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# interfaces.spm.preprocess

## ApplyDeformations

Link to code

Inputs:

    [Mandatory]
    deformation_field: (an existing file name)
    in_files: (an existing file name)
    reference_volume: (an existing file name)

    [Optional]
    deformation_field: (an existing file name)
    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_files: (an existing file name)
    interp: (0 <= an integer <= 7)
            degree of b-spline used for interpolation
    matlab_cmd: (a string)
            matlab command to use
    mfile: (a boolean, nipype default value: True)
            Run m-code using m-file
    paths: (a directory name)
            Paths to add to matlabpath
    reference_volume: (an existing file name)
    use_mcr: (a boolean)
            Run m-code using SPM MCR
    use_v8struct: (a boolean, nipype default value: True)
            Generate SPM8 and higher compatible jobs

Outputs:

    out_files: (an existing file name)

## Coregister

Link to code

Use spm_coreg for estimating cross-modality rigid body alignment

http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=39

### Examples

    >>> import nipype.interfaces.spm as spm
    >>> coreg = spm.Coregister()
    >>> coreg.inputs.target = 'functional.nii'
    >>> coreg.inputs.source = 'structural.nii'
    >>> coreg.run()

Inputs:

    [Mandatory]
    source: (an existing file name)
            file to register to target
    target: (an existing file name)
            reference file to register to

    [Optional]
    apply_to_files: (an existing file name)
            files to apply transformation to
    cost_function: ('mi' or 'nmi' or 'ecc' or 'ncc')
            cost function, one of: 'mi' - Mutual Information,
             'nmi' - Normalised Mutual Information,
             'ecc' - Entropy Correlation Coefficient,
             'ncc' - Normalised Cross Correlation
    fwhm: (a list of from 2 to 2 items which are a float)
            gaussian smoothing kernel width (mm)
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    jobtype: ('estwrite' or 'estimate' or 'write', nipype default value:
             estwrite)
            one of: estimate, write, estwrite
    matlab_cmd: (a string)
            matlab command to use
    mfile: (a boolean, nipype default value: True)
            Run m-code using m-file
    out_prefix: (a string, nipype default value: r)
            coregistered output prefix
    paths: (a directory name)
            Paths to add to matlabpath
    separation: (a list of items which are a float)
            sampling separation in mm
    source: (an existing file name)
            file to register to target
    target: (an existing file name)
            reference file to register to
    tolerance: (a list of items which are a float)
            acceptable tolerance for each of 12 params
    use_mcr: (a boolean)
            Run m-code using SPM MCR
    use_v8struct: (a boolean, nipype default value: True)
            Generate SPM8 and higher compatible jobs
    write_interp: (0 <= an integer <= 7)
            degree of b-spline used for interpolation
    write_mask: (a boolean)
            True/False mask output image
    write_wrap: (a list of from 3 to 3 items which are an integer)
            Check if interpolation should wrap in [x,y,z]

Outputs:

    coregistered_files: (an existing file name)
            Coregistered other files
    coregistered_source: (an existing file name)
            Coregistered source files

## CreateWarped

Link to code

Apply a flow field estimated by DARTEL to create warped images

http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=202

### Examples

    >>> import nipype.interfaces.spm as spm
    >>> create_warped = spm.CreateWarped()
    >>> create_warped.inputs.image_files = ['rc1s1.nii', 'rc1s2.nii']
    >>> create_warped.inputs.flowfield_files = ['u_rc1s1_Template.nii', 'u_rc1s2_Template.nii']
    >>> create_warped.run()

Inputs:

    [Mandatory]
    flowfield_files: (an existing file name)
            DARTEL flow fields u_rc1*
    image_files: (an existing file name)
            A list of files to be warped

