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