Home · Quickstart · Documentation · Citation · NiPy

# interfaces.fsl.preprocess

## ApplyWarp

Link to code

Wraps command applywarp

Use FSL’s applywarp to apply the results of a FNIRT registration

### Examples

    >>> from nipype.interfaces import fsl
    >>> from nipype.testing import example_data
    >>> aw = fsl.ApplyWarp()
    >>> aw.inputs.in_file = example_data('structural.nii')
    >>> aw.inputs.ref_file = example_data('mni.nii')
    >>> aw.inputs.field_file = 'my_coefficients_filed.nii'
    >>> res = aw.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            image to be warped
    ref_file: (an existing file name)
            reference image

    [Optional]
    abswarp: (a boolean)
            treat warp field as absolute: x' = w(x)
            mutually_exclusive: relwarp
    args: (a string)
            Additional parameters to the command
    datatype: ('char' or 'short' or 'int' or 'float' or 'double')
            Force output data type [char short int float double].
    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
    field_file: (an existing file name)
            file containing warp field
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    interp: ('nn' or 'trilinear' or 'sinc' or 'spline')
            interpolation method
    mask_file: (an existing file name)
            filename for mask image (in reference space)
    out_file: (a file name)
            output filename
    output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
            FSL output type
    postmat: (an existing file name)
            filename for post-transform (affine matrix)
    premat: (an existing file name)
            filename for pre-transform (affine matrix)
    relwarp: (a boolean)
            treat warp field as relative: x' = x + w(x)
            mutually_exclusive: abswarp
    superlevel: ('a' or an integer)
            level of intermediary supersampling, a for 'automatic' or integer level. Default = 2
    supersample: (a boolean)
            intermediary supersampling of output, default is off

Outputs:

    out_file: (an existing file name)
            Warped output file

## ApplyXfm

Link to code

Wraps command flirt

Currently just a light wrapper around FLIRT, with no modifications

ApplyXfm is used to apply an existing tranform to an image

### Examples

    >>> import nipype.interfaces.fsl as fsl
    >>> from nipype.testing import example_data
    >>> applyxfm = fsl.ApplyXfm()
    >>> applyxfm.inputs.in_file = example_data('structural.nii')
    >>> applyxfm.inputs.in_matrix_file = example_data('trans.mat')
    >>> applyxfm.inputs.out_file = 'newfile.nii'
    >>> applyxfm.inputs.reference = example_data('mni.nii')
    >>> applyxfm.inputs.apply_xfm = True
    >>> result = applyxfm.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            input file
    reference: (an existing file name)
            reference file

    [Optional]
    angle_rep: ('quaternion' or 'euler')
            representation of rotation angles
    apply_xfm: (a boolean, nipype default value: True)
            apply transformation supplied by in_matrix_file
            requires: in_matrix_file
    args: (a string)
            Additional parameters to the command
    bins: (an integer)
            number of histogram bins
    coarse_search: (an integer)
            coarse search delta angle
    cost: ('mutualinfo' or 'corratio' or 'normcorr' or 'normmi' or 'leastsq' or 'labeldiff')
            cost function
    cost_func: ('mutualinfo' or 'corratio' or 'normcorr' or 'normmi' or 'leastsq' or
             'labeldiff')
            cost function
    datatype: ('char' or 'short' or 'int' or 'float' or 'double')
            force output data type
    display_init: (a boolean)
            display initial matrix
    dof: (an integer)
            number of transform degrees of freedom
    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
    fine_search: (an integer)
            fine search delta angle
    force_scaling: (a boolean)
            force rescaling even for low-res images
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    in_matrix_file: (a file name)
            input 4x4 affine matrix
    in_weight: (an existing file name)
            File for input weighting volume
    interp: ('trilinear' or 'nearestneighbour' or 'sinc')
            final interpolation method used in reslicing
    min_sampling: (a float)
            set minimum voxel dimension for sampling
    no_clamp: (a boolean)
            do not use intensity clamping
    no_resample: (a boolean)
            do not change input sampling
    no_resample_blur: (a boolean)
            do not use blurring on downsampling
    no_search: (a boolean)
            set all angular searches to ranges 0 to 0
    out_file: (a file name)
            registered output file
    out_matrix_file: (a file name)
            output affine matrix in 4x4 asciii format
    output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
            FSL output type
    padding_size: (an integer)
            for applyxfm: interpolates outside image by size
    ref_weight: (an existing file name)
            File for reference weighting volume
    rigid2D: (a boolean)
            use 2D rigid body mode - ignores dof
    schedule: (an existing file name)
            replaces default schedule
    searchr_x: (a list of from 2 to 2 items which are an integer)
            search angles along x-axis, in degrees
    searchr_y: (a list of from 2 to 2 items which are an integer)
            search angles along y-axis, in degrees
    searchr_z: (a list of from 2 to 2 items which are an integer)
            search angles along z-axis, in degrees
    sinc_width: (an integer)
            full-width in voxels
    sinc_window: ('rectangular' or 'hanning' or 'blackman')
            sinc window
    uses_qform: (a boolean)
            initialize using sform or qform
    verbose: (an integer)
            verbose mode, 0 is least

