Code: file:///tmp/buildd/nipype-0.5.3/nipype/interfaces/fsl/dti.py#L199
Wraps command bedpostx
Deprecated! Please use create_bedpostx_pipeline instead
>>> from nipype.interfaces import fsl
>>> bedp = fsl.BEDPOSTX(bpx_directory='subjdir', bvecs='bvecs', bvals='bvals', dwi='diffusion.nii', mask='mask.nii', fibres=1)
>>> bedp.cmdline
'bedpostx subjdir -n 1'
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
[Optional]
args: (a string)
Additional parameters to the command
bpx_directory: (a directory name, nipype default value: bedpostx)
the name for this subjects bedpostx folder
burn_period: (an integer)
burnin period
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
fibres: (an integer)
number of fibres per voxel
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
jumps: (an integer)
number of jumps
output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
FSL output type
sampling: (an integer)
sample every
weight: (a float)
ARD weight, more weight means less secondary fibres per voxel
Outputs:
bpx_out_directory: (an existing directory name)
path/name of directory with all bedpostx output files for this subject
dyads: (a list of items which are a file name)
a list of path/name of mean of PDD distribution in vector form
mean_fsamples: (a list of items which are a file name)
a list of path/name of 3D volume with mean of distribution on f anisotropy
mean_phsamples: (a list of items which are a file name)
a list of path/name of 3D volume with mean of distribution on phi
mean_thsamples: (a list of items which are a file name)
a list of path/name of 3D volume with mean of distribution on theta
merged_fsamples: (a list of items which are a file name)
a list of path/name of 4D volume with samples from the distribution on anisotropic
volume fraction
merged_phsamples: (a list of items which are a file name)
a list of path/name of file with samples from the distribution on phi
merged_thsamples: (a list of items which are a file name)
a list of path/name of 4D volume with samples from the distribution on theta
xfms_directory: (an existing directory name)
path/name of directory with the tranformation matrices
Code: file:///tmp/buildd/nipype-0.5.3/nipype/interfaces/fsl/dti.py#L70
Wraps command dtifit
Use FSL dtifit command for fitting a diffusion tensor model at each voxel
>>> 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
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
[Optional]
args: (a string)
Additional parameters to the command
base_name: (a string, nipype default value: dtifit_)
base_name that all output files will start with
cni: (an existing file name)
input counfound regressors
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
little_bit: (a boolean)
only process small area of brain
max_x: (an integer)
max x
max_y: (an integer)
max y
max_z: (an integer)
max z
min_x: (an integer)
min x
min_y: (an integer)
min y
min_z: (an integer)
min z
output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
FSL output type
save_tensor: (a boolean)
save the elements of the tensor
sse: (a boolean)
output sum of squared errors
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
tensor: (an existing file name)
path/name of file with the 4D tensor volume
Code: file:///tmp/buildd/nipype-0.5.3/nipype/interfaces/fsl/dti.py#L775
Wraps command distancemap
Use FSL’s distancemap to generate a map of the distance to the nearest nonzero voxel.
>>> 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
[Optional]
args: (a string)
Additional parameters to the command
distance_map: (a file name)
distance map to write
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
invert_input: (a boolean)
invert input image
local_max_file: (a boolean or a file name)
write an image of the local maxima
mask_file: (an existing file name)
binary mask to contrain calculations
output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
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
Code: file:///tmp/buildd/nipype-0.5.3/nipype/interfaces/fsl/dti.py#L113
Wraps command eddy_correct
Deprecated! Please use create_eddy_correct_pipeline instead
>>> from nipype.interfaces import fsl
>>> eddyc = fsl.EddyCorrect(in_file='diffusion.nii', out_file="diffusion_edc.nii", ref_num=0)
>>> eddyc.cmdline
'eddy_correct diffusion.nii diffusion_edc.nii 0'
Inputs:
[Mandatory]
in_file: (an existing file name)
4D input file
ref_num: (an integer)
reference number
[Optional]
args: (a string)
Additional parameters to the command
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
out_file: (a file name)
4D output file
output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
FSL output type
Outputs:
eddy_corrected: (an existing file name)
path/name of 4D eddy corrected output file
Code: file:///tmp/buildd/nipype-0.5.3/nipype/interfaces/fsl/dti.py#L620
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.
