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.

Mandatory Inputs:

  • 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.

Optional Inputs:

  • args (a 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) – Maximum number of iterations (must be list of integers in the form [J,K,L…]: J = coarsest resolution iterations, K = middle resolution iterations, L = fine resolution iterations. Maps to a command-line argument: -m %s.

  • num_threads (an integer) – Number of ITK threads to use. (Nipype default value: 1)

  • out_prefix (a 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)

Outputs:

  • 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'
Mandatory Inputs:

  • 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.

Optional Inputs:

  • args (a 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) – Maximum number of iterations (must be list of integers in the form [J,K,L…]: J = coarsest resolution iterations, K = middle resolution iterations, L = fine resolution iterations. Maps to a command-line argument: -m %s.

  • num_threads (an integer) – Number of ITK threads to use. (Nipype default value: 1)

  • out_prefix (a 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)

Outputs:

  • 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'
Mandatory Inputs:

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).

Optional Inputs:

  • args (a 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) – 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) – Maximum number of iterations (must be list of integers in the form [J,K,L…]: J = coarsest resolution iterations, K = middle resolution iterations, L = fine resolution iterations. Maps to a command-line argument: -m %s.

  • num_cores (an integer) – 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) – Number of ITK threads to use. (Nipype default value: 1)

  • out_prefix (a 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.

Outputs:

  • 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.