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# interfaces.ants.registration

## MeasureImageSimilarity

Link to code

Wraps command MeasureImageSimilarity

### Examples

    >>> from nipype.interfaces.ants import MeasureImageSimilarity
    >>> sim = MeasureImageSimilarity()
    >>> sim.inputs.dimension = 3
    >>> sim.inputs.metric = 'MI'
    >>> sim.inputs.fixed_image = 'T1.nii'
    >>> sim.inputs.moving_image = 'resting.nii'
    >>> sim.inputs.metric_weight = 1.0
    >>> sim.inputs.radius_or_number_of_bins = 5
    >>> sim.inputs.sampling_strategy = 'Regular'
    >>> sim.inputs.sampling_percentage = 1.0
    >>> sim.inputs.fixed_image_mask = 'mask.nii'
    >>> sim.inputs.moving_image_mask = 'mask.nii.gz'
    >>> sim.cmdline
    'MeasureImageSimilarity --dimensionality 3 --masks ["mask.nii","mask.nii.gz"] --metric MI["T1.nii","resting.nii",1.0,5,Regular,1.0]'

Inputs:

    [Mandatory]
    fixed_image: (an existing file name)
            Image to which the moving image is warped
    metric: (u'CC' or u'MI' or u'Mattes' or u'MeanSquares' or u'Demons'
             or u'GC')
            flag: %s
    moving_image: (an existing file name)
            Image to apply transformation to (generally a coregistered
            functional)
    radius_or_number_of_bins: (an integer (int or long))
            The number of bins in each stage for the MI and Mattes metric, or
            the radius for other metrics
            requires: metric
    sampling_percentage: (0.0 <= a floating point number <= 1.0)
            Percentage of points accessible to the sampling strategy over which
            to optimize the metric.
            requires: metric

    [Optional]
    args: (a unicode string)
            Additional parameters to the command
            flag: %s
    dimension: (2 or 3 or 4)
            Dimensionality of the fixed/moving image pair
            flag: --dimensionality %d, position: 1
    environ: (a dictionary with keys which are a newbytes or None or a
             newstr or None and with values which are a newbytes or None or a
             newstr or None, nipype default value: {})
            Environment variables
    fixed_image_mask: (an existing file name)
            mask used to limit metric sampling region of the fixed image
            flag: %s
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    metric_weight: (a float, nipype default value: 0.0)
            The "metricWeight" variable is not used.
            requires: metric
    moving_image_mask: (an existing file name)
            mask used to limit metric sampling region of the moving image
            requires: fixed_image_mask
    num_threads: (an integer (int or long), nipype default value: 1)
            Number of ITK threads to use
    sampling_strategy: (u'None' or u'Regular' or u'Random', nipype
             default value: None)
            Manner of choosing point set over which to optimize the metric.
            Defaults to "None" (i.e. a dense sampling of one sample per voxel).
            requires: metric
    terminal_output: (u'stream' or u'allatonce' or u'file' or u'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

Outputs:

    similarity: (a float)

## Registration

Link to code

Wraps command antsRegistration

antsRegistration registers a `moving_image` to a `fixed_image`, using a predefined (sequence of) cost function(s) and transformation operations. The cost function is defined using one or more ‘metrics’, specifically local cross-correlation (`CC`), Mean Squares (`MeanSquares`), Demons (`Demons`), global correlation (`GC`), or Mutual Information (`Mattes` or `MI`).

ANTS can use both linear (`Translation`, `Rigid`, `Affine`, `CompositeAffine`, or `Translation`) and non-linear transformations (`BSpline`, `GaussianDisplacementField`, `TimeVaryingVelocityField`, `TimeVaryingBSplineVelocityField`, `SyN`, `BSplineSyN`, `Exponential`, or `BSplineExponential`). Usually, registration is done in multiple stages. For example first an Affine, then a Rigid, and ultimately a non-linear (Syn)-transformation.

antsRegistration can be initialized using one ore more transforms from moving_image to fixed_image with the `initial_moving_transform`-input. For example, when you already have a warpfield that corrects for geometrical distortions in an EPI (functional) image, that you want to apply before an Affine registration to a structural image. You could put this transform into ‘intial_moving_transform’.

