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# interfaces.fsl.dti

## BEDPOSTX

Link to code

Wraps command bedpostx

Deprecated! Please use create_bedpostx_pipeline instead

### Example

    >>> 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

## 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
    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

## 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

    [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

## EddyCorrect

Link to code

Wraps command eddy_correct

Deprecated! Please use create_eddy_correct_pipeline instead

### Example

    >>> 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

## 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 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

## 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)
    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)

## 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: (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

## 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 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

## 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)

    [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

## 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
    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

## XFibres

Link to code

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