(
exists=True,
desc=(
"reference vol to define seed space in simple mode "
"- diffusion space assumed if absent"
),
argstr="--seedref=%s",
)
out_dir = Directory(
exists=True,
argstr="--dir=%s",
desc="directory to put the final volumes in",
genfile=True,
)
force_dir = traits.Bool(
True,
desc=(
"use the actual directory name given - i.e. "
"do not add + to make a new directory"
),
argstr="--forcedir",
usedefault=True,
)
opd = traits.Bool(
True, desc="outputs path distributions", argstr="--opd", usedefault=True
)
correct_path_distribution = traits.Bool(
desc=("correct path distribution " "for the length of the " "pathways"),
argstr="--pd",
)
os2t = traits.Bool(desc="Outputs seeds to targets", argstr="--os2t")
# paths_file = File('nipype_fdtpaths', usedefault=True, argstr='--out=%s',
# desc='produces an output file (default is fdt_paths)')
avoid_mp = File(
exists=True,
desc=("reject pathways passing through locations given by " "this mask"),
argstr="--avoid=%s",
)
stop_mask = File(
exists=True,
argstr="--stop=%s",
desc="stop tracking at locations given by this mask file",
)
xfm = File(
exists=True,
argstr="--xfm=%s",
desc=(
"transformation matrix taking seed space to DTI space "
"(either FLIRT matrix or FNIRT warp_field) - default is "
"identity"
),
)
inv_xfm = File(
argstr="--invxfm=%s",
desc=(
"transformation matrix taking DTI space to seed "
"space (compulsory when using a warp_field for "
"seeds_to_dti)"
),
)
n_samples = traits.Int(
5000,
argstr="--nsamples=%d",
desc="number of samples - default=5000",
usedefault=True,
)
n_steps = traits.Int(
argstr="--nsteps=%d", desc="number of steps per sample - default=2000"
)
dist_thresh = traits.Float(
argstr="--distthresh=%.3f",
desc=("discards samples shorter than this " "threshold (in mm - default=0)"),
)
c_thresh = traits.Float(
argstr="--cthr=%.3f", desc="curvature threshold - default=0.2"
)
sample_random_points = traits.Bool(
argstr="--sampvox", desc=("sample random points within " "seed voxels")
)
step_length = traits.Float(
argstr="--steplength=%.3f", desc="step_length in mm - default=0.5"
)
loop_check = traits.Bool(
argstr="--loopcheck",
desc=(
"perform loop_checks on paths - slower, "
"but allows lower curvature threshold"
),
)
use_anisotropy = traits.Bool(
argstr="--usef", desc="use anisotropy to constrain tracking"
)
rand_fib = traits.Enum(
0,
1,
2,
3,
argstr="--randfib=%d",
desc=(
"options: 0 - default, 1 - to randomly "
"sample initial fibres (with f > fibthresh), "
"2 - to sample in proportion fibres (with "
"f>fibthresh) to f, 3 - to sample ALL "
"populations at random (even if "
"f<fibthresh)"
),
)
fibst = traits.Int(
argstr="--fibst=%d",
desc=(
"force a starting fibre for tracking - "
"default=1, i.e. first fibre orientation. Only "
"works if randfib==0"
),
)
mod_euler = traits.Bool(argstr="--modeuler", desc="use modified euler streamlining")
random_seed = traits.Bool(argstr="--rseed", desc="random seed")
s2tastext = traits.Bool(
argstr="--s2tastext",
desc=(
"output seed-to-target counts as a text "
"file (useful when seeding from a mesh)"
),
)
verbose = traits.Enum(
0,
1,
2,
desc=("Verbose level, [0-2]. Level 2 is required to " "output particle files."),
argstr="--verbose=%d",
)
class ProbTrackXInputSpec(ProbTrackXBaseInputSpec):
mode = traits.Enum(
"simple",
"two_mask_symm",
"seedmask",
desc=(