>>> 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 file name)
a list of input volumes or a singleMatrixFile
[Optional]
args: (a string)
Additional parameters to the command
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
out_file: (a file name)
file with the resulting segmentation
output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
FSL output type
Outputs:
out_file: (an existing file name)
output file indexed in order of input files
Code: file:///tmp/buildd/nipype-0.5.3/nipype/interfaces/fsl/dti.py#L920
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)
theta_vol: (an existing file name)
[Optional]
args: (a string)
Additional parameters to the command
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
mask: (an existing file name)
output: (a file name, nipype default value: dyads)
output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
FSL output type
perc: (a float)
the {perc}% angle of the output cone of uncertainty (output will be in degrees)
Outputs:
dispersion: (an existing file name)
dyads: (an existing file name)
Code: file:///tmp/buildd/nipype-0.5.3/nipype/interfaces/fsl/dti.py#L376
Wraps command probtrackx
Use FSL probtrackx for tractography on bedpostx results
>>> 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: (an existing file name)
mask: (an existing file name)
bet binary mask file in diffusion space
phsamples: (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)
seed volume(s), or voxel(s)or freesurfer label file
thsamples: (an existing file name)
[Optional]
args: (a string)
Additional parameters to the command
avoid_mp: (an existing file name)
reject pathways passing through locations given by this mask
c_thresh: (a float)
curvature threshold - default=0.2
correct_path_distribution: (a boolean)
correct path distribution for the length of the pathways
dist_thresh: (a float)
discards samples shorter than this threshold (in mm - default=0)
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
fibst: (an integer)
force a starting fibre for tracking - default=1, i.e. first fibre orientation. Only
works if randfib==0
force_dir: (a boolean, nipype default value: True)
use the actual directory name given - i.e. do not add + to make a new directory
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
inv_xfm: (a file name)
transformation matrix taking DTI space to seed space (compulsory when using a warp_field
for seeds_to_dti)
loop_check: (a boolean)
perform loop_checks on paths - slower, but allows lower curvature threshold
mask2: (an existing file name)
second bet binary mask (in diffusion space) in twomask_symm mode
mesh: (an existing file name)
Freesurfer-type surface descriptor (in ascii format)
mod_euler: (a boolean)
use modified euler streamlining
mode: ('simple' or 'two_mask_symm' or 'seedmask')
options: simple (single seed voxel), seedmask (mask of seed voxels), twomask_symm (two
bet binary masks)
n_samples: (an integer, nipype default value: 5000)
number of samples - default=5000
n_steps: (an integer)
number of steps per sample - default=2000
network: (a boolean)
activate network mode - only keep paths going through at least one seed mask (required
if multiple seed masks)
opd: (a boolean, nipype default value: True)
outputs path distributions
os2t: (a boolean)
Outputs seeds to targets
out_dir: (an existing directory name)
directory to put the final volumes in
output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
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)
random_seed: (a boolean)
random seed
s2tastext: (a boolean)
output seed-to-target counts as a text file (useful when seeding from a mesh)
sample_random_points: (a boolean)
sample random points within seed voxels
samples_base_name: (a string, nipype default value: merged)
the rootname/base_name for samples files
seed_ref: (an existing file name)
reference vol to define seed space in simple mode - diffusion space assumed if absent
step_length: (a float)
step_length in mm - default=0.5
stop_mask: (an existing file name)
stop tracking at locations given by this mask file
target_masks: (a file name)
list of target masks - required for seeds_to_targets classification
use_anisotropy: (a boolean)
use anisotropy to constrain tracking
verbose: (0 or 1 or 2)
Verbose level, [0-2].Level 2 is required to output particle files.
waypoints: (an existing file name)
waypoint mask or ascii list of waypoint masks - only keep paths going through ALL the
masks
xfm: (an existing file name)
transformation matrix taking seed space to DTI space (either FLIRT matrix or FNIRT
warp_field) - default is identity
Outputs:
fdt_paths: (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 a file name)
Files describing all of the tract samples. Generated only if verbose is set to 2
targets: (a list of items which are a 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
Code: file:///tmp/buildd/nipype-0.5.3/nipype/interfaces/fsl/dti.py#L579
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>
>>> 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 file name)
a list of input volumes
threshold: (an integer)
threshold indicating minimum number of seed voxels entering this mask region
[Optional]
args: (a string)
Additional parameters to the command
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
FSL output type
Outputs:
out_files: (a list of items which are a file name)
path/name of output volume after thresholding
Code: file:///tmp/buildd/nipype-0.5.3/nipype/interfaces/fsl/dti.py#L687
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.
