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
nipype.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]
n_fibres: (a long integer >= 1, nipype default value: 2)
Maximum number of fibres to fit in each voxel
argument: ``-n %d``
bvecs: (an existing file name)
b vectors file
dwi: (an existing file name)
diffusion weighted image data file
bvals: (an existing file name)
b values file
out_dir: (a directory name, nipype default value: bedpostx)
output directory
argument: ``%s``, position: 1
mask: (an existing file name)
bet binary mask file
[Optional]
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
no_spat: (a boolean)
Initialise with tensor, not spatially
argument: ``--nospat``
mutually_exclusive: no_spat, non_linear, cnlinear
cnlinear: (a boolean)
Initialise with constrained nonlinear fitting
argument: ``--cnonlinear``
mutually_exclusive: no_spat, non_linear, cnlinear
all_ard: (a boolean)
Turn ARD on on all fibres
argument: ``--allard``
mutually_exclusive: no_ard, all_ard
fudge: (an integer (int or long))
ARD fudge factor
argument: ``-w %d``
rician: (a boolean)
use Rician noise modeling
argument: ``--rician``
no_ard: (a boolean)
Turn ARD off on all fibres
argument: ``--noard``
mutually_exclusive: no_ard, all_ard
f0_noard: (a boolean)
Noise floor model: add to the model an unattenuated signal
compartment f0
argument: ``--f0``
mutually_exclusive: f0_noard, f0_ard
model: (1 or 2 or 3)
use monoexponential (1, default, required for single-shell) or
multiexponential (2, multi-shell) model
argument: ``-model %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``
n_jumps: (an integer (int or long), nipype default value: 5000)
Num of jumps to be made by MCMC
argument: ``-j %d``
gradnonlin: (a boolean)
consider gradient nonlinearities, default off
argument: ``-g``
seed: (an integer (int or long))
seed for pseudo random number generator
argument: ``--seed=%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``
use_gpu: (a boolean)
Use the GPU version of bedpostx
logdir: (a directory name)
argument: ``--logdir=%s``
force_dir: (a boolean, nipype default value: True)
use the actual directory name given (do not add + to make a new
directory)
argument: ``--forcedir``
sample_every: (a long integer >= 0, nipype default value: 1)
Num of jumps for each sample (MCMC)
argument: ``-s %d``
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
output_type: ('NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI' or
'NIFTI_PAIR')
FSL output type
burn_in: (a long integer >= 0, nipype default value: 0)
Total num of jumps at start of MCMC to be discarded
argument: ``-b %d``
grad_dev: (an existing file name)
grad_dev file, if gradnonlin, -g is True
non_linear: (a boolean)
Initialise with nonlinear fitting
argument: ``--nonlinear``
mutually_exclusive: no_spat, non_linear, cnlinear
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:
merged_thsamples: (a list of items which are an existing file name)
Samples from the distribution on theta
merged_fsamples: (a list of items which are an existing file name)
Samples from the distribution on anisotropic volume fraction
merged_phsamples: (a list of items which are an existing file name)
Samples from the distribution on phi
mean_thsamples: (a list of items which are an existing file name)
Mean of distribution on theta
dyads: (a list of items which are an existing file name)
Mean of PDD distribution in vector form.