Outputs:

    out_file: (an existing file name)
            path/name of registered file (if generated)
    out_matrix_file: (an existing file name)
            path/name of calculated affine transform (if generated)

## BET

Link to code

Wraps command bet

Use FSL BET command for skull stripping.

For complete details, see the BET Documentation.

### Examples

    >>> from nipype.interfaces import fsl
    >>> from nipype.testing import  example_data
    >>> btr = fsl.BET()
    >>> btr.inputs.in_file = example_data('structural.nii')
    >>> btr.inputs.frac = 0.7
    >>> res = btr.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            input file to skull strip

    [Optional]
    args: (a string)
            Additional parameters to the command
    center: (a list of at most 3 items which are an integer)
            center of gravity in voxels
    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
    frac: (a float)
            fractional intensity threshold
    functional: (a boolean)
            apply to 4D fMRI data
            mutually_exclusive: functional, reduce_bias, robust, padding, remove_eyes, surfaces,
             t2_guided
    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: (a boolean)
            create binary mask image
    mesh: (a boolean)
            generate a vtk mesh brain surface
    no_output: (a boolean)
            Don't generate segmented output
    out_file: (a file name)
            name of output skull stripped image
    outline: (a boolean)
            create surface outline image
    output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
            FSL output type
    padding: (a boolean)
            improve BET if FOV is very small in Z (by temporarily padding end slices)
            mutually_exclusive: functional, reduce_bias, robust, padding, remove_eyes, surfaces,
             t2_guided
    radius: (an integer)
            head radius
    reduce_bias: (a boolean)
            bias field and neck cleanup
            mutually_exclusive: functional, reduce_bias, robust, padding, remove_eyes, surfaces,
             t2_guided
    remove_eyes: (a boolean)
            eye & optic nerve cleanup (can be useful in SIENA)
            mutually_exclusive: functional, reduce_bias, robust, padding, remove_eyes, surfaces,
             t2_guided
    robust: (a boolean)
            robust brain centre estimation (iterates BET several times)
            mutually_exclusive: functional, reduce_bias, robust, padding, remove_eyes, surfaces,
             t2_guided
    skull: (a boolean)
            create skull image
    surfaces: (a boolean)
            run bet2 and then betsurf to get additional skull and scalp surfaces (includes
            registrations)
            mutually_exclusive: functional, reduce_bias, robust, padding, remove_eyes, surfaces,
             t2_guided
    t2_guided: (a file name)
            as with creating surfaces, when also feeding in non-brain-extracted T2 (includes
            registrations)
            mutually_exclusive: functional, reduce_bias, robust, padding, remove_eyes, surfaces,
             t2_guided
    threshold: (a boolean)
            apply thresholding to segmented brain image and mask
    vertical_gradient: (a float)
            vertical gradient in fractional intensity threshold (-1, 1)