>>> 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)
[Optional]
alt_data_file: (an existing file name)
4D non-FA data to project onto skeleton
alt_skeleton: (an existing file name)
alternate skeleton to use
args: (a string)
Additional parameters to the command
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 value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
FSL output type
project_data: (a boolean)
project data onto skeleton
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
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
Code: file:///tmp/buildd/nipype-0.5.3/nipype/interfaces/fsl/dti.py#L521
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>
>>> 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
ref_vol: (an existing file name)
filename for reference (target) volume
[Optional]
affine_mat: (an existing file name)
filename for affine transformation matrix
args: (a string)
Additional parameters to the command
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
interpolation: ('nearestneighbour' or 'trilinear' or 'sinc' or 'spline')
interpolation method : nearestneighbour, trilinear (default), sinc or spline
mask: (an existing file name)
brain mask in input space
out_file: (a file name)
filename for output registered vector or tensor field
output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
FSL output type
ref_mask: (an existing file name)
brain mask in output space (useful for speed up of nonlinear reg)
rotation_mat: (an existing file name)
filename for secondary affine matrixif set, this will be used for the rotation of the
vector/tensor field
rotation_warp: (an existing file name)
filename for secondary warp fieldif set, this will be used for the rotation of the
vector/tensor field
warp_field: (an existing file name)
filename for 4D warp field for nonlinear registration
Outputs:
out_file: (an existing file name)
path/name of filename for the registered vector or tensor field
Code: file:///tmp/buildd/nipype-0.5.3/nipype/interfaces/fsl/dti.py#L870
Wraps command xfibres
Perform model parameters estimation for local (voxelwise) diffusion parameters
Inputs:
[Mandatory]
bvals: (an existing file name)
bvecs: (an existing file name)
dwi: (an existing file name)
mask: (an existing file name)
[Optional]
all_ard: (a boolean)
Turn ARD on on all fibres
mutually_exclusive: no_ard, all_ard
args: (a string)
Additional parameters to the command
burn_in: (an integer >= 0)
Total num of jumps at start of MCMC to be discarded
burn_in_no_ard: (an integer >= 0)
num of burnin jumps before the ard is imposed
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
force_dir: (a boolean, nipype default value: True)
use the actual directory name given - i.e. do not add + to make a new directory
fudge: (an integer)
ARD fudge factor
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
logdir: (a directory name, nipype default value: logdir)
model: (an integer)
Which model to use. 1=mono-exponential (default and required for single shell).
2=continous exponential (for multi-shell experiments)
n_fibres: (an integer >= 1)
Maximum nukmber of fibres to fit in each voxel
n_jumps: (an integer >= 1)
Num of jumps to be made by MCMC
no_ard: (a boolean)
Turn ARD off on all fibres
mutually_exclusive: no_ard, all_ard
no_spat: (a boolean)
Initialise with tensor, not spatially
mutually_exclusive: no_spat, non_linear
non_linear: (a boolean)
Initialise with nonlinear fitting
mutually_exclusive: no_spat, non_linear
output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
FSL output type
sample_every: (an integer >= 0)
Num of jumps for each sample (MCMC)
seed: (an integer)
seed for pseudo random number generator
update_proposal_every: (an integer >= 1)
Num of jumps for each update to the proposal density std (MCMC)
Outputs:
dyads: (an existing file name)
Mean of PDD distribution in vector form.
fsamples: (an existing file name)
Samples from the distribution on anisotropic volume fraction
mean_S0samples: (an existing file name)
Samples from S0 distribution
mean_dsamples: (an existing file name)
Mean of distribution on diffusivity d
mean_fsamples: (an existing file name)
Mean of distribution on f anisotropy
phsamples: (an existing file name)
Samples from the distribution on phi
thsamples: (an existing file name)
Samples from the distribution on theta