The Registration-interface can output the resulting transform(s) that map moving_image to fixed_image in a single file as a `composite_transform` (if `write_composite_transform` is set to `True`), or a list of transforms as `forwards_transforms`. It can also output inverse transforms (from `fixed_image` to `moving_image`) in a similar fashion using `inverse_composite_transform`. Note that the order of `forward_transforms` is in ‘natural’ order: the first element should be applied first, the last element should be applied last.

Note, however, that ANTS tools always apply lists of transformations in reverse order (the last transformation in the list is applied first). Therefore, if the output forward_transforms is a list, one can not directly feed it into, for example, `ants.ApplyTransforms`. To make `ants.ApplyTransforms` apply the transformations in the same order as `ants.Registration`, you have to provide the list of transformations in reverse order from `forward_transforms`. `reverse_forward_transforms` outputs `forward_transforms` in reverse order and can be used for this purpose. Note also that, because `composite_transform` is always a single file, this output is preferred for most use-cases.

More information can be found in the ANTS manual.

See below for some useful examples.

### Examples

Set up a Registation node with some default settings. This Node registers ‘fixed1.nii’ to ‘moving1.nii’ by first fitting a linear ‘Affine’ transformation, and then a non-linear ‘SyN’ transformation, both using the Mutual Information-cost metric.

The registration is initailized by first applying the (linear) transform trans.mat.

    >>> import copy, pprint
    >>> from nipype.interfaces.ants import Registration
    >>> reg = Registration()
    >>> reg.inputs.fixed_image = 'fixed1.nii'
    >>> reg.inputs.moving_image = 'moving1.nii'
    >>> reg.inputs.output_transform_prefix = "output_"
    >>> reg.inputs.initial_moving_transform = 'trans.mat'
    >>> reg.inputs.transforms = ['Affine', 'SyN']
    >>> reg.inputs.transform_parameters = [(2.0,), (0.25, 3.0, 0.0)]
    >>> reg.inputs.number_of_iterations = [[1500, 200], [100, 50, 30]]
    >>> reg.inputs.dimension = 3
    >>> reg.inputs.write_composite_transform = True
    >>> reg.inputs.collapse_output_transforms = False
    >>> reg.inputs.initialize_transforms_per_stage = False
    >>> reg.inputs.metric = ['Mattes']*2
    >>> reg.inputs.metric_weight = [1]*2 # Default (value ignored currently by ANTs)
    >>> reg.inputs.radius_or_number_of_bins = [32]*2
    >>> reg.inputs.sampling_strategy = ['Random', None]
    >>> reg.inputs.sampling_percentage = [0.05, None]
    >>> reg.inputs.convergence_threshold = [1.e-8, 1.e-9]
    >>> reg.inputs.convergence_window_size = [20]*2
    >>> reg.inputs.smoothing_sigmas = [[1,0], [2,1,0]]
    >>> reg.inputs.sigma_units = ['vox'] * 2
    >>> reg.inputs.shrink_factors = [[2,1], [3,2,1]]
    >>> reg.inputs.use_estimate_learning_rate_once = [True, True]
    >>> reg.inputs.use_histogram_matching = [True, True] # This is the default
    >>> reg.inputs.output_warped_image = 'output_warped_image.nii.gz'
    >>> reg.cmdline
    'antsRegistration --collapse-output-transforms 0 --dimensionality 3 --initial-moving-transform [ trans.mat, 0 ] --initialize-transforms-per-stage 0 --interpolation Linear --output [ output_, output_warped_image.nii.gz ] --transform Affine[ 2.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32, Random, 0.05 ] --convergence [ 1500x200, 1e-08, 20 ] --smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --transform SyN[ 0.25, 3.0, 0.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32 ] --convergence [ 100x50x30, 1e-09, 20 ] --smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 1.0 ]  --write-composite-transform 1'
    >>> reg.run()

Same as reg1, but first invert the initial transform (‘trans.mat’) before applying it.

