NIPY logo
Home · Quickstart · Documentation · Citation · NiPy
Loading

Table Of Contents

Versions

ReleaseDevel
0.7.0pre-0.8
Download Github

Links

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