Outputs:

    inskull_mask_file: (a file name)
            path/name of inskull mask (if generated)
    inskull_mesh_file: (a file name)
            path/name of inskull mesh outline (if generated)
    mask_file: (a file name)
            path/name of binary brain mask (if generated)
    meshfile: (a file name)
            path/name of vtk mesh file (if generated)
    out_file: (a file name)
            path/name of skullstripped file (if generated)
    outline_file: (a file name)
            path/name of outline file (if generated)
    outskin_mask_file: (a file name)
            path/name of outskin mask (if generated)
    outskin_mesh_file: (a file name)
            path/name of outskin mesh outline (if generated)
    outskull_mask_file: (a file name)
            path/name of outskull mask (if generated)
    outskull_mesh_file: (a file name)
            path/name of outskull mesh outline (if generated)
    skull_mask_file: (a file name)
            path/name of skull mask (if generated)

## FAST

Link to code

Wraps command fast

Use FSL FAST for segmenting and bias correction.

For complete details, see the FAST Documentation.

### Examples

    >>> from nipype.interfaces import fsl
    >>> from nipype.testing import example_data

Assign options through the `inputs` attribute:

    >>> fastr = fsl.FAST()
    >>> fastr.inputs.in_files = example_data('structural.nii')
    >>> out = fastr.run()

Inputs:

    [Mandatory]
    in_files: (an existing file name)
            image, or multi-channel set of images, to be segmented

    [Optional]
    args: (a string)
            Additional parameters to the command
    bias_iters: (1 <= an integer <= 10)
            number of main-loop iterations during bias-field removal
    bias_lowpass: (4 <= an integer <= 40)
            bias field smoothing extent (FWHM) in mm
    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
    hyper: (0.0 <= a floating point number <= 1.0)
            segmentation spatial smoothness
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    img_type: (1 or 2 or 3)
            int specifying type of image: (1 = T1, 2 = T2, 3 = PD)
    init_seg_smooth: (0.0001 <= a floating point number <= 0.1)
            initial segmentation spatial smoothness (during bias field estimation)
    init_transform: (an existing file name)
            <standard2input.mat> initialise using priors
    iters_afterbias: (an integer >= 1)
            number of main-loop iterations after bias-field removal
    manual_seg: (an existing file name)
            Filename containing intensities
    mixel_smooth: (0.0 <= a floating point number <= 1.0)
            spatial smoothness for mixeltype
    no_bias: (a boolean)
            do not remove bias field
    no_pve: (a boolean)
            turn off PVE (partial volume estimation)
    number_classes: (1 <= an integer <= 10)
            number of tissue-type classes
    other_priors: (a list of from 3 to 3 items which are a file name)
            alternative prior images
    out_basename: (a file name)
            base name of output files
    output_biascorrected: (a boolean)
            output restored image (bias-corrected image)
    output_biasfield: (a boolean)
            output estimated bias field
    output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
            FSL output type
    probability_maps: (a boolean)
            outputs individual probability maps
    segment_iters: (1 <= an integer <= 50)
            number of segmentation-initialisation iterations
    segments: (a boolean)
            outputs a separate binary image for each tissue type
    use_priors: (a boolean)
            use priors throughout
    verbose: (a boolean)
            switch on diagnostic messages

Outputs:

    bias_field: (a file name)
    mixeltype: (a file name)
            path/name of mixeltype volume file _mixeltype
    partial_volume_files: (a file name)
    partial_volume_map: (a file name)
            path/name of partial volume file _pveseg
    probability_maps: (a file name)
    restored_image: (a file name)
    tissue_class_files: (a file name)
    tissue_class_map: (an existing file name)
            path/name of binary segmented volume file one val for each class  _seg

## FIRST

Link to code

Wraps command run_first_all

Use FSL’s run_first_all command to segment subcortical volumes

http://www.fmrib.ox.ac.uk/fsl/first/index.html

### Examples

    >>> from nipype.interfaces import fsl
    >>> first = fsl.FIRST()
    >>> first.inputs.in_file = 'structural.nii'
    >>> first.inputs.out_file = 'segmented.nii'
    >>> res = first.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            input data file
    out_file: (a file name, nipype default value: segmented)
            output data file

