nipype.interfaces.spm.preprocess module

SPM wrappers for preprocessing data

ApplyDeformations

Link to code

Bases: SPMCommand

deformation_field : a pathlike object or string representing an existing file in_files : a list of items which are a pathlike object or string representing an existing file reference_volume : a pathlike object or string representing an existing file

interp : 0 <= a long integer <= 7
Degree of b-spline used for interpolation.
matlab_cmd : a unicode string
Matlab command to use.
mfile : a boolean
Run m-code using m-file. (Nipype default value: True)
paths : a list of items which are a pathlike object or string representing a directory
Paths to add to matlabpath.
use_mcr : a boolean
Run m-code using SPM MCR.
use_v8struct : a boolean
Generate SPM8 and higher compatible jobs. (Nipype default value: True)

out_files : a list of items which are a pathlike object or string representing an existing file

Coregister

Link to code

Bases: SPMCommand

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() # doctest: +SKIP
source : a list of items which are a pathlike object or string representing an existing file
File to register to target.
target : a pathlike object or string representing an existing file
Reference file to register to.
apply_to_files : a list of items which are a pathlike object or string representing an existing file
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).
jobtype : ‘estwrite’ or ‘estimate’ or ‘write’
One of: estimate, write, estwrite. (Nipype default value: estwrite)
matlab_cmd : a unicode string
Matlab command to use.
mfile : a boolean
Run m-code using m-file. (Nipype default value: True)
out_prefix : a string
Coregistered output prefix. (Nipype default value: r)
paths : a list of items which are a pathlike object or string representing a directory
Paths to add to matlabpath.
separation : a list of items which are a float
Sampling separation in mm.
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
Generate SPM8 and higher compatible jobs. (Nipype default value: True)
write_interp : 0 <= a long 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 (int or long)
Check if interpolation should wrap in [x,y,z].
coregistered_files : a list of items which are a pathlike object or string representing an existing file
Coregistered other files.
coregistered_source : a list of items which are a pathlike object or string representing an existing file
Coregistered source files.

CreateWarped

Link to code

Bases: SPMCommand

Apply a flow field estimated by DARTEL to create warped images

http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=190

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() # doctest: +SKIP
flowfield_files : a list of items which are a pathlike object or string representing an existing file
DARTEL flow fields u_rc1*.
image_files : a list of items which are a pathlike object or string representing an existing file
A list of files to be warped.
interp : 0 <= a long integer <= 7
Degree of b-spline used for interpolation.
iterations : 0 <= a long integer <= 9
The number of iterations: log2(number of time steps).
matlab_cmd : a unicode string
Matlab command to use.
mfile : a boolean
Run m-code using m-file. (Nipype default value: True)
modulate : a boolean
Modulate images.
paths : a list of items which are a pathlike object or string representing a directory
Paths to add to matlabpath.
use_mcr : a boolean
Run m-code using SPM MCR.
use_v8struct : a boolean
Generate SPM8 and higher compatible jobs. (Nipype default value: True)

warped_files : a list of items which are a pathlike object or string representing an existing file

DARTEL

Link to code

Bases: SPMCommand

Use spm DARTEL to create a template and flow fields

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

Examples

>>> import nipype.interfaces.spm as spm
>>> dartel = spm.DARTEL()
>>> dartel.inputs.image_files = [['rc1s1.nii','rc1s2.nii'],['rc2s1.nii', 'rc2s2.nii']]
>>> dartel.run() # doctest: +SKIP
image_files : a list of items which are a list of items which are a pathlike object or string representing an existing file
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 <= a long 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 unicode string
Matlab command to use.
mfile : a boolean
Run m-code using m-file. (Nipype default value: True)
optimization_parameters : a tuple of the form: (a float, 1 <= a long integer <= 8, 1 <= a long integer <= 8)

Optimization settings a tuple:

  • LM regularization
  • cycles of multigrid solver
  • relaxation iterations
paths : a list of items which are a pathlike object or string representing a directory
Paths to add to matlabpath.
regularization_form : ‘Linear’ or ‘Membrane’ or ‘Bending’
Form of regularization energy term.
template_prefix : a unicode string
Prefix for template. (Nipype default value: Template)
use_mcr : a boolean
Run m-code using SPM MCR.
use_v8struct : a boolean
Generate SPM8 and higher compatible jobs. (Nipype default value: True)
dartel_flow_fields : a list of items which are a pathlike object or string representing an existing file
DARTEL flow fields.
final_template_file : a pathlike object or string representing an existing file
Final DARTEL template.
template_files : a list of items which are a pathlike object or string representing an existing file
Templates from different stages of iteration.