    >>> reg.inputs.invert_initial_moving_transform = True
    >>> reg1 = copy.deepcopy(reg)
    >>> reg1.inputs.winsorize_lower_quantile = 0.025
    >>> reg1.cmdline
    'antsRegistration --collapse-output-transforms 0 --dimensionality 3 --initial-moving-transform [ trans.mat, 1 ] --initialize-transforms-per-stage 0 --interpolation Linear --output [ output_, output_warped_image.nii.gz ] --transform Affine[ 2.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32, Random, 0.05 ] --convergence [ 1500x200, 1e-08, 20 ] --smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --transform SyN[ 0.25, 3.0, 0.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32 ] --convergence [ 100x50x30, 1e-09, 20 ] --smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --winsorize-image-intensities [ 0.025, 1.0 ]  --write-composite-transform 1'
    >>> reg1.run()

Clip extremely high intensity data points using winsorize_upper_quantile. All data points higher than the 0.975 quantile are set to the value of the 0.975 quantile.

    >>> reg2 = copy.deepcopy(reg)
    >>> reg2.inputs.winsorize_upper_quantile = 0.975
    >>> reg2.cmdline
    'antsRegistration --collapse-output-transforms 0 --dimensionality 3 --initial-moving-transform [ trans.mat, 1 ] --initialize-transforms-per-stage 0 --interpolation Linear --output [ output_, output_warped_image.nii.gz ] --transform Affine[ 2.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32, Random, 0.05 ] --convergence [ 1500x200, 1e-08, 20 ] --smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --transform SyN[ 0.25, 3.0, 0.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32 ] --convergence [ 100x50x30, 1e-09, 20 ] --smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 0.975 ]  --write-composite-transform 1'

Clip extremely low intensity data points using winsorize_lower_quantile. All data points lower than the 0.025 quantile are set to the original value at the 0.025 quantile.

    >>> reg3 = copy.deepcopy(reg)
    >>> reg3.inputs.winsorize_lower_quantile = 0.025
    >>> reg3.inputs.winsorize_upper_quantile = 0.975
    >>> reg3.cmdline
    'antsRegistration --collapse-output-transforms 0 --dimensionality 3 --initial-moving-transform [ trans.mat, 1 ] --initialize-transforms-per-stage 0 --interpolation Linear --output [ output_, output_warped_image.nii.gz ] --transform Affine[ 2.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32, Random, 0.05 ] --convergence [ 1500x200, 1e-08, 20 ] --smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --transform SyN[ 0.25, 3.0, 0.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32 ] --convergence [ 100x50x30, 1e-09, 20 ] --smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --winsorize-image-intensities [ 0.025, 0.975 ]  --write-composite-transform 1'

Use float instead of double for computations (saves memory usage)

    >>> reg3a = copy.deepcopy(reg)
    >>> reg3a.inputs.float = True
    >>> reg3a.cmdline
    'antsRegistration --collapse-output-transforms 0 --dimensionality 3 --float 1 --initial-moving-transform [ trans.mat, 1 ] --initialize-transforms-per-stage 0 --interpolation Linear --output [ output_, output_warped_image.nii.gz ] --transform Affine[ 2.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32, Random, 0.05 ] --convergence [ 1500x200, 1e-08, 20 ] --smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --transform SyN[ 0.25, 3.0, 0.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32 ] --convergence [ 100x50x30, 1e-09, 20 ] --smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 1.0 ]  --write-composite-transform 1'

Force to use double instead of float for computations (more precision and memory usage).

    >>> reg3b = copy.deepcopy(reg)
    >>> reg3b.inputs.float = False
    >>> reg3b.cmdline
    'antsRegistration --collapse-output-transforms 0 --dimensionality 3 --float 0 --initial-moving-transform [ trans.mat, 1 ] --initialize-transforms-per-stage 0 --interpolation Linear --output [ output_, output_warped_image.nii.gz ] --transform Affine[ 2.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32, Random, 0.05 ] --convergence [ 1500x200, 1e-08, 20 ] --smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --transform SyN[ 0.25, 3.0, 0.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32 ] --convergence [ 100x50x30, 1e-09, 20 ] --smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 1.0 ]  --write-composite-transform 1'

‘collapse_output_transforms’ can be used to put all transformation in a single ‘composite_transform’- file. Note that forward_transforms will now be an empty list.

