医学图像配准工具ANTs的学习——相关函数以及参数介绍

ants.registration(fixed, moving, type_of_transform=‘SyN’, initial_transform=None, outprefix=’’, mask=None, grad_step=0.2, flow_sigma=3, total_sigma=0, aff_metric=‘mattes’, aff_sampling=32, aff_random_sampling_rate=0.2, syn_metric=‘mattes’, syn_sampling=32, reg_iterations=(40, 20, 0), aff_iterations=(2100, 1200, 1200, 10), aff_shrink_factors=(6, 4, 2, 1), aff_smoothing_sigmas=(3, 2, 1, 0), write_composite_transform=False, random_seed=None, verbose=False, multivariate_extras=None, **kwargs)

Register a pair of images either through the full or simplified interface to the ANTs registration method.

ANTsR function: antsRegistration

Parameters:

• fixed (ANTsImage) – fixed image to which we register the moving image.
• moving (ANTsImage) – moving image to be mapped to fixed space.
• type_of_transform (string) – A linear or non-linear registration type. Mutual information metric by default. See Notes below for more.
• initial_transform (list of strings (optional)) – transforms to prepend.
• outprefix (string) – output will be named with this prefix.
• mask (ANTsImage (optional)) – mask the registration.
• grad_step (scalar) – gradient step size (not for all tx)
• flow_sigma (scalar) – smoothing for update field
• total_sigma (scalar) – smoothing for total field
• aff_metric (string) – the metric for the affine part (GC, mattes,
  meansquares)
• aff_sampling (scalar) – the nbins or radius parameter for the syn metric
• aff_random_sampling_rate (scalar) – the fraction of points used to estimate the metric. this can impact speed but also reproducibility and/or accuracy.
• syn_metric (string) – the metric for the syn part (CC, mattes, meansquares, demons)
• syn_sampling (scalar) – the nbins or radius parameter for the syn metric
• reg_iterations (list/tuple of python:integers) – vector of iterations for syn. we will set the smoothing and multi-resolution parameters based on the length of this vector.
• aff_iterations (list/tuple of python:integers) –vector of iterations for low-dimensional (translation, rigid, affine) registration.
• aff_shrink_factors (list/tuple of python:integers) – vector of multi-resolution shrink factors for low-dimensional (translation,rigid, affine) registration.
• aff_smoothing_sigmas (list/tuple of python:integers) – vector of multi-resolution smoothing factors for low-dimensional (translation,
  rigid, affine) registration.
• random_seed (integer) – random seed to improve reproducibility. note that the number of ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS should be 1 if you want perfect reproducibility. write_composite_transform (boolean) – Boolean specifying whether or not the composite transform (and its inverse, if it exists) should be written to an hdf5 composite file. This is false by default so that only the transform for each stage is written to file.
• verbose (boolean) – request verbose output (useful for debugging)
• multivariate_extras (additional metrics for multi-metric registration) – list of additional images and metrics which will trigger the use of multiple metrics in the registration process in the deformable stage. Each multivariate metric needs 5 entries: name of metric, fixed, moving, weight, samplingParam. the list of lists should be of the form ( (“nameOfMetric2”, img, img, weight, metricParam ) ). Another example would be ( ( “MeanSquares”, f2, m2,0.5, 0), ( “CC”, f2, m2, 0.5, 2 ) ) . This is only compatible with the SyNOnly transformation.

kwargs : keyword args
extra arguments
Returns:

• warpedmovout: Moving image warped to space of fixed image.
• warpedfixout: Fixed image warped to space of moving image.
• fwdtransforms: Transforms to move from moving to fixed image.
• invtransforms: Transforms to move from fixed to moving image.
  Return type :dict containing follow key/value pairs

Notes
type_of_transform can be one of:

• “Translation”: Translation transformation.
• “Rigid”: Rigid transformation: Only rotation and translation.
• “Similarity”: Similarity transformation: scaling, rotation and
  translation.
• “QuickRigid”: Rigid transformation: Only rotation and translation. May be useful for quick visualization fixes.’
• “DenseRigid”: Rigid transformation: Only rotation and translation. Employs dense sampling during metric estimation.’
• “BOLDRigid”: Rigid transformation: Parameters typical for BOLD to BOLD intrasubject registration’.’
• “Affine”: Affine transformation: Rigid + scaling.
• “AffineFast”: Fast version of Affine.
• “BOLDAffine”: Affine transformation: Parameters typical for BOLD to BOLD intrasubject registration’.’
• “TRSAA”: translation, rigid, similarity, affine (twice). please set regIterations if using this option. this would be used in cases where you want a really high quality affine mapping (perhaps with mask).
• “Elastic”: Elastic deformation: Affine + deformable.
• “ElasticSyN”: Symmetric normalization: Affine + deformable transformation, with mutual information as optimization metric and elastic regularization.
• “SyN”: Symmetric normalization: Affine + deformable transformation,with mutual information as optimization metric.
• “SyNRA”: Symmetric normalization: Rigid + Affine + deformable transformation, with mutual information as optimization metric.
• “SyNOnly”: Symmetric normalization: no initial transformation,with mutual information as optimization metric. Assumes images are aligned by an inital transformation. Can be useful if you want to run an unmasked affine followed by masked deformable registration.
• “SyNCC”: SyN, but with cross-correlation as the metric.
• “SyNabp”: SyN optimized for abpBrainExtraction.
• “SyNBold”: SyN, but optimized for registrations between BOLD and T1 images.
• “SyNBoldAff”: SyN, but optimized for registrations between BOLD and T1 images, with additional affine step.
• “SyNAggro”: SyN, but with more aggressive registration(fine-scale matching and more deformation). Takes more time than SyN.
• “TV