DARTELNorm2MNI

Link to code

Bases: SPMCommand

Use spm DARTEL to normalize data to MNI space

http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=188

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() # doctest: +SKIP
apply_to_files : a list of items which are a pathlike object or string representing an existing file
Files to apply the transform to.
flowfield_files : a list of items which are a pathlike object or string representing an existing file
DARTEL flow fields u_rc1*.
template_file : a pathlike object or string representing an existing file
DARTEL template.
bounding_box : a tuple of the form: (a float, a float, a float, a float, a float, a float)
Voxel sizes for output file.
fwhm : a list of from 3 to 3 items which are a float or a float
3-list of fwhm for each dimension.
matlab_cmd : a unicode string
Matlab command to use.
mfile : a boolean
Run m-code using m-file. (Nipype default value: True)
modulate : a boolean
Modulate out images - no modulation preserves concentrations.
paths : a list of items which are a pathlike object or string representing a directory
Paths to add to matlabpath.
use_mcr : a boolean
Run m-code using SPM MCR.
use_v8struct : a boolean
Generate SPM8 and higher compatible jobs. (Nipype default value: True)
voxel_size : a tuple of the form: (a float, a float, a float)
Voxel sizes for output file.
normalization_parameter_file : a pathlike object or string representing an existing file
Transform parameters to MNI space.
normalized_files : a list of items which are a pathlike object or string representing an existing file
Normalized files in MNI space.

FieldMap

Link to code

Bases: SPMCommand

Use the fieldmap toolbox from spm to calculate the voxel displacement map (VDM).

http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=173

Important

This interface does not deal with real/imag magnitude images nor with the two phase files case.

Examples

>>> from nipype.interfaces.spm import FieldMap
>>> fm = FieldMap()
>>> fm.inputs.phase_file = 'phase.nii'
>>> fm.inputs.magnitude_file = 'magnitude.nii'
>>> fm.inputs.echo_times = (5.19, 7.65)
>>> fm.inputs.blip_direction = 1
>>> fm.inputs.total_readout_time = 15.6
>>> fm.inputs.epi_file = 'epi.nii'
>>> fm.run() # doctest: +SKIP
blip_direction : 1 or -1
Polarity of the phase-encode blips.
echo_times : a tuple of the form: (a float, a float)
Short and long echo times.
epi_file : a pathlike object or string representing an existing file
EPI to unwarp.
magnitude_file : a pathlike object or string representing an existing file
Presubstracted magnitude file.
phase_file : a pathlike object or string representing an existing file
Presubstracted phase file.
total_readout_time : a float
Total EPI readout time.
anat_file : a pathlike object or string representing an existing file
Anatomical image for comparison.
epifm : a boolean
Epi-based field map. (Nipype default value: False)
jacobian_modulation : a boolean
Jacobian modulation. (Nipype default value: False)
jobtype : ‘calculatevdm’ or ‘applyvdm’
One of: calculatevdm, applyvdm. (Nipype default value: calculatevdm)
mask_fwhm : a long integer >= 0
Gaussian smoothing kernel width. (Nipype default value: 5)
maskbrain : a boolean
Masking or no masking of the brain. (Nipype default value: True)
matchanat : a boolean
Match anatomical image to EPI. (Nipype default value: True)
matchvdm : a boolean
Match VDM to EPI. (Nipype default value: True)
matlab_cmd : a unicode string
Matlab command to use.
method : ‘Mark3D’ or ‘Mark2D’ or ‘Huttonish’
One of: Mark3D, Mark2D, Huttonish. (Nipype default value: Mark3D)
mfile : a boolean
Run m-code using m-file. (Nipype default value: True)
ndilate : a long integer >= 0
Number of erosions. (Nipype default value: 4)
nerode : a long integer >= 0
Number of erosions. (Nipype default value: 2)
pad : a long integer >= 0
Padding kernel width. (Nipype default value: 0)
paths : a list of items which are a pathlike object or string representing a directory
Paths to add to matlabpath.
reg : a float
Regularization value used in the segmentation. (Nipype default value: 0.02)
sessname : a unicode string
VDM filename extension. (Nipype default value: _run-)
template : a pathlike object or string representing an existing file
Template image for brain masking.
thresh : a float
Threshold used to create brain mask from segmented data. (Nipype default value: 0.5)
unwarp_fwhm : a long integer >= 0
Gaussian smoothing kernel width. (Nipype default value: 10)
use_mcr : a boolean
Run m-code using SPM MCR.
use_v8struct : a boolean
Generate SPM8 and higher compatible jobs. (Nipype default value: True)
writeunwarped : a boolean
Write unwarped EPI. (Nipype default value: False)
ws : a boolean
Weighted smoothing. (Nipype default value: True)
vdm : a pathlike object or string representing an existing file
Voxel difference map.