    [Optional]
    affine_file: (an existing file name)
            Affine matrix to use (e.g. img2std.mat) (does not re-run registration)
    args: (a string)
            Additional parameters to the command
    brain_extracted: (a boolean)
            Input structural image is already brain-extracted
    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
    list_of_specific_structures: (a list of at least 1 items which are a string)
            Runs only on the specified structures (e.g. L_Hipp, R_HippL_Accu, R_Accu, L_Amyg,
            R_AmygL_Caud, R_Caud, L_Pall, R_PallL_Puta, R_Puta, L_Thal, R_Thal, BrStem
    method: ('auto' or 'fast' or 'none')
            Method must be one of auto, fast, none, or it can be entered using the
            'method_as_numerical_threshold' input
            mutually_exclusive: method_as_numerical_threshold
    method_as_numerical_threshold: (a float)
            Specify a numerical threshold value or use the 'method' input to choose auto, fast, or
            none
    no_cleanup: (a boolean)
            Input structural image is already brain-extracted
    output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
            FSL output type
    verbose: (a boolean)
            Use verbose logging.

Outputs:

    bvars: (an existing file name)
            bvars for each subcortical region
    original_segmentations: (an existing file name)
            3D image file containing the segmented regions as integer values. Uses CMA labelling
    segmentation_file: (an existing file name)
            4D image file containing a single volume per segmented region
    vtk_surfaces: (an existing file name)
            VTK format meshes for each subcortical region

## FLIRT

Link to code

Wraps command flirt

Use FSL FLIRT for coregistration.

For complete details, see the FLIRT Documentation.

To print out the command line help, use:
  fsl.FLIRT().inputs_help()

### Examples

    >>> from nipype.interfaces import fsl
    >>> from nipype.testing import example_data
    >>> flt = fsl.FLIRT(bins=640, cost_func='mutualinfo')
    >>> flt.inputs.in_file = example_data('structural.nii')
    >>> flt.inputs.reference = example_data('mni.nii')
    >>> flt.inputs.out_file = 'moved_subject.nii'
    >>> flt.inputs.out_matrix_file = 'subject_to_template.mat'
    >>> res = flt.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            input file
    reference: (an existing file name)
            reference file

    [Optional]
    angle_rep: ('quaternion' or 'euler')
            representation of rotation angles
    apply_xfm: (a boolean)
            apply transformation supplied by in_matrix_file
            requires: in_matrix_file
    args: (a string)
            Additional parameters to the command
    bins: (an integer)
            number of histogram bins
    coarse_search: (an integer)
            coarse search delta angle
    cost: ('mutualinfo' or 'corratio' or 'normcorr' or 'normmi' or 'leastsq' or 'labeldiff')
            cost function
    cost_func: ('mutualinfo' or 'corratio' or 'normcorr' or 'normmi' or 'leastsq' or
             'labeldiff')
            cost function
    datatype: ('char' or 'short' or 'int' or 'float' or 'double')
            force output data type
    display_init: (a boolean)
            display initial matrix
    dof: (an integer)
            number of transform degrees of freedom
    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
    fine_search: (an integer)
            fine search delta angle
    force_scaling: (a boolean)
            force rescaling even for low-res images
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    in_matrix_file: (a file name)
            input 4x4 affine matrix
    in_weight: (an existing file name)
            File for input weighting volume
    interp: ('trilinear' or 'nearestneighbour' or 'sinc')
            final interpolation method used in reslicing
    min_sampling: (a float)
            set minimum voxel dimension for sampling
    no_clamp: (a boolean)
            do not use intensity clamping
    no_resample: (a boolean)
            do not change input sampling
    no_resample_blur: (a boolean)
            do not use blurring on downsampling
    no_search: (a boolean)
            set all angular searches to ranges 0 to 0
    out_file: (a file name)
            registered output file
    out_matrix_file: (a file name)
            output affine matrix in 4x4 asciii format
    output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
            FSL output type
    padding_size: (an integer)
            for applyxfm: interpolates outside image by size
    ref_weight: (an existing file name)
            File for reference weighting volume
    rigid2D: (a boolean)
            use 2D rigid body mode - ignores dof
    schedule: (an existing file name)
            replaces default schedule
    searchr_x: (a list of from 2 to 2 items which are an integer)
            search angles along x-axis, in degrees
    searchr_y: (a list of from 2 to 2 items which are an integer)
            search angles along y-axis, in degrees
    searchr_z: (a list of from 2 to 2 items which are an integer)
            search angles along z-axis, in degrees
    sinc_width: (an integer)
            full-width in voxels
    sinc_window: ('rectangular' or 'hanning' or 'blackman')
            sinc window
    uses_qform: (a boolean)
            initialize using sform or qform
    verbose: (an integer)
            verbose mode, 0 is least

