interfaces.fsl.dti

BEDPOSTX5

Link to code

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 --forcedir -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
n_fibres: (an integer >= 1, nipype default value: 1)
        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: (an integer >= 0)
        Total num of jumps at start of MCMC to be discarded
        flag: -b %d
burn_in_no_ard: (an integer >= 0)
        num of burnin jumps before the ard is imposed
        flag: --burninnoard=%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 newbytes or None or a
         newstr or None and with values which are a newbytes or None or a
         newstr or None, 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
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)
        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))
        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: (u'NIFTI_PAIR' or u'NIFTI_PAIR_GZ' or u'NIFTI_GZ' or
         u'NIFTI')
        FSL output type
rician: (a boolean)
        use Rician noise modeling
        flag: --rician
sample_every: (an integer >= 0)
        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
terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored
update_proposal_every: (an integer >= 1)
        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

DTIFit

Link to code

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 newbytes or None or a
         newstr or None and with values which are a newbytes or None or a
         newstr or None, nipype default value: {})
        Environment variables
gradnonlin: (an existing file name)
        gradient non linearities
        flag: --gradnonlin=%s
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
        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: (u'NIFTI_PAIR' or u'NIFTI_PAIR_GZ' or u'NIFTI_GZ' or
         u'NIFTI')
        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
terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored

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

DistanceMap

Link to code

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 newbytes or None or a
         newstr or None and with values which are a newbytes or None or a
         newstr or None, 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
        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: (u'NIFTI_PAIR' or u'NIFTI_PAIR_GZ' or u'NIFTI_GZ' or
         u'NIFTI')
        FSL output type
terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored

Outputs:

distance_map: (an existing file name)
        value is distance to nearest nonzero voxels
local_max_file: (a file name)
        image of local maxima

FindTheBiggest

Link to code

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 newbytes or None or a
         newstr or None and with values which are a newbytes or None or a
         newstr or None, 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
        flag: %s, position: 2
output_type: (u'NIFTI_PAIR' or u'NIFTI_PAIR_GZ' or u'NIFTI_GZ' or
         u'NIFTI')
        FSL output type
terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored

Outputs:

out_file: (an existing file name)
        output file indexed in order of input files
        flag: %s

MakeDyadicVectors

Link to code

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 newbytes or None or a
         newstr or None and with values which are a newbytes or None or a
         newstr or None, 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)
        flag: %s, position: 2
output: (a file name, nipype default value: dyads)
        flag: %s, position: 3
output_type: (u'NIFTI_PAIR' or u'NIFTI_PAIR_GZ' or u'NIFTI_GZ' or
         u'NIFTI')
        FSL output type
perc: (a float)
        the {perc}% angle of the output cone of uncertainty (output will be
        in degrees)
        flag: %f, position: 4
terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored

Outputs:

dispersion: (an existing file name)
dyads: (an existing file name)

ProbTrackX

Link to code

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 newbytes or None or a
         newstr or None and with values which are a newbytes or None or a
         newstr or None, 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
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)
        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: (u'simple' or u'two_mask_symm' or u'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: (u'NIFTI_PAIR' or u'NIFTI_PAIR_GZ' or u'NIFTI_GZ' or
         u'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)
        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
terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored
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

ProbTrackX2

Link to code

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 newbytes or None or a
         newstr or None and with values which are a newbytes or None or a
         newstr or None, 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
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)
        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: (u'caret' or u'freesurfer' or u'first' or u'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: (u'NIFTI_PAIR' or u'NIFTI_PAIR_GZ' or u'NIFTI_GZ' or
         u'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)
        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
terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored
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: (u'OR' or u'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

ProjThresh

Link to code

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 newbytes or None or a
         newstr or None and with values which are a newbytes or None or a
         newstr or None, 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: (u'NIFTI_PAIR' or u'NIFTI_PAIR_GZ' or u'NIFTI_GZ' or
         u'NIFTI')
        FSL output type
terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored

Outputs:

out_files: (a list of items which are an existing file name)
        path/name of output volume after thresholding

TractSkeleton

Link to code

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 newbytes or None or a
         newstr or None and with values which are a newbytes or None or a
         newstr or None, 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: (u'NIFTI_PAIR' or u'NIFTI_PAIR_GZ' or u'NIFTI_GZ' or
         u'NIFTI')
        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
terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored
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

VecReg

Link to code

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 newbytes or None or a
         newstr or None and with values which are a newbytes or None or a
         newstr or None, 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: (u'nearestneighbour' or u'trilinear' or u'sinc' or
         u'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: (u'NIFTI_PAIR' or u'NIFTI_PAIR_GZ' or u'NIFTI_GZ' or
         u'NIFTI')
        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
terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored
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

XFibres5

Link to code

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: (an integer >= 1, nipype default value: 1)
        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: (an integer >= 0)
        Total num of jumps at start of MCMC to be discarded
        flag: --burnin=%d
burn_in_no_ard: (an integer >= 0)
        num of burnin jumps before the ard is imposed
        flag: --burninnoard=%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 newbytes or None or a
         newstr or None and with values which are a newbytes or None or a
         newstr or None, 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
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: .)
        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))
        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: (u'NIFTI_PAIR' or u'NIFTI_PAIR_GZ' or u'NIFTI_GZ' or
         u'NIFTI')
        FSL output type
rician: (a boolean)
        use Rician noise modeling
        flag: --rician
sample_every: (an integer >= 0)
        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
terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored
update_proposal_every: (an integer >= 1)
        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