dyads_dispersion: (a list of items which are an existing file name)
Dispersion
mean_phsamples: (a list of items which are an existing file name)
Mean of distribution on phi
mean_fsamples: (a list of items which are an existing file name)
Mean of distribution on f anisotropy
mean_S0samples: (an existing file name)
Mean of distribution on T2wbaseline signal intensity S0
mean_dsamples: (an existing file name)
Mean of distribution on diffusivity d
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: (an existing file name)
diffusion weighted image data file
argument: ``-k %s``, position: 0
bvecs: (an existing file name)
b vectors file
argument: ``-r %s``, position: 3
bvals: (an existing file name)
b values file
argument: ``-b %s``, position: 4
mask: (an existing file name)
bet binary mask file
argument: ``-m %s``, position: 2
[Optional]
max_y: (an integer (int or long))
max y
argument: ``-Y %d``
gradnonlin: (an existing file name)
gradient non linearities
argument: ``--gradnonlin=%s``
little_bit: (a boolean)
only process small area of brain
argument: ``--littlebit``
cni: (an existing file name)
input counfound regressors
argument: ``--cni=%s``
min_x: (an integer (int or long))
min x
argument: ``-x %d``
sse: (a boolean)
output sum of squared errors
argument: ``--sse``
min_z: (an integer (int or long))
min z
argument: ``-z %d``
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
base_name: (a unicode string, nipype default value: dtifit_)
base_name that all output files will start with
argument: ``-o %s``, position: 1
save_tensor: (a boolean)
save the elements of the tensor
argument: ``--save_tensor``
output_type: ('NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI' or
'NIFTI_PAIR')
FSL output type
max_x: (an integer (int or long))
max x
argument: ``-X %d``
min_y: (an integer (int or long))
min y
argument: ``-y %d``
max_z: (an integer (int or long))
max z
argument: ``-Z %d``
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
Outputs:
L1: (an existing file name)
path/name of file with the 1st eigenvalue
tensor: (an existing file name)
path/name of file with the 4D tensor volume
FA: (an existing file name)
path/name of file with the fractional anisotropy
MO: (an existing file name)
path/name of file with the mode of anisotropy
L3: (an existing file name)
path/name of file with the 3rd eigenvalue
V1: (an existing file name)
path/name of file with the 1st eigenvector
V3: (an existing file name)
path/name of file with the 3rd eigenvector
S0: (an existing file name)
path/name of file with the raw T2 signal with no diffusion weighting
sse: (an existing file name)
path/name of file with the summed squared error
V2: (an existing file name)
path/name of file with the 2nd eigenvector
L2: (an existing file name)
path/name of file with the 2nd eigenvalue
MD: (an existing file name)
path/name of file with the mean diffusivity
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()
Inputs:
[Mandatory]
in_file: (an existing file name)
image to calculate distance values for
argument: ``--in=%s``
[Optional]
invert_input: (a boolean)
invert input image
argument: ``--invert``
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
output_type: ('NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI' or
'NIFTI_PAIR')
FSL output type
mask_file: (an existing file name)
binary mask to contrain calculations
argument: ``--mask=%s``
local_max_file: (a boolean or a file name)
write an image of the local maxima
argument: ``--localmax=%s``
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
distance_map: (a file name)
distance map to write
argument: ``--out=%s``
Outputs:
local_max_file: (a file name)
image of local maxima
distance_map: (an existing file name)
value is distance to nearest nonzero voxels
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 an existing file name)
a list of input volumes or a singleMatrixFile
argument: ``%s``, position: 0
[Optional]
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
out_file: (a file name)
file with the resulting segmentation
argument: ``%s``, position: 2
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
output_type: ('NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI' or
'NIFTI_PAIR')
FSL output type
Outputs:
out_file: (an existing file name)
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]
phi_vol: (an existing file name)
argument: ``%s``, position: 1
theta_vol: (an existing file name)
argument: ``%s``, position: 0
[Optional]
output: (a file name, nipype default value: dyads)
argument: ``%s``, position: 3
