Inputs:
[Mandatory]
deformation_field: (an existing file name)
in_files: (an existing file name)
reference_volume: (an existing file name)
[Optional]
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
interp: (0 <= an integer <= 7)
degree of b-spline used for interpolation
matlab_cmd: (a string)
matlab command to use
mfile: (a boolean, nipype default value: True)
Run m-code using m-file
paths: (a directory name)
Paths to add to matlabpath
use_mcr: (a boolean)
Run m-code using SPM MCR
Outputs:
out_files: (an existing file name)
Use spm_coreg for estimating cross-modality rigid body alignment
http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=39
>>> import nipype.interfaces.spm as spm
>>> coreg = spm.Coregister()
>>> coreg.inputs.target = 'functional.nii'
>>> coreg.inputs.source = 'structural.nii'
>>> coreg.run()
Inputs:
[Mandatory]
source: (an existing file name)
file to register to target
target: (an existing file name)
reference file to register to
[Optional]
apply_to_files: (an existing file name)
files to apply transformation to
cost_function: ('mi' or 'nmi' or 'ecc' or 'ncc')
cost function, one of: 'mi' - Mutual Information,
'nmi' - Normalised Mutual Information,
'ecc' - Entropy Correlation Coefficient,
'ncc' - Normalised Cross Correlation
fwhm: (a float)
gaussian smoothing kernel width (mm)
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
jobtype: ('estwrite' or 'estimate' or 'write', nipype default value: estwrite)
one of: estimate, write, estwrite
matlab_cmd: (a 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 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
write_interp: (an integer >= 0)
degree of b-spline used for interpolation
write_mask: (a boolean)
True/False mask output image
write_wrap: (a list of from 3 to 3 items which are an integer)
Check if interpolation should wrap in [x,y,z]
Outputs:
coregistered_files: (an existing file name)
Coregistered other files
coregistered_source: (an existing file name)
Coregistered source files
Apply a flow field estimated by DARTEL to create warped images
http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=202
>>> 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: (an existing file name)
DARTEL flow fields u_rc1*
image_files: (an existing file name)
A list of files to be warped
[Optional]
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
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, nipype default value: True)
Run m-code using m-file
paths: (a directory name)
Paths to add to matlabpath
use_mcr: (a boolean)
Run m-code using SPM MCR
Outputs:
warped_files: (a list of items which are an existing file name)
Use spm DARTEL to create a template and flow fields
http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=197
>>> 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 file name)
A list of files to be segmented
[Optional]
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
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, nipype default value: True)
Run m-code using m-file
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 directory name)
Paths to add to matlabpath
regularization_form: ('Linear' or 'Membrane' or 'Bending')
Form of regularization energy term
template_prefix: (a string, nipype default value: Template)
Prefix for template
use_mcr: (a boolean)
Run m-code using SPM MCR
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
Use spm DARTEL to normalize data to MNI space
http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=200
>>> 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: (an existing file name)
Files to apply the transform to
flowfield_files: (an existing file name)
DARTEL flow fields u_rc1*
template_file: (an existing file name)
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
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
matlab_cmd: (a 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 directory name)
Paths to add to matlabpath
use_mcr: (a boolean)
Run m-code using SPM MCR
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: (an existing file name)
Normalized files in MNI space
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=185
>>> 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: (an existing file name)
A list of files to be segmented
[Optional]
affine_regularization: ('mni' or 'eastern' or 'subj' or 'none')
mni, eastern, subj, none
channel_info: (a 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
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
matlab_cmd: (a string)
matlab command to use
mfile: (a boolean, nipype default value: True)
Run m-code using m-file
paths: (a directory name)
Paths to add to matlabpath
sampling_distance: (a float)
Sampling distance on data for parameter estimation
tissues: (a list of items which are a tuple of the form: (a tuple of the form: (an
existing file name, 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 [Modulated, Unmodualted] - a tuple of two boolean
values
use_mcr: (a boolean)
Run m-code using SPM MCR
warping_regularization: (a float)
Aproximate distance between sampling points.