    [Optional]
    flowfield_files: (an existing file name)
            DARTEL flow fields u_rc1*
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    image_files: (an existing file name)
            A list of files to be warped
    interp: (0 <= an integer <= 7)
            degree of b-spline used for interpolation
    iterations: (0 <= an integer <= 9)
            The number of iterations: log2(number of time steps)
    matlab_cmd: (a string)
            matlab command to use
    mfile: (a boolean, nipype default value: True)
            Run m-code using m-file
    paths: (a directory name)
            Paths to add to matlabpath
    use_mcr: (a boolean)
            Run m-code using SPM MCR
    use_v8struct: (a boolean, nipype default value: True)
            Generate SPM8 and higher compatible jobs

Outputs:

    warped_files: (a list of items which are an existing file name)

## DARTEL

Link to code

Use spm DARTEL to create a template and flow fields

http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=197

### Examples

    >>> import nipype.interfaces.spm as spm
    >>> dartel = spm.DARTEL()
    >>> dartel.inputs.image_files = [['rc1s1.nii','rc1s2.nii'],['rc2s1.nii', 'rc2s2.nii']]
    >>> dartel.run()

Inputs:

    [Mandatory]
    image_files: (a list of items which are a list of items which are an
             existing file name)
            A list of files to be segmented

    [Optional]
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    image_files: (a list of items which are a list of items which are an
             existing file name)
            A list of files to be segmented
    iteration_parameters: (a list of from 3 to 12 items which are a tuple
             of the form: (1 <= an integer <= 10, a tuple of the form: (a float,
             a float, a float), 1 or 2 or 4 or 8 or 16 or 32 or 64 or 128 or 256
             or 512, 0 or 0.5 or 1 or 2 or 4 or 8 or 16 or 32))
            List of tuples for each iteration
             - Inner iterations
             - Regularization parameters
             - Time points for deformation model
             - smoothing parameter
    matlab_cmd: (a string)
            matlab command to use
    mfile: (a boolean, nipype default value: True)
            Run m-code using m-file
    optimization_parameters: (a tuple of the form: (a float, 1 <= an
             integer <= 8, 1 <= an integer <= 8))
            Optimization settings a tuple
             - LM regularization
             - cycles of multigrid solver
             - relaxation iterations
    paths: (a directory name)
            Paths to add to matlabpath
    regularization_form: ('Linear' or 'Membrane' or 'Bending')
            Form of regularization energy term
    template_prefix: (a string, nipype default value: Template)
            Prefix for template
    use_mcr: (a boolean)
            Run m-code using SPM MCR
    use_v8struct: (a boolean, nipype default value: True)
            Generate SPM8 and higher compatible jobs

Outputs:

    dartel_flow_fields: (a list of items which are an existing file name)
            DARTEL flow fields
    final_template_file: (an existing file name)
            final DARTEL template
    template_files: (a list of items which are an existing file name)
            Templates from different stages of iteration

## DARTELNorm2MNI

Link to code

Use spm DARTEL to normalize data to MNI space

http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=200

### Examples

    >>> import nipype.interfaces.spm as spm
    >>> nm = spm.DARTELNorm2MNI()
    >>> nm.inputs.template_file = 'Template_6.nii'
    >>> nm.inputs.flowfield_files = ['u_rc1s1_Template.nii', 'u_rc1s3_Template.nii']
    >>> nm.inputs.apply_to_files = ['c1s1.nii', 'c1s3.nii']
    >>> nm.inputs.modulate = True
    >>> nm.run()

Inputs:

    [Mandatory]
    apply_to_files: (an existing file name)
            Files to apply the transform to
    flowfield_files: (an existing file name)
            DARTEL flow fields u_rc1*
    template_file: (an existing file name)
            DARTEL template