output_type: ('NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI' or
'NIFTI_PAIR')
FSL output type
perc: (a float)
the {perc}% angle of the output cone of uncertainty (output will be
in degrees)
argument: ``%f``, position: 4
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
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
mask: (an existing file name)
argument: ``%s``, position: 2
Outputs:
dyads: (an existing file name)
dispersion: (an existing file name)
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]
seed: (an existing file name or a list of items which are an existing
file name 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``
thsamples: (a list of items which are an existing file name)
fsamples: (a list of items which are an existing file name)
phsamples: (a list of items which are an existing file name)
mask: (an existing file name)
bet binary mask file in diffusion space
argument: ``-m %s``
[Optional]
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``
stop_mask: (an existing file name)
stop tracking at locations given by this mask file
argument: ``--stop=%s``
mod_euler: (a boolean)
use modified euler streamlining
argument: ``--modeuler``
target_masks: (a list of items which are a file name)
list of target masks - required for seeds_to_targets classification
argument: ``--targetmasks=%s``
mask2: (an existing file name)
second bet binary mask (in diffusion space) in twomask_symm mode
argument: ``--mask2=%s``
sample_random_points: (a boolean)
sample random points within seed voxels
argument: ``--sampvox``
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``
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``
random_seed: (a boolean)
random seed
argument: ``--rseed``
waypoints: (an existing file name)
waypoint mask or ascii list of waypoint masks - only keep paths
going through ALL the masks
argument: ``--waypoints=%s``
n_samples: (an integer (int or long), nipype default value: 5000)
number of samples - default=5000
argument: ``--nsamples=%d``
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``
xfm: (an existing file name)
transformation matrix taking seed space to DTI space (either FLIRT
matrix or FNIRT warp_field) - default is identity
argument: ``--xfm=%s``
samples_base_name: (a unicode string, nipype default value: merged)
the rootname/base_name for samples files
argument: ``--samples=%s``
seed_ref: (an existing file name)
reference vol to define seed space in simple mode - diffusion space
assumed if absent
argument: ``--seedref=%s``
os2t: (a boolean)
Outputs seeds to targets
argument: ``--os2t``
out_dir: (an existing directory name)
directory to put the final volumes in
argument: ``--dir=%s``
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
n_steps: (an integer (int or long))
number of steps per sample - default=2000
argument: ``--nsteps=%d``
mesh: (an existing file name)
Freesurfer-type surface descriptor (in ascii format)
argument: ``--mesh=%s``
verbose: (0 or 1 or 2)
Verbose level, [0-2]. Level 2 is required to output particle files.
argument: ``--verbose=%d``
dist_thresh: (a float)
discards samples shorter than this threshold (in mm - default=0)
argument: ``--distthresh=%.3f``
loop_check: (a boolean)
perform loop_checks on paths - slower, but allows lower curvature
threshold
argument: ``--loopcheck``
c_thresh: (a float)
curvature threshold - default=0.2
argument: ``--cthr=%.3f``
network: (a boolean)
activate network mode - only keep paths going through at least one
seed mask (required if multiple seed masks)
argument: ``--network``
output_type: ('NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI' or
'NIFTI_PAIR')
FSL output type
opd: (a boolean, nipype default value: True)
outputs path distributions
argument: ``--opd``
use_anisotropy: (a boolean)
use anisotropy to constrain tracking
argument: ``--usef``
avoid_mp: (an existing file name)
reject pathways passing through locations given by this mask
argument: ``--avoid=%s``
correct_path_distribution: (a boolean)
correct path distribution for the length of the pathways
argument: ``--pd``
step_length: (a float)
step_length in mm - default=0.5
argument: ``--steplength=%.3f``
s2tastext: (a boolean)
output seed-to-target counts as a text file (useful when seeding
from a mesh)
argument: ``--s2tastext``
inv_xfm: (a file name)
transformation matrix taking DTI space to seed space (compulsory
when using a warp_field for seeds_to_dti)
argument: ``--invxfm=%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:
way_total: (an existing file name)
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