Outputs:

    out_file: (an existing file name)
            path/name of registered file (if generated)
    out_matrix_file: (an existing file name)
            path/name of calculated affine transform (if generated)

## FNIRT

Link to code

Wraps command fnirt

Use FSL FNIRT for non-linear registration.

### Examples

    >>> from nipype.interfaces import fsl
    >>> from nipype.testing import example_data
    >>> fnt = fsl.FNIRT(affine_file=example_data('trans.mat'))
    >>> res = fnt.run(ref_file=example_data('mni.nii', in_file=example_data('structural.nii'))

T1 -> Mni153

    >>> from nipype.interfaces import fsl
    >>> fnirt_mprage = fsl.FNIRT()
    >>> fnirt_mprage.inputs.in_fwhm = [8, 4, 2, 2]
    >>> fnirt_mprage.inputs.subsampling_scheme = [4, 2, 1, 1]

Specify the resolution of the warps

    >>> fnirt_mprage.inputs.warp_resolution = (6, 6, 6)
    >>> res = fnirt_mprage.run(in_file='structural.nii', ref_file='mni.nii', warped_file='warped.nii', fieldcoeff_file='fieldcoeff.nii')

We can check the command line and confirm that it’s what we expect.

    >>> fnirt_mprage.cmdline
    'fnirt --cout=fieldcoeff.nii --in=structural.nii --infwhm=8,4,2,2 --ref=mni.nii --subsamp=4,2,1,1 --warpres=6,6,6 --iout=warped.nii'

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            name of input image
    ref_file: (an existing file name)
            name of reference image

