nipype.interfaces.spm.preprocess module

SPM wrappers for preprocessing data

ApplyDeformations

Link to code

Bases: SPMCommand

Mandatory Inputs:

  • 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)

Optional Inputs:

  • interp (0 <= an integer <= 7) – Degree of b-spline used for interpolation.

  • matlab_cmd (a 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)

Outputs:

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

ApplyVDM

Link to code

Bases: SPMCommand

Use the fieldmap toolbox from spm to apply the voxel displacement map (VDM) to some epi files.

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.

Mandatory Inputs:

  • in_files (a list of items which are a pathlike object or string representing an existing file) – List of filenames to apply the vdm to.

  • vdmfile (a pathlike object or string representing a file) – Voxel displacement map to use.

Optional Inputs:

  • distortion_direction (an integer) – Phase encode direction input data have been acquired with. (Nipype default value: 2)

  • interpolation (0 <= an integer <= 7) – Degree of b-spline used for interpolation.

  • matlab_cmd (a string) – Matlab command to use.

  • mfile (a boolean) – Run m-code using m-file. (Nipype default value: True)

  • out_prefix (a string) – Fieldmap corrected 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.

  • 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_mask (a boolean) – True/False mask time series images.

  • write_which (a list of items which are a value of class ‘int’) – If the first value is non-zero, reslice all images. If the second value is non-zero, reslice a mean image. (Nipype default value: [2, 1])

  • 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 (a pathlike object or string representing an existing file) – Mean image.

  • out_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) – These will be the fieldmap corrected files.

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()
Mandatory Inputs:

  • 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.

Optional Inputs:

  • 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 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 <= 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 (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()
Mandatory Inputs:

  • 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.

Optional Inputs:

  • 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) – 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)

Outputs:

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()
Mandatory Inputs:

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.

Optional Inputs:

  • 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) – Run m-code using m-file. (Nipype default value: True)

  • 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 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 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)

Outputs:

  • 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()
Mandatory Inputs:

  • 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.

Optional Inputs:

  • 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 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.

Outputs:

  • 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()
Mandatory Inputs:

  • 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.

Optional Inputs:

  • 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’) – Must be ‘calculatevdm’; to apply VDM, use the ApplyVDM interface. (Nipype default value: calculatevdm)

  • mask_fwhm (an 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 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 (an integer >= 0) – Number of erosions. (Nipype default value: 4)

  • nerode (an integer >= 0) – Number of erosions. (Nipype default value: 2)

  • pad (an 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 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 (an 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)

Outputs:

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()

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()
Optional Inputs:

  • 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 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), 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 [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].

Outputs:

  • 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()

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()
Mandatory Inputs:

channel_files (a list of items which are a pathlike object or string representing an existing file) – A list of files to be segmented.

Optional Inputs:

  • 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 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), 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 [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].

Outputs:

  • 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()
Mandatory Inputs:

  • 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.

Optional Inputs:

  • 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 string) – Matlab command to use.

  • mfile (a boolean) – Run m-code using m-file. (Nipype default value: True)

  • nonlinear_iterations (an integer) – 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 <= an 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) – Check if interpolation should wrap in [x,y,z] - list of bools.

Outputs:

  • 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()
Mandatory Inputs:

  • 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.

Optional Inputs:

  • 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 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 <= an 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.

Outputs:

  • 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()
Mandatory Inputs:

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.

Optional Inputs:

  • 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. (Nipype default value: estwrite)

  • matlab_cmd (a 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) – 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 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) – Check if interpolation should wrap in [x,y,z].

Outputs:

  • 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()
Mandatory Inputs:

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.

Optional Inputs:

  • est_basis_func (a list of from 2 to 2 items which are an integer) – 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) – 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 <= an 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) – 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 <= an integer <= 7) – Degree of b-spline used for interpolation.

  • matlab_cmd (a 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 <= an 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) – 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) – Check if interpolation should wrap in [x,y,z].

Outputs:

  • 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()
Mandatory Inputs:

data (a list of items which are a pathlike object or string representing an existing file) – One scan per subject.

Optional Inputs:

  • 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) – 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 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].

Outputs:

  • 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()
Mandatory Inputs:

  • 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) – Number of slices in a volume.

  • ref_slice (an integer 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 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).

Optional Inputs:

  • matlab_cmd (a 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)

Outputs:

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()
Mandatory Inputs:

in_files (a list of items which are a pathlike object or string representing an existing file) – List of files to smooth.

Optional Inputs:

  • data_type (an integer) – 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 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)

Outputs:

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()
Mandatory Inputs:

in_files (a list of items which are a pathlike object or string representing an existing file) – A list of files to be segmented.

Optional Inputs:

  • 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) – 0=None,1=light,2=thorough. (Nipype default value: 1)

  • csf_dartel (0 <= an integer <= 2) – 0=None,1=rigid(SPM8 default),2=affine. (Nipype default value: 0)

  • csf_modulated_normalized (0 <= an 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, an integer, an integer, an integer, an integer, an integer)) – Number of gaussians for each tissue class. (Nipype default value: (2, 2, 2, 3, 4, 2))

  • gm_dartel (0 <= an integer <= 2) – 0=None,1=rigid(SPM8 default),2=affine. (Nipype default value: 0)

  • gm_modulated_normalized (0 <= an 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 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 <= an 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 <= an 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 <= an integer <= 2) – 0=None,1=rigid(SPM8 default),2=affine. (Nipype default value: 0)

  • wm_modulated_normalized (0 <= an 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)

Outputs:

  • 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.