MultiChannelNewSegment

Link to code

Bases: SPMCommand

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

http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=45

Examples

>>> import nipype.interfaces.spm as spm
>>> seg = spm.MultiChannelNewSegment()
>>> seg.inputs.channels = [('structural.nii',(0.0001, 60, (True, True)))]
>>> seg.run() # doctest: +SKIP

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 = MultiChannelNewSegment()
>>> channel1= ('T1.nii',(0.0001, 60, (True, True)))
>>> channel2= ('T2.nii',(0.0001, 60, (True, True)))
>>> seg.inputs.channels = [channel1, channel2]
>>> 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() # doctest: +SKIP
affine_regularization : ‘mni’ or ‘eastern’ or ‘subj’ or ‘none’
Mni, eastern, subj, none .
channels : a list of items which are a tuple of the form: (a list of items which are a pathlike object or string representing an existing file, a tuple of the form: (a float, a float, a tuple of the form: (a boolean, a boolean)))
A list of tuples (one per each channel) with the following fields:
  • a list of channel files (only 1rst channel files will be segmented)
  • a tuple with the following channel-specific info fields: - bias reguralisation (0-10) - FWHM of Gaussian smoothness of bias - which maps to save (Field, Corrected) - a tuple of two boolean values.
matlab_cmd : a unicode string
Matlab command to use.
mfile : a boolean
Run m-code using m-file. (Nipype default value: True)
paths : a list of items which are a pathlike object or string representing a directory
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: (a pathlike object or string representing an existing file, an integer (int or long)), an integer (int or long), 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 [Unmodulated, Modulated] - a tuple of two boolean values.
use_mcr : a boolean
Run m-code using SPM MCR.
use_v8struct : a boolean
Generate SPM8 and higher compatible jobs. (Nipype default value: True)
warping_regularization : a list of from 5 to 5 items which are a float or a float
Warping regularization parameter(s). Accepts float or list of floats (the latter is required by SPM12).
write_deformation_fields : a list of from 2 to 2 items which are a boolean
Which deformation fields to write:[Inverse, Forward].
bias_corrected_images : a list of items which are a pathlike object or string representing an existing file
Bias corrected images.
bias_field_images : a list of items which are a pathlike object or string representing an existing file
Bias field images.
dartel_input_images : a list of items which are a list of items which are a pathlike object or string representing an existing file
Dartel imported class images.

forward_deformation_field : a list of items which are a pathlike object or string representing an existing file inverse_deformation_field : a list of items which are a pathlike object or string representing an existing file modulated_class_images : a list of items which are a list of items which are a pathlike object or string representing an existing file

Modulated+normalized class images.
native_class_images : a list of items which are a list of items which are a pathlike object or string representing an existing file
Native space probability maps.
normalized_class_images : a list of items which are a list of items which are a pathlike object or string representing an existing file
Normalized class images.
transformation_mat : a list of items which are a pathlike object or string representing an existing file
Normalization transformation.

