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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