interfaces.ants.legacy¶
GenWarpFields¶
Wraps the executable command antsIntroduction.sh
.
Inputs:
[Mandatory]
reference_image: (a pathlike object or string representing an
existing file)
template file to warp to
argument: ``-r %s``
input_image: (a pathlike object or string representing an existing
file)
input image to warp to template
argument: ``-i %s``
[Optional]
dimension: (3 or 2, nipype default value: 3)
image dimension (2 or 3)
argument: ``-d %d``, position: 1
force_proceed: (a boolean)
force script to proceed even if headers may be incompatible
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)
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
argument: ``-m %s``
bias_field_correction: (a boolean)
Applies bias field correction to moving image
argument: ``-n 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)
argument: ``-s %s``
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
argument: ``-t %s``
out_prefix: (a unicode string, nipype default value: ants_)
Prefix that is prepended to all output files (default = ants_)
argument: ``-o %s``
quality_check: (a boolean)
Perform a quality check of the result
argument: ``-q 1``
num_threads: (an integer (int or long), nipype default value: 1)
Number of ITK threads to use
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
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', nipype default value: {})
Environment variables
Outputs:
affine_transformation: (a pathlike object or string representing an
existing file)
affine (prefix_Affine.txt)
warp_field: (a pathlike object or string representing an existing
file)
warp field (prefix_Warp.nii)
inverse_warp_field: (a pathlike object or string representing an
existing file)
inverse warp field (prefix_InverseWarp.nii)
input_file: (a pathlike object or string representing an existing
file)
input image (prefix_repaired.nii)
output_file: (a pathlike object or string representing an existing
file)
output image (prefix_deformed.nii)
antsIntroduction¶
Wraps the executable command 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'
Inputs:
[Mandatory]
reference_image: (a pathlike object or string representing an
existing file)
template file to warp to
argument: ``-r %s``
input_image: (a pathlike object or string representing an existing
file)
input image to warp to template
argument: ``-i %s``
[Optional]
dimension: (3 or 2, nipype default value: 3)
image dimension (2 or 3)
argument: ``-d %d``, position: 1
force_proceed: (a boolean)
force script to proceed even if headers may be incompatible
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)
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
argument: ``-m %s``
bias_field_correction: (a boolean)
Applies bias field correction to moving image
argument: ``-n 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)
argument: ``-s %s``
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
argument: ``-t %s``
out_prefix: (a unicode string, nipype default value: ants_)
Prefix that is prepended to all output files (default = ants_)
argument: ``-o %s``
quality_check: (a boolean)
Perform a quality check of the result
argument: ``-q 1``
num_threads: (an integer (int or long), nipype default value: 1)
Number of ITK threads to use
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
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', nipype default value: {})
Environment variables
Outputs:
affine_transformation: (a pathlike object or string representing an
existing file)
affine (prefix_Affine.txt)
warp_field: (a pathlike object or string representing an existing
file)
warp field (prefix_Warp.nii)
inverse_warp_field: (a pathlike object or string representing an
existing file)
inverse warp field (prefix_InverseWarp.nii)
input_file: (a pathlike object or string representing an existing
file)
input image (prefix_repaired.nii)
output_file: (a pathlike object or string representing an existing
file)
output image (prefix_deformed.nii)
buildtemplateparallel¶
Wraps the executable command 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'
Inputs:
[Mandatory]
in_files: (a list of items which are a pathlike object or string
representing an existing file)
list of images to generate template from
argument: ``%s``, position: -1
[Optional]
dimension: (3 or 2 or 4, nipype default value: 3)
image dimension (2, 3 or 4)
argument: ``-d %d``, position: 1
out_prefix: (a unicode string, nipype default value: antsTMPL_)
Prefix that is prepended to all output files (default = antsTMPL_)
argument: ``-o %s``
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
argument: ``-c %d``
gradient_step_size: (a float)
smaller magnitude results in more cautious steps (default = .25)
argument: ``-g %f``
iteration_limit: (an integer (int or long), nipype default value: 4)
iterations of template construction
argument: ``-i %d``
num_cores: (an integer (int or long))
Requires parallelization = 2 (PEXEC). Sets number of cpu cores to
use
argument: ``-j %d``
requires: parallelization
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
argument: ``-m %s``
bias_field_correction: (a boolean)
Applies bias field correction to moving image
argument: ``-n 1``
rigid_body_registration: (a boolean)
registers inputs before creating template (useful if no initial
template available)
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)
argument: ``-s %s``
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
argument: ``-t %s``
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.
num_threads: (an integer (int or long), nipype default value: 1)
Number of ITK threads to use
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
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', nipype default value: {})
Environment variables
Outputs:
final_template_file: (a pathlike object or string representing an
existing file)
final ANTS template
template_files: (a list of items which are a pathlike object or
string representing an existing file)
Templates from different stages of iteration
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)