interfaces.spm.preprocess¶
ApplyDeformations¶
Inputs:
[Mandatory]
deformation_field: (an existing file name)
in_files: (a list of items which are an existing, uncompressed file
(valid extensions: [.img, .nii, .hdr]))
reference_volume: (an existing, uncompressed file (valid extensions:
[.img, .nii, .hdr]))
[Optional]
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, nipype default value: True)
Run m-code using m-file
paths: (a list of items which are a directory name)
Paths to add to matlabpath
use_mcr: (a boolean)
Run m-code using SPM MCR
use_v8struct: (a boolean, nipype default value: True)
Generate SPM8 and higher compatible jobs
Outputs:
out_files: (a list of items which are an existing file name)
References:: None
Coregister¶
Use spm_coreg for estimating cross-modality rigid body alignment
http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=39
Examples¶
>>> import nipype.interfaces.spm as spm
>>> coreg = spm.Coregister()
>>> coreg.inputs.target = 'functional.nii'
>>> coreg.inputs.source = 'structural.nii'
>>> coreg.run()
Inputs:
[Mandatory]
source: (a list of items which are an existing, uncompressed file
(valid extensions: [.img, .nii, .hdr]))
file to register to target
target: (an existing, uncompressed file (valid extensions: [.img,
.nii, .hdr]))
reference file to register to
[Optional]
apply_to_files: (a list of items which are an existing file name)
files to apply transformation to
cost_function: ('mi' or 'nmi' or 'ecc' or 'ncc')
cost function, one of:
'mi' - Mutual Information,
'nmi' - Normalised Mutual Information,
'ecc' - Entropy Correlation Coefficient,
'ncc' - Normalised Cross Correlation
fwhm: (a list of from 2 to 2 items which are a float)
gaussian smoothing kernel width (mm)
jobtype: ('estwrite' or 'estimate' or 'write', nipype default value:
estwrite)
one of: estimate, write, estwrite
matlab_cmd: (a unicode string)
matlab command to use
mfile: (a boolean, nipype default value: True)
Run m-code using m-file
out_prefix: (a string, nipype default value: r)
coregistered output prefix
paths: (a list of items which are a directory name)
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, nipype default value: True)
Generate SPM8 and higher compatible jobs
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]
Outputs:
coregistered_files: (a list of items which are an existing file name)
Coregistered other files
coregistered_source: (a list of items which are an existing file
name)
Coregistered source files
References:: None
CreateWarped¶
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()
Inputs:
[Mandatory]
flowfield_files: (a list of items which are an existing, uncompressed
file (valid extensions: [.img, .nii, .hdr]))
DARTEL flow fields u_rc1*
image_files: (a list of items which are an existing, uncompressed
file (valid extensions: [.img, .nii, .hdr]))
A list of files to be warped
[Optional]
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, nipype default value: True)
Run m-code using m-file
modulate: (a boolean)
Modulate images
paths: (a list of items which are a directory name)
Paths to add to matlabpath
use_mcr: (a boolean)
Run m-code using SPM MCR
use_v8struct: (a boolean, nipype default value: True)
Generate SPM8 and higher compatible jobs
Outputs:
warped_files: (a list of items which are an existing file name)
References:: None
DARTEL¶
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()
Inputs:
[Mandatory]
image_files: (a list of items which are a list of items which are an
existing, uncompressed file (valid extensions: [.img, .nii, .hdr]))
A list of files to be segmented
[Optional]
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, nipype default value: True)
Run m-code using m-file
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 directory name)
Paths to add to matlabpath
regularization_form: ('Linear' or 'Membrane' or 'Bending')
Form of regularization energy term
template_prefix: (a unicode string, nipype default value: Template)
Prefix for template
use_mcr: (a boolean)
Run m-code using SPM MCR
use_v8struct: (a boolean, nipype default value: True)
Generate SPM8 and higher compatible jobs
Outputs:
dartel_flow_fields: (a list of items which are an existing file name)
DARTEL flow fields
final_template_file: (an existing file name)
final DARTEL template
template_files: (a list of items which are an existing file name)
Templates from different stages of iteration
References:: None
DARTELNorm2MNI¶
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()
Inputs:
[Mandatory]