    [Optional]
    affine_file: (an existing file name)
            name of file containing affine transform
    apply_inmask: (a list of items which are 0 or 1)
            list of iterations to use input mask on (1 to use, 0 to skip)
            mutually_exclusive: skip_inmask
    apply_intensity_mapping: (a list of items which are 0 or 1)
            List of subsampling levels to apply intensity mapping for (0 to skip, 1 to apply)
            mutually_exclusive: skip_intensity_mapping
    apply_refmask: (a list of items which are 0 or 1)
            list of iterations to use reference mask on (1 to use, 0 to skip)
            mutually_exclusive: skip_refmask
    args: (a string)
            Additional parameters to the command
    bias_regularization_lambda: (a float)
            Weight of regularisation for bias-field, default 10000
    biasfield_resolution: (a tuple of the form: (an integer, an integer, an integer))
            Resolution (in mm) of bias-field modelling local intensities, default 50, 50, 50
    config_file: ('T1_2_MNI152_2mm' or 'FA_2_FMRIB58_1mm' or an existing file name)
            Name of config file specifying command line arguments
    derive_from_ref: (a boolean)
            If true, ref image is used to calculate derivatives. Default false
    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
    field_file: (a boolean or a file name)
            name of output file with field or true
    fieldcoeff_file: (a boolean or a file name)
            name of output file with field coefficients or true
    hessian_precision: ('double' or 'float')
            Precision for representing Hessian, double or float. Default double
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    in_fwhm: (a list of items which are an integer)
            FWHM (in mm) of gaussian smoothing kernel for input volume, default [6, 4, 2, 2]
    in_intensitymap_file: (an existing file name)
            name of file/files containing initial intensity mapingusually generated by previos fnirt
            run
    inmask_file: (an existing file name)
            name of file with mask in input image space
    inmask_val: (a float)
            Value to mask out in --in image. Default =0.0
    intensity_mapping_model: ('none' or 'global_linear' or 'global_non_linearlocal_linear' or
             'global_non_linear_with_bias' or 'local_non_linear')
            Model for intensity-mapping
    intensity_mapping_order: (an integer)
            Order of poynomial for mapping intensities, default 5
    inwarp_file: (an existing file name)
            name of file containing initial non-linear warps
    jacobian_file: (a boolean or a file name)
            name of file for writing out the Jacobianof the field (for diagnostic or VBM purposes)
    jacobian_range: (a tuple of the form: (a float, a float))
            Allowed range of Jacobian determinants, default 0.01, 100.0
    log_file: (a file name)
            Name of log-file
    max_nonlin_iter: (a list of items which are an integer)
            Max # of non-linear iterations list, default [5, 5, 5, 5]
    modulatedref_file: (a boolean or a file name)
            name of file for writing out intensity modulated--ref (for diagnostic purposes)
    out_intensitymap_file: (a boolean or a file name)
            name of files for writing information pertaining to intensity mapping
    output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
            FSL output type
    ref_fwhm: (a list of items which are an integer)
            FWHM (in mm) of gaussian smoothing kernel for ref volume, default [4, 2, 0, 0]
    refmask_file: (an existing file name)
            name of file with mask in reference space
    refmask_val: (a float)
            Value to mask out in --ref image. Default =0.0
    regularization_lambda: (a list of items which are a float)
            Weight of regularisation, default depending on --ssqlambda and --regmod switches. See
            user documetation.
    regularization_model: ('membrane_energy' or 'bending_energy')
            Model for regularisation of warp-field [membrane_energy bending_energy], default
            bending_energy
    skip_implicit_in_masking: (a boolean)
            skip implicit masking  based on valuein --in image. Default = 0
    skip_implicit_ref_masking: (a boolean)
            skip implicit masking  based on valuein --ref image. Default = 0
    skip_inmask: (a boolean)
            skip specified inmask if set, default false
            mutually_exclusive: apply_inmask
    skip_intensity_mapping: (a boolean)
            Skip estimate intensity-mapping default false
            mutually_exclusive: apply_intensity_mapping
    skip_lambda_ssq: (a boolean)
            If true, lambda is not weighted by current ssq, default false
    skip_refmask: (a boolean)
            Skip specified refmask if set, default false
            mutually_exclusive: apply_refmask
    spline_order: (an integer)
            Order of spline, 2->Qadratic spline, 3->Cubic spline. Default=3
    subsampling_scheme: (a list of items which are an integer)
            sub-sampling scheme, list, default [4, 2, 1, 1]
    warp_resolution: (a tuple of the form: (an integer, an integer, an integer))
            (approximate) resolution (in mm) of warp basis in x-, y- and z-direction, default 10,
            10, 10
    warped_file: (a file name)
            name of output image

Outputs:

    field_file: (a file name)
            file with warp field
    fieldcoeff_file: (an existing file name)
            file with field coefficients
    jacobian_file: (a file name)
            file containing Jacobian of the field
    log_file: (a file name)
            Name of log-file
    modulatedref_file: (a file name)
            file containing intensity modulated --ref
    out_intensitymap_file: (a file name)
            file containing info pertaining to intensity mapping
    warped_file: (an existing file name)
            warped image

## FUGUE

Link to code

Wraps command fugue

Use FSL FUGUE to unwarp epi’s with fieldmaps

### Examples

Please insert examples for use of this command

Inputs:

    [Mandatory]