    >>> # Test collapse transforms flag
    >>> reg4 = copy.deepcopy(reg)
    >>> reg4.inputs.save_state = 'trans.mat'
    >>> reg4.inputs.restore_state = 'trans.mat'
    >>> reg4.inputs.initialize_transforms_per_stage = True
    >>> reg4.inputs.collapse_output_transforms = True
    >>> outputs = reg4._list_outputs()
    >>> pprint.pprint(outputs)
    {'composite_transform': '.../nipype/testing/data/output_Composite.h5',
     'elapsed_time': <undefined>,
     'forward_invert_flags': [],
     'forward_transforms': [],
     'inverse_composite_transform': '.../nipype/testing/data/output_InverseComposite.h5',
     'inverse_warped_image': <undefined>,
     'metric_value': <undefined>,
     'reverse_invert_flags': [],
     'reverse_transforms': [],
     'save_state': '.../nipype/testing/data/trans.mat',
     'warped_image': '.../nipype/testing/data/output_warped_image.nii.gz'}
    >>> reg4.cmdline
    'antsRegistration --collapse-output-transforms 1 --dimensionality 3 --initial-moving-transform [ trans.mat, 1 ] --initialize-transforms-per-stage 1 --interpolation Linear --output [ output_, output_warped_image.nii.gz ] --restore-state trans.mat --save-state trans.mat --transform Affine[ 2.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32, Random, 0.05 ] --convergence [ 1500x200, 1e-08, 20 ] --smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --transform SyN[ 0.25, 3.0, 0.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32 ] --convergence [ 100x50x30, 1e-09, 20 ] --smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 1.0 ]  --write-composite-transform 1'

    >>> # Test collapse transforms flag
    >>> reg4b = copy.deepcopy(reg4)
    >>> reg4b.inputs.write_composite_transform = False
    >>> outputs = reg4b._list_outputs()
    >>> pprint.pprint(outputs)
    {'composite_transform': <undefined>,
     'elapsed_time': <undefined>,
     'forward_invert_flags': [False, False],
     'forward_transforms': ['.../nipype/testing/data/output_0GenericAffine.mat',
     '.../nipype/testing/data/output_1Warp.nii.gz'],
     'inverse_composite_transform': <undefined>,
     'inverse_warped_image': <undefined>,
     'metric_value': <undefined>,
     'reverse_invert_flags': [True, False],
     'reverse_transforms': ['.../nipype/testing/data/output_0GenericAffine.mat',     '.../nipype/testing/data/output_1InverseWarp.nii.gz'],
     'save_state': '.../nipype/testing/data/trans.mat',
     'warped_image': '.../nipype/testing/data/output_warped_image.nii.gz'}
    >>> reg4b.aggregate_outputs()
    >>> reg4b.cmdline
    'antsRegistration --collapse-output-transforms 1 --dimensionality 3 --initial-moving-transform [ trans.mat, 1 ] --initialize-transforms-per-stage 1 --interpolation Linear --output [ output_, output_warped_image.nii.gz ] --restore-state trans.mat --save-state trans.mat --transform Affine[ 2.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32, Random, 0.05 ] --convergence [ 1500x200, 1e-08, 20 ] --smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --transform SyN[ 0.25, 3.0, 0.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32 ] --convergence [ 100x50x30, 1e-09, 20 ] --smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 1.0 ]  --write-composite-transform 0'

One can use multiple similarity metrics in a single registration stage.The Node below first performs a linear registation using only the Mutual Information (‘Mattes’)-metric. In a second stage, it performs a non-linear registration (‘Syn’) using both a Mutual Information and a local cross-correlation (‘CC’)-metric. Both metrics are weighted equally (‘metric_weight’ is .5 for both). The Mutual Information- metric uses 32 bins. The local cross-correlations (correlations between every voxel’s neighborhoods) is computed with a radius of 4.

    >>> # Test multiple metrics per stage
    >>> reg5 = copy.deepcopy(reg)
    >>> reg5.inputs.fixed_image = 'fixed1.nii'
    >>> reg5.inputs.moving_image = 'moving1.nii'
    >>> reg5.inputs.metric = ['Mattes', ['Mattes', 'CC']]
    >>> reg5.inputs.metric_weight = [1, [.5,.5]]
    >>> reg5.inputs.radius_or_number_of_bins = [32, [32, 4] ]
    >>> reg5.inputs.sampling_strategy = ['Random', None] # use default strategy in second stage
    >>> reg5.inputs.sampling_percentage = [0.05, [0.05, 0.10]]
    >>> reg5.cmdline
    'antsRegistration --collapse-output-transforms 0 --dimensionality 3 --initial-moving-transform [ trans.mat, 1 ] --initialize-transforms-per-stage 0 --interpolation Linear --output [ output_, output_warped_image.nii.gz ] --transform Affine[ 2.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32, Random, 0.05 ] --convergence [ 1500x200, 1e-08, 20 ] --smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --transform SyN[ 0.25, 3.0, 0.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 0.5, 32, None, 0.05 ] --metric CC[ fixed1.nii, moving1.nii, 0.5, 4, None, 0.1 ] --convergence [ 100x50x30, 1e-09, 20 ] --smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 1.0 ]  --write-composite-transform 1'