apply_to_files: (a list of items which are an existing, uncompressed
file (valid extensions: [.img, .nii, .hdr]))
Files to apply the transform to
flowfield_files: (a list of items which are an existing, uncompressed
file (valid extensions: [.img, .nii, .hdr]))
DARTEL flow fields u_rc1*
template_file: (an existing, uncompressed file (valid extensions:
[.img, .nii, .hdr]))
DARTEL template
[Optional]
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, nipype default value: True)
Run m-code using m-file
modulate: (a boolean)
Modulate out images - no modulation preserves concentrations
paths: (a list of items which are a directory name)
Paths to add to matlabpath
use_mcr: (a boolean)
Run m-code using SPM MCR
use_v8struct: (a boolean, nipype default value: True)
Generate SPM8 and higher compatible jobs
voxel_size: (a tuple of the form: (a float, a float, a float))
Voxel sizes for output file
Outputs:
normalization_parameter_file: (an existing file name)
Transform parameters to MNI space
normalized_files: (a list of items which are an existing file name)
Normalized files in MNI space
References:: None
FieldMap¶
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
To do¶
Deal with real/imag magnitude images and 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()
Inputs:
[Mandatory]
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: (an existing file name)
EPI to unwarp
magnitude_file: (an existing file name)
presubstracted magnitude file
phase_file: (an existing file name)
presubstracted phase file
total_readout_time: (a float)
total EPI readout time
[Optional]
anat_file: (an existing file name)
anatomical image for comparison
epifm: (a boolean, nipype default value: False)
epi-based field map
jacobian_modulation: (a boolean, nipype default value: False)
jacobian modulation
jobtype: ('calculatevdm' or 'applyvdm', nipype default value:
calculatevdm)
one of: calculatevdm, applyvdm
mask_fwhm: (a long integer >= 0, nipype default value: 5)
gaussian smoothing kernel width
maskbrain: (a boolean, nipype default value: True)
masking or no masking of the brain
matchanat: (a boolean, nipype default value: True)
match anatomical image to EPI
matchvdm: (a boolean, nipype default value: True)
match VDM to EPI
matlab_cmd: (a unicode string)
matlab command to use
method: ('Mark3D' or 'Mark2D' or 'Huttonish', nipype default value:
Mark3D)
One of: Mark3D, Mark2D, Huttonish
mfile: (a boolean, nipype default value: True)
Run m-code using m-file
ndilate: (a long integer >= 0, nipype default value: 4)
number of erosions
nerode: (a long integer >= 0, nipype default value: 2)
number of erosions
pad: (a long integer >= 0, nipype default value: 0)
padding kernel width
paths: (a list of items which are a directory name)
Paths to add to matlabpath
reg: (a float, nipype default value: 0.02)
regularization value used in the segmentation
sessname: (a unicode string, nipype default value: _run-)
VDM filename extension
template: (an existing file name)
template image for brain masking
thresh: (a float, nipype default value: 0.5)
threshold used to create brain mask from segmented data
unwarp_fwhm: (a long integer >= 0, nipype default value: 10)
gaussian smoothing kernel width
use_mcr: (a boolean)
Run m-code using SPM MCR
use_v8struct: (a boolean, nipype default value: True)
Generate SPM8 and higher compatible jobs
writeunwarped: (a boolean, nipype default value: False)
write unwarped EPI
ws: (a boolean, nipype default value: True)
weighted smoothing
Outputs:
vdm: (an existing file name)
voxel difference map
References:: None
NewSegment¶
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()
Inputs:
[Mandatory]
channel_files: (a list of items which are an existing, uncompressed
file (valid extensions: [.img, .nii, .hdr]))
A list of files to be segmented
[Optional]
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 (Corrected, Field) - a tuple of two boolean
values
matlab_cmd: (a unicode string)
matlab command to use
mfile: (a boolean, nipype default value: True)
Run m-code using m-file
paths: (a list of items which are a directory name)
Paths to add to matlabpath
sampling_distance: (a float)
Sampling distance on data for parameter estimation
tissues: (a list of items which are a tuple of the form: (a tuple of
the form: (an existing, uncompressed file (valid extensions: [.img,
.nii, .hdr]), 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, nipype default value: True)
Generate SPM8 and higher compatible jobs
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 an existing file
name)
