interfaces.spm.preprocess¶
ApplyDeformations¶
Inputs:
[Mandatory]
in_files: (a list of items which are an existing, uncompressed file
(valid extensions: [.hdr, .nii, .img]))
deformation_field: (an existing file name)
reference_volume: (an existing, uncompressed file (valid extensions:
[.hdr, .nii, .img]))
[Optional]
interp: (0 <= a long integer <= 7)
degree of b-spline used for interpolation
matlab_cmd: (a unicode string)
matlab command to use
paths: (a list of items which are a directory name)
Paths to add to matlabpath
mfile: (a boolean, nipype default value: True)
Run m-code using m-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
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]
target: (an existing, uncompressed file (valid extensions: [.hdr,
.nii, .img]))
reference file to register to
source: (a list of items which are an existing, uncompressed file
(valid extensions: [.hdr, .nii, .img]))
file to register to target
[Optional]
jobtype: ('estwrite' or 'estimate' or 'write', nipype default value:
estwrite)
one of: estimate, write, estwrite
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)
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
write_interp: (0 <= a long integer <= 7)
degree of b-spline used for interpolation
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]
write_mask: (a boolean)
True/False mask output image
out_prefix: (a string, nipype default value: r)
coregistered output prefix
matlab_cmd: (a unicode string)
matlab command to use
paths: (a list of items which are a directory name)
Paths to add to matlabpath
mfile: (a boolean, nipype default value: True)
Run m-code using m-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
Outputs:
coregistered_source: (a list of items which are an existing file
name)
Coregistered source files
coregistered_files: (a list of items which are an existing file name)
Coregistered other 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]
image_files: (a list of items which are an existing, uncompressed
file (valid extensions: [.hdr, .nii, .img]))
A list of files to be warped
flowfield_files: (a list of items which are an existing, uncompressed
file (valid extensions: [.hdr, .nii, .img]))
DARTEL flow fields u_rc1*
[Optional]
iterations: (0 <= a long integer <= 9)
The number of iterations: log2(number of time steps)
interp: (0 <= a long integer <= 7)
degree of b-spline used for interpolation
modulate: (a boolean)
Modulate images
matlab_cmd: (a unicode string)
matlab command to use
paths: (a list of items which are a directory name)
Paths to add to matlabpath
mfile: (a boolean, nipype default value: True)
Run m-code using m-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
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: [.hdr, .nii,
.img]))
A list of files to be segmented
[Optional]
template_prefix: (a unicode string, nipype default value: Template)
Prefix for template
regularization_form: ('Linear' or 'Membrane' or 'Bending')
Form of regularization energy term
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
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
matlab_cmd: (a unicode string)
matlab command to use
paths: (a list of items which are a directory name)
Paths to add to matlabpath
mfile: (a boolean, nipype default value: True)
Run m-code using m-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
Outputs:
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
dartel_flow_fields: (a list of items which are an existing file name)
DARTEL flow fields
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]
template_file: (an existing, uncompressed file (valid extensions:
[.hdr, .nii, .img]))
DARTEL template
flowfield_files: (a list of items which are an existing, uncompressed
file (valid extensions: [.hdr, .nii, .img]))
DARTEL flow fields u_rc1*
apply_to_files: (a list of items which are an existing, uncompressed
file (valid extensions: [.hdr, .nii, .img]))
Files to apply the transform to
[Optional]
voxel_size: (a tuple of the form: (a float, a float, a float))
Voxel sizes for output file
bounding_box: (a tuple of the form: (a float, a float, a float, a
float, a float, a float))
Voxel sizes for output file
modulate: (a boolean)
Modulate out images - no modulation preserves concentrations
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
paths: (a list of items which are a directory name)
Paths to add to matlabpath
mfile: (a boolean, nipype default value: True)
Run m-code using m-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
Outputs:
normalized_files: (a list of items which are an existing file name)
Normalized files in MNI space
normalization_parameter_file: (an existing file name)
Transform parameters to 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]
phase_file: (an existing file name)
presubstracted phase file
magnitude_file: (an existing file name)
presubstracted magnitude file
echo_times: (a tuple of the form: (a float, a float))
short and long echo times
blip_direction: (1 or -1)
polarity of the phase-encode blips
total_readout_time: (a float)