ANTS Registration can also use multiple modalities to perform the registration. Here it is assumed that fixed1.nii and fixed2.nii are in the same space, and so are moving1.nii and moving2.nii. First, a linear registration is performed matching fixed1.nii to moving1.nii, then a non-linear registration is performed to match fixed2.nii to moving2.nii, starting from the transformation of the first step.

    >>> # Test multiple inputS
    >>> reg6 = copy.deepcopy(reg5)
    >>> reg6.inputs.fixed_image = ['fixed1.nii', 'fixed2.nii']
    >>> reg6.inputs.moving_image = ['moving1.nii', 'moving2.nii']
    >>> reg6.cmdline
    'antsRegistration --collapse-output-transforms 0 --dimensionality 3 --initial-moving-transform [ trans.mat, 1 ] --initialize-transforms-per-stage 0 --interpolation Linear --output [ output_, output_warped_image.nii.gz ] --transform Affine[ 2.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32, Random, 0.05 ] --convergence [ 1500x200, 1e-08, 20 ] --smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --transform SyN[ 0.25, 3.0, 0.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 0.5, 32, None, 0.05 ] --metric CC[ fixed2.nii, moving2.nii, 0.5, 4, None, 0.1 ] --convergence [ 100x50x30, 1e-09, 20 ] --smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 1.0 ]  --write-composite-transform 1'

Different methods can be used for the interpolation when applying transformations.

    >>> # Test Interpolation Parameters (BSpline)
    >>> reg7a = copy.deepcopy(reg)
    >>> reg7a.inputs.interpolation = 'BSpline'
    >>> reg7a.inputs.interpolation_parameters = (3,)
    >>> reg7a.cmdline
    'antsRegistration --collapse-output-transforms 0 --dimensionality 3 --initial-moving-transform [ trans.mat, 1 ] --initialize-transforms-per-stage 0 --interpolation BSpline[ 3 ] --output [ output_, output_warped_image.nii.gz ] --transform Affine[ 2.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32, Random, 0.05 ] --convergence [ 1500x200, 1e-08, 20 ] --smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --transform SyN[ 0.25, 3.0, 0.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32 ] --convergence [ 100x50x30, 1e-09, 20 ] --smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 1.0 ]  --write-composite-transform 1'

    >>> # Test Interpolation Parameters (MultiLabel/Gaussian)
    >>> reg7b = copy.deepcopy(reg)
    >>> reg7b.inputs.interpolation = 'Gaussian'
    >>> reg7b.inputs.interpolation_parameters = (1.0, 1.0)
    >>> reg7b.cmdline
    'antsRegistration --collapse-output-transforms 0 --dimensionality 3 --initial-moving-transform [ trans.mat, 1 ] --initialize-transforms-per-stage 0 --interpolation Gaussian[ 1.0, 1.0 ] --output [ output_, output_warped_image.nii.gz ] --transform Affine[ 2.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32, Random, 0.05 ] --convergence [ 1500x200, 1e-08, 20 ] --smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --transform SyN[ 0.25, 3.0, 0.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32 ] --convergence [ 100x50x30, 1e-09, 20 ] --smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 1.0 ]  --write-composite-transform 1'

BSplineSyN non-linear registration with custom parameters.

    >>> # Test Extended Transform Parameters
    >>> reg8 = copy.deepcopy(reg)
    >>> reg8.inputs.transforms = ['Affine', 'BSplineSyN']
    >>> reg8.inputs.transform_parameters = [(2.0,), (0.25, 26, 0, 3)]
    >>> reg8.cmdline
    'antsRegistration --collapse-output-transforms 0 --dimensionality 3 --initial-moving-transform [ trans.mat, 1 ] --initialize-transforms-per-stage 0 --interpolation Linear --output [ output_, output_warped_image.nii.gz ] --transform Affine[ 2.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32, Random, 0.05 ] --convergence [ 1500x200, 1e-08, 20 ] --smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --transform BSplineSyN[ 0.25, 26, 0, 3 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32 ] --convergence [ 100x50x30, 1e-09, 20 ] --smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 1.0 ]  --write-composite-transform 1'

Mask the fixed image in the second stage of the registration (but not the first).