NewSegment

Link to code

Bases: SPMCommand

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=43

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() # doctest: +SKIP

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() # doctest: +SKIP
channel_files : a list of items which are a pathlike object or string representing an existing file
A list of files to be segmented.
affine_regularization : ‘mni’ or ‘eastern’ or ‘subj’ or ‘none’
Mni, eastern, subj, none .
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 (Field, Corrected) - a tuple of two boolean values.
matlab_cmd : a unicode string
Matlab command to use.
mfile : a boolean
Run m-code using m-file. (Nipype default value: True)
paths : a list of items which are a pathlike object or string representing a directory
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: (a pathlike object or string representing an existing file, an integer (int or long)), an integer (int or long), 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 [Unmodulated, Modulated] - a tuple of two boolean values.
use_mcr : a boolean
Run m-code using SPM MCR.
use_v8struct : a boolean
Generate SPM8 and higher compatible jobs. (Nipype default value: True)
warping_regularization : a list of from 5 to 5 items which are a float or a float
Warping regularization parameter(s). Accepts float or list of floats (the latter is required by SPM12).
write_deformation_fields : a list of from 2 to 2 items which are a boolean
Which deformation fields to write:[Inverse, Forward].
bias_corrected_images : a list of items which are a pathlike object or string representing an existing file
Bias corrected images.
bias_field_images : a list of items which are a pathlike object or string representing an existing file
Bias field images.
dartel_input_images : a list of items which are a list of items which are a pathlike object or string representing an existing file
Dartel imported class images.

forward_deformation_field : a list of items which are a pathlike object or string representing an existing file inverse_deformation_field : a list of items which are a pathlike object or string representing an existing file modulated_class_images : a list of items which are a list of items which are a pathlike object or string representing an existing file

Modulated+normalized class images.
native_class_images : a list of items which are a list of items which are a pathlike object or string representing an existing file
Native space probability maps.
normalized_class_images : a list of items which are a list of items which are a pathlike object or string representing an existing file
Normalized class images.
transformation_mat : a list of items which are a pathlike object or string representing an existing file
Normalization transformation.

Normalize

Link to code

Bases: SPMCommand

use spm_normalise for warping an image to a template

http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=203

Examples

>>> import nipype.interfaces.spm as spm
>>> norm = spm.Normalize()
>>> norm.inputs.source = 'functional.nii'
>>> norm.run() # doctest: +SKIP
parameter_file : a pathlike object or string representing a file
Normalization parameter file*_sn.mat. Mutually exclusive with inputs: source, template.
source : a list of items which are a pathlike object or string representing an existing file
File to normalize to template. Mutually exclusive with inputs: parameter_file.
template : a pathlike object or string representing an existing file
Template file to normalize to. Mutually exclusive with inputs: parameter_file.
DCT_period_cutoff : a float
Cutoff of for DCT bases.
affine_regularization_type : ‘mni’ or ‘size’ or ‘none’
Mni, size, none.
apply_to_files : a list of items which are a pathlike object or string representing an existing file or a list of items which are a pathlike object or string representing an existing file
Files to apply transformation to.
jobtype : ‘estwrite’ or ‘est’ or ‘write’
Estimate, Write or do both. (Nipype default value: estwrite)
matlab_cmd : a unicode string
Matlab command to use.
mfile : a boolean
Run m-code using m-file. (Nipype default value: True)
nonlinear_iterations : an integer (int or long)
Number of iterations of nonlinear warping.
nonlinear_regularization : a float
The amount of the regularization for the nonlinear part of the normalization.
out_prefix : a string
Normalized output prefix. (Nipype default value: w)
paths : a list of items which are a pathlike object or string representing a directory
Paths to add to matlabpath.
source_image_smoothing : a float
Source smoothing.
source_weight : a pathlike object or string representing a file
Name of weighting image for source.
template_image_smoothing : a float
Template smoothing.
template_weight : a pathlike object or string representing a file
Name of weighting image for template.
use_mcr : a boolean
Run m-code using SPM MCR.
use_v8struct : a boolean
Generate SPM8 and higher compatible jobs. (Nipype default value: True)
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.
write_interp : 0 <= a long integer <= 7
Degree of b-spline used for interpolation.
write_preserve : a boolean
True/False warped images are modulated.
write_voxel_sizes : a list of from 3 to 3 items which are a float
3-element list.
write_wrap : a list of items which are an integer (int or long)
Check if interpolation should wrap in [x,y,z] - list of bools.
normalization_parameters : a list of items which are a pathlike object or string representing an existing file
MAT files containing the normalization parameters.
normalized_files : a list of items which are a pathlike object or string representing an existing file
Normalized other files.
normalized_source : a list of items which are a pathlike object or string representing an existing file
Normalized source files.