total EPI readout time
epi_file: (an existing file name)
EPI to unwarp
[Optional]
jobtype: ('calculatevdm' or 'applyvdm', nipype default value:
calculatevdm)
one of: calculatevdm, applyvdm
maskbrain: (a boolean, nipype default value: True)
masking or no masking of the brain
epifm: (a boolean, nipype default value: False)
epi-based field map
jacobian_modulation: (a boolean, nipype default value: False)
jacobian modulation
method: ('Mark3D' or 'Mark2D' or 'Huttonish', nipype default value:
Mark3D)
One of: Mark3D, Mark2D, Huttonish
unwarp_fwhm: (a long integer >= 0, nipype default value: 10)
gaussian smoothing kernel width
pad: (a long integer >= 0, nipype default value: 0)
padding kernel width
ws: (a boolean, nipype default value: True)
weighted smoothing
template: (an existing file name)
template image for brain masking
mask_fwhm: (a long integer >= 0, nipype default value: 5)
gaussian smoothing kernel width
nerode: (a long integer >= 0, nipype default value: 2)
number of erosions
ndilate: (a long integer >= 0, nipype default value: 4)
number of erosions
thresh: (a float, nipype default value: 0.5)
threshold used to create brain mask from segmented data
reg: (a float, nipype default value: 0.02)
regularization value used in the segmentation
matchvdm: (a boolean, nipype default value: True)
match VDM to EPI
sessname: (a unicode string, nipype default value: _run-)
VDM filename extension
writeunwarped: (a boolean, nipype default value: False)
write unwarped EPI
anat_file: (an existing file name)
anatomical image for comparison
matchanat: (a boolean, nipype default value: True)
match anatomical image to EPI
matlab_cmd: (a unicode string)
matlab command to use
paths: (a list of items which are a directory name)
Paths to add to matlabpath
mfile: (a boolean, nipype default value: True)
Run m-code using m-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
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: [.hdr, .nii, .img]))
A list of files to be segmented
[Optional]
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
tissues: (a list of items which are a tuple of the form: (a tuple of
the form: (an existing, uncompressed file (valid extensions:
[.hdr, .nii, .img]), 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
affine_regularization: ('mni' or 'eastern' or 'subj' or 'none')
mni, eastern, subj, none
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)
sampling_distance: (a float)
Sampling distance on data for parameter estimation
write_deformation_fields: (a list of from 2 to 2 items which are a
boolean)
Which deformation fields to write:[Inverse, Forward]
matlab_cmd: (a unicode string)
matlab command to use
paths: (a list of items which are a directory name)
Paths to add to matlabpath
mfile: (a boolean, nipype default value: True)
Run m-code using m-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
Outputs:
native_class_images: (a list of items which are a list of items which
are an existing file name)
native space probability maps
dartel_input_images: (a list of items which are a list of items which
are an existing file name)
dartel imported class images
normalized_class_images: (a list of items which are a list of items
which are an existing file name)
normalized class images
modulated_class_images: (a list of items which are a list of items
which are an existing file name)
modulated+normalized class images
transformation_mat: (a list of items which are an existing file name)
Normalization transformation
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
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)
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]
template: (an existing file name)
template file to normalize to
mutually_exclusive: parameter_file
source: (a list of items which are an existing, uncompressed file
(valid extensions: [.hdr, .nii, .img]))
file to normalize to template
mutually_exclusive: parameter_file
parameter_file: (a file name)
normalization parameter file*_sn.mat
mutually_exclusive: source, template
[Optional]
jobtype: ('estwrite' or 'est' or 'write', nipype default value:
estwrite)
Estimate, Write or do both
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
source_weight: (a file name)
name of weighting image for source
template_weight: (a file name)
name of weighting image for template
source_image_smoothing: (a float)
source smoothing
template_image_smoothing: (a float)
template smoothing
affine_regularization_type: ('mni' or 'size' or 'none')
mni, size, none
DCT_period_cutoff: (a float)
Cutoff of for DCT bases
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
write_preserve: (a boolean)
True/False warped images are modulated
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_voxel_sizes: (a list of from 3 to 3 items which are a float)
3-element list
write_interp: (0 <= a long integer <= 7)
degree of b-spline used for interpolation
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
out_prefix: (a string, nipype default value: w)
normalized output prefix
matlab_cmd: (a unicode string)
matlab command to use