bias corrected images
bias_field_images: (a list of items which are an existing file name)
bias field images
dartel_input_images: (a list of items which are a list of items which
are an existing file name)
dartel imported class images
forward_deformation_field: (a list of items which are an existing
file name)
inverse_deformation_field: (a list of items which are an existing
file name)
modulated_class_images: (a list of items which are a list of items
which are an existing file name)
modulated+normalized class images
native_class_images: (a list of items which are a list of items which
are an existing file name)
native space probability maps
normalized_class_images: (a list of items which are a list of items
which are an existing file name)
normalized class images
transformation_mat: (a list of items which are an existing file name)
Normalization transformation
References:: None
Normalize¶
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()
Inputs:
[Mandatory]
parameter_file: (a file name)
normalization parameter file*_sn.mat
mutually_exclusive: source, template
source: (a list of items which are an existing, uncompressed file
(valid extensions: [.img, .nii, .hdr]))
file to normalize to template
mutually_exclusive: parameter_file
template: (an existing file name)
template file to normalize to
mutually_exclusive: parameter_file
[Optional]
DCT_period_cutoff: (a float)
Cutoff of for DCT bases
affine_regularization_type: ('mni' or 'size' or 'none')
mni, size, none
apply_to_files: (a list of items which are an existing file name or a
list of items which are an existing file name)
files to apply transformation to
jobtype: ('estwrite' or 'est' or 'write', nipype default value:
estwrite)
Estimate, Write or do both
matlab_cmd: (a unicode string)
matlab command to use
mfile: (a boolean, nipype default value: True)
Run m-code using m-file
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, nipype default value: w)
normalized output prefix
paths: (a list of items which are a directory name)
Paths to add to matlabpath
source_image_smoothing: (a float)
source smoothing
source_weight: (a file name)
name of weighting image for source
template_image_smoothing: (a float)
template smoothing
template_weight: (a file name)
name of weighting image for template
use_mcr: (a boolean)
Run m-code using SPM MCR
use_v8struct: (a boolean, nipype default value: True)
Generate SPM8 and higher compatible jobs
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
Outputs:
normalization_parameters: (a list of items which are an existing file
name)
MAT files containing the normalization parameters
normalized_files: (a list of items which are an existing file name)
Normalized other files
normalized_source: (a list of items which are an existing file name)
Normalized source files
References:: None
Normalize12¶
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()
Inputs:
[Mandatory]
deformation_file: (a uncompressed file (valid extensions: [.img,
.nii, .hdr]))
file y_*.nii containing 3 deformation fields for the deformation in
x, y and z dimension
mutually_exclusive: image_to_align, tpm
image_to_align: (an existing, uncompressed file (valid extensions:
[.img, .nii, .hdr]))
file to estimate normalization parameters with
mutually_exclusive: deformation_file
[Optional]
affine_regularization_type: ('mni' or 'size' or 'none')
mni, size, none
apply_to_files: (a list of items which are an existing, uncompressed
file (valid extensions: [.img, .nii, .hdr]) or a list of items
which are an existing, uncompressed file (valid extensions: [.img,
.nii, .hdr]))
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', nipype default value:
estwrite)
Estimate, Write or do Both
matlab_cmd: (a unicode string)
matlab command to use
mfile: (a boolean, nipype default value: True)
Run m-code using m-file
out_prefix: (a string, nipype default value: w)
Normalized output prefix
paths: (a list of items which are a directory name)
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: (an existing file name)
template in form of tissue probablitiy maps to normalize to
mutually_exclusive: deformation_file
use_mcr: (a boolean)
Run m-code using SPM MCR
use_v8struct: (a boolean, nipype default value: True)
Generate SPM8 and higher compatible jobs
warping_regularization: (a 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
Outputs:
deformation_field: (a list of items which are an existing file name)
NIfTI file containing 3 deformation fields for the deformation in x,
y and z dimension