    >>> # Test masking
    >>> reg9 = copy.deepcopy(reg)
    >>> reg9.inputs.fixed_image_masks = ['NULL', 'fixed1.nii']
    >>> reg9.cmdline
    'antsRegistration --collapse-output-transforms 0 --dimensionality 3 --initial-moving-transform [ trans.mat, 1 ] --initialize-transforms-per-stage 0 --interpolation Linear --output [ output_, output_warped_image.nii.gz ] --transform Affine[ 2.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32, Random, 0.05 ] --convergence [ 1500x200, 1e-08, 20 ] --smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --masks [ NULL, NULL ] --transform SyN[ 0.25, 3.0, 0.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32 ] --convergence [ 100x50x30, 1e-09, 20 ] --smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --masks [ fixed1.nii, NULL ] --winsorize-image-intensities [ 0.0, 1.0 ]  --write-composite-transform 1'

Here we use both a warpfield and a linear transformation, before registration commences. Note that the first transformation that needs to be applied (‘ants_Warp.nii.gz’) is last in the list of ‘initial_moving_transform’.

    >>> # Test initialization with multiple transforms matrices (e.g., unwarp and affine transform)
    >>> reg10 = copy.deepcopy(reg)
    >>> reg10.inputs.initial_moving_transform = ['func_to_struct.mat', 'ants_Warp.nii.gz']
    >>> reg10.inputs.invert_initial_moving_transform = [False, False]
    >>> reg10.cmdline
    'antsRegistration --collapse-output-transforms 0 --dimensionality 3 --initial-moving-transform [ func_to_struct.mat, 0 ] [ ants_Warp.nii.gz, 0 ] --initialize-transforms-per-stage 0 --interpolation Linear --output [ output_, output_warped_image.nii.gz ] --transform Affine[ 2.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32, Random, 0.05 ] --convergence [ 1500x200, 1e-08, 20 ] --smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --transform SyN[ 0.25, 3.0, 0.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32 ] --convergence [ 100x50x30, 1e-09, 20 ] --smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 1.0 ]  --write-composite-transform 1'

Inputs:

    [Mandatory]
    fixed_image: (a list of items which are an existing file name)
            Image to which the moving_image should be transformed(usually a
            structural image)
    metric: (a list of items which are u'CC' or u'MeanSquares' or
             u'Demons' or u'GC' or u'MI' or u'Mattes' or a list of items which
             are u'CC' or u'MeanSquares' or u'Demons' or u'GC' or u'MI' or
             u'Mattes')
            the metric(s) to use for each stage. Note that multiple metrics per
            stage are not supported in ANTS 1.9.1 and earlier.
    metric_weight: (a list of items which are a float or a list of items
             which are a float, nipype default value: [1.0])
            the metric weight(s) for each stage. The weights must sum to 1 per
            stage.
            requires: metric
    moving_image: (a list of items which are an existing file name)
            Image that will be registered to the space of fixed_image. This is
            theimage on which the transformations will be applied to
    shrink_factors: (a list of items which are a list of items which are
             an integer (int or long))
    smoothing_sigmas: (a list of items which are a list of items which
             are a float)
    transforms: (a list of items which are u'Rigid' or u'Affine' or
             u'CompositeAffine' or u'Similarity' or u'Translation' or u'BSpline'
             or u'GaussianDisplacementField' or u'TimeVaryingVelocityField' or
             u'TimeVaryingBSplineVelocityField' or u'SyN' or u'BSplineSyN' or
             u'Exponential' or u'BSplineExponential')
            flag: %s