write_deformation_fields: (a list of from 2 to 2 items which are a boolean)
Which deformation fields to write:[Inverse, Forward]
Outputs:
bias_corrected_images: (an existing file name)
bias corrected images
bias_field_images: (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: (an existing file name)
inverse_deformation_field: (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: (an existing file name)
Normalization transformation
use spm_normalise for warping an image to a template
http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=51
>>> 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: (an existing file name)
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 (opt)
affine_regularization_type: ('mni' or 'size' or 'none')
mni, size, none (opt)
apply_to_files: (an existing file name)
files to apply transformation to (opt)
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
jobtype: ('estwrite' or 'est' or 'write', nipype default value: estwrite)
one of: est, write, estwrite (opt, estwrite)
matlab_cmd: (a string)
matlab command to use
mfile: (a boolean, nipype default value: True)
Run m-code using m-file
nonlinear_iterations: (an integer)
Number of iterations of nonlinear warping (opt)
nonlinear_regularization: (a float)
the amount of the regularization for the nonlinear part of the normalization (opt)
out_prefix: (a string, nipype default value: w)
normalized output prefix
paths: (a directory name)
Paths to add to matlabpath
source_image_smoothing: (a float)
source smoothing (opt)
source_weight: (a file name)
name of weighting image for source (opt)
template_image_smoothing: (a float)
template smoothing (opt)
template_weight: (a file name)
name of weighting image for template (opt)
use_mcr: (a boolean)
Run m-code using SPM MCR
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 (opt)
write_interp: (an integer >= 0)
degree of b-spline used for interpolation
write_preserve: (a boolean)
True/False warped images are modulated (opt,)
write_voxel_sizes: (a list of from 3 to 3 items which are a float)
3-element list (opt)
write_wrap: (a list of items which are an integer)
Check if interpolation should wrap in [x,y,z] - list of bools (opt)
Outputs:
normalization_parameters: (an existing file name)
MAT files containing the normalization parameters
normalized_files: (an existing file name)
Normalized other files
normalized_source: (an existing file name)
Normalized source files
Use spm_realign for estimating within modality rigid body alignment
http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=25
>>> import nipype.interfaces.spm as spm
>>> realign = spm.Realign()
>>> realign.inputs.in_files = 'functional.nii'
>>> realign.inputs.register_to_mean = True
>>> realign.run()
Inputs:
[Mandatory]
in_files: (a list of items which are an existing file name or an existing file name)
list of filenames to realign
[Optional]
fwhm: (a floating point number >= 0.0)
gaussian smoothing kernel width
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
interp: (0 <= an 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 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 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
weight_img: (an existing file name)
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 type 'int', nipype default value: [1,
1])
determines which images to reslice
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: (an existing file name)
Mean image file from the realignment
realigned_files: (a list of items which are an existing file name or an existing file
name)
Realigned files
realignment_parameters: (an existing file name)
Estimated translation and rotation parameters
use spm_segment to separate structural images into different tissue classes.
http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=43
>>> import nipype.interfaces.spm as spm
>>> seg = spm.Segment()
>>> seg.inputs.data = 'structural.nii'
>>> seg.run()
Inputs:
[Mandatory]
data: (an existing file name)
one scan per subject
[Optional]
affine_regularization: ('mni' or 'eastern' or 'subj' or 'none' 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]
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
mask_image: (an existing file name)
Binary image to restrict parameter estimation
matlab_cmd: (a string)
matlab command to use
mfile: (a boolean, nipype default value: True)
Run m-code using m-file
paths: (a directory name)
Paths to add to matlabpath
sampling_distance: (a float)
Sampling distance on data for parameter estimation
save_bias_corrected: (a boolean)
True/False produce a bias corrected image
tissue_prob_maps: (a list of items which are an existing file name)
list of gray, white & csf prob. (opt,)
use_mcr: (a boolean)
Run m-code using SPM MCR
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:
inverse_transformation_mat: (an existing file name)
Inverse normalization info
modulated_csf_image: (an existing file name)
modulated, normalized csf probability map
modulated_gm_image: (an existing file name)
modulated, normalized grey probability map
modulated_input_image: (an existing file name)
modulated version of input image
modulated_wm_image: (an existing file name)
modulated, normalized white probability map
native_csf_image: (an existing file name)
native space csf probability map
native_gm_image: (an existing file name)
native space grey probability map
native_wm_image: (an existing file name)
native space white probability map
normalized_csf_image: (an existing file name)
normalized csf probability map
normalized_gm_image: (an existing file name)
normalized grey probability map
normalized_wm_image: (an existing file name)
normalized white probability map
transformation_mat: (an existing file name)
Normalization transformation
Use spm to perform slice timing correction.