paths: (a list of items which are a directory name)
Paths to add to matlabpath
mfile: (a boolean, nipype default value: True)
Run m-code using m-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
Outputs:
normalization_parameters: (a list of items which are an existing file
name)
MAT files containing the normalization parameters
normalized_source: (a list of items which are an existing file name)
Normalized source files
normalized_files: (a list of items which are an existing file name)
Normalized other 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]
image_to_align: (an existing, uncompressed file (valid extensions:
[.hdr, .nii, .img]))
file to estimate normalization parameters with
mutually_exclusive: deformation_file
deformation_file: (a uncompressed file (valid extensions: [.hdr,
.nii, .img]))
file y_*.nii containing 3 deformation fields for the deformation in
x, y and z dimension
mutually_exclusive: image_to_align, tpm
[Optional]
apply_to_files: (a list of items which are an existing, uncompressed
file (valid extensions: [.hdr, .nii, .img]) or a list of items
which are an existing, uncompressed file (valid extensions: [.hdr,
.nii, .img]))
files to apply transformation to
jobtype: ('estwrite' or 'est' or 'write', nipype default value:
estwrite)
Estimate, Write or do Both
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)
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
tpm: (an existing file name)
template in form of tissue probablitiy maps to normalize to
mutually_exclusive: deformation_file
affine_regularization_type: ('mni' or 'size' or 'none')
mni, size, none
warping_regularization: (a list of from 5 to 5 items which are a
float)
controls balance between parameters and data
smoothness: (a float)
value (in mm) to smooth the data before normalization
sampling_distance: (a float)
Sampling distance on data for parameter estimation
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_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
write_interp: (0 <= a long integer <= 7)
degree of b-spline used for interpolation
out_prefix: (a string, nipype default value: w)
Normalized output prefix
matlab_cmd: (a unicode string)
matlab command to use
paths: (a list of items which are a directory name)
Paths to add to matlabpath
mfile: (a boolean, nipype default value: True)
Run m-code using m-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
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_image: (a list of items which are an existing file name)
Normalized file that needed to be aligned
normalized_files: (a list of items which are an existing file name)
Normalized other files
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: [.hdr, .nii, .img]) or a list of items which
are an existing, uncompressed file (valid extensions: [.hdr, .nii,
.img]))
list of filenames to realign
[Optional]
jobtype: ('estwrite' or 'estimate' or 'write', nipype default value:
estwrite)
one of: estimate, write, estwrite
quality: (0.0 <= a floating point number <= 1.0)
0.1 = fast, 1.0 = precise
fwhm: (a floating point number >= 0.0)
gaussian smoothing kernel width
separation: (a floating point number >= 0.0)
sampling separation in mm
register_to_mean: (a boolean)
Indicate whether realignment is done to the mean image
weight_img: (an existing file name)
filename of weighting image
interp: (0 <= a long integer <= 7)
degree of b-spline used for interpolation
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_which: (a list of items which are a value of class 'int',
nipype default value: [2, 1])
determines which images to reslice
write_interp: (0 <= a long integer <= 7)
degree of b-spline used for interpolation
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]
write_mask: (a boolean)
True/False mask output image
out_prefix: (a string, nipype default value: r)
realigned output prefix
matlab_cmd: (a unicode string)
matlab command to use
paths: (a list of items which are a directory name)
Paths to add to matlabpath
mfile: (a boolean, nipype default value: True)
Run m-code using m-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
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: [.hdr, .nii, .img]) or a list of items which
are an existing, uncompressed file (valid extensions: [.hdr, .nii,
.img]))
list of filenames to realign and unwarp
[Optional]
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
fwhm: (a floating point number >= 0.0)
gaussian smoothing kernel width
separation: (a floating point number >= 0.0)
sampling separation in mm
register_to_mean: (a boolean)
Indicate whether realignment is done to the mean image
weight_img: (an existing file name)
filename of weighting image
interp: (0 <= a long integer <= 7)
degree of b-spline used for interpolation
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]
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_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_reg_factor: (a list of items which are a value of class 'int',
nipype default value: [100000])
Regularisation factor. Default: 100000 (medium).