    [Optional]
    args: (a unicode string)
            Additional parameters to the command
            flag: %s
    collapse_output_transforms: (a boolean, nipype default value: False)
            Collapse output transforms. Specifically, enabling this option
            combines all adjacent linear transforms and composes all adjacent
            displacement field transforms before writing the results to disk.
            flag: --collapse-output-transforms %d
    convergence_threshold: (a list of at least 1 items which are a float,
             nipype default value: [1e-06])
            requires: number_of_iterations
    convergence_window_size: (a list of at least 1 items which are an
             integer (int or long), nipype default value: [10])
            requires: convergence_threshold
    dimension: (3 or 2, nipype default value: 3)
            image dimension (2 or 3)
            flag: --dimensionality %d
    environ: (a dictionary with keys which are a newbytes or None or a
             newstr or None and with values which are a newbytes or None or a
             newstr or None, nipype default value: {})
            Environment variables
    fixed_image_mask: (an existing file name)
            Mask used to limit metric sampling region of the fixed imagein all
            stages
            flag: %s
            mutually_exclusive: fixed_image_masks
    fixed_image_masks: (a list of items which are an existing file name
             or u'NULL')
            Masks used to limit metric sampling region of the fixed image,
            defined per registration stage(Use "NULL" to omit a mask at a given
            stage)
            mutually_exclusive: fixed_image_mask
    float: (a boolean)
            Use float instead of double for computations.
            flag: --float %d
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    initial_moving_transform: (a list of items which are an existing file
             name)
            A transform or a list of transforms that should be appliedbefore the
            registration begins. Note that, when a list is given,the
            transformations are applied in reverse order.
            flag: %s
            mutually_exclusive: initial_moving_transform_com
    initial_moving_transform_com: (0 or 1 or 2)
            Align the moving_image nad fixed_image befor registration usingthe
            geometric center of the images (=0), the image intensities (=1),or
            the origin of the images (=2)
            flag: %s
            mutually_exclusive: initial_moving_transform
    initialize_transforms_per_stage: (a boolean, nipype default value:
             False)
            Initialize linear transforms from the previous stage. By enabling
            this option, the current linear stage transform is directly
            intialized from the previous stages linear transform; this allows
            multiple linear stages to be run where each stage directly updates
            the estimated linear transform from the previous stage. (e.g.
            Translation -> Rigid -> Affine).
            flag: --initialize-transforms-per-stage %d
    interpolation: (u'Linear' or u'NearestNeighbor' or
             u'CosineWindowedSinc' or u'WelchWindowedSinc' or
             u'HammingWindowedSinc' or u'LanczosWindowedSinc' or u'BSpline' or
             u'MultiLabel' or u'Gaussian', nipype default value: Linear)
            flag: %s
    interpolation_parameters: (a tuple of the form: (an integer (int or
             long)) or a tuple of the form: (a float, a float))
    invert_initial_moving_transform: (a list of items which are a
             boolean)
            One boolean or a list of booleans that indicatewhether the
            inverse(s) of the transform(s) definedin initial_moving_transform
            should be used.
            mutually_exclusive: initial_moving_transform_com
            requires: initial_moving_transform
    metric_item_trait: (u'CC' or u'MeanSquares' or u'Demons' or u'GC' or
             u'MI' or u'Mattes')
    metric_stage_trait: (u'CC' or u'MeanSquares' or u'Demons' or u'GC' or
             u'MI' or u'Mattes' or a list of items which are u'CC' or
             u'MeanSquares' or u'Demons' or u'GC' or u'MI' or u'Mattes')
    metric_weight_item_trait: (a float)
    metric_weight_stage_trait: (a float or a list of items which are a
             float)
    moving_image_mask: (an existing file name)
            mask used to limit metric sampling region of the moving imagein all
            stages
            mutually_exclusive: moving_image_masks
            requires: fixed_image_mask
    moving_image_masks: (a list of items which are an existing file name
             or u'NULL')
            Masks used to limit metric sampling region of the moving image,
            defined per registration stage(Use "NULL" to omit a mask at a given
            stage)
            mutually_exclusive: moving_image_mask
    num_threads: (an integer (int or long), nipype default value: 1)
            Number of ITK threads to use
    number_of_iterations: (a list of items which are a list of items
             which are an integer (int or long))
    output_inverse_warped_image: (a boolean or a file name)
            requires: output_warped_image
    output_transform_prefix: (a unicode string, nipype default value:
             transform)
            flag: %s
    output_warped_image: (a boolean or a file name)
