interfaces.fsl.dti¶
BEDPOSTX5¶
Wraps the executable command bedpostx.
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.
Note
Consider using
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'
Inputs:
[Mandatory]
dwi: (a pathlike object or string representing an existing file)
diffusion weighted image data file
mask: (a pathlike object or string representing an existing file)
bet binary mask file
bvecs: (a pathlike object or string representing an existing file)
b vectors file
bvals: (a pathlike object or string representing an existing file)
b values file
n_fibres: (a long integer >= 1, nipype default value: 2)
Maximum number of fibres to fit in each voxel
argument: ``-n %d``
out_dir: (a pathlike object or string representing a directory,
nipype default value: bedpostx)
output directory
argument: ``%s``, position: 1
[Optional]
logdir: (a pathlike object or string representing a directory)
argument: ``--logdir=%s``
model: (1 or 2 or 3)
use monoexponential (1, default, required for single-shell) or
multiexponential (2, multi-shell) model
argument: ``-model %d``
fudge: (an integer (int or long))
ARD fudge factor
argument: ``-w %d``
n_jumps: (an integer (int or long), nipype default value: 5000)
Num of jumps to be made by MCMC
argument: ``-j %d``
burn_in: (a long integer >= 0, nipype default value: 0)
Total num of jumps at start of MCMC to be discarded
argument: ``-b %d``
sample_every: (a long integer >= 0, nipype default value: 1)
Num of jumps for each sample (MCMC)
argument: ``-s %d``
gradnonlin: (a boolean)
consider gradient nonlinearities, default off
argument: ``-g``
grad_dev: (a pathlike object or string representing an existing file)
grad_dev file, if gradnonlin, -g is True
use_gpu: (a boolean)
Use the GPU version of bedpostx
burn_in_no_ard: (a long integer >= 0, nipype default value: 0)
num of burnin jumps before the ard is imposed
argument: ``--burnin_noard=%d``
update_proposal_every: (a long integer >= 1, nipype default value:
40)
Num of jumps for each update to the proposal density std (MCMC)
argument: ``--updateproposalevery=%d``
seed: (an integer (int or long))
seed for pseudo random number generator
argument: ``--seed=%d``
no_ard: (a boolean)
Turn ARD off on all fibres
argument: ``--noard``
mutually_exclusive: no_ard, all_ard
all_ard: (a boolean)
Turn ARD on on all fibres
argument: ``--allard``
mutually_exclusive: no_ard, all_ard
no_spat: (a boolean)
Initialise with tensor, not spatially
argument: ``--nospat``
mutually_exclusive: no_spat, non_linear, cnlinear
non_linear: (a boolean)
Initialise with nonlinear fitting
argument: ``--nonlinear``
mutually_exclusive: no_spat, non_linear, cnlinear
cnlinear: (a boolean)
Initialise with constrained nonlinear fitting
argument: ``--cnonlinear``
mutually_exclusive: no_spat, non_linear, cnlinear
rician: (a boolean)
use Rician noise modeling
argument: ``--rician``
f0_noard: (a boolean)
Noise floor model: add to the model an unattenuated signal
compartment f0
argument: ``--f0``
mutually_exclusive: f0_noard, f0_ard
f0_ard: (a boolean)
Noise floor model: add to the model an unattenuated signal
compartment f0
argument: ``--f0 --ardf0``
mutually_exclusive: f0_noard, f0_ard, all_ard
force_dir: (a boolean, nipype default value: True)
use the actual directory name given (do not add + to make a new
directory)
argument: ``--forcedir``
output_type: ('NIFTI' or 'NIFTI_PAIR' or 'NIFTI_GZ' or
'NIFTI_PAIR_GZ')
FSL output type
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
of class 'str' and with values which are a bytes or None or a
value of class 'str', nipype default value: {})
Environment variables
Outputs:
mean_dsamples: (a pathlike object or string representing an existing
file)
Mean of distribution on diffusivity d
mean_fsamples: (a list of items which are a pathlike object or string
representing an existing file)
Mean of distribution on f anisotropy
mean_S0samples: (a pathlike object or string representing an existing
file)
Mean of distribution on T2wbaseline signal intensity S0
mean_phsamples: (a list of items which are a pathlike object or
string representing an existing file)
Mean of distribution on phi
mean_thsamples: (a list of items which are a pathlike object or
string representing an existing file)
Mean of distribution on theta
merged_thsamples: (a list of items which are a pathlike object or
string representing an existing file)
Samples from the distribution on theta
merged_phsamples: (a list of items which are a pathlike object or
string representing an existing file)
Samples from the distribution on phi
merged_fsamples: (a list of items which are a pathlike object or
string representing an existing file)
Samples from the distribution on anisotropic volume fraction
dyads: (a list of items which are a pathlike object or string
representing an existing file)
Mean of PDD distribution in vector form.
dyads_dispersion: (a list of items which are a pathlike object or
string representing an existing file)
Dispersion
References:¶
None
DTIFit¶
Wraps the executable command dtifit.