normalized_files: (a list of items which are an existing file name)
Normalized other files
normalized_image: (a list of items which are an existing file name)
Normalized file that needed to be aligned
References:: None
Realign¶
Use spm_realign for estimating within modality rigid body alignment
http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=25
Examples¶
>>> import nipype.interfaces.spm as spm
>>> realign = spm.Realign()
>>> realign.inputs.in_files = 'functional.nii'
>>> realign.inputs.register_to_mean = True
>>> realign.run()
Inputs:
[Mandatory]
in_files: (a list of items which are an existing, uncompressed file
(valid extensions: [.img, .nii, .hdr]) or a list of items which are
an existing, uncompressed file (valid extensions: [.img, .nii,
.hdr]))
list of filenames to realign
[Optional]
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', nipype default value:
estwrite)
one of: estimate, write, estwrite
matlab_cmd: (a unicode string)
matlab command to use
mfile: (a boolean, nipype default value: True)
Run m-code using m-file
out_prefix: (a string, nipype default value: r)
realigned output prefix
paths: (a list of items which are a directory name)
Paths to add to matlabpath
quality: (0.0 <= a floating point number <= 1.0)
0.1 = fast, 1.0 = precise
register_to_mean: (a boolean)
Indicate whether realignment is done to the mean image
separation: (a floating point number >= 0.0)
sampling separation in mm
use_mcr: (a boolean)
Run m-code using SPM MCR
use_v8struct: (a boolean, nipype default value: True)
Generate SPM8 and higher compatible jobs
weight_img: (an existing file name)
filename of weighting image
wrap: (a list of from 3 to 3 items which are an integer (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',
nipype default value: [2, 1])
determines which images to reslice
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]
Outputs:
mean_image: (an existing file name)
Mean image file from the realignment
modified_in_files: (a list of items which are a list of items which
are an existing file name or an existing file name)
Copies of all files passed to in_files. Headers will have been
modified to align all images with the first, or optionally to first
do that, extract a mean image, and re-align to that mean image.
realigned_files: (a list of items which are a list of items which are
an existing file name or an existing file name)
If jobtype is write or estwrite, these will be the resliced files.
Otherwise, they will be copies of in_files that have had their
headers rewritten.
realignment_parameters: (a list of items which are an existing file
name)
Estimated translation and rotation parameters
References:: None
RealignUnwarp¶
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()
Inputs:
[Mandatory]
in_files: (a list of items which are an existing, uncompressed file
(valid extensions: [.img, .nii, .hdr]) or a list of items which are
an existing, uncompressed file (valid extensions: [.img, .nii,
.hdr]))
list of filenames to realign and unwarp
[Optional]
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', nipype default value: [5])
Number of iterations.
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',
nipype default value: [100000])
Regularisation factor. Default: 100000 (medium).
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, nipype default value: Average)
Point in position space to perform Taylor-expansion around.
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, nipype default value: True)
Run m-code using m-file
out_prefix: (a string, nipype default value: u)
realigned and unwarped output prefix
paths: (a list of items which are a directory name)
Paths to add to matlabpath
phase_map: (a file name)
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',
nipype default value: [2, 1])
determines which images to reslice
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, nipype default value: True)
Generate SPM8 and higher compatible jobs
weight_img: (an existing file name)
filename of weighting image
wrap: (a list of from 3 to 3 items which are an integer (int or
long))
Check if interpolation should wrap in [x,y,z]
Outputs:
mean_image: (an existing file name)
Mean image file from the realignment & unwarping
modified_in_files: (a list of items which are a list of items which
are an existing file name or an existing file name)
Copies of all files passed to in_files. Headers will have been
modified to align all images with the first, or optionally to first
do that, extract a mean image, and re-align to that mean image.