    radius_bins_item_trait: (an integer (int or long))
    radius_bins_stage_trait: (an integer (int or long) or a list of items
             which are an integer (int or long))
    radius_or_number_of_bins: (a list of items which are an integer (int
             or long) or a list of items which are an integer (int or long),
             nipype default value: [5])
            the number of bins in each stage for the MI and Mattes metric, the
            radius for other metrics
            requires: metric_weight
    restore_state: (an existing file name)
            Filename for restoring the internal restorable state of the
            registration
            flag: --restore-state %s
    restrict_deformation: (a list of items which are a list of items
             which are 0 or 1)
            This option allows the user to restrict the optimization of the
            displacement field, translation, rigid or affine transform on a per-
            component basis. For example, if one wants to limit the deformation
            or rotation of 3-D volume to the first two dimensions, this is
            possible by specifying a weight vector of '1x1x0' for a deformation
            field or '1x1x0x1x1x0' for a rigid transformation. Low-dimensional
            restriction only works if there are no preceding transformations.
    sampling_percentage: (a list of items which are 0.0 <= a floating
             point number <= 1.0 or None or a list of items which are 0.0 <= a
             floating point number <= 1.0 or None)
            the metric sampling percentage(s) to use for each stage
            requires: sampling_strategy
    sampling_percentage_item_trait: (0.0 <= a floating point number <=
             1.0 or None)
    sampling_percentage_stage_trait: (0.0 <= a floating point number <=
             1.0 or None or a list of items which are 0.0 <= a floating point
             number <= 1.0 or None)
    sampling_strategy: (a list of items which are u'None' or u'Regular'
             or u'Random' or None or a list of items which are u'None' or
             u'Regular' or u'Random' or None)
            the metric sampling strategy (strategies) for each stage
            requires: metric_weight
    sampling_strategy_item_trait: (u'None' or u'Regular' or u'Random' or
             None)
    sampling_strategy_stage_trait: (u'None' or u'Regular' or u'Random' or
             None or a list of items which are u'None' or u'Regular' or
             u'Random' or None)
    save_state: (a file name)
            Filename for saving the internal restorable state of the
            registration
            flag: --save-state %s
    sigma_units: (a list of items which are u'mm' or u'vox')
            units for smoothing sigmas
            requires: smoothing_sigmas
    terminal_output: (u'stream' or u'allatonce' or u'file' or u'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
    transform_parameters: (a list of items which are a tuple of the form:
             (a float) or a tuple of the form: (a float, a float, a float) or a
             tuple of the form: (a float, an integer (int or long), an integer
             (int or long), an integer (int or long)) or a tuple of the form: (a
             float, an integer (int or long), a float, a float, a float, a
             float) or a tuple of the form: (a float, a float, a float, an
             integer (int or long)) or a tuple of the form: (a float, an integer
             (int or long), an integer (int or long), an integer (int or long),
             an integer (int or long)))
    use_estimate_learning_rate_once: (a list of items which are a
             boolean)
    use_histogram_matching: (a boolean or a list of items which are a
             boolean, nipype default value: True)
            Histogram match the images before registration.
    verbose: (a boolean)
            flag: -v
    winsorize_lower_quantile: (0.0 <= a floating point number <= 1.0,
             nipype default value: 0.0)
            The Lower quantile to clip image ranges
            flag: %s
    winsorize_upper_quantile: (0.0 <= a floating point number <= 1.0,
             nipype default value: 1.0)
            The Upper quantile to clip image ranges
            flag: %s
    write_composite_transform: (a boolean, nipype default value: False)
            flag: --write-composite-transform %d

Outputs:

    composite_transform: (an existing file name)
            Composite transform file
    elapsed_time: (a float)
            the total elapsed time as reported by ANTs
    forward_invert_flags: (a list of items which are a boolean)
            List of flags corresponding to the forward transforms
    forward_transforms: (a list of items which are an existing file name)
            List of output transforms for forward registration
    inverse_composite_transform: (a file name)
            Inverse composite transform file
    inverse_warped_image: (a file name)
            Outputs the inverse of the warped image
    metric_value: (a float)
            the final value of metric
    reverse_invert_flags: (a list of items which are a boolean)
            List of flags corresponding to the reverse transforms
    reverse_transforms: (a list of items which are an existing file name)
            List of output transforms for reverse registration
    save_state: (a file name)
            The saved registration state to be restored
    warped_image: (a file name)
            Outputs warped image