    [Optional]
    apply_to_files: (an existing file name)
            Files to apply the transform to
    bounding_box: (a tuple of the form: (a float, a float, a float, a
             float, a float, a float))
            Voxel sizes for output file
    flowfield_files: (an existing file name)
            DARTEL flow fields u_rc1*
    fwhm: (a list of from 3 to 3 items which are a float or a float)
            3-list of fwhm for each dimension
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    matlab_cmd: (a string)
            matlab command to use
    mfile: (a boolean, nipype default value: True)
            Run m-code using m-file
    modulate: (a boolean)
            Modulate out images - no modulation preserves concentrations
    paths: (a directory name)
            Paths to add to matlabpath
    template_file: (an existing file name)
            DARTEL template
    use_mcr: (a boolean)
            Run m-code using SPM MCR
    use_v8struct: (a boolean, nipype default value: True)
            Generate SPM8 and higher compatible jobs
    voxel_size: (a tuple of the form: (a float, a float, a float))
            Voxel sizes for output file

Outputs:

    normalization_parameter_file: (an existing file name)
            Transform parameters to MNI space
    normalized_files: (an existing file name)
            Normalized files in MNI space

## NewSegment

Link to code

Use spm_preproc8 (New Segment) to separate structural images into different tissue classes. Supports multiple modalities.

NOTE: This interface currently supports single channel input only

http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=185

### Examples

    >>> import nipype.interfaces.spm as spm
    >>> seg = spm.NewSegment()
    >>> seg.inputs.channel_files = 'structural.nii'
    >>> seg.inputs.channel_info = (0.0001, 60, (True, True))
    >>> seg.run()

For VBM pre-processing [http://www.fil.ion.ucl.ac.uk/~john/misc/VBMclass10.pdf], TPM.nii should be replaced by /path/to/spm8/toolbox/Seg/TPM.nii

    >>> seg = NewSegment()
    >>> seg.inputs.channel_files = 'structural.nii'
    >>> tissue1 = (('TPM.nii', 1), 2, (True,True), (False, False))
    >>> tissue2 = (('TPM.nii', 2), 2, (True,True), (False, False))
    >>> tissue3 = (('TPM.nii', 3), 2, (True,False), (False, False))
    >>> tissue4 = (('TPM.nii', 4), 2, (False,False), (False, False))
    >>> tissue5 = (('TPM.nii', 5), 2, (False,False), (False, False))
    >>> seg.inputs.tissues = [tissue1, tissue2, tissue3, tissue4, tissue5]
    >>> seg.run()

Inputs:

    [Mandatory]
    channel_files: (an existing file name)
            A list of files to be segmented

    [Optional]
    affine_regularization: ('mni' or 'eastern' or 'subj' or 'none')
            mni, eastern, subj, none
    channel_files: (an existing file name)
            A list of files to be segmented
    channel_info: (a tuple of the form: (a float, a float, a tuple of the
             form: (a boolean, a boolean)))
            A tuple with the following fields:
             - bias reguralisation (0-10)
             - FWHM of Gaussian smoothness of bias
             - which maps to save (Corrected, Field) - a tuple of two boolean
            values
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    matlab_cmd: (a string)
            matlab command to use
    mfile: (a boolean, nipype default value: True)
            Run m-code using m-file
    paths: (a directory name)
            Paths to add to matlabpath
    sampling_distance: (a float)
            Sampling distance on data for parameter estimation
    tissues: (a list of items which are a tuple of the form: (a tuple of
             the form: (an existing file name, an integer), an integer, a tuple
             of the form: (a boolean, a boolean), a tuple of the form: (a
             boolean, a boolean)))
            A list of tuples (one per tissue) with the following fields:
             - tissue probability map (4D), 1-based index to frame
             - number of gaussians
             - which maps to save [Native, DARTEL] - a tuple of two boolean
            values
             - which maps to save [Modulated, Unmodualted] - a tuple of two
            boolean values
    use_mcr: (a boolean)
            Run m-code using SPM MCR
    use_v8struct: (a boolean, nipype default value: True)
            Generate SPM8 and higher compatible jobs
    warping_regularization: (a float)
            Aproximate distance between sampling points.
    write_deformation_fields: (a list of from 2 to 2 items which are a
             boolean)
            Which deformation fields to write:[Inverse, Forward]