est_jacobian_deformations: (a boolean)
Jacobian deformations. In theory a good idea to include them, in
practice a bad idea. Default: No.
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_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_unwarp_fwhm: (a floating point number >= 0.0)
gaussian smoothing kernel width for unwarp
est_re_est_mov_par: (a boolean)
Re-estimate movement parameters at each unwarping iteration.
est_num_of_iterations: (a list of items which are a value of class
'int', nipype default value: [5])
Number of iterations.
est_taylor_expansion_point: (a string, nipype default value: Average)
Point in position space to perform Taylor-expansion around.
reslice_which: (a list of items which are a value of class 'int',
nipype default value: [2, 1])
determines which images to reslice
reslice_interp: (0 <= a long integer <= 7)
degree of b-spline used for interpolation
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]
reslice_mask: (a boolean)
True/False mask output image
out_prefix: (a string, nipype default value: u)
realigned and unwarped output prefix
matlab_cmd: (a unicode string)
matlab command to use
paths: (a list of items which are a directory name)
Paths to add to matlabpath
mfile: (a boolean, nipype default value: True)
Run m-code using m-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
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: [.hdr, .nii, .img]))
one scan per subject
[Optional]
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]
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]
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]
save_bias_corrected: (a boolean)
True/False produce a bias corrected image
clean_masks: ('no' or 'light' or 'thorough')
clean using estimated brain mask ('no','light','thorough')
tissue_prob_maps: (a list of items which are an existing file name)
list of gray, white & csf prob. (opt,)
gaussians_per_class: (a list of items which are an integer (int or
long))
num Gaussians capture intensity distribution
affine_regularization: ('mni' or 'eastern' or 'subj' or 'none' or '')
Possible options: "mni", "eastern", "subj", "none" (no
reguralisation), "" (no affine registration)
warping_regularization: (a float)
Controls balance between parameters and data
warp_frequency_cutoff: (a float)
Cutoff of DCT bases
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)
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
sampling_distance: (a float)
Sampling distance on data for parameter estimation
mask_image: (an existing file name)
Binary image to restrict parameter estimation
matlab_cmd: (a unicode string)
matlab command to use
paths: (a list of items which are a directory name)
Paths to add to matlabpath
mfile: (a boolean, nipype default value: True)
Run m-code using m-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
Outputs:
native_gm_image: (a file name)
native space grey probability map
normalized_gm_image: (a file name)
normalized grey probability map
modulated_gm_image: (a file name)
modulated, normalized grey probability map
native_wm_image: (a file name)
native space white probability map
normalized_wm_image: (a file name)
normalized white probability map
modulated_wm_image: (a file name)
modulated, normalized white probability map
native_csf_image: (a file name)
native space csf probability map
normalized_csf_image: (a file name)
normalized csf probability map
modulated_csf_image: (a file name)
modulated, normalized csf probability map
modulated_input_image: (a file name)
bias-corrected version of input image
bias_corrected_image: (a file name)
bias-corrected version of input image
transformation_mat: (an existing file name)
Normalization transformation
inverse_transformation_mat: (an existing file name)
Inverse normalization info
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: [.hdr, .nii, .img])
or an existing, uncompressed file (valid extensions: [.hdr, .nii,
.img]))
list of filenames to apply slice timing
num_slices: (an integer (int or long))
number of slices in a volume
time_repetition: (a float)
time between volume acquisitions(start to start time)
time_acquisition: (a float)
time of volume acquisition. usuallycalculated as TR-(TR/num_slices)
slice_order: (a list of items which are a float)
1-based order or onset (in ms) in which slices are acquired
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)
[Optional]
out_prefix: (a string, nipype default value: a)
slicetimed output prefix
matlab_cmd: (a unicode string)
matlab command to use
