Wraps command antsIntroduction.sh
Inputs:
[Mandatory]
input_image: (an existing file name)
input image to warp to template
reference_image: (an existing file name)
template file to warp to
[Optional]
args: (a string)
Additional parameters to the command
bias_field_correction: (a boolean)
Applies bias field correction to moving image
dimension: (3 or 2, nipype default value: 3)
image dimension (2 or 3)
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
force_proceed: (a boolean)
force script to proceed even if headers may be incompatible
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
inverse_warp_template_labels: (a boolean)
Applies inverse warp to the template labels to estimate label positions in target space
(use for template-based segmentation)
max_iterations: (a list of items which are an integer)
maximum number of iterations (must be list of integers in the form [J,K,L...]: J =
coarsest resolution iterations, K = middle resolution interations, L = fine resolution
iterations
num_threads: (an integer, nipype default value: -1)
Number of ITK threads to use
out_prefix: (a string, nipype default value: ants_)
Prefix that is prepended to all output files (default = ants_)
quality_check: (a boolean)
Perform a quality check of the result
similarity_metric: ('PR' or 'CC' or 'MI' or 'MSQ')
Type of similartiy metric used for registration (CC = cross correlation, MI = mutual
information, PR = probability mapping, MSQ = mean square difference)
transformation_model: ('GR' or 'EL' or 'SY' or 'S2' or 'EX' or 'DD' or 'RI' or 'RA',
nipype default value: GR)
Type of transofmration model used for registration (EL = elastic transformation model,
SY = SyN with time, arbitrary number of time points, S2 = SyN with time optimized for 2
time points, GR = greedy SyN, EX = exponential, DD = diffeomorphic demons style
exponential mapping, RI = purely rigid, RA = affine rigid
Outputs:
affine_transformation: (an existing file name)
affine (prefix_Affine.txt)
input_file: (an existing file name)
input image (prefix_repaired.nii)
inverse_warp_field: (an existing file name)
inverse warp field (prefix_InverseWarp.nii)
output_file: (an existing file name)
output image (prefix_deformed.nii)
warp_field: (an existing file name)
warp field (prefix_Warp.nii)
Wraps command antsIntroduction.sh
Uses ANTS to generate matrices to warp data from one space to another.
>>> from nipype.interfaces.ants.legacy import antsIntroduction
>>> warp = antsIntroduction()
>>> warp.inputs.reference_image = 'Template_6.nii'
>>> warp.inputs.input_image = 'structural.nii'
>>> warp.inputs.max_iterations = [30,90,20]
>>> warp.cmdline
'antsIntroduction.sh -d 3 -i structural.nii -m 30x90x20 -o ants_ -r Template_6.nii -t GR'
Inputs:
[Mandatory]
input_image: (an existing file name)
input image to warp to template
reference_image: (an existing file name)
template file to warp to
[Optional]
args: (a string)
Additional parameters to the command
bias_field_correction: (a boolean)
Applies bias field correction to moving image
dimension: (3 or 2, nipype default value: 3)
image dimension (2 or 3)
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
force_proceed: (a boolean)
force script to proceed even if headers may be incompatible
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
inverse_warp_template_labels: (a boolean)
Applies inverse warp to the template labels to estimate label positions in target space
(use for template-based segmentation)
max_iterations: (a list of items which are an integer)
maximum number of iterations (must be list of integers in the form [J,K,L...]: J =
coarsest resolution iterations, K = middle resolution interations, L = fine resolution
iterations
num_threads: (an integer, nipype default value: -1)
Number of ITK threads to use
out_prefix: (a string, nipype default value: ants_)
Prefix that is prepended to all output files (default = ants_)
quality_check: (a boolean)
Perform a quality check of the result
similarity_metric: ('PR' or 'CC' or 'MI' or 'MSQ')
Type of similartiy metric used for registration (CC = cross correlation, MI = mutual
information, PR = probability mapping, MSQ = mean square difference)
transformation_model: ('GR' or 'EL' or 'SY' or 'S2' or 'EX' or 'DD' or 'RI' or 'RA',
nipype default value: GR)
Type of transofmration model used for registration (EL = elastic transformation model,
SY = SyN with time, arbitrary number of time points, S2 = SyN with time optimized for 2
time points, GR = greedy SyN, EX = exponential, DD = diffeomorphic demons style
exponential mapping, RI = purely rigid, RA = affine rigid
Outputs:
affine_transformation: (an existing file name)
affine (prefix_Affine.txt)
input_file: (an existing file name)
input image (prefix_repaired.nii)
inverse_warp_field: (an existing file name)
inverse warp field (prefix_InverseWarp.nii)
output_file: (an existing file name)
output image (prefix_deformed.nii)
warp_field: (an existing file name)
warp field (prefix_Warp.nii)
Wraps command buildtemplateparallel.sh
Generate a optimal average template
Warning
This can take a VERY long time to complete
>>> from nipype.interfaces.ants.legacy import buildtemplateparallel
>>> tmpl = buildtemplateparallel()
>>> tmpl.inputs.in_files = ['T1.nii', 'structural.nii']
>>> tmpl.inputs.max_iterations = [30, 90, 20]
>>> tmpl.cmdline
'buildtemplateparallel.sh -d 3 -i 4 -m 30x90x20 -o antsTMPL_ -c 0 -t GR T1.nii structural.nii'
Inputs:
[Mandatory]
in_files: (a list of items which are an existing file name)
list of images to generate template from
[Optional]
args: (a string)
Additional parameters to the command
bias_field_correction: (a boolean)
Applies bias field correction to moving image
dimension: (3 or 2, nipype default value: 3)
image dimension (2 or 3)
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
gradient_step_size: (a float)
smaller magnitude results in more cautious steps (default = .25)
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_limit: (an integer, nipype default value: 4)
iterations of template construction
max_iterations: (a list of items which are an integer)
maximum number of iterations (must be list of integers in the form [J,K,L...]: J =
coarsest resolution iterations, K = middle resolution interations, L = fine resolution
iterations
num_cores: (an integer)
Requires parallelization = 2 (PEXEC). Sets number of cpu cores to use
requires: parallelization
num_threads: (an integer, nipype default value: -1)
Number of ITK threads to use
out_prefix: (a string, nipype default value: antsTMPL_)
Prefix that is prepended to all output files (default = antsTMPL_)
parallelization: (0 or 1 or 2, nipype default value: 0)
control for parallel processing (0 = serial, 1 = use PBS, 2 = use PEXEC, 3 = use Apple
XGrid
rigid_body_registration: (a boolean)
registers inputs before creating template (useful if no initial template available)
similarity_metric: ('PR' or 'CC' or 'MI' or 'MSQ')
Type of similartiy metric used for registration (CC = cross correlation, MI = mutual
information, PR = probability mapping, MSQ = mean square difference)
transformation_model: ('GR' or 'EL' or 'SY' or 'S2' or 'EX' or 'DD', nipype default
value: GR)
Type of transofmration model used for registration (EL = elastic transformation model,
SY = SyN with time, arbitrary number of time points, S2 = SyN with time optimized for 2
time points, GR = greedy SyN, EX = exponential, DD = diffeomorphic demons style
exponential mapping
use_first_as_target: (a boolean)
uses first volume as target of all inputs. When not used, an unbiased average image is
used to start.
Outputs:
final_template_file: (an existing file name)
final ANTS template
subject_outfiles: (an existing file name)
Outputs for each input image. Includes warp field, inverse warp, Affine, original image
(repaired) and warped image (deformed)
template_files: (an existing file name)
Templates from different stages of iteration