Outputs:

    bias_corrected_images: (an existing file name)
            bias corrected images
    bias_field_images: (an existing file name)
            bias field images
    dartel_input_images: (a list of items which are a list of items which
             are an existing file name)
            dartel imported class images
    forward_deformation_field: (an existing file name)
    inverse_deformation_field: (an existing file name)
    modulated_class_images: (a list of items which are a list of items
             which are an existing file name)
            modulated+normalized class images
    native_class_images: (a list of items which are a list of items which
             are an existing file name)
            native space probability maps
    normalized_class_images: (a list of items which are a list of items
             which are an existing file name)
            normalized class images
    transformation_mat: (an existing file name)
            Normalization transformation

## Normalize

Link to code

use spm_normalise for warping an image to a template

http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=51

### Examples

    >>> import nipype.interfaces.spm as spm
    >>> norm = spm.Normalize()
    >>> norm.inputs.source = 'functional.nii'
    >>> norm.run()

Inputs:

    [Mandatory]
    parameter_file: (a file name)
            normalization parameter file*_sn.mat
            mutually_exclusive: source, template
    source: (an existing file name)
            file to normalize to template
            mutually_exclusive: parameter_file
    template: (an existing file name)
            template file to normalize to
            mutually_exclusive: parameter_file

    [Optional]
    DCT_period_cutoff: (a float)
            Cutoff of for DCT bases (opt)
    affine_regularization_type: ('mni' or 'size' or 'none')
            mni, size, none (opt)
    apply_to_files: (an existing file name or a list of items which are
             an existing file name)
            files to apply transformation to (opt)
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    jobtype: ('estwrite' or 'est' or 'write', nipype default value:
             estwrite)
            one of: est, write, estwrite (opt, estwrite)
    matlab_cmd: (a string)
            matlab command to use
    mfile: (a boolean, nipype default value: True)
            Run m-code using m-file
    nonlinear_iterations: (an integer)
            Number of iterations of nonlinear warping (opt)
    nonlinear_regularization: (a float)
            the amount of the regularization for the nonlinear part of the
            normalization (opt)
    out_prefix: (a string, nipype default value: w)
            normalized output prefix
    parameter_file: (a file name)
            normalization parameter file*_sn.mat
            mutually_exclusive: source, template
    paths: (a directory name)
            Paths to add to matlabpath
    source: (an existing file name)
            file to normalize to template
            mutually_exclusive: parameter_file
    source_image_smoothing: (a float)
            source smoothing (opt)
    source_weight: (a file name)
            name of weighting image for source (opt)
    template: (an existing file name)
            template file to normalize to
            mutually_exclusive: parameter_file
    template_image_smoothing: (a float)
            template smoothing (opt)
    template_weight: (a file name)
            name of weighting image for template (opt)
    use_mcr: (a boolean)
            Run m-code using SPM MCR
    use_v8struct: (a boolean, nipype default value: True)
            Generate SPM8 and higher compatible jobs
    write_bounding_box: (a list of from 2 to 2 items which are a list of
             from 3 to 3 items which are a float)
            3x2-element list of lists (opt)
    write_interp: (0 <= an integer <= 7)
            degree of b-spline used for interpolation
    write_preserve: (a boolean)
            True/False warped images are modulated (opt,)
    write_voxel_sizes: (a list of from 3 to 3 items which are a float)
            3-element list (opt)
    write_wrap: (a list of items which are an integer)
            Check if interpolation should wrap in [x,y,z] - list of bools (opt)

Outputs:

    normalization_parameters: (an existing file name)
            MAT files containing the normalization parameters
    normalized_files: (an existing file name)
            Normalized other files
    normalized_source: (an existing file name)
            Normalized source files

## Realign

Link to code

Use spm_realign for estimating within modality rigid body alignment

http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=25

### Examples

    >>> import nipype.interfaces.spm as spm
    >>> realign = spm.Realign()
    >>> realign.inputs.in_files = 'functional.nii'
    >>> realign.inputs.register_to_mean = True
    >>> realign.run()

Inputs:

    [Mandatory]
    in_files: (a list of items which are an existing file name or an
             existing file name)
            list of filenames to realign
    register_to_mean: (a boolean, nipype default value: True)
            Indicate whether realignment is done to the mean image

    [Optional]
    fwhm: (a floating point number >= 0.0)
            gaussian smoothing kernel width
    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_files: (a list of items which are an existing file name or an
             existing file name)
            list of filenames to realign
    interp: (0 <= an integer <= 7)
            degree of b-spline used for interpolation
    jobtype: ('estwrite' or 'estimate' or 'write', nipype default value:
             estwrite)
            one of: estimate, write, estwrite
    matlab_cmd: (a string)
            matlab command to use
    mfile: (a boolean, nipype default value: True)
            Run m-code using m-file
    out_prefix: (a string, nipype default value: r)
            realigned output prefix
    paths: (a directory name)
            Paths to add to matlabpath
    quality: (0.0 <= a floating point number <= 1.0)
            0.1 = fast, 1.0 = precise
    register_to_mean: (a boolean, nipype default value: True)
            Indicate whether realignment is done to the mean image
    separation: (a floating point number >= 0.0)
            sampling separation in mm
    use_mcr: (a boolean)
            Run m-code using SPM MCR
    use_v8struct: (a boolean, nipype default value: True)
            Generate SPM8 and higher compatible jobs
    weight_img: (an existing file name)
            filename of weighting image
    wrap: (a list of from 3 to 3 items which are an integer)
            Check if interpolation should wrap in [x,y,z]
    write_interp: (0 <= an integer <= 7)
            degree of b-spline used for interpolation
    write_mask: (a boolean)
            True/False mask output image
    write_which: (a list of items which are a value of type 'int', nipype
             default value: [2, 1])
            determines which images to reslice
    write_wrap: (a list of from 3 to 3 items which are an integer)
            Check if interpolation should wrap in [x,y,z]

Outputs:

    mean_image: (an existing file name)
            Mean image file from the realignment
    modified_in_files: (a list of items which are an existing file name
             or an existing file name)
            Copies of all files passed to in_files. Headers will have been
            modified to align all images with the first, or optionally to first
            do that, extract a mean image, and re-align to that mean image.
    realigned_files: (a list of items which are an existing file name or
             an existing file name)
            If jobtype is write or estwrite, these will be the resliced files.
            Otherwise, they will be copies of in_files that have had their
            headers rewritten.
    realignment_parameters: (an existing file name)
            Estimated translation and rotation parameters

## Segment

Link to code

use spm_segment to separate structural images into different tissue classes.

http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=43

### Examples

    >>> import nipype.interfaces.spm as spm
    >>> seg = spm.Segment()
    >>> seg.inputs.data = 'structural.nii'
    >>> seg.run()

Inputs:

    [Mandatory]
    data: (an existing file name)
            one scan per subject

    [Optional]
    affine_regularization: ('mni' or 'eastern' or 'subj' or 'none' or '')
            Possible options: "mni", "eastern", "subj", "none" (no
            reguralisation), "" (no affine registration)
    bias_fwhm: (30 or 40 or 50 or 60 or 70 or 80 or 90 or 100 or 110 or
             120 or 130 or 'Inf')
            FWHM of Gaussian smoothness of bias
    bias_regularization: (0 or 1e-05 or 0.0001 or 0.001 or 0.01 or 0.1 or
             1 or 10)
            no(0) - extremely heavy (10)
    clean_masks: ('no' or 'light' or 'thorough')
            clean using estimated brain mask ('no','light','thorough')
    csf_output_type: (a list of from 3 to 3 items which are a boolean)
            Options to produce CSF images: c3*.img, wc3*.img and mwc3*.img.
             None: [False,False,False],
             Native Space: [False,False,True],
             Unmodulated Normalised: [False,True,False],
             Modulated Normalised: [True,False,False],
             Native + Unmodulated Normalised: [False,True,True],
             Native + Modulated Normalised: [True,False,True],
             Native + Modulated + Unmodulated: [True,True,True],
             Modulated + Unmodulated Normalised: [True,True,False]
    data: (an existing file name)
            one scan per subject
    gaussians_per_class: (a list of items which are an integer)
            num Gaussians capture intensity distribution
    gm_output_type: (a list of from 3 to 3 items which are a boolean)
            Options to produce grey matter images: c1*.img, wc1*.img and
            mwc1*.img.
             None: [False,False,False],
             Native Space: [False,False,True],
             Unmodulated Normalised: [False,True,False],
             Modulated Normalised: [True,False,False],
             Native + Unmodulated Normalised: [False,True,True],
             Native + Modulated Normalised: [True,False,True],
             Native + Modulated + Unmodulated: [True,True,True],
             Modulated + Unmodulated Normalised: [True,True,False]
    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_image: (an existing file name)
            Binary image to restrict parameter estimation
    matlab_cmd: (a string)
            matlab command to use
    mfile: (a boolean, nipype default value: True)
            Run m-code using m-file
    paths: (a directory name)
            Paths to add to matlabpath
    sampling_distance: (a float)
            Sampling distance on data for parameter estimation
    save_bias_corrected: (a boolean)
            True/False produce a bias corrected image
    tissue_prob_maps: (a list of items which are an existing file name)
            list of gray, white & csf prob. (opt,)
    use_mcr: (a boolean)
            Run m-code using SPM MCR
    use_v8struct: (a boolean, nipype default value: True)
            Generate SPM8 and higher compatible jobs
    warp_frequency_cutoff: (a float)
            Cutoff of DCT bases
    warping_regularization: (a float)
            Controls balance between parameters and data
    wm_output_type: (a list of from 3 to 3 items which are a boolean)
            Options to produce white matter images: c2*.img, wc2*.img and
            mwc2*.img.
             None: [False,False,False],
             Native Space: [False,False,True],
             Unmodulated Normalised: [False,True,False],
             Modulated Normalised: [True,False,False],
             Native + Unmodulated Normalised: [False,True,True],
             Native + Modulated Normalised: [True,False,True],
             Native + Modulated + Unmodulated: [True,True,True],
             Modulated + Unmodulated Normalised: [True,True,False]

Outputs:

    bias_corrected_image: (a file name)
            bias-corrected version of input image
    inverse_transformation_mat: (an existing file name)
            Inverse normalization info
    modulated_csf_image: (a file name)
            modulated, normalized csf probability map
    modulated_gm_image: (a file name)
            modulated, normalized grey probability map
    modulated_input_image: (a file name)
            bias-corrected version of input image
    modulated_wm_image: (a file name)
            modulated, normalized white probability map
    native_csf_image: (a file name)
            native space csf probability map
    native_gm_image: (a file name)
            native space grey probability map
    native_wm_image: (a file name)
            native space white probability map
    normalized_csf_image: (a file name)
            normalized csf probability map
    normalized_gm_image: (a file name)
            normalized grey probability map
    normalized_wm_image: (a file name)
            normalized white probability map
    transformation_mat: (an existing file name)
            Normalization transformation

## SliceTiming

Link to code

Use spm to perform slice timing correction.

http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=19

### Examples

    >>> from nipype.interfaces.spm import SliceTiming
    >>> st = SliceTiming()
    >>> st.inputs.in_files = 'functional.nii'
    >>> st.inputs.num_slices = 32
    >>> st.inputs.time_repetition = 6.0
    >>> st.inputs.time_acquisition = 6. - 6./32.
    >>> st.inputs.slice_order = range(32,0,-1)
    >>> st.inputs.ref_slice = 1
    >>> st.run()

Inputs:

    [Mandatory]
    in_files: (a list of items which are an existing file name or an
             existing file name)
            list of filenames to apply slice timing
    num_slices: (an integer)
            number of slices in a volume
    ref_slice: (an integer)
            1-based Number of the reference slice
    slice_order: (a list of items which are an integer)
            1-based order in which slices are acquired
    time_acquisition: (a float)
            time of volume acquisition. usually calculated as TR-(TR/num_slices)
    time_repetition: (a float)
            time between volume acquisitions (start to start time)