realigned_unwarped_files: (a list of items which are a list of items
which are an existing file name or an existing file name)
Realigned and unwarped files written to disc.
realignment_parameters: (a list of items which are an existing file
name)
Estimated translation and rotation parameters
References:: None
Segment¶
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()
Inputs:
[Mandatory]
data: (a list of items which are an existing, uncompressed file
(valid extensions: [.img, .nii, .hdr]))
one scan per subject
[Optional]
affine_regularization: ('mni' or 'eastern' or 'subj' or 'none' or '')
Possible options: "mni", "eastern", "subj", "none" (no
reguralisation), "" (no affine registration)
bias_fwhm: (30 or 40 or 50 or 60 or 70 or 80 or 90 or 100 or 110 or
120 or 130 or 'Inf')
FWHM of Gaussian smoothness of bias
bias_regularization: (0 or 1e-05 or 0.0001 or 0.001 or 0.01 or 0.1 or
1 or 10)
no(0) - extremely heavy (10)
clean_masks: ('no' or 'light' or 'thorough')
clean using estimated brain mask ('no','light','thorough')
csf_output_type: (a list of from 3 to 3 items which are a boolean)
Options to produce CSF images: c3*.img, wc3*.img and mwc3*.img.
None: [False,False,False],
Native Space: [False,False,True],
Unmodulated Normalised: [False,True,False],
Modulated Normalised: [True,False,False],
Native + Unmodulated Normalised: [False,True,True],
Native + Modulated Normalised: [True,False,True],
Native + Modulated + Unmodulated: [True,True,True],
Modulated + Unmodulated Normalised: [True,True,False]
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: (an existing file name)
Binary image to restrict parameter estimation
matlab_cmd: (a unicode string)
matlab command to use
mfile: (a boolean, nipype default value: True)
Run m-code using m-file
paths: (a list of items which are a directory name)
Paths to add to matlabpath
sampling_distance: (a float)
Sampling distance on data for parameter estimation
save_bias_corrected: (a boolean)
True/False produce a bias corrected image
tissue_prob_maps: (a list of items which are an existing file name)
list of gray, white & csf prob. (opt,)
use_mcr: (a boolean)
Run m-code using SPM MCR
use_v8struct: (a boolean, nipype default value: True)
Generate SPM8 and higher compatible jobs
warp_frequency_cutoff: (a float)
Cutoff of DCT bases
warping_regularization: (a float)
Controls balance between parameters and data
wm_output_type: (a list of from 3 to 3 items which are a boolean)
Options to produce white matter images: c2*.img, wc2*.img and
mwc2*.img.
None: [False,False,False],
Native Space: [False,False,True],
Unmodulated Normalised: [False,True,False],
Modulated Normalised: [True,False,False],
Native + Unmodulated Normalised: [False,True,True],
Native + Modulated Normalised: [True,False,True],
Native + Modulated + Unmodulated: [True,True,True],
Modulated + Unmodulated Normalised: [True,True,False]
Outputs:
bias_corrected_image: (a file name)
bias-corrected version of input image
inverse_transformation_mat: (an existing file name)
Inverse normalization info
modulated_csf_image: (a file name)
modulated, normalized csf probability map
modulated_gm_image: (a file name)
modulated, normalized grey probability map
modulated_input_image: (a file name)
bias-corrected version of input image
modulated_wm_image: (a file name)
modulated, normalized white probability map
native_csf_image: (a file name)
native space csf probability map
native_gm_image: (a file name)
native space grey probability map
native_wm_image: (a file name)
native space white probability map
normalized_csf_image: (a file name)
normalized csf probability map
normalized_gm_image: (a file name)
normalized grey probability map
normalized_wm_image: (a file name)
normalized white probability map
transformation_mat: (an existing file name)
Normalization transformation
References:: None
SliceTiming¶
Use spm to perform slice timing correction.
http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=19
Examples¶
>>> from nipype.interfaces.spm import SliceTiming
>>> st = SliceTiming()
>>> st.inputs.in_files = 'functional.nii'
>>> st.inputs.num_slices = 32
>>> st.inputs.time_repetition = 6.0
>>> st.inputs.time_acquisition = 6. - 6./32.