    [Optional]
    args: (a string)
            Additional parameters to the command
    asym_se_time: (a float)
            set the fieldmap asymmetric spin echo time (sec)
    despike_2dfilter: (a boolean)
            apply a 2D de-spiking filter
    despike_theshold: (a float)
            specify the threshold for de-spiking (default=3.0)
    dwell_time: (a float)
            set the EPI dwell time per phase-encode line - same as echo spacing - (sec)
    dwell_to_asym_ratio: (a float)
            set the dwell to asym time ratio
    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
    fmap_in_file: (an existing file name)
            filename for loading fieldmap (rad/s)
    fmap_out_file: (a file name)
            filename for saving fieldmap (rad/s)
    fourier_order: (an integer)
            apply Fourier (sinusoidal) fitting of order N
    icorr: (a boolean)
            apply intensity correction to unwarping (pixel shift method only)
            requires: shift_in_file
    icorr_only: (a boolean)
            apply intensity correction only
            requires: unwarped_file
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    in_file: (an existing file name)
            filename of input volume
    mask_file: (an existing file name)
            filename for loading valid mask
    median_2dfilter: (a boolean)
            apply 2D median filtering
    no_extend: (a boolean)
            do not apply rigid-body extrapolation to the fieldmap
    no_gap_fill: (a boolean)
            do not apply gap-filling measure to the fieldmap
    nokspace: (a boolean)
            do not use k-space forward warping
    output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
            FSL output type
    pava: (a boolean)
            apply monotonic enforcement via PAVA
    phase_conjugate: (a boolean)
            apply phase conjugate method of unwarping
    phasemap_file: (an existing file name)
            filename for input phase image
    poly_order: (an integer)
            apply polynomial fitting of order N
    save_unmasked_fmap: (a boolean or a file name)
            saves the unmasked fieldmap when using --savefmap
            requires: fmap_out_file
    save_unmasked_shift: (a boolean or a file name)
            saves the unmasked shiftmap when using --saveshift
            requires: shift_out_file
    shift_in_file: (an existing file name)
            filename for reading pixel shift volume
    shift_out_file: (a file name)
            filename for saving pixel shift volume
    smooth2d: (a float)
            apply 2D Gaussian smoothing of sigma N (in mm)
    smooth3d: (a float)
            apply 3D Gaussian smoothing of sigma N (in mm)
    unwarp_direction: ('x' or 'y' or 'z' or 'x-' or 'y-' or 'z-')
            specifies direction of warping (default y)
    unwarped_file: (a file name)
            apply unwarping and save as filename

Outputs:

    unwarped_file: (an existing file name)
            unwarped file

## MCFLIRT

Link to code

Wraps command mcflirt

Use FSL MCFLIRT to do within-modality motion correction.

For complete details, see the MCFLIRT Documentation.

### Examples

    >>> from nipype.interfaces import fsl
    >>> from nipype.testing import example_data
    >>> mcflt = fsl.MCFLIRT(in_file=example_data('functional.nii'), cost='mutualinfo')
    >>> res = mcflt.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            timeseries to motion-correct

    [Optional]
    args: (a string)
            Additional parameters to the command
    bins: (an integer)
            number of histogram bins
    cost: ('mutualinfo' or 'woods' or 'corratio' or 'normcorr' or 'normmi' or 'leastsquares')
            cost function to optimize
    dof: (an integer)
            degrees of freedom for the transformation
    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
    init: (an existing file name)
            inital transformation matrix
    interpolation: ('spline' or 'nn' or 'sinc')
            interpolation method for transformation
    mean_vol: (a boolean)
            register to mean volume
    out_file: (a file name)
            file to write
    output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
            FSL output type
    ref_file: (an existing file name)
            target image for motion correction
    ref_vol: (an integer)
            volume to align frames to
    rotation: (an integer)
            scaling factor for rotation tolerances
    save_mats: (a boolean)
            save transformation matrices
    save_plots: (a boolean)
            save transformation parameters
    save_rms: (a boolean)
            save rms displacement parameters
    scaling: (a float)
            scaling factor to use
    smooth: (a float)
            smoothing factor for the cost function
    stages: (an integer)
            stages (if 4, perform final search with sinc interpolation
    stats_imgs: (a boolean)
            produce variance and std. dev. images
    use_contour: (a boolean)
            run search on contour images
    use_gradient: (a boolean)
            run search on gradient images

Outputs:

    mat_file: (an existing file name)
            transformation matrices
    mean_img: (an existing file name)
            mean timeseries image
    out_file: (an existing file name)
            motion-corrected timeseries
    par_file: (an existing file name)
            text-file with motion parameters
    rms_files: (an existing file name)
            absolute and relative displacement parameters
    std_img: (an existing file name)
            standard deviation image
    variance_img: (an existing file name)
            variance image

## PRELUDE

Link to code

Wraps command prelude

Use FSL prelude to do phase unwrapping

### Examples

Please insert examples for use of this command

Inputs:

    [Mandatory]
    complex_phase_file: (an existing file name)
            complex phase input volume
            mutually_exclusive: magnitude_file, phase_file
    magnitude_file: (an existing file name)
            file containing magnitude image
            mutually_exclusive: complex_phase_file
    phase_file: (an existing file name)
            raw phase file
            mutually_exclusive: complex_phase_file

    [Optional]
    args: (a string)
            Additional parameters to the command
    end: (an integer)
            final image number to process (default Inf)
    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
    label_file: (a file name)
            saving the area labels output
    labelprocess2d: (a boolean)
            does label processing in 2D (slice at a time)
    mask_file: (an existing file name)
            filename of mask input volume
    num_partitions: (an integer)
            number of phase partitions to use
    output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
            FSL output type
    process2d: (a boolean)
            does all processing in 2D (slice at a time)
            mutually_exclusive: labelprocess2d
    process3d: (a boolean)
            forces all processing to be full 3D
            mutually_exclusive: labelprocess2d, process2d
    rawphase_file: (a file name)
            saving the raw phase output
    removeramps: (a boolean)
            remove phase ramps during unwrapping
    savemask_file: (a file name)
            saving the mask volume
    start: (an integer)
            first image number to process (default 0)
    threshold: (a float)
            intensity threshold for masking
    unwrapped_phase_file: (a file name)
            file containing unwrapepd phase

Outputs:

    unwrapped_phase_file: (an existing file name)
            unwrapped phase file

## SUSAN

Link to code

Wraps command susan

use FSL SUSAN to perform smoothing

### Examples

    >>> from nipype.interfaces import fsl
    >>> from nipype.testing import example_data
    >>> print anatfile
    anatomical.nii
    >>> sus = fsl.SUSAN()
    >>> sus.inputs.in_file = example_data('structural.nii')
    >>> sus.inputs.brightness_threshold = 2000.0
    >>> sus.inputs.fwhm = 8.0
    >>> result = sus.run()

Inputs:

    [Mandatory]
    brightness_threshold: (a float)
            brightness threshold and should be greater than noise level and less than contrast of
            edges to be preserved.
    fwhm: (a float)
            fwhm of smoothing, in mm, gets converted using sqrt(8*log(2))
    in_file: (an existing file name)
            filename of input timeseries

    [Optional]
    args: (a string)
            Additional parameters to the command
    dimension: (3 or 2, nipype default value: 3)
            within-plane (2) or fully 3D (3)
    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)
            output file name
    output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
            FSL output type
    usans: (a list of at most 2 items which are a tuple of the form: (an existing file name,
             a float), nipype default value: [])
            determines whether the smoothing area (USAN) is to be found from secondary images (0, 1
            or 2). A negative value for any brightness threshold will auto-set the threshold at 10%
            of the robust range
    use_median: (1 or 0, nipype default value: 1)
            whether to use a local median filter in the cases where single-point noise is detected

Outputs:

    smoothed_file: (an existing file name)
            smoothed output file

## SliceTimer

Link to code

Wraps command slicetimer

use FSL slicetimer to perform slice timing correction.

### Examples

    >>> from nipype.interfaces import fsl
    >>> from nipype.testing import example_data
    >>> st = fsl.SliceTimer()
    >>> st.inputs.in_file = example_data('functional.nii')
    >>> st.inputs.interleaved = True
    >>> result = st.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            filename of input timeseries

    [Optional]
    args: (a string)
            Additional parameters to the command
    custom_order: (an existing file name)
            filename of single-column custom interleave order file (first slice is referred to as 1
            not 0)
    custom_timings: (an existing file name)
            slice timings, in fractions of TR, range 0:1 (default is 0.5 = no shift)
    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
    global_shift: (a float)
            shift in fraction of TR, range 0:1 (default is 0.5 = no shift)
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    index_dir: (a boolean)
            slice indexing from top to bottom
    interleaved: (a boolean)
            use interleaved acquisition
    out_file: (a file name)
            filename of output timeseries
    output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
            FSL output type
    slice_direction: (1 or 2 or 3)
            direction of slice acquisition (x=1, y=2, z=3) - default is z
    time_repetition: (a float)
            Specify TR of data - default is 3s

Outputs:

    slice_time_corrected_file: (an existing file name)
            slice time corrected file