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'
Inputs:
[Mandatory]
dwi: (a pathlike object or string representing an existing file)
diffusion weighted image data file
argument: ``-k %s``, position: 0
mask: (a pathlike object or string representing an existing file)
bet binary mask file
argument: ``-m %s``, position: 2
bvecs: (a pathlike object or string representing an existing file)
b vectors file
argument: ``-r %s``, position: 3
bvals: (a pathlike object or string representing an existing file)
b values file
argument: ``-b %s``, position: 4
[Optional]
base_name: (a unicode string, nipype default value: dtifit_)
base_name that all output files will start with
argument: ``-o %s``, position: 1
min_z: (an integer (int or long))
min z
argument: ``-z %d``
max_z: (an integer (int or long))
max z
argument: ``-Z %d``
min_y: (an integer (int or long))
min y
argument: ``-y %d``
max_y: (an integer (int or long))
max y
argument: ``-Y %d``
min_x: (an integer (int or long))
min x
argument: ``-x %d``
max_x: (an integer (int or long))
max x
argument: ``-X %d``
save_tensor: (a boolean)
save the elements of the tensor
argument: ``--save_tensor``
sse: (a boolean)
output sum of squared errors
argument: ``--sse``
cni: (a pathlike object or string representing an existing file)
input counfound regressors
argument: ``--cni=%s``
little_bit: (a boolean)
only process small area of brain
argument: ``--littlebit``
gradnonlin: (a pathlike object or string representing an existing
file)
gradient non linearities
argument: ``--gradnonlin=%s``
output_type: ('NIFTI' or 'NIFTI_PAIR' or 'NIFTI_GZ' or
'NIFTI_PAIR_GZ')
FSL output type
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
of class 'str' and with values which are a bytes or None or a
value of class 'str', nipype default value: {})
Environment variables
Outputs:
V1: (a pathlike object or string representing an existing file)
path/name of file with the 1st eigenvector
V2: (a pathlike object or string representing an existing file)
path/name of file with the 2nd eigenvector
V3: (a pathlike object or string representing an existing file)
path/name of file with the 3rd eigenvector
L1: (a pathlike object or string representing an existing file)
path/name of file with the 1st eigenvalue
L2: (a pathlike object or string representing an existing file)
path/name of file with the 2nd eigenvalue
L3: (a pathlike object or string representing an existing file)
path/name of file with the 3rd eigenvalue
MD: (a pathlike object or string representing an existing file)
path/name of file with the mean diffusivity
FA: (a pathlike object or string representing an existing file)
path/name of file with the fractional anisotropy
MO: (a pathlike object or string representing an existing file)
path/name of file with the mode of anisotropy
S0: (a pathlike object or string representing an existing file)
path/name of file with the raw T2 signal with no diffusion weighting
tensor: (a pathlike object or string representing an existing file)
path/name of file with the 4D tensor volume
sse: (a pathlike object or string representing an existing file)
path/name of file with the summed squared error
References:¶
None
DistanceMap¶
Wraps the executable command distancemap.
Use FSL’s distancemap to generate a map of the distance to the nearest nonzero voxel.
Example¶
>>> import nipype.interfaces.fsl as fsl
>>> mapper = fsl.DistanceMap()
>>> mapper.inputs.in_file = "skeleton_mask.nii.gz"
>>> mapper.run() # doctest: +SKIP
Inputs:
[Mandatory]
in_file: (a pathlike object or string representing an existing file)
image to calculate distance values for
argument: ``--in=%s``
[Optional]
mask_file: (a pathlike object or string representing an existing
file)
binary mask to contrain calculations
argument: ``--mask=%s``
invert_input: (a boolean)
invert input image
argument: ``--invert``
local_max_file: (a boolean or a pathlike object or string
representing a file)
write an image of the local maxima
argument: ``--localmax=%s``
distance_map: (a pathlike object or string representing a file)
distance map to write
argument: ``--out=%s``
output_type: ('NIFTI' or 'NIFTI_PAIR' or 'NIFTI_GZ' or
'NIFTI_PAIR_GZ')
FSL output type
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
of class 'str' and with values which are a bytes or None or a
value of class 'str', nipype default value: {})
Environment variables
Outputs:
distance_map: (a pathlike object or string representing an existing
file)
value is distance to nearest nonzero voxels
local_max_file: (a pathlike object or string representing a file)
image of local maxima
References:¶
None
FindTheBiggest¶
Wraps the executable command find_the_biggest.