"options: simple (single seed voxel), seedmask "
"(mask of seed voxels), twomask_symm (two bet "
"binary masks)"
),
argstr="--mode=%s",
genfile=True,
)
mask2 = File(
exists=True,
desc=("second bet binary mask (in diffusion space) in " "twomask_symm mode"),
argstr="--mask2=%s",
)
mesh = File(
exists=True,
desc="Freesurfer-type surface descriptor (in ascii format)",
argstr="--mesh=%s",
)
class ProbTrackXOutputSpec(TraitedSpec):
log = File(
exists=True, desc="path/name of a text record of the command that was run"
)
fdt_paths = OutputMultiPath(
File(exists=True),
desc=(
"path/name of a 3D image file "
"containing the output connectivity "
"distribution to the seed mask"
),
)
way_total = File(
exists=True,
desc=(
"path/name of a text file containing a single "
"number corresponding to the total number of "
"generated tracts that have not been rejected by "
"inclusion/exclusion mask criteria"
),
)
targets = traits.List(
File(exists=True), desc=("a list with all generated seeds_to_target " "files")
)
particle_files = traits.List(
File(exists=True),
desc=(
"Files describing all of the tract "
"samples. Generated only if verbose is "
"set to 2"
),
)
class ProbTrackX(FSLCommand):
""" Use FSL probtrackx for tractography on bedpostx results
Examples
--------
>>> from nipype.interfaces import fsl
>>> pbx = fsl.ProbTrackX(samples_base_name='merged', mask='mask.nii', \
seed='MASK_average_thal_right.nii', mode='seedmask', \
xfm='trans.mat', n_samples=3, n_steps=10, force_dir=True, opd=True, \
os2t=True, target_masks = ['targets_MASK1.nii', 'targets_MASK2.nii'], \
thsamples='merged_thsamples.nii', fsamples='merged_fsamples.nii', \
phsamples='merged_phsamples.nii', out_dir='.')
>>> pbx.cmdline
'probtrackx --forcedir -m mask.nii --mode=seedmask --nsamples=3 --nsteps=10 --opd --os2t --dir=. --samples=merged --seed=MASK_average_thal_right.nii --targetmasks=targets.txt --xfm=trans.mat'
"""
_cmd = "probtrackx"
input_spec = ProbTrackXInputSpec
output_spec = ProbTrackXOutputSpec
def __init__(self, **inputs):
warnings.warn(
("Deprecated: Please use create_bedpostx_pipeline " "instead"),
DeprecationWarning,
)
return super(ProbTrackX, self).__init__(**inputs)
def _run_interface(self, runtime):
for i in range(1, len(self.inputs.thsamples) + 1):
_, _, ext = split_filename(self.inputs.thsamples[i - 1])
copyfile(
self.inputs.thsamples[i - 1],
self.inputs.samples_base_name + "_th%dsamples" % i + ext,
copy=False,
)
_, _, ext = split_filename(self.inputs.thsamples[i - 1])
copyfile(
self.inputs.phsamples[i - 1],
self.inputs.samples_base_name + "_ph%dsamples" % i + ext,
copy=False,
)
_, _, ext = split_filename(self.inputs.thsamples[i - 1])
copyfile(
self.inputs.fsamples[i - 1],
self.inputs.samples_base_name + "_f%dsamples" % i + ext,
copy=False,
)
if isdefined(self.inputs.target_masks):
f = open("targets.txt", "w")
for target in self.inputs.target_masks:
f.write("%s\n" % target)
f.close()
if isinstance(self.inputs.seed, list):
f = open("seeds.txt", "w")
for seed in self.inputs.seed:
if isinstance(seed, list):
f.write("%s\n" % (" ".join([str(s) for s in seed])))
else:
f.write("%s\n" % seed)
f.close()
runtime = super(ProbTrackX, self)._run_interface(runtime)
if runtime.stderr:
self.raise_exception(runtime)
return runtime
def _format_arg(self, name, spec, value):
if name == "target_masks" and isdefined(value):