particle_files: (a list of items which are an existing file name)
Files describing all of the tract samples. Generated only if verbose
is set to 2
fdt_paths: (a list of items which are an existing file name)
path/name of a 3D image file containing the output connectivity
distribution to the seed mask
targets: (a list of items which are an existing file name)
a list with all generated seeds_to_target files
log: (an existing file name)
path/name of a text record of the command that was run
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]
seed: (an existing file name or a list of items which are an existing
file name 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``
thsamples: (a list of items which are an existing file name)
fsamples: (a list of items which are an existing file name)
phsamples: (a list of items which are an existing file name)
mask: (an existing file name)
bet binary mask file in diffusion space
argument: ``-m %s``
[Optional]
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``
target_masks: (a list of items which are a file name)
list of target masks - required for seeds_to_targets classification
argument: ``--targetmasks=%s``
sample_random_points: (a boolean)
sample random points within seed voxels
argument: ``--sampvox``
waypoints: (an existing file name)
waypoint mask or ascii list of waypoint masks - only keep paths
going through ALL the masks
argument: ``--waypoints=%s``
distthresh3: (a float)
Discards samples (in matrix3) shorter than this threshold (in mm -
default=0)
argument: ``--distthresh3=%.3f``
lrtarget3: (an existing file name)
Column-space mask used for Nxn connectivity matrix
argument: ``--lrtarget3=%s``
omatrix3: (a boolean)
Output matrix3 (NxN connectivity matrix)
argument: ``--omatrix3``
requires: target3, lrtarget3
xfm: (an existing file name)
transformation matrix taking seed space to DTI space (either FLIRT
matrix or FNIRT warp_field) - default is identity
argument: ``--xfm=%s``
out_dir: (an existing directory name)
directory to put the final volumes in
argument: ``--dir=%s``
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
n_steps: (an integer (int or long))
number of steps per sample - default=2000
argument: ``--nsteps=%d``
target2: (an existing file name)
Low resolution binary brain mask for storing connectivity
distribution in matrix2 mode
argument: ``--target2=%s``
verbose: (0 or 1 or 2)
Verbose level, [0-2]. Level 2 is required to output particle files.
argument: ``--verbose=%d``
dist_thresh: (a float)
discards samples shorter than this threshold (in mm - default=0)
argument: ``--distthresh=%.3f``
os2t: (a boolean)
Outputs seeds to targets
argument: ``--os2t``
network: (a boolean)
activate network mode - only keep paths going through at least one
seed mask (required if multiple seed masks)
argument: ``--network``
omatrix4: (a boolean)
Output matrix4 - DtiMaskToSeed (special Oxford Sparse Format)
argument: ``--omatrix4``
onewaycondition: (a boolean)
Apply waypoint conditions to each half tract separately
argument: ``--onewaycondition``
avoid_mp: (an existing file name)
reject pathways passing through locations given by this mask
argument: ``--avoid=%s``
fopd: (an existing file name)
Other mask for binning tract distribution
argument: ``--fopd=%s``
target4: (an existing file name)
Brain mask in DTI space
argument: ``--target4=%s``
c_thresh: (a float)
curvature threshold - default=0.2
argument: ``--cthr=%.3f``
step_length: (a float)
step_length in mm - default=0.5
argument: ``--steplength=%.3f``
s2tastext: (a boolean)
output seed-to-target counts as a text file (useful when seeding
from a mesh)
argument: ``--s2tastext``
samples_base_name: (a unicode string, nipype default value: merged)
the rootname/base_name for samples files
argument: ``--samples=%s``
stop_mask: (an existing file name)
stop tracking at locations given by this mask file
argument: ``--stop=%s``
mod_euler: (a boolean)
use modified euler streamlining
argument: ``--modeuler``
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``
waycond: ('OR' or 'AND')
Waypoint condition. Either "AND" (default) or "OR"
argument: ``--waycond=%s``
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``
random_seed: (a boolean)
random seed
argument: ``--rseed``
omatrix2: (a boolean)
Output matrix2 - SeedToLowResMask
argument: ``--omatrix2``
requires: target2
simple: (a boolean)
rack from a list of voxels (seed must be a ASCII list of
coordinates)
argument: ``--simple``
omatrix1: (a boolean)
Output matrix1 - SeedToSeed Connectivity
argument: ``--omatrix1``
wayorder: (a boolean)