Use FSL find_the_biggest for performing hard segmentation on the outputs of connectivity-based thresholding in probtrack. For complete details, see the FDT Documentation.
Example¶
>>> from nipype.interfaces import fsl
>>> ldir = ['seeds_to_M1.nii', 'seeds_to_M2.nii']
>>> fBig = fsl.FindTheBiggest(in_files=ldir, out_file='biggestSegmentation')
>>> fBig.cmdline
'find_the_biggest seeds_to_M1.nii seeds_to_M2.nii biggestSegmentation'
Inputs:
[Mandatory]
in_files: (a list of items which are a pathlike object or string
representing an existing file)
a list of input volumes or a singleMatrixFile
argument: ``%s``, position: 0
[Optional]
out_file: (a pathlike object or string representing a file)
file with the resulting segmentation
argument: ``%s``, position: 2
output_type: ('NIFTI' or 'NIFTI_PAIR' or 'NIFTI_GZ' or
'NIFTI_PAIR_GZ')
FSL output type
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
of class 'str' and with values which are a bytes or None or a
value of class 'str', nipype default value: {})
Environment variables
Outputs:
out_file: (a pathlike object or string representing an existing file)
output file indexed in order of input files
argument: ``%s``
References:¶
None
MakeDyadicVectors¶
Wraps the executable command make_dyadic_vectors.
Create vector volume representing mean principal diffusion direction and its uncertainty (dispersion)
Inputs:
[Mandatory]
theta_vol: (a pathlike object or string representing an existing
file)
argument: ``%s``, position: 0
phi_vol: (a pathlike object or string representing an existing file)
argument: ``%s``, position: 1
[Optional]
mask: (a pathlike object or string representing an existing file)
argument: ``%s``, position: 2
output: (a pathlike object or string representing a file, nipype
default value: dyads)
argument: ``%s``, position: 3
perc: (a float)
the {perc}% angle of the output cone of uncertainty (output will be
in degrees)
argument: ``%f``, position: 4
output_type: ('NIFTI' or 'NIFTI_PAIR' or 'NIFTI_GZ' or
'NIFTI_PAIR_GZ')
FSL output type
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
of class 'str' and with values which are a bytes or None or a
value of class 'str', nipype default value: {})
Environment variables
Outputs:
dyads: (a pathlike object or string representing an existing file)
dispersion: (a pathlike object or string representing an existing
file)
References:¶
None
ProbTrackX¶
Wraps the executable command probtrackx.
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'
Inputs:
[Mandatory]
thsamples: (a list of items which are a pathlike object or string
representing an existing file)
phsamples: (a list of items which are a pathlike object or string
representing an existing file)
fsamples: (a list of items which are a pathlike object or string
representing an existing file)
mask: (a pathlike object or string representing an existing file)
bet binary mask file in diffusion space
argument: ``-m %s``
seed: (a pathlike object or string representing an existing file or a
list of items which are a pathlike object or string representing
an existing file or a list of items which are a list of from 3 to
3 items which are an integer (int or long))
seed volume(s), or voxel(s) or freesurfer label file
argument: ``--seed=%s``
[Optional]
mode: ('simple' or 'two_mask_symm' or 'seedmask')
options: simple (single seed voxel), seedmask (mask of seed voxels),
twomask_symm (two bet binary masks)
argument: ``--mode=%s``
mask2: (a pathlike object or string representing an existing file)
second bet binary mask (in diffusion space) in twomask_symm mode
argument: ``--mask2=%s``
mesh: (a pathlike object or string representing an existing file)
Freesurfer-type surface descriptor (in ascii format)
argument: ``--mesh=%s``
samples_base_name: (a unicode string, nipype default value: merged)
the rootname/base_name for samples files
argument: ``--samples=%s``
target_masks: (a list of items which are a pathlike object or string
representing a file)
list of target masks - required for seeds_to_targets classification
argument: ``--targetmasks=%s``
waypoints: (a pathlike object or string representing an existing
file)
waypoint mask or ascii list of waypoint masks - only keep paths
going through ALL the masks
argument: ``--waypoints=%s``
network: (a boolean)
activate network mode - only keep paths going through at least one
seed mask (required if multiple seed masks)
argument: ``--network``