fname = "targets.txt"
return super(ProbTrackX, self)._format_arg(name, spec, [fname])
elif name == "seed" and isinstance(value, list):
fname = "seeds.txt"
return super(ProbTrackX, self)._format_arg(name, spec, fname)
else:
return super(ProbTrackX, self)._format_arg(name, spec, value)
def _list_outputs(self):
outputs = self.output_spec().get()
if not isdefined(self.inputs.out_dir):
out_dir = self._gen_filename("out_dir")
else:
out_dir = self.inputs.out_dir
outputs["log"] = os.path.abspath(os.path.join(out_dir, "probtrackx.log"))
# utputs['way_total'] = os.path.abspath(os.path.join(out_dir,
# 'waytotal'))
if isdefined(self.inputs.opd is True):
if isinstance(self.inputs.seed, list) and isinstance(
self.inputs.seed[0], list
):
outputs["fdt_paths"] = []
for seed in self.inputs.seed:
outputs["fdt_paths"].append(
os.path.abspath(
self._gen_fname(
("fdt_paths_%s" % ("_".join([str(s) for s in seed]))),
cwd=out_dir,
suffix="",
)
)
)
else:
outputs["fdt_paths"] = os.path.abspath(
self._gen_fname("fdt_paths", cwd=out_dir, suffix="")
)
# handle seeds-to-target output files
if isdefined(self.inputs.target_masks):
outputs["targets"] = []
for target in self.inputs.target_masks:
outputs["targets"].append(
os.path.abspath(
self._gen_fname(
"seeds_to_" + os.path.split(target)[1],
cwd=out_dir,
suffix="",
)
)
)
if isdefined(self.inputs.verbose) and self.inputs.verbose == 2:
outputs["particle_files"] = [
os.path.abspath(os.path.join(out_dir, "particle%d" % i))
for i in range(self.inputs.n_samples)
]
return outputs
def _gen_filename(self, name):
if name == "out_dir":
return os.getcwd()
elif name == "mode":
if isinstance(self.inputs.seed, list) and isinstance(
self.inputs.seed[0], list
):
return "simple"
else:
return "seedmask"
class ProbTrackX2InputSpec(ProbTrackXBaseInputSpec):
simple = traits.Bool(
desc=(
"rack from a list of voxels (seed must be a " "ASCII list of coordinates)"
),
argstr="--simple",
)
fopd = File(
exists=True,
desc="Other mask for binning tract distribution",
argstr="--fopd=%s",
)
waycond = traits.Enum(
"OR",
"AND",
argstr="--waycond=%s",
desc=('Waypoint condition. Either "AND" (default) ' 'or "OR"'),
)
wayorder = traits.Bool(
desc=(
"Reject streamlines that do not hit "
"waypoints in given order. Only valid if "
"waycond=AND"
),
argstr="--wayorder",
)
onewaycondition = traits.Bool(
desc=("Apply waypoint conditions to each " "half tract separately"),
argstr="--onewaycondition",
)
omatrix1 = traits.Bool(
desc="Output matrix1 - SeedToSeed Connectivity", argstr="--omatrix1"
)
distthresh1 = traits.Float(
argstr="--distthresh1=%.3f",
desc=(
"Discards samples (in matrix1) shorter "
"than this threshold (in mm - "
"default=0)"
),
)
omatrix2 = traits.Bool(
desc="Output matrix2 - SeedToLowResMask",
argstr="--omatrix2",
requires=["target2"],
)
target2 = File(
exists=True,
desc=(
"Low resolution binary brain mask for storing "
"connectivity distribution in matrix2 mode"
),
argstr="--target2=%s",
)
omatrix3 = traits.Bool(
desc="Output matrix3 (NxN connectivity matrix)",
argstr="--omatrix3",
requires=["target3", "lrtarget3"],
)
target3 = File(
exists=True,
desc=("Mask used for NxN connectivity matrix (or Nxn if " "lrtarget3 is set)"),
argstr="--target3=%s",
)
lrtarget3 = File(
exists=True,
desc="Column-space mask used for Nxn connectivity matrix",