Normalize12

Link to code

Bases: SPMCommand

uses SPM12’s new Normalise routine for warping an image to a template. Spatial normalisation is now done via the segmentation routine (which was known as New Segment in SPM8). Note that the normalisation in SPM12 is done towards a file containing multiple tissue probability maps, which was not the case in SPM8.

http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=49

Examples

>>> import nipype.interfaces.spm as spm
>>> norm12 = spm.Normalize12()
>>> norm12.inputs.image_to_align = 'structural.nii'
>>> norm12.inputs.apply_to_files = 'functional.nii'
>>> norm12.run() # doctest: +SKIP
deformation_file : a pathlike object or string representing a file
File y_*.nii containing 3 deformation fields for the deformation in x, y and z dimension. Mutually exclusive with inputs: image_to_align, tpm.
image_to_align : a pathlike object or string representing an existing file
File to estimate normalization parameters with. Mutually exclusive with inputs: deformation_file.
affine_regularization_type : ‘mni’ or ‘size’ or ‘none’
Mni, size, none.
apply_to_files : a list of items which are a pathlike object or string representing an existing file or a list of items which are a pathlike object or string representing an existing file
Files to apply transformation to.
bias_fwhm : 30 or 40 or 50 or 60 or 70 or 80 or 90 or 100 or 110 or 120 or 130 or 140 or 150 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).
jobtype : ‘estwrite’ or ‘est’ or ‘write’
Estimate, Write or do Both. (Nipype default value: estwrite)
matlab_cmd : a unicode string
Matlab command to use.
mfile : a boolean
Run m-code using m-file. (Nipype default value: True)
out_prefix : a string
Normalized output prefix. (Nipype default value: w)
paths : a list of items which are a pathlike object or string representing a directory
Paths to add to matlabpath.
sampling_distance : a float
Sampling distance on data for parameter estimation.
smoothness : a float
Value (in mm) to smooth the data before normalization.
tpm : a pathlike object or string representing an existing file
Template in form of tissue probablitiy maps to normalize to. Mutually exclusive with inputs: deformation_file.
use_mcr : a boolean
Run m-code using SPM MCR.
use_v8struct : a boolean
Generate SPM8 and higher compatible jobs. (Nipype default value: True)
warping_regularization : a list of from 5 to 5 items which are a float
Controls balance between parameters and data.
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 representing the bounding box (in mm) to be written.
write_interp : 0 <= a long integer <= 7
Degree of b-spline used for interpolation.
write_voxel_sizes : a list of from 3 to 3 items which are a float
3-element list representing the voxel sizes (in mm) of the written normalised images.
deformation_field : a list of items which are a pathlike object or string representing an existing file
NIfTI file containing 3 deformation fields for the deformation in x, y and z dimension.
normalized_files : a list of items which are a pathlike object or string representing an existing file
Normalized other files.
normalized_image : a list of items which are a pathlike object or string representing an existing file
Normalized file that needed to be aligned.

Realign

Link to code

Bases: SPMCommand

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() # doctest: +SKIP
in_files : a list of items which are a pathlike object or string representing an existing file or a list of items which are a pathlike object or string representing an existing file
List of filenames to realign.
fwhm : a floating point number >= 0.0
Gaussian smoothing kernel width.
interp : 0 <= a long integer <= 7
Degree of b-spline used for interpolation.
jobtype : ‘estwrite’ or ‘estimate’ or ‘write’
One of: estimate, write, estwrite. (Nipype default value: estwrite)
matlab_cmd : a unicode string
Matlab command to use.
mfile : a boolean
Run m-code using m-file. (Nipype default value: True)
out_prefix : a string
Realigned output prefix. (Nipype default value: r)
paths : a list of items which are a pathlike object or string representing a directory
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.
use_mcr : a boolean
Run m-code using SPM MCR.
use_v8struct : a boolean
Generate SPM8 and higher compatible jobs. (Nipype default value: True)
weight_img : a pathlike object or string representing an existing file
Filename of weighting image.
wrap : a list of from 3 to 3 items which are an integer (int or long)
Check if interpolation should wrap in [x,y,z].
write_interp : 0 <= a long 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 class ‘int’
Determines which images to reslice. (Nipype default value: [2, 1])
write_wrap : a list of from 3 to 3 items which are an integer (int or long)
Check if interpolation should wrap in [x,y,z].
mean_image : a pathlike object or string representing an existing file
Mean image file from the realignment.
modified_in_files : a list of items which are a list of items which are a pathlike object or string representing an existing file or a pathlike object or string representing an existing file
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 a list of items which are a pathlike object or string representing an existing file or a pathlike object or string representing an existing file
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 : a list of items which are a pathlike object or string representing an existing file
Estimated translation and rotation parameters.

RealignUnwarp

Link to code

Bases: SPMCommand

Use spm_uw_estimate for estimating within subject registration and unwarping of time series. Function accepts only one single field map. If in_files is a list of files they will be treated as separate sessions but associated to the same fieldmap.

http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=31

Examples

>>> import nipype.interfaces.spm as spm
>>> realignUnwarp = spm.RealignUnwarp()
>>> realignUnwarp.inputs.in_files = ['functional.nii', 'functional2.nii']
>>> realignUnwarp.inputs.phase_map = 'voxeldisplacemap.vdm'
>>> realignUnwarp.inputs.register_to_mean = True
>>> realignUnwarp.run() # doctest: +SKIP
in_files : a list of items which are a pathlike object or string representing an existing file or a list of items which are a pathlike object or string representing an existing file
List of filenames to realign and unwarp.
est_basis_func : a list of from 2 to 2 items which are an integer (int or long)
Number of basis functions to use for each dimension.
est_first_order_effects : a list of from 1 to 6 items which are an integer (int or long)
First order effects should only depend on pitch and roll, i.e. [4 5].
est_jacobian_deformations : a boolean
Jacobian deformations. In theory a good idea to include them, in practice a bad idea. Default: No.
est_num_of_iterations : a list of items which are a value of class ‘int’
Number of iterations. (Nipype default value: [5])
est_re_est_mov_par : a boolean
Re-estimate movement parameters at each unwarping iteration.
est_reg_factor : a list of items which are a value of class ‘int’
Regularisation factor. Default: 100000 (medium). (Nipype default value: [100000])
est_reg_order : 0 <= a long integer <= 3
This parameter determines how to balance the compromise between likelihood maximization and smoothness maximization of the estimated field.
est_second_order_effects : a list of from 1 to 6 items which are an integer (int or long)
List of second order terms to model second derivatives of.
est_taylor_expansion_point : a string
Point in position space to perform Taylor-expansion around. (Nipype default value: Average)
est_unwarp_fwhm : a floating point number >= 0.0
Gaussian smoothing kernel width for unwarp.
fwhm : a floating point number >= 0.0
Gaussian smoothing kernel width.
interp : 0 <= a long integer <= 7
Degree of b-spline used for interpolation.
matlab_cmd : a unicode string
Matlab command to use.
mfile : a boolean
Run m-code using m-file. (Nipype default value: True)
out_prefix : a string
Realigned and unwarped output prefix. (Nipype default value: u)
paths : a list of items which are a pathlike object or string representing a directory
Paths to add to matlabpath.
phase_map : a pathlike object or string representing a file
Voxel displacement map to use in unwarping. Unlike SPM standard behaviour, the same map will be used for all sessions.
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.
reslice_interp : 0 <= a long integer <= 7
Degree of b-spline used for interpolation.
reslice_mask : a boolean
True/False mask output image.
reslice_which : a list of items which are a value of class ‘int’
Determines which images to reslice. (Nipype default value: [2, 1])
reslice_wrap : a list of from 3 to 3 items which are an integer (int or long)
Check if interpolation should wrap in [x,y,z].
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
Generate SPM8 and higher compatible jobs. (Nipype default value: True)
weight_img : a pathlike object or string representing an existing file
Filename of weighting image.
wrap : a list of from 3 to 3 items which are an integer (int or long)
Check if interpolation should wrap in [x,y,z].
mean_image : a pathlike object or string representing an existing file
Mean image file from the realignment & unwarping.
modified_in_files : a list of items which are a list of items which are a pathlike object or string representing an existing file or a pathlike object or string representing an existing file
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_unwarped_files : a list of items which are a list of items which are a pathlike object or string representing an existing file or a pathlike object or string representing an existing file
Realigned and unwarped files written to disc.
realignment_parameters : a list of items which are a pathlike object or string representing an existing file
Estimated translation and rotation parameters.

Segment

Link to code

Bases: SPMCommand

use spm_segment to separate structural images into different tissue classes.

http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=209

Examples

>>> import nipype.interfaces.spm as spm
>>> seg = spm.Segment()
>>> seg.inputs.data = 'structural.nii'
>>> seg.run() # doctest: +SKIP
data : a list of items which are a pathlike object or string representing an existing file
One scan per subject.
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].
gaussians_per_class : a list of items which are an integer (int or long)
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].
mask_image : a pathlike object or string representing an existing file
Binary image to restrict parameter estimation .
matlab_cmd : a unicode string
Matlab command to use.
mfile : a boolean
Run m-code using m-file. (Nipype default value: True)
paths : a list of items which are a pathlike object or string representing a directory
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 a pathlike object or string representing an existing file
List of gray, white & csf prob. (opt,).
use_mcr : a boolean
Run m-code using SPM MCR.
use_v8struct : a boolean
Generate SPM8 and higher compatible jobs. (Nipype default value: True)
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].
bias_corrected_image : a pathlike object or string representing a file
Bias-corrected version of input image.
inverse_transformation_mat : a pathlike object or string representing an existing file
Inverse normalization info.
modulated_csf_image : a pathlike object or string representing a file
Modulated, normalized csf probability map.
modulated_gm_image : a pathlike object or string representing a file
Modulated, normalized grey probability map.
modulated_input_image : a pathlike object or string representing a file
Bias-corrected version of input image.
modulated_wm_image : a pathlike object or string representing a file
Modulated, normalized white probability map.
native_csf_image : a pathlike object or string representing a file
Native space csf probability map.
native_gm_image : a pathlike object or string representing a file
Native space grey probability map.
native_wm_image : a pathlike object or string representing a file
Native space white probability map.
normalized_csf_image : a pathlike object or string representing a file
Normalized csf probability map.
normalized_gm_image : a pathlike object or string representing a file
Normalized grey probability map.
normalized_wm_image : a pathlike object or string representing a file
Normalized white probability map.
transformation_mat : a pathlike object or string representing an existing file
Normalization transformation.

SliceTiming

Link to code

Bases: SPMCommand

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 = list(range(32,0,-1))
>>> st.inputs.ref_slice = 1
>>> st.run() # doctest: +SKIP
in_files : a list of items which are a list of items which are a pathlike object or string representing an existing file or a pathlike object or string representing an existing file
List of filenames to apply slice timing.
num_slices : an integer (int or long)
Number of slices in a volume.
ref_slice : an integer (int or long) or a float
1-based Number of the reference slice or reference time point if slice_order is in onsets (ms).
slice_order : a list of items which are an integer (int or long) or a float
1-based order or onset (in ms) 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).
matlab_cmd : a unicode string
Matlab command to use.
mfile : a boolean
Run m-code using m-file. (Nipype default value: True)
out_prefix : a string
Slicetimed output prefix. (Nipype default value: a)
paths : a list of items which are a pathlike object or string representing a directory
Paths to add to matlabpath.
use_mcr : a boolean
Run m-code using SPM MCR.
use_v8struct : a boolean
Generate SPM8 and higher compatible jobs. (Nipype default value: True)
timecorrected_files : a list of items which are a list of items which are a pathlike object or string representing an existing file or a pathlike object or string representing an existing file
Slice time corrected files.

Smooth

Link to code

Bases: SPMCommand

Use spm_smooth for 3D Gaussian smoothing of image volumes.

http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=55

Examples

>>> import nipype.interfaces.spm as spm
>>> smooth = spm.Smooth()
>>> smooth.inputs.in_files = 'functional.nii'
>>> smooth.inputs.fwhm = [4, 4, 4]
>>> smooth.run() # doctest: +SKIP
in_files : a list of items which are a pathlike object or string representing an existing file
List of files to smooth.
data_type : an integer (int or long)
Data type of the output images.
fwhm : a list of from 3 to 3 items which are a float or a float
3-list of fwhm for each dimension.
implicit_masking : a boolean
A mask implied by a particular voxel value.
matlab_cmd : a unicode string
Matlab command to use.
mfile : a boolean
Run m-code using m-file. (Nipype default value: True)
out_prefix : a string
Smoothed output prefix. (Nipype default value: s)
paths : a list of items which are a pathlike object or string representing a directory
Paths to add to matlabpath.
use_mcr : a boolean
Run m-code using SPM MCR.
use_v8struct : a boolean
Generate SPM8 and higher compatible jobs. (Nipype default value: True)
smoothed_files : a list of items which are a pathlike object or string representing an existing file
Smoothed files.

VBMSegment

Link to code

Bases: SPMCommand

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() # doctest: +SKIP
in_files : a list of items which are a pathlike object or string representing an existing file
A list of files to be segmented.
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’
FWHM of Gaussian smoothness of bias. (Nipype default value: 60)
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). (Nipype default value: 0.0001)
cleanup_partitions : an integer (int or long)
0=None,1=light,2=thorough. (Nipype default value: 1)
csf_dartel : 0 <= a long integer <= 2
0=None,1=rigid(SPM8 default),2=affine. (Nipype default value: 0)
csf_modulated_normalized : 0 <= a long integer <= 2
0=none,1=affine+non-linear(SPM8 default),2=non-linear only. (Nipype default value: 2)
csf_native : a boolean
(Nipype default value: False)
csf_normalized : a boolean
(Nipype default value: False)

dartel_template : a pathlike object or string representing an existing file deformation_field : a tuple of the form: (a boolean, a boolean)

Forward and inverse field. (Nipype default value: (0, 0))
display_results : a boolean
(Nipype default value: True)
gaussians_per_class : a tuple of the form: (an integer (int or long), an integer (int or long), an integer (int or long), an integer (int or long), an integer (int or long), an integer (int or long))
Number of gaussians for each tissue class. (Nipype default value: (2, 2, 2, 3, 4, 2))
gm_dartel : 0 <= a long integer <= 2
0=None,1=rigid(SPM8 default),2=affine. (Nipype default value: 0)
gm_modulated_normalized : 0 <= a long integer <= 2
0=none,1=affine+non-linear(SPM8 default),2=non-linear only. (Nipype default value: 2)
gm_native : a boolean
(Nipype default value: False)
gm_normalized : a boolean
(Nipype default value: False)
jacobian_determinant : a boolean
(Nipype default value: False)
matlab_cmd : a unicode string
Matlab command to use.
mfile : a boolean
Run m-code using m-file. (Nipype default value: True)
mrf_weighting : a float
(Nipype default value: 0.15)
paths : a list of items which are a pathlike object or string representing a directory
Paths to add to matlabpath.
pve_label_dartel : 0 <= a long integer <= 2
0=None,1=rigid(SPM8 default),2=affine. (Nipype default value: 0)
pve_label_native : a boolean
(Nipype default value: False)
pve_label_normalized : a boolean
(Nipype default value: False)
sampling_distance : a float
Sampling distance on data for parameter estimation. (Nipype default value: 3)
spatial_normalization : ‘high’ or ‘low’
(Nipype default value: high)
tissues : a pathlike object or string representing an existing file
Tissue probability map.
use_mcr : a boolean
Run m-code using SPM MCR.
use_sanlm_denoising_filter : 0 <= a long integer <= 2
0=No denoising, 1=denoising,2=denoising multi-threaded. (Nipype default value: 2)
use_v8struct : a boolean
Generate SPM8 and higher compatible jobs. (Nipype default value: True)
warping_regularization : a float
Controls balance between parameters and data. (Nipype default value: 4)
wm_dartel : 0 <= a long integer <= 2
0=None,1=rigid(SPM8 default),2=affine. (Nipype default value: 0)
wm_modulated_normalized : 0 <= a long integer <= 2
0=none,1=affine+non-linear(SPM8 default),2=non-linear only. (Nipype default value: 2)
wm_native : a boolean
(Nipype default value: False)
wm_normalized : a boolean
(Nipype default value: False)
bias_corrected_images : a list of items which are a pathlike object or string representing an existing file
Bias corrected images.
dartel_input_images : a list of items which are a list of items which are a pathlike object or string representing an existing file
Dartel imported class images.

forward_deformation_field : a list of items which are a pathlike object or string representing an existing file inverse_deformation_field : a list of items which are a pathlike object or string representing an existing file jacobian_determinant_images : a list of items which are a pathlike object or string representing an existing file modulated_class_images : a list of items which are a list of items which are a pathlike object or string representing an existing file

Modulated+normalized class images.
native_class_images : a list of items which are a list of items which are a pathlike object or string representing an existing file
Native space probability maps.
normalized_bias_corrected_images : a list of items which are a pathlike object or string representing an existing file
Bias corrected images.
normalized_class_images : a list of items which are a list of items which are a pathlike object or string representing an existing file
Normalized class images.

pve_label_native_images : a list of items which are a pathlike object or string representing an existing file pve_label_normalized_images : a list of items which are a pathlike object or string representing an existing file pve_label_registered_images : a list of items which are a pathlike object or string representing an existing file transformation_mat : a list of items which are a pathlike object or string representing an existing file

Normalization transformation.