seed_ref: (a pathlike object or string representing an existing file)
reference vol to define seed space in simple mode - diffusion space
assumed if absent
argument: ``--seedref=%s``
out_dir: (a pathlike object or string representing an existing
directory)
directory to put the final volumes in
argument: ``--dir=%s``
force_dir: (a boolean, nipype default value: True)
use the actual directory name given - i.e. do not add + to make a
new directory
argument: ``--forcedir``
opd: (a boolean, nipype default value: True)
outputs path distributions
argument: ``--opd``
correct_path_distribution: (a boolean)
correct path distribution for the length of the pathways
argument: ``--pd``
os2t: (a boolean)
Outputs seeds to targets
argument: ``--os2t``
avoid_mp: (a pathlike object or string representing an existing file)
reject pathways passing through locations given by this mask
argument: ``--avoid=%s``
stop_mask: (a pathlike object or string representing an existing
file)
stop tracking at locations given by this mask file
argument: ``--stop=%s``
xfm: (a pathlike object or string representing an existing file)
transformation matrix taking seed space to DTI space (either FLIRT
matrix or FNIRT warp_field) - default is identity
argument: ``--xfm=%s``
inv_xfm: (a pathlike object or string representing a file)
transformation matrix taking DTI space to seed space (compulsory
when using a warp_field for seeds_to_dti)
argument: ``--invxfm=%s``
n_samples: (an integer (int or long), nipype default value: 5000)
number of samples - default=5000
argument: ``--nsamples=%d``
n_steps: (an integer (int or long))
number of steps per sample - default=2000
argument: ``--nsteps=%d``
dist_thresh: (a float)
discards samples shorter than this threshold (in mm - default=0)
argument: ``--distthresh=%.3f``
c_thresh: (a float)
curvature threshold - default=0.2
argument: ``--cthr=%.3f``
sample_random_points: (a boolean)
sample random points within seed voxels
argument: ``--sampvox``
step_length: (a float)
step_length in mm - default=0.5
argument: ``--steplength=%.3f``
loop_check: (a boolean)
perform loop_checks on paths - slower, but allows lower curvature
threshold
argument: ``--loopcheck``
use_anisotropy: (a boolean)
use anisotropy to constrain tracking
argument: ``--usef``
rand_fib: (0 or 1 or 2 or 3)
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)
argument: ``--randfib=%d``
fibst: (an integer (int or long))
force a starting fibre for tracking - default=1, i.e. first fibre
orientation. Only works if randfib==0
argument: ``--fibst=%d``
mod_euler: (a boolean)
use modified euler streamlining
argument: ``--modeuler``
random_seed: (a boolean)
random seed
argument: ``--rseed``
s2tastext: (a boolean)
output seed-to-target counts as a text file (useful when seeding
from a mesh)
argument: ``--s2tastext``
verbose: (0 or 1 or 2)
Verbose level, [0-2]. Level 2 is required to output particle files.
argument: ``--verbose=%d``
output_type: ('NIFTI' or 'NIFTI_PAIR' or 'NIFTI_GZ' or
'NIFTI_PAIR_GZ')
FSL output type
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
of class 'str' and with values which are a bytes or None or a
value of class 'str', nipype default value: {})
Environment variables
Outputs:
log: (a pathlike object or string representing an existing file)
path/name of a text record of the command that was run
fdt_paths: (a list of items which are a pathlike object or string
representing an existing file)
path/name of a 3D image file containing the output connectivity
distribution to the seed mask
way_total: (a pathlike object or string representing an existing
file)
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: (a list of items which are a pathlike object or string
representing an existing file)
a list with all generated seeds_to_target files
particle_files: (a list of items which are a pathlike object or
string representing an existing file)
Files describing all of the tract samples. Generated only if verbose
is set to 2
References:¶
None
ProbTrackX2¶
Wraps the executable command probtrackx2.
Use FSL probtrackx2 for tractography on bedpostx results
Examples¶
>>> from nipype.interfaces import fsl
>>> pbx2 = fsl.ProbTrackX2()
>>> pbx2.inputs.seed = 'seed_source.nii.gz'
>>> pbx2.inputs.thsamples = 'merged_th1samples.nii.gz'
>>> pbx2.inputs.fsamples = 'merged_f1samples.nii.gz'
>>> pbx2.inputs.phsamples = 'merged_ph1samples.nii.gz'
>>> pbx2.inputs.mask = 'nodif_brain_mask.nii.gz'
>>> pbx2.inputs.out_dir = '.'