    [Optional]
    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_files: (a list of items which are an existing file name or an
             existing file name)
            list of filenames to apply slice timing
    matlab_cmd: (a string)
            matlab command to use
    mfile: (a boolean, nipype default value: True)
            Run m-code using m-file
    num_slices: (an integer)
            number of slices in a volume
    out_prefix: (a string, nipype default value: a)
            slicetimed output prefix
    paths: (a directory name)
            Paths to add to matlabpath
    ref_slice: (an integer)
            1-based Number of the reference slice
    slice_order: (a list of items which are an integer)
            1-based order in which slices are acquired
    time_acquisition: (a float)
            time of volume acquisition. usually calculated as TR-(TR/num_slices)
    time_repetition: (a float)
            time between volume acquisitions (start to start time)
    use_mcr: (a boolean)
            Run m-code using SPM MCR
    use_v8struct: (a boolean, nipype default value: True)
            Generate SPM8 and higher compatible jobs

Outputs:

    timecorrected_files: (a list of items which are an existing file name
             or an existing file name)
            slice time corrected files

## Smooth

Link to code

Use spm_smooth for 3D Gaussian smoothing of image volumes.

http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=57

### Examples

    >>> import nipype.interfaces.spm as spm
    >>> smooth = spm.Smooth()
    >>> smooth.inputs.in_files = 'functional.nii'
    >>> smooth.inputs.fwhm = [4, 4, 4]
    >>> smooth.run()

Inputs:

    [Mandatory]
    in_files: (an existing file name)
            list of files to smooth

    [Optional]
    data_type: (an integer)
            Data type of the output images (opt)
    fwhm: (a list of from 3 to 3 items which are a float or a float)
            3-list of fwhm for each dimension (opt)
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    implicit_masking: (a boolean)
            A mask implied by a particular voxel value
    in_files: (an existing file name)
            list of files to smooth
    matlab_cmd: (a string)
            matlab command to use
    mfile: (a boolean, nipype default value: True)
            Run m-code using m-file
    out_prefix: (a string, nipype default value: s)
            smoothed output prefix
    paths: (a directory name)
            Paths to add to matlabpath
    use_mcr: (a boolean)
            Run m-code using SPM MCR
    use_v8struct: (a boolean, nipype default value: True)
            Generate SPM8 and higher compatible jobs

Outputs:

    smoothed_files: (an existing file name)
            smoothed files

## VBMSegment

Link to code

Use VBM8 toolbox to separate structural images into different tissue classes.

### Example

    >>> import nipype.interfaces.spm as spm
    >>> seg = spm.VBMSegment()
    >>> seg.inputs.tissues = 'TPM.nii'
    >>> seg.inputs.dartel_template = 'Template_1_IXI550_MNI152.nii'
    >>> seg.inputs.bias_corrected_native = True
    >>> seg.inputs.gm_native = True
    >>> seg.inputs.wm_native = True
    >>> seg.inputs.csf_native = True
    >>> seg.inputs.pve_label_native = True
    >>> seg.inputs.deformation_field = (True, False)
    >>> seg.run()

Inputs:

    [Mandatory]
    in_files: (an existing file name)
            A list of files to be segmented

