nipype.interfaces.ants.legacy module

ANTS Legacy Interfaces

These interfaces are for programs that have been deprecated by ANTs, but are preserved for backwards compatibility.

GenWarpFields

Link to code

Bases: antsIntroduction

Wrapped executable: antsIntroduction.sh.

input_image : a pathlike object or string representing an existing file
Input image to warp to template. Maps to a command-line argument: -i %s.
reference_image : a pathlike object or string representing an existing file
Template file to warp to. Maps to a command-line argument: -r %s.
args : a unicode string
Additional parameters to the command. Maps to a command-line argument: %s.
bias_field_correction : a boolean
Applies bias field correction to moving image. Maps to a command-line argument: -n 1.
dimension : 3 or 2
Image dimension (2 or 3). Maps to a command-line argument: -d %d (position: 1). (Nipype default value: 3)
environ : a dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’
Environment variables. (Nipype default value: {})
force_proceed : a boolean
Force script to proceed even if headers may be incompatible. Maps to a command-line argument: -f 1.
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). Maps to a command-line argument: -l.
max_iterations : a list of items which are an integer (int or long)
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. Maps to a command-line argument: -m %s.
num_threads : an integer (int or long)
Number of ITK threads to use. (Nipype default value: 1)
out_prefix : a unicode string
Prefix that is prepended to all output files (default = ants_). Maps to a command-line argument: -o %s. (Nipype default value: ants_)
quality_check : a boolean
Perform a quality check of the result. Maps to a command-line argument: -q 1.
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). Maps to a command-line argument: -s %s.
transformation_model : ‘GR’ or ‘EL’ or ‘SY’ or ‘S2’ or ‘EX’ or ‘DD’ or ‘RI’ or ‘RA’
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. Maps to a command-line argument: -t %s. (Nipype default value: GR)
affine_transformation : a pathlike object or string representing an existing file
Affine (prefix_Affine.txt).
input_file : a pathlike object or string representing an existing file
Input image (prefix_repaired.nii).
inverse_warp_field : a pathlike object or string representing an existing file
Inverse warp field (prefix_InverseWarp.nii).
output_file : a pathlike object or string representing an existing file
Output image (prefix_deformed.nii).
warp_field : a pathlike object or string representing an existing file
Warp field (prefix_Warp.nii).

antsIntroduction

Link to code

Bases: ANTSCommand

Wrapped executable: antsIntroduction.sh.

Uses ANTS to generate matrices to warp data from one space to another.

Examples

>>> 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'
input_image : a pathlike object or string representing an existing file
Input image to warp to template. Maps to a command-line argument: -i %s.
reference_image : a pathlike object or string representing an existing file
Template file to warp to. Maps to a command-line argument: -r %s.
args : a unicode string
Additional parameters to the command. Maps to a command-line argument: %s.
bias_field_correction : a boolean
Applies bias field correction to moving image. Maps to a command-line argument: -n 1.
dimension : 3 or 2
Image dimension (2 or 3). Maps to a command-line argument: -d %d (position: 1). (Nipype default value: 3)
environ : a dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’
Environment variables. (Nipype default value: {})
force_proceed : a boolean
Force script to proceed even if headers may be incompatible. Maps to a command-line argument: -f 1.
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). Maps to a command-line argument: -l.
max_iterations : a list of items which are an integer (int or long)
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. Maps to a command-line argument: -m %s.
num_threads : an integer (int or long)
Number of ITK threads to use. (Nipype default value: 1)
out_prefix : a unicode string
Prefix that is prepended to all output files (default = ants_). Maps to a command-line argument: -o %s. (Nipype default value: ants_)
quality_check : a boolean
Perform a quality check of the result. Maps to a command-line argument: -q 1.
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). Maps to a command-line argument: -s %s.
transformation_model : ‘GR’ or ‘EL’ or ‘SY’ or ‘S2’ or ‘EX’ or ‘DD’ or ‘RI’ or ‘RA’
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. Maps to a command-line argument: -t %s. (Nipype default value: GR)
affine_transformation : a pathlike object or string representing an existing file
Affine (prefix_Affine.txt).
input_file : a pathlike object or string representing an existing file
Input image (prefix_repaired.nii).
inverse_warp_field : a pathlike object or string representing an existing file
Inverse warp field (prefix_InverseWarp.nii).
output_file : a pathlike object or string representing an existing file
Output image (prefix_deformed.nii).
warp_field : a pathlike object or string representing an existing file
Warp field (prefix_Warp.nii).

buildtemplateparallel

Link to code

Bases: ANTSCommand

Wrapped executable: buildtemplateparallel.sh.

Generate a optimal average template

Warning

This can take a VERY long time to complete

Examples

>>> 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'
in_files : a list of items which are a pathlike object or string representing an existing file
List of images to generate template from. Maps to a command-line argument: %s (position: -1).
args : a unicode string
Additional parameters to the command. Maps to a command-line argument: %s.
bias_field_correction : a boolean
Applies bias field correction to moving image. Maps to a command-line argument: -n 1.
dimension : 3 or 2 or 4
Image dimension (2, 3 or 4). Maps to a command-line argument: -d %d (position: 1). (Nipype default value: 3)
environ : a dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’
Environment variables. (Nipype default value: {})
gradient_step_size : a float
Smaller magnitude results in more cautious steps (default = .25). Maps to a command-line argument: -g %f.
iteration_limit : an integer (int or long)
Iterations of template construction. Maps to a command-line argument: -i %d. (Nipype default value: 4)
max_iterations : a list of items which are an integer (int or long)
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. Maps to a command-line argument: -m %s.
num_cores : an integer (int or long)
Requires parallelization = 2 (PEXEC). Sets number of cpu cores to use. Maps to a command-line argument: -j %d. Requires inputs: parallelization.
num_threads : an integer (int or long)
Number of ITK threads to use. (Nipype default value: 1)
out_prefix : a unicode string
Prefix that is prepended to all output files (default = antsTMPL_). Maps to a command-line argument: -o %s. (Nipype default value: antsTMPL_)
parallelization : 0 or 1 or 2
Control for parallel processing (0 = serial, 1 = use PBS, 2 = use PEXEC, 3 = use Apple XGrid. Maps to a command-line argument: -c %d. (Nipype default value: 0)
rigid_body_registration : a boolean
Registers inputs before creating template (useful if no initial template available). Maps to a command-line argument: -r 1.
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). Maps to a command-line argument: -s %s.
transformation_model : ‘GR’ or ‘EL’ or ‘SY’ or ‘S2’ or ‘EX’ or ‘DD’
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. Maps to a command-line argument: -t %s. (Nipype default value: GR)
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.
final_template_file : a pathlike object or string representing an existing file
Final ANTS template.
subject_outfiles : a list of items which are a pathlike object or string representing an existing file
Outputs for each input image. Includes warp field, inverse warp, Affine, original image (repaired) and warped image (deformed).
template_files : a list of items which are a pathlike object or string representing an existing file
Templates from different stages of iteration.