>>> pbx2.inputs.n_samples = 3
>>> pbx2.inputs.n_steps = 10
>>> pbx2.cmdline
'probtrackx2 --forcedir -m nodif_brain_mask.nii.gz --nsamples=3 --nsteps=10 --opd --dir=. --samples=merged --seed=seed_source.nii.gz'
Inputs:
[Mandatory]
thsamples: (a list of items which are a pathlike object or string
representing an existing file)
phsamples: (a list of items which are a pathlike object or string
representing an existing file)
fsamples: (a list of items which are a pathlike object or string
representing an existing file)
mask: (a pathlike object or string representing an existing file)
bet binary mask file in diffusion space
argument: ``-m %s``
seed: (a pathlike object or string representing an existing file or a
list of items which are a pathlike object or string representing
an existing file or a list of items which are a list of from 3 to
3 items which are an integer (int or long))
seed volume(s), or voxel(s) or freesurfer label file
argument: ``--seed=%s``
[Optional]
simple: (a boolean)
rack from a list of voxels (seed must be a ASCII list of
coordinates)
argument: ``--simple``
fopd: (a pathlike object or string representing an existing file)
Other mask for binning tract distribution
argument: ``--fopd=%s``
waycond: ('OR' or 'AND')
Waypoint condition. Either "AND" (default) or "OR"
argument: ``--waycond=%s``
wayorder: (a boolean)
Reject streamlines that do not hit waypoints in given order. Only
valid if waycond=AND
argument: ``--wayorder``
onewaycondition: (a boolean)
Apply waypoint conditions to each half tract separately
argument: ``--onewaycondition``
omatrix1: (a boolean)
Output matrix1 - SeedToSeed Connectivity
argument: ``--omatrix1``
distthresh1: (a float)
Discards samples (in matrix1) shorter than this threshold (in mm -
default=0)
argument: ``--distthresh1=%.3f``
omatrix2: (a boolean)
Output matrix2 - SeedToLowResMask
argument: ``--omatrix2``
requires: target2
target2: (a pathlike object or string representing an existing file)
Low resolution binary brain mask for storing connectivity
distribution in matrix2 mode
argument: ``--target2=%s``
omatrix3: (a boolean)
Output matrix3 (NxN connectivity matrix)
argument: ``--omatrix3``
requires: target3, lrtarget3
target3: (a pathlike object or string representing an existing file)
Mask used for NxN connectivity matrix (or Nxn if lrtarget3 is set)
argument: ``--target3=%s``
lrtarget3: (a pathlike object or string representing an existing
file)
Column-space mask used for Nxn connectivity matrix
argument: ``--lrtarget3=%s``
distthresh3: (a float)
Discards samples (in matrix3) shorter than this threshold (in mm -
default=0)
argument: ``--distthresh3=%.3f``
omatrix4: (a boolean)
Output matrix4 - DtiMaskToSeed (special Oxford Sparse Format)
argument: ``--omatrix4``
colmask4: (a pathlike object or string representing an existing file)
Mask for columns of matrix4 (default=seed mask)
argument: ``--colmask4=%s``
target4: (a pathlike object or string representing an existing file)
Brain mask in DTI space
argument: ``--target4=%s``
meshspace: ('caret' or 'freesurfer' or 'first' or 'vox')
Mesh reference space - either "caret" (default) or "freesurfer" or
"first" or "vox"
argument: ``--meshspace=%s``
samples_base_name: (a unicode string, nipype default value: merged)
the rootname/base_name for samples files
argument: ``--samples=%s``
target_masks: (a list of items which are a pathlike object or string
representing a file)
list of target masks - required for seeds_to_targets classification
argument: ``--targetmasks=%s``
waypoints: (a pathlike object or string representing an existing
file)
waypoint mask or ascii list of waypoint masks - only keep paths
going through ALL the masks
argument: ``--waypoints=%s``
network: (a boolean)
activate network mode - only keep paths going through at least one
seed mask (required if multiple seed masks)
argument: ``--network``
seed_ref: (a pathlike object or string representing an existing file)
reference vol to define seed space in simple mode - diffusion space
assumed if absent
argument: ``--seedref=%s``
out_dir: (a pathlike object or string representing an existing
directory)
directory to put the final volumes in
argument: ``--dir=%s``
force_dir: (a boolean, nipype default value: True)
use the actual directory name given - i.e. do not add + to make a
new directory
argument: ``--forcedir``
opd: (a boolean, nipype default value: True)
outputs path distributions
argument: ``--opd``
correct_path_distribution: (a boolean)
correct path distribution for the length of the pathways
argument: ``--pd``
os2t: (a boolean)
Outputs seeds to targets
argument: ``--os2t``
avoid_mp: (a pathlike object or string representing an existing file)
reject pathways passing through locations given by this mask
argument: ``--avoid=%s``
stop_mask: (a pathlike object or string representing an existing
file)
stop tracking at locations given by this mask file
argument: ``--stop=%s``
