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# interfaces.ants.legacy

## GenWarpFields

Link to code

Wraps command antsIntroduction.sh

Inputs:

    [Mandatory]
    input_image: (an existing file name)
            input image to warp to template
            flag: -i %s
    reference_image: (an existing file name)
            template file to warp to
            flag: -r %s

    [Optional]
    args: (a string)
            Additional parameters to the command
            flag: %s
    bias_field_correction: (a boolean)
            Applies bias field correction to moving image
            flag: -n 1
    dimension: (3 or 2, nipype default value: 3)
            image dimension (2 or 3)
            flag: -d %d, position: 1
    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
            flag: -f 1
    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)
            flag: -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 interations, L = fine resolution iterations
            flag: -m %s
    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_)
            flag: -o %s
    quality_check: (a boolean)
            Perform a quality check of the result
            flag: -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)
            flag: -s %s
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output: `stream` - displays to terminal immediately
            (default), `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored
    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
            flag: -t %s

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)

## antsIntroduction

Link to code

Wraps 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]
    input_image: (an existing file name)
            input image to warp to template
            flag: -i %s
    reference_image: (an existing file name)
            template file to warp to
            flag: -r %s

    [Optional]
    args: (a string)
            Additional parameters to the command
            flag: %s
    bias_field_correction: (a boolean)
            Applies bias field correction to moving image
            flag: -n 1
    dimension: (3 or 2, nipype default value: 3)
            image dimension (2 or 3)
            flag: -d %d, position: 1
    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
            flag: -f 1
    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)
            flag: -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 interations, L = fine resolution iterations
            flag: -m %s
    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_)
            flag: -o %s
    quality_check: (a boolean)
            Perform a quality check of the result
            flag: -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)
            flag: -s %s
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output: `stream` - displays to terminal immediately
            (default), `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored
    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
            flag: -t %s

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)

## buildtemplateparallel

Link to code

Wraps 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 an existing file name)
            list of images to generate template from
            flag: %s, position: -1

    [Optional]
    args: (a string)
            Additional parameters to the command
            flag: %s
    bias_field_correction: (a boolean)
            Applies bias field correction to moving image
            flag: -n 1
    dimension: (3 or 2, nipype default value: 3)
            image dimension (2 or 3)
            flag: -d %d, position: 1
    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)
            flag: -g %f
    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
            flag: -i %d
    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
            flag: -m %s
    num_cores: (an integer)
            Requires parallelization = 2 (PEXEC). Sets number of cpu cores to
            use
            flag: -j %d
            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_)
            flag: -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
            flag: -c %d
    rigid_body_registration: (a boolean)
            registers inputs before creating template (useful if no initial
            template available)
            flag: -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)
            flag: -s %s
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output: `stream` - displays to terminal immediately
            (default), `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored
    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
            flag: -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.

Outputs:

    final_template_file: (an existing file name)
            final ANTS template
    subject_outfiles: (a list of items which are an existing file name)
            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 an existing file name)
            Templates from different stages of iteration