>>> st.inputs.slice_order = list(range(32,0,-1))
>>> st.inputs.ref_slice = 1
>>> st.run()
Inputs:
[Mandatory]
in_files: (a list of items which are a list of items which are an
existing, uncompressed file (valid extensions: [.img, .nii, .hdr])
or an existing, uncompressed file (valid extensions: [.img, .nii,
.hdr]))
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))
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 a float)
1-based order or onset (in ms) in which slices are acquired
time_acquisition: (a float)
time of volume acquisition. usuallycalculated as TR-(TR/num_slices)
time_repetition: (a float)
time between volume acquisitions(start to start time)
[Optional]
matlab_cmd: (a unicode string)
matlab command to use
mfile: (a boolean, nipype default value: True)
Run m-code using m-file
out_prefix: (a string, nipype default value: a)
slicetimed output prefix
paths: (a list of items which are a directory name)
Paths to add to matlabpath
use_mcr: (a boolean)
Run m-code using SPM MCR
use_v8struct: (a boolean, nipype default value: True)
Generate SPM8 and higher compatible jobs
Outputs:
timecorrected_files: (a list of items which are a list of items which
are an existing file name or an existing file name)
slice time corrected files
References:: None
Smooth¶
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()
Inputs:
[Mandatory]
in_files: (a list of items which are an existing, uncompressed file
(valid extensions: [.img, .nii, .hdr]))
list of files to smooth
[Optional]
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 particularvoxel value
matlab_cmd: (a unicode string)
matlab command to use
mfile: (a boolean, nipype default value: True)
Run m-code using m-file
out_prefix: (a string, nipype default value: s)
smoothed output prefix
paths: (a list of items which are a directory name)
Paths to add to matlabpath
use_mcr: (a boolean)
Run m-code using SPM MCR
use_v8struct: (a boolean, nipype default value: True)
Generate SPM8 and higher compatible jobs
Outputs:
smoothed_files: (a list of items which are an existing file name)
smoothed files
References:: None
VBMSegment¶
Use VBM8 toolbox to separate structural images into different tissue classes.
Example¶
>>> import nipype.interfaces.spm as spm
>>> seg = spm.VBMSegment()
>>> seg.inputs.tissues = 'TPM.nii'
>>> seg.inputs.dartel_template = 'Template_1_IXI550_MNI152.nii'
>>> seg.inputs.bias_corrected_native = True
>>> seg.inputs.gm_native = True
>>> seg.inputs.wm_native = True
>>> seg.inputs.csf_native = True
>>> seg.inputs.pve_label_native = True
>>> seg.inputs.deformation_field = (True, False)
>>> seg.run()
Inputs:
[Mandatory]
in_files: (a list of items which are an existing, uncompressed file
(valid extensions: [.img, .nii, .hdr]))
A list of files to be segmented
[Optional]
bias_corrected_affine: (a boolean, nipype default value: False)
bias_corrected_native: (a boolean, nipype default value: False)
bias_corrected_normalized: (a boolean, nipype default value: True)
bias_fwhm: (30 or 40 or 50 or 60 or 70 or 80 or 90 or 100 or 110 or
120 or 130 or 'Inf', nipype default value: 60)
FWHM of Gaussian smoothness of bias
bias_regularization: (0 or 1e-05 or 0.0001 or 0.001 or 0.01 or 0.1 or
1 or 10, nipype default value: 0.0001)
no(0) - extremely heavy (10)
cleanup_partitions: (an integer (int or long), nipype default value:
1)
0=None,1=light,2=thorough
csf_dartel: (0 <= a long integer <= 2, nipype default value: 0)
0=None,1=rigid(SPM8 default),2=affine
csf_modulated_normalized: (0 <= a long integer <= 2, nipype default
value: 2)
0=none,1=affine+non-linear(SPM8 default),2=non-linear only
csf_native: (a boolean, nipype default value: False)
csf_normalized: (a boolean, nipype default value: False)
