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nipype.interfaces.spm.preprocess

Coregister

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]
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 float)
        gaussian smoothing kernel width (mm)
jobtype : ('estwrite' or 'estimate' or 'write')
        one of: estimate, write, estwrite
matlab_cmd : (a string)
        Unknown
mfile : (a boolean)
        Run m-code using m-file
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
tolerance : (a list of items which are a float)
        acceptable tolerance for each of 12 params
write_interp : (an integer >= 0)
        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 a boolean)
        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

NewSegment

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

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.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_info : (a list of items which are a tuple of the form: (a float, a float, a tuple of the form: (a boolean, a boolean)))
            A list of tuples (one per channel/modality)
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
    matlab_cmd : (a string)
            Unknown
    mfile : (a boolean)
            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 list of items which are an existing file name, 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
        - 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
    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:

native_class_images : (an existing file name)
        native space grey probability map
transformation_mat : (an existing file name)
        Normalization transformation

Normalize

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
        exclusive: source,template
source : (an existing file name)
        file to normalize to template
        exclusive: parameter_file
template : (an existing file name)
        template file to normalize to
        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)
        files to apply transformation to (opt)
jobtype : ('estwrite' or 'estimate' or 'write')
        one of: estimate, write, estwrite (opt, estwrite)
matlab_cmd : (a string)
        Unknown
mfile : (a boolean)
        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)
paths : (a directory name)
        Paths to add to matlabpath
source_image_smoothing : (a float)
        source smoothing (opt)
source_weight : (a file name)
        name of weighting image for source (opt)
template_image_smoothing : (a float)
        template smoothing (opt)
template_weight : (a file name)
        name of weighting image for template (opt)
write_bounding_box : (a list of from 6 to 6 items which are a float)
        6-element list (opt)
write_interp : (an integer >= 0)
        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 a boolean)
        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

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

[Optional]
fwhm : (a floating point number >= 0.0)
        gaussian smoothing kernel width
interp : (0 <= an integer <= 7)
        degree of b-spline used for interpolation
jobtype : ('estwrite' or 'estimate' or 'write')
        one of: estimate, write, estwrite
matlab_cmd : (a string)
        Unknown
mfile : (a boolean)
        Run m-code using m-file
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)
        Indicate whether realignment is done to the mean image
separation : (a floating point number >= 0.0)
        sampling separation in mm
weight_img : (an existing file name)
        filename of weighting image
wrap : (a tuple of the form: (an integer, an integer, 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 tuple of the form: (an integer, an integer))
        determines which images to reslice
write_wrap : (a tuple of the form: (an integer, an integer, an integer))
        Check if interpolation should wrap in [x,y,z]

Outputs:

mean_image : (an existing file name)
        Mean image file from the realignment
realigned_files : (a list of items which are an existing file name or an existing file name)
        Realigned files
realignment_parameters : (an existing file name)
        Estimated translation and rotation parameters

Segment

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')
        mni, eastern, subj, none
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 1.0000000000000001e-05 or 0.0001 or 0.001 or 0.01 or 0.10000000000000001 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: [0,0,0],
    Native Space: [0,0,1],
    Unmodulated Normalised: [0,1,0],
    Modulated Normalised: [1,0,0],
    Native + Unmodulated Normalised: [0,1,1],
    Native + Modulated Normalised: [1,0,1],
    Native + Modulated + Unmodulated: [1,1,1],
    Modulated + Unmodulated Normalised: [1,1,0]
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: [0,0,0],
    Native Space: [0,0,1],
    Unmodulated Normalised: [0,1,0],
    Modulated Normalised: [1,0,0],
    Native + Unmodulated Normalised: [0,1,1],
    Native + Modulated Normalised: [1,0,1],
    Native + Modulated + Unmodulated: [1,1,1],
    Modulated + Unmodulated Normalised: [1,1,0]
mask_image : (an existing file name)
        Binary image to restrict parameter estimation
matlab_cmd : (a string)
        Unknown
mfile : (a boolean)
        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,)
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: [0,0,0],
    Native Space: [0,0,1],
    Unmodulated Normalised: [0,1,0],
    Modulated Normalised: [1,0,0],
    Native + Unmodulated Normalised: [0,1,1],
    Native + Modulated Normalised: [1,0,1],
    Native + Modulated + Unmodulated: [1,1,1],
    Modulated + Unmodulated Normalised: [1,1,0]

Outputs:

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

SliceTiming

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

[Optional]
matlab_cmd : (a string)
        Unknown
mfile : (a boolean)
        Run m-code using m-file
num_slices : (an integer)
        number of slices in a volume
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)

Outputs:

timecorrected_files : (a file name)
        Unknown

Smooth

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)
matlab_cmd : (a string)
        Unknown
mfile : (a boolean)
        Run m-code using m-file
paths : (a directory name)
        Paths to add to matlabpath

Outputs:

smoothed_files : (an existing file name)
        smoothed files