http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=19
>>> from nipype.interfaces.spm import SliceTiming
>>> st = SliceTiming()
>>> st.inputs.in_files = 'functional.nii'
>>> st.inputs.num_slices = 32
>>> st.inputs.time_repetition = 6.0
>>> st.inputs.time_acquisition = 6. - 6./32.
>>> st.inputs.slice_order = range(32,0,-1)
>>> st.inputs.ref_slice = 1
>>> st.run()
Inputs:
[Mandatory]
in_files: (a list of items which are an existing file name or an existing file name)
list of filenames to apply slice timing
num_slices: (an integer)
number of slices in a volume
ref_slice: (an integer)
1-based Number of the reference slice
slice_order: (a list of items which are an integer)
1-based order in which slices are acquired
time_acquisition: (a float)
time of volume acquisition. usually calculated as TR-(TR/num_slices)
time_repetition: (a float)
time between volume acquisitions (start to start time)
[Optional]
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
matlab_cmd: (a 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 directory name)
Paths to add to matlabpath
use_mcr: (a boolean)
Run m-code using SPM MCR
Outputs:
timecorrected_files: (a file name)
Use spm_smooth for 3D Gaussian smoothing of image volumes.
http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=57
>>> import nipype.interfaces.spm as spm
>>> smooth = spm.Smooth()
>>> smooth.inputs.in_files = 'functional.nii'
>>> smooth.inputs.fwhm = [4, 4, 4]
>>> smooth.run()
Inputs:
[Mandatory]
in_files: (an existing file name)
list of files to smooth
[Optional]
data_type: (an integer)
Data type of the output images (opt)
fwhm: (a list of from 3 to 3 items which are a float or a float)
3-list of fwhm for each dimension (opt)
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
implicit_masking: (a boolean)
A mask implied by a particular voxel value
matlab_cmd: (a 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 directory name)
Paths to add to matlabpath
use_mcr: (a boolean)
Run m-code using SPM MCR
Outputs:
smoothed_files: (an existing file name)
smoothed files
Use VBM8 toolbox to separate structural images into different tissue classes.
>>> 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: (an existing file name)
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, nipype default value: 1)
0=None,1=light,2=thorough
csf_dartel: (0 <= an integer <= 2, nipype default value: 0)
0=None,1=rigid(SPM8 default),2=affine
csf_modulated_normalized: (0 <= an 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 file name)
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, an integer, an integer, an
integer, an integer, an integer), nipype default value: (2, 2, 2, 3, 4, 2))
number of gaussians for each tissue class
gm_dartel: (0 <= an integer <= 2, nipype default value: 0)
0=None,1=rigid(SPM8 default),2=affine
gm_modulated_normalized: (0 <= an 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)
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
jacobian_determinant: (a boolean, nipype default value: False)
matlab_cmd: (a 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 directory name)
Paths to add to matlabpath
pve_label_dartel: (0 <= an 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 file name)
tissue probability map
use_mcr: (a boolean)
Run m-code using SPM MCR
use_sanlm_denoising_filter: (0 <= an integer <= 2, nipype default value: 2)
0=No denoising, 1=denoising,2=denoising multi-threaded
warping_regularization: (a float, nipype default value: 4)
Controls balance between parameters and data
wm_dartel: (0 <= an integer <= 2, nipype default value: 0)
0=None,1=rigid(SPM8 default),2=affine
wm_modulated_normalized: (0 <= an 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: (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: (an existing file name)
inverse_deformation_field: (an existing file name)
jacobian_determinant_images: (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: (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: (an existing file name)
pve_label_normalized_images: (an existing file name)
pve_label_registered_images: (an existing file name)
transformation_mat: (an existing file name)
Normalization transformation