Reject streamlines that do not hit waypoints in given order. Only
valid if waycond=AND
argument: ``--wayorder``
n_samples: (an integer (int or long), nipype default value: 5000)
number of samples - default=5000
argument: ``--nsamples=%d``
distthresh1: (a float)
Discards samples (in matrix1) shorter than this threshold (in mm -
default=0)
argument: ``--distthresh1=%.3f``
seed_ref: (an existing file name)
reference vol to define seed space in simple mode - diffusion space
assumed if absent
argument: ``--seedref=%s``
loop_check: (a boolean)
perform loop_checks on paths - slower, but allows lower curvature
threshold
argument: ``--loopcheck``
colmask4: (an existing file name)
Mask for columns of matrix4 (default=seed mask)
argument: ``--colmask4=%s``
meshspace: ('caret' or 'freesurfer' or 'first' or 'vox')
Mesh reference space - either "caret" (default) or "freesurfer" or
"first" or "vox"
argument: ``--meshspace=%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``
use_anisotropy: (a boolean)
use anisotropy to constrain tracking
argument: ``--usef``
target3: (an existing file name)
Mask used for NxN connectivity matrix (or Nxn if lrtarget3 is set)
argument: ``--target3=%s``
output_type: ('NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI' or
'NIFTI_PAIR')
FSL output type
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
inv_xfm: (a file name)
transformation matrix taking DTI space to seed space (compulsory
when using a warp_field for seeds_to_dti)
argument: ``--invxfm=%s``
Outputs:
network_matrix: (an existing file name)
the network matrix generated by --omatrix1 option
particle_files: (a list of items which are an existing file name)
Files describing all of the tract samples. Generated only if verbose
is set to 2
matrix2_dot: (an existing file name)
Output matrix2.dot - SeedToLowResMask
targets: (a list of items which are an existing file name)
a list with all generated seeds_to_target files
matrix3_dot: (an existing file name)
Output matrix3 - NxN connectivity matrix
log: (an existing file name)
path/name of a text record of the command that was run
matrix1_dot: (an existing file name)
Output matrix1.dot - SeedToSeed Connectivity
way_total: (an existing file name)
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
fdt_paths: (a list of items which are an existing file name)
path/name of a 3D image file containing the output connectivity
distribution to the seed mask
lookup_tractspace: (an existing file name)
lookup_tractspace generated by --omatrix2 option
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 an existing file name)
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]
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
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
output_type: ('NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI' or
'NIFTI_PAIR')
FSL output type
Outputs:
out_files: (a list of items which are an existing file name)
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()
Inputs:
[Mandatory]
in_file: (an existing file name)
input image (typcially mean FA volume)
argument: ``-i %s``
[Optional]
projected_data: (a file name)
input data projected onto skeleton
project_data: (a boolean)
project data onto skeleton
argument: ``-p %.3f %s %s %s %s``
requires: threshold, distance_map, data_file
alt_data_file: (an existing file name)
4D non-FA data to project onto skeleton
argument: ``-a %s``
alt_skeleton: (an existing file name)
alternate skeleton to use
argument: ``-s %s``
output_type: ('NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI' or
'NIFTI_PAIR')
FSL output type
data_file: (an existing file name)
4D data to project onto skeleton (usually FA)
threshold: (a float)
skeleton threshold value
search_mask_file: (an existing file name)
mask in which to use alternate search rule
mutually_exclusive: use_cingulum_mask
skeleton_file: (a boolean or a file name)
write out skeleton image
argument: ``-o %s``
use_cingulum_mask: (a boolean, nipype default value: True)
perform alternate search using built-in cingulum mask
mutually_exclusive: search_mask_file
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
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
distance_map: (an existing file name)
distance map image
Outputs:
projected_data: (a file name)
input data projected onto skeleton
skeleton_file: (a file name)
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: (an existing file name)
filename for input vector or tensor field
argument: ``-i %s``
ref_vol: (an existing file name)
filename for reference (target) volume
argument: ``-r %s``