    [Optional]
    bias_corrected_affine: (a boolean, nipype default value: False)
    bias_corrected_native: (a boolean, nipype default value: False)
    bias_corrected_normalized: (a boolean, nipype default value: True)
    bias_fwhm: (30 or 40 or 50 or 60 or 70 or 80 or 90 or 100 or 110 or
             120 or 130 or 'Inf', nipype default value: 60)
            FWHM of Gaussian smoothness of bias
    bias_regularization: (0 or 1e-05 or 0.0001 or 0.001 or 0.01 or 0.1 or
             1 or 10, nipype default value: 0.0001)
            no(0) - extremely heavy (10)
    cleanup_partitions: (an integer, nipype default value: 1)
            0=None,1=light,2=thorough
    csf_dartel: (0 <= an integer <= 2, nipype default value: 0)
            0=None,1=rigid(SPM8 default),2=affine
    csf_modulated_normalized: (0 <= an integer <= 2, nipype default
             value: 2)
            0=none,1=affine+non-linear(SPM8 default),2=non-linear only
    csf_native: (a boolean, nipype default value: False)
    csf_normalized: (a boolean, nipype default value: False)
    dartel_template: (an existing file name)
    deformation_field: (a tuple of the form: (a boolean, a boolean),
             nipype default value: (0, 0))
            forward and inverse field
    display_results: (a boolean, nipype default value: True)
    gaussians_per_class: (a tuple of the form: (an integer, an integer,
             an integer, an integer, an integer, an integer), nipype default
             value: (2, 2, 2, 3, 4, 2))
            number of gaussians for each tissue class
    gm_dartel: (0 <= an integer <= 2, nipype default value: 0)
            0=None,1=rigid(SPM8 default),2=affine
    gm_modulated_normalized: (0 <= an integer <= 2, nipype default value:
             2)
            0=none,1=affine+non-linear(SPM8 default),2=non-linear only
    gm_native: (a boolean, nipype default value: False)
    gm_normalized: (a boolean, nipype default value: False)
    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_files: (an existing file name)
            A list of files to be segmented
    jacobian_determinant: (a boolean, nipype default value: False)
    matlab_cmd: (a string)
            matlab command to use
    mfile: (a boolean, nipype default value: True)
            Run m-code using m-file
    mrf_weighting: (a float, nipype default value: 0.15)
    paths: (a directory name)
            Paths to add to matlabpath
    pve_label_dartel: (0 <= an integer <= 2, nipype default value: 0)
            0=None,1=rigid(SPM8 default),2=affine
    pve_label_native: (a boolean, nipype default value: False)
    pve_label_normalized: (a boolean, nipype default value: False)
    sampling_distance: (a float, nipype default value: 3)
            Sampling distance on data for parameter estimation
    spatial_normalization: ('high' or 'low', nipype default value: high)
    tissues: (an existing file name)
            tissue probability map
    use_mcr: (a boolean)
            Run m-code using SPM MCR
    use_sanlm_denoising_filter: (0 <= an integer <= 2, nipype default
             value: 2)
            0=No denoising, 1=denoising,2=denoising multi-threaded
    use_v8struct: (a boolean, nipype default value: True)
            Generate SPM8 and higher compatible jobs
    warping_regularization: (a float, nipype default value: 4)
            Controls balance between parameters and data
    wm_dartel: (0 <= an integer <= 2, nipype default value: 0)
            0=None,1=rigid(SPM8 default),2=affine
    wm_modulated_normalized: (0 <= an integer <= 2, nipype default value:
             2)
            0=none,1=affine+non-linear(SPM8 default),2=non-linear only
    wm_native: (a boolean, nipype default value: False)
    wm_normalized: (a boolean, nipype default value: False)

Outputs:

    bias_corrected_images: (an existing file name)
            bias corrected images
    dartel_input_images: (a list of items which are a list of items which
             are an existing file name)
            dartel imported class images
    forward_deformation_field: (an existing file name)
    inverse_deformation_field: (an existing file name)
    jacobian_determinant_images: (an existing file name)
    modulated_class_images: (a list of items which are a list of items
             which are an existing file name)
            modulated+normalized class images
    native_class_images: (a list of items which are a list of items which
             are an existing file name)
            native space probability maps
    normalized_bias_corrected_images: (an existing file name)
            bias corrected images
    normalized_class_images: (a list of items which are a list of items
             which are an existing file name)
            normalized class images
    pve_label_native_images: (an existing file name)
    pve_label_normalized_images: (an existing file name)
    pve_label_registered_images: (an existing file name)
    transformation_mat: (an existing file name)
            Normalization transformation