xfm: (a pathlike object or string representing an existing file)
transformation matrix taking seed space to DTI space (either FLIRT
matrix or FNIRT warp_field) - default is identity
argument: ``--xfm=%s``
inv_xfm: (a pathlike object or string representing a file)
transformation matrix taking DTI space to seed space (compulsory
when using a warp_field for seeds_to_dti)
argument: ``--invxfm=%s``
n_samples: (an integer (int or long), nipype default value: 5000)
number of samples - default=5000
argument: ``--nsamples=%d``
n_steps: (an integer (int or long))
number of steps per sample - default=2000
argument: ``--nsteps=%d``
dist_thresh: (a float)
discards samples shorter than this threshold (in mm - default=0)
argument: ``--distthresh=%.3f``
c_thresh: (a float)
curvature threshold - default=0.2
argument: ``--cthr=%.3f``
sample_random_points: (a boolean)
sample random points within seed voxels
argument: ``--sampvox``
step_length: (a float)
step_length in mm - default=0.5
argument: ``--steplength=%.3f``
loop_check: (a boolean)
perform loop_checks on paths - slower, but allows lower curvature
threshold
argument: ``--loopcheck``
use_anisotropy: (a boolean)
use anisotropy to constrain tracking
argument: ``--usef``
rand_fib: (0 or 1 or 2 or 3)
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)
argument: ``--randfib=%d``
fibst: (an integer (int or long))
force a starting fibre for tracking - default=1, i.e. first fibre
orientation. Only works if randfib==0
argument: ``--fibst=%d``
mod_euler: (a boolean)
use modified euler streamlining
argument: ``--modeuler``
random_seed: (a boolean)
random seed
argument: ``--rseed``
s2tastext: (a boolean)
output seed-to-target counts as a text file (useful when seeding
from a mesh)
argument: ``--s2tastext``
verbose: (0 or 1 or 2)
Verbose level, [0-2]. Level 2 is required to output particle files.
argument: ``--verbose=%d``
output_type: ('NIFTI' or 'NIFTI_PAIR' or 'NIFTI_GZ' or
'NIFTI_PAIR_GZ')
FSL output type
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
of class 'str' and with values which are a bytes or None or a
value of class 'str', nipype default value: {})
Environment variables
Outputs:
network_matrix: (a pathlike object or string representing an existing
file)
the network matrix generated by --omatrix1 option
matrix1_dot: (a pathlike object or string representing an existing
file)
Output matrix1.dot - SeedToSeed Connectivity
lookup_tractspace: (a pathlike object or string representing an
existing file)
lookup_tractspace generated by --omatrix2 option
matrix2_dot: (a pathlike object or string representing an existing
file)
Output matrix2.dot - SeedToLowResMask
matrix3_dot: (a pathlike object or string representing an existing
file)
Output matrix3 - NxN connectivity matrix
log: (a pathlike object or string representing an existing file)
path/name of a text record of the command that was run
fdt_paths: (a list of items which are a pathlike object or string
representing an existing file)
path/name of a 3D image file containing the output connectivity
distribution to the seed mask
way_total: (a pathlike object or string representing an existing
file)
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: (a list of items which are a pathlike object or string
representing an existing file)
a list with all generated seeds_to_target files
particle_files: (a list of items which are a pathlike object or
string representing an existing file)
Files describing all of the tract samples. Generated only if verbose
is set to 2
References:¶
None
ProjThresh¶
Wraps the executable command proj_thresh.
Use FSL proj_thresh for thresholding some outputs of probtrack For complete details, see the FDT Documentation <http://www.fmrib.ox.ac.uk/fsl/fdt/fdt_thresh.html>
Example¶
>>> from nipype.interfaces import fsl
>>> ldir = ['seeds_to_M1.nii', 'seeds_to_M2.nii']
>>> pThresh = fsl.ProjThresh(in_files=ldir, threshold=3)
>>> pThresh.cmdline
'proj_thresh seeds_to_M1.nii seeds_to_M2.nii 3'
Inputs:
[Mandatory]
in_files: (a list of items which are a pathlike object or string
representing an existing file)
a list of input volumes
argument: ``%s``, position: 0
threshold: (an integer (int or long))
threshold indicating minimum number of seed voxels entering this
mask region
argument: ``%d``, position: 1
[Optional]
output_type: ('NIFTI' or 'NIFTI_PAIR' or 'NIFTI_GZ' or
'NIFTI_PAIR_GZ')
FSL output type
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
of class 'str' and with values which are a bytes or None or a
value of class 'str', nipype default value: {})
Environment variables
Outputs:
out_files: (a list of items which are a pathlike object or string
representing an existing file)
path/name of output volume after thresholding
References:¶
None
TractSkeleton¶
Wraps the executable command tbss_skeleton.
Use FSL’s tbss_skeleton to skeletonise an FA image or project arbitrary values onto a skeleton.