[Optional]
ref_mask: (an existing file name)
brain mask in output space (useful for speed up of nonlinear reg)
argument: ``--refmask=%s``
out_file: (a file name)
filename for output registered vector or tensor field
argument: ``-o %s``
rotation_warp: (an existing file name)
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``
rotation_mat: (an existing file name)
filename for secondary affine matrix if set, this will be used for
the rotation of the vector/tensor field
argument: ``--rotmat=%s``
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
output_type: ('NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI' or
'NIFTI_PAIR')
FSL output type
warp_field: (an existing file name)
filename for 4D warp field for nonlinear registration
argument: ``-w %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
affine_mat: (an existing file name)
filename for affine transformation matrix
argument: ``-t %s``
mask: (an existing file name)
brain mask in input space
argument: ``-m %s``
Outputs:
out_file: (an existing file name)
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]
n_fibres: (a long integer >= 1, nipype default value: 2)
Maximum number of fibres to fit in each voxel
argument: ``--nfibres=%d``
dwi: (an existing file name)
diffusion weighted image data file
argument: ``--data=%s``
bvecs: (an existing file name)
b vectors file
argument: ``--bvecs=%s``
bvals: (an existing file name)
b values file
argument: ``--bvals=%s``
mask: (an existing file name)
brain binary mask file (i.e. from BET)
argument: ``--mask=%s``
[Optional]
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
no_spat: (a boolean)
Initialise with tensor, not spatially
argument: ``--nospat``
mutually_exclusive: no_spat, non_linear, cnlinear
cnlinear: (a boolean)
Initialise with constrained nonlinear fitting
argument: ``--cnonlinear``
mutually_exclusive: no_spat, non_linear, cnlinear
all_ard: (a boolean)
Turn ARD on on all fibres
argument: ``--allard``
mutually_exclusive: no_ard, all_ard
fudge: (an integer (int or long))
ARD fudge factor
argument: ``--fudge=%d``
no_ard: (a boolean)
Turn ARD off on all fibres
argument: ``--noard``
mutually_exclusive: no_ard, all_ard
f0_noard: (a boolean)
Noise floor model: add to the model an unattenuated signal
compartment f0
argument: ``--f0``
mutually_exclusive: f0_noard, f0_ard
model: (1 or 2 or 3)
use monoexponential (1, default, required for single-shell) or
multiexponential (2, multi-shell) model
argument: ``--model=%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``
n_jumps: (an integer (int or long), nipype default value: 5000)
Num of jumps to be made by MCMC
argument: ``--njumps=%d``
gradnonlin: (an existing file name)
gradient file corresponding to slice
argument: ``--gradnonlin=%s``
seed: (an integer (int or long))
seed for pseudo random number generator
argument: ``--seed=%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``
logdir: (a directory name, nipype default value: .)
argument: ``--logdir=%s``
rician: (a boolean)
use Rician noise modeling
argument: ``--rician``
force_dir: (a boolean, nipype default value: True)
use the actual directory name given (do not add + to make a new
directory)
argument: ``--forcedir``
sample_every: (a long integer >= 0, nipype default value: 1)
Num of jumps for each sample (MCMC)
argument: ``--sampleevery=%d``
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
output_type: ('NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI' or
'NIFTI_PAIR')
FSL output type
burn_in: (a long integer >= 0, nipype default value: 0)
Total num of jumps at start of MCMC to be discarded
argument: ``--burnin=%d``
non_linear: (a boolean)
Initialise with nonlinear fitting
argument: ``--nonlinear``
mutually_exclusive: no_spat, non_linear, cnlinear
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_fsamples: (a list of items which are an existing file name)
Mean of distribution on f anisotropy
phsamples: (a list of items which are an existing file name)
phi samples, per fiber
mean_tausamples: (an existing file name)
Mean of distribution on tau samples (only with rician noise)
dyads: (a list of items which are an existing file name)
Mean of PDD distribution in vector form.
thsamples: (a list of items which are an existing file name)
theta samples, per fiber
fsamples: (a list of items which are an existing file name)
Samples from the distribution on f anisotropy
mean_dsamples: (an existing file name)
Mean of distribution on diffusivity d
mean_S0samples: (an existing file name)
Mean of distribution on T2wbaseline signal intensity S0
References:¶
None