interfaces.ants.registration¶
Registration¶
Wraps command antsRegistration
Examples¶
>>> 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.invert_initial_moving_transform = True
>>> 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'
>>> 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()
>>> 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'
>>> 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'
>>> 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'
>>> 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'
>>> # 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',
'forward_invert_flags': [],
'forward_transforms': [],
'inverse_composite_transform': '.../nipype/testing/data/output_InverseComposite.h5',
'inverse_warped_image': <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>,
'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>,
'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'
>>> # 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'
>>> # 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'
>>> # 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'
>>> # 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'
Inputs:
[Mandatory]
fixed_image: (a list of items which are an existing file name)
image to apply transformation to (generally a coregistered
functional)
metric: (a list of items which are 'CC' or 'MeanSquares' or 'Demons'
or 'GC' or 'MI' or 'Mattes' or a list of items which are 'CC' or
'MeanSquares' or 'Demons' or 'GC' or 'MI' or '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 to apply transformation to (generally a coregistered
functional)
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 'Rigid' or 'Affine' or
'CompositeAffine' or 'Similarity' or 'Translation' or 'BSpline' or
'GaussianDisplacementField' or 'TimeVaryingVelocityField' or
'TimeVaryingBSplineVelocityField' or 'SyN' or 'BSplineSyN' or
'Exponential' or 'BSplineExponential')
flag: %s
[Optional]
args: (a 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 value of type 'str' and
with values which are a value of type 'str', 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
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: (an existing file name)
flag: %s
mutually_exclusive: initial_moving_transform_com
initial_moving_transform_com: (0 or 1 or 2)
Use center of mass for moving transform
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: ('Linear' or 'NearestNeighbor' or 'CosineWindowedSinc'
or 'WelchWindowedSinc' or 'HammingWindowedSinc' or
'LanczosWindowedSinc' or 'BSpline' or 'MultiLabel' or '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 boolean)
mutually_exclusive: initial_moving_transform_com
requires: initial_moving_transform
metric_item_trait: ('CC' or 'MeanSquares' or 'Demons' or 'GC' or 'MI'
or 'Mattes')
metric_stage_trait: ('CC' or 'MeanSquares' or 'Demons' or 'GC' or
'MI' or 'Mattes' or a list of items which are 'CC' or 'MeanSquares'
or 'Demons' or 'GC' or 'MI' or '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 image
requires: fixed_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 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
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 'None' or 'Regular' or
'Random' or None or a list of items which are 'None' or 'Regular'
or 'Random' or None)
the metric sampling strategy (strategies) for each stage
requires: metric_weight
sampling_strategy_item_trait: ('None' or 'Regular' or 'Random' or
None)
sampling_strategy_stage_trait: ('None' or 'Regular' or 'Random' or
None or a list of items which are 'None' or 'Regular' or '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 'mm' or 'vox')
units for smoothing sigmas
requires: smoothing_sigmas
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
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)
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
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
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