paths: (a list of items which are a directory name)
Paths to add to matlabpath
mfile: (a boolean, nipype default value: True)
Run m-code using m-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
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: [.hdr, .nii, .img]))
list of files to smooth
[Optional]
fwhm: (a list of from 3 to 3 items which are a float or a float)
3-list of fwhm for each dimension
data_type: (an integer (int or long))
Data type of the output images
implicit_masking: (a boolean)
A mask implied by a particularvoxel value
out_prefix: (a string, nipype default value: s)
smoothed output prefix
matlab_cmd: (a unicode string)
matlab command to use
paths: (a list of items which are a directory name)
Paths to add to matlabpath
mfile: (a boolean, nipype default value: True)
Run m-code using m-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
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: [.hdr, .nii, .img]))
A list of files to be segmented
[Optional]
tissues: (an existing, uncompressed file (valid extensions: [.hdr,
.nii, .img]))
tissue probability map
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
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)
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
sampling_distance: (a float, nipype default value: 3)
Sampling distance on data for parameter estimation
warping_regularization: (a float, nipype default value: 4)
Controls balance between parameters and data
spatial_normalization: ('high' or 'low', nipype default value: high)
dartel_template: (an existing, uncompressed file (valid extensions:
[.hdr, .nii, .img]))
use_sanlm_denoising_filter: (0 <= a long integer <= 2, nipype default
value: 2)
0=No denoising, 1=denoising,2=denoising multi-threaded
mrf_weighting: (a float, nipype default value: 0.15)
cleanup_partitions: (an integer (int or long), nipype default value:
1)
0=None,1=light,2=thorough
display_results: (a boolean, nipype default value: True)
gm_native: (a boolean, nipype default value: False)
gm_normalized: (a boolean, nipype default value: False)
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_dartel: (0 <= a long integer <= 2, nipype default value: 0)
0=None,1=rigid(SPM8 default),2=affine
wm_native: (a boolean, nipype default value: False)
wm_normalized: (a boolean, nipype default value: False)
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_dartel: (0 <= a long integer <= 2, nipype default value: 0)
0=None,1=rigid(SPM8 default),2=affine
csf_native: (a boolean, nipype default value: False)
csf_normalized: (a boolean, nipype default value: False)
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_dartel: (0 <= a long integer <= 2, nipype default value: 0)
0=None,1=rigid(SPM8 default),2=affine
bias_corrected_native: (a boolean, nipype default value: False)
bias_corrected_normalized: (a boolean, nipype default value: True)
bias_corrected_affine: (a boolean, nipype default value: False)
pve_label_native: (a boolean, nipype default value: False)
pve_label_normalized: (a boolean, nipype default value: False)
pve_label_dartel: (0 <= a long integer <= 2, nipype default value: 0)
0=None,1=rigid(SPM8 default),2=affine
jacobian_determinant: (a boolean, nipype default value: False)
deformation_field: (a tuple of the form: (a boolean, a boolean),
nipype default value: (0, 0))
forward and inverse field
matlab_cmd: (a unicode string)
matlab command to use
paths: (a list of items which are a directory name)
Paths to add to matlabpath
mfile: (a boolean, nipype default value: True)
Run m-code using m-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
Outputs:
native_class_images: (a list of items which are a list of items which
are an existing file name)
native space probability maps
dartel_input_images: (a list of items which are a list of items which
are an existing file name)
dartel imported class images
normalized_class_images: (a list of items which are a list of items
which are an existing file name)
normalized class images
modulated_class_images: (a list of items which are a list of items
which are an existing file name)
modulated+normalized class images
transformation_mat: (a list of items which are an existing file name)
Normalization transformation
bias_corrected_images: (a list of items which are an existing file
name)
bias corrected images
normalized_bias_corrected_images: (a list of items which are an
existing file name)
bias corrected 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)
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)
References:¶
None