There are two ways to use this interface. To create a skeleton from an FA
image, just supply the in_file and set skeleton_file to True (or
specify a skeleton filename. To project values onto a skeleton, you must
set project_data to True, and then also supply values for
threshold, distance_map, and data_file. The
search_mask_file and use_cingulum_mask inputs are also used in data
projection, but use_cingulum_mask is set to True by default. This mask
controls where the projection algorithm searches within a circular space
around a tract, rather than in a single perpindicular direction.
Example¶
>>> import nipype.interfaces.fsl as fsl
>>> skeletor = fsl.TractSkeleton()
>>> skeletor.inputs.in_file = "all_FA.nii.gz"
>>> skeletor.inputs.skeleton_file = True
>>> skeletor.run() # doctest: +SKIP
Inputs:
[Mandatory]
in_file: (a pathlike object or string representing an existing file)
input image (typcially mean FA volume)
argument: ``-i %s``
[Optional]
project_data: (a boolean)
project data onto skeleton
argument: ``-p %.3f %s %s %s %s``
requires: threshold, distance_map, data_file
threshold: (a float)
skeleton threshold value
distance_map: (a pathlike object or string representing an existing
file)
distance map image
search_mask_file: (a pathlike object or string representing an
existing file)
mask in which to use alternate search rule
mutually_exclusive: use_cingulum_mask
use_cingulum_mask: (a boolean, nipype default value: True)
perform alternate search using built-in cingulum mask
mutually_exclusive: search_mask_file
data_file: (a pathlike object or string representing an existing
file)
4D data to project onto skeleton (usually FA)
alt_data_file: (a pathlike object or string representing an existing
file)
4D non-FA data to project onto skeleton
argument: ``-a %s``
alt_skeleton: (a pathlike object or string representing an existing
file)
alternate skeleton to use
argument: ``-s %s``
projected_data: (a pathlike object or string representing a file)
input data projected onto skeleton
skeleton_file: (a boolean or a pathlike object or string representing
a file)
write out skeleton image
argument: ``-o %s``
output_type: ('NIFTI' or 'NIFTI_PAIR' or 'NIFTI_GZ' or
'NIFTI_PAIR_GZ')
FSL output type
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
of class 'str' and with values which are a bytes or None or a
value of class 'str', nipype default value: {})
Environment variables
Outputs:
projected_data: (a pathlike object or string representing a file)
input data projected onto skeleton
skeleton_file: (a pathlike object or string representing a file)
tract skeleton image
References:¶
None
VecReg¶
Wraps the executable command vecreg.
Use FSL vecreg for registering vector data For complete details, see the FDT Documentation <http://www.fmrib.ox.ac.uk/fsl/fdt/fdt_vecreg.html>
Example¶
>>> from nipype.interfaces import fsl
>>> vreg = fsl.VecReg(in_file='diffusion.nii', affine_mat='trans.mat', ref_vol='mni.nii', out_file='diffusion_vreg.nii')
>>> vreg.cmdline
'vecreg -t trans.mat -i diffusion.nii -o diffusion_vreg.nii -r mni.nii'
Inputs:
[Mandatory]
in_file: (a pathlike object or string representing an existing file)
filename for input vector or tensor field
argument: ``-i %s``
ref_vol: (a pathlike object or string representing an existing file)
filename for reference (target) volume
argument: ``-r %s``
[Optional]
out_file: (a pathlike object or string representing a file)
filename for output registered vector or tensor field
argument: ``-o %s``
affine_mat: (a pathlike object or string representing an existing
file)
filename for affine transformation matrix
argument: ``-t %s``
warp_field: (a pathlike object or string representing an existing
file)
filename for 4D warp field for nonlinear registration
argument: ``-w %s``
rotation_mat: (a pathlike object or string representing an existing
file)
filename for secondary affine matrix if set, this will be used for
the rotation of the vector/tensor field
argument: ``--rotmat=%s``
rotation_warp: (a pathlike object or string representing an existing
file)
filename for secondary warp field if set, this will be used for the
rotation of the vector/tensor field
argument: ``--rotwarp=%s``
interpolation: ('nearestneighbour' or 'trilinear' or 'sinc' or
'spline')
interpolation method : nearestneighbour, trilinear (default), sinc
or spline
argument: ``--interp=%s``
mask: (a pathlike object or string representing an existing file)
brain mask in input space
argument: ``-m %s``
ref_mask: (a pathlike object or string representing an existing file)
brain mask in output space (useful for speed up of nonlinear reg)
argument: ``--refmask=%s``
output_type: ('NIFTI' or 'NIFTI_PAIR' or 'NIFTI_GZ' or
'NIFTI_PAIR_GZ')
FSL output type
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
of class 'str' and with values which are a bytes or None or a
value of class 'str', nipype default value: {})
Environment variables
Outputs:
out_file: (a pathlike object or string representing an existing file)
path/name of filename for the registered vector or tensor field
References:¶
None
XFibres5¶
Wraps the executable command xfibres.
Perform model parameters estimation for local (voxelwise) diffusion parameters
Inputs:
[Mandatory]
dwi: (a pathlike object or string representing an existing file)
diffusion weighted image data file
argument: ``--data=%s``
mask: (a pathlike object or string representing an existing file)
brain binary mask file (i.e. from BET)
argument: ``--mask=%s``
bvecs: (a pathlike object or string representing an existing file)
b vectors file
argument: ``--bvecs=%s``
bvals: (a pathlike object or string representing an existing file)
b values file
argument: ``--bvals=%s``
n_fibres: (a long integer >= 1, nipype default value: 2)
Maximum number of fibres to fit in each voxel
argument: ``--nfibres=%d``
[Optional]
gradnonlin: (a pathlike object or string representing an existing
file)
gradient file corresponding to slice
argument: ``--gradnonlin=%s``
logdir: (a pathlike object or string representing a directory, nipype
default value: .)
argument: ``--logdir=%s``
model: (1 or 2 or 3)
use monoexponential (1, default, required for single-shell) or
multiexponential (2, multi-shell) model
argument: ``--model=%d``
fudge: (an integer (int or long))
ARD fudge factor
argument: ``--fudge=%d``
n_jumps: (an integer (int or long), nipype default value: 5000)
Num of jumps to be made by MCMC
argument: ``--njumps=%d``
burn_in: (a long integer >= 0, nipype default value: 0)
Total num of jumps at start of MCMC to be discarded
argument: ``--burnin=%d``
burn_in_no_ard: (a long integer >= 0, nipype default value: 0)
num of burnin jumps before the ard is imposed
argument: ``--burnin_noard=%d``
sample_every: (a long integer >= 0, nipype default value: 1)
Num of jumps for each sample (MCMC)
argument: ``--sampleevery=%d``
update_proposal_every: (a long integer >= 1, nipype default value:
40)
Num of jumps for each update to the proposal density std (MCMC)
argument: ``--updateproposalevery=%d``
seed: (an integer (int or long))
seed for pseudo random number generator
argument: ``--seed=%d``
no_ard: (a boolean)
Turn ARD off on all fibres
argument: ``--noard``
mutually_exclusive: no_ard, all_ard
all_ard: (a boolean)
Turn ARD on on all fibres
argument: ``--allard``
mutually_exclusive: no_ard, all_ard
no_spat: (a boolean)
Initialise with tensor, not spatially
argument: ``--nospat``
mutually_exclusive: no_spat, non_linear, cnlinear
non_linear: (a boolean)
Initialise with nonlinear fitting
argument: ``--nonlinear``
mutually_exclusive: no_spat, non_linear, cnlinear
cnlinear: (a boolean)
Initialise with constrained nonlinear fitting
argument: ``--cnonlinear``
mutually_exclusive: no_spat, non_linear, cnlinear
rician: (a boolean)
use Rician noise modeling
argument: ``--rician``
f0_noard: (a boolean)
Noise floor model: add to the model an unattenuated signal
compartment f0
argument: ``--f0``
mutually_exclusive: f0_noard, f0_ard
f0_ard: (a boolean)
Noise floor model: add to the model an unattenuated signal
compartment f0
argument: ``--f0 --ardf0``
mutually_exclusive: f0_noard, f0_ard, all_ard
force_dir: (a boolean, nipype default value: True)
use the actual directory name given (do not add + to make a new
directory)
argument: ``--forcedir``
output_type: ('NIFTI' or 'NIFTI_PAIR' or 'NIFTI_GZ' or
'NIFTI_PAIR_GZ')
FSL output type
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
of class 'str' and with values which are a bytes or None or a
value of class 'str', nipype default value: {})
Environment variables
Outputs:
dyads: (a list of items which are a pathlike object or string
representing an existing file)
Mean of PDD distribution in vector form.
fsamples: (a list of items which are a pathlike object or string
representing an existing file)
Samples from the distribution on f anisotropy
mean_dsamples: (a pathlike object or string representing an existing
file)
Mean of distribution on diffusivity d
mean_fsamples: (a list of items which are a pathlike object or string
representing an existing file)
Mean of distribution on f anisotropy
mean_S0samples: (a pathlike object or string representing an existing
file)
Mean of distribution on T2wbaseline signal intensity S0
mean_tausamples: (a pathlike object or string representing an
existing file)
Mean of distribution on tau samples (only with rician noise)
phsamples: (a list of items which are a pathlike object or string
representing an existing file)
phi samples, per fiber
thsamples: (a list of items which are a pathlike object or string
representing an existing file)
theta samples, per fiber
References:¶
None