dartel_template: (an existing, uncompressed file (valid extensions:
[.img, .nii, .hdr]))
deformation_field: (a tuple of the form: (a boolean, a boolean),
nipype default value: (0, 0))
forward and inverse field
display_results: (a boolean, nipype default value: True)
gaussians_per_class: (a tuple of the form: (an integer (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)),
nipype default value: (2, 2, 2, 3, 4, 2))
number of gaussians for each tissue class
gm_dartel: (0 <= a long integer <= 2, nipype default value: 0)
0=None,1=rigid(SPM8 default),2=affine
gm_modulated_normalized: (0 <= a long integer <= 2, nipype default
value: 2)
0=none,1=affine+non-linear(SPM8 default),2=non-linear only
gm_native: (a boolean, nipype default value: False)
gm_normalized: (a boolean, nipype default value: False)
jacobian_determinant: (a boolean, nipype default value: False)
matlab_cmd: (a unicode string)
matlab command to use
mfile: (a boolean, nipype default value: True)
Run m-code using m-file
mrf_weighting: (a float, nipype default value: 0.15)
paths: (a list of items which are a directory name)
Paths to add to matlabpath
pve_label_dartel: (0 <= a long integer <= 2, nipype default value: 0)
0=None,1=rigid(SPM8 default),2=affine
pve_label_native: (a boolean, nipype default value: False)
pve_label_normalized: (a boolean, nipype default value: False)
sampling_distance: (a float, nipype default value: 3)
Sampling distance on data for parameter estimation
spatial_normalization: ('high' or 'low', nipype default value: high)
tissues: (an existing, uncompressed file (valid extensions: [.img,
.nii, .hdr]))
tissue probability map
use_mcr: (a boolean)
Run m-code using SPM MCR
use_sanlm_denoising_filter: (0 <= a long integer <= 2, nipype default
value: 2)
0=No denoising, 1=denoising,2=denoising multi-threaded
use_v8struct: (a boolean, nipype default value: True)
Generate SPM8 and higher compatible jobs
warping_regularization: (a float, nipype default value: 4)
Controls balance between parameters and data
wm_dartel: (0 <= a long integer <= 2, nipype default value: 0)
0=None,1=rigid(SPM8 default),2=affine
wm_modulated_normalized: (0 <= a long integer <= 2, nipype default
value: 2)
0=none,1=affine+non-linear(SPM8 default),2=non-linear only
wm_native: (a boolean, nipype default value: False)
wm_normalized: (a boolean, nipype default value: False)
Outputs:
bias_corrected_images: (a list of items which are an existing file
name)
bias corrected images
dartel_input_images: (a list of items which are a list of items which
are an existing file name)
dartel imported class images
forward_deformation_field: (a list of items which are an existing
file name)
inverse_deformation_field: (a list of items which are an existing
file name)
jacobian_determinant_images: (a list of items which are an existing
file name)
modulated_class_images: (a list of items which are a list of items
which are an existing file name)
modulated+normalized class images
native_class_images: (a list of items which are a list of items which
are an existing file name)
native space probability maps
normalized_bias_corrected_images: (a list of items which are an
existing file name)
bias corrected images
normalized_class_images: (a list of items which are a list of items
which are an existing file name)
normalized class images
pve_label_native_images: (a list of items which are an existing file
name)
pve_label_normalized_images: (a list of items which are an existing
file name)
pve_label_registered_images: (a list of items which are an existing
file name)
transformation_mat: (a list of items which are an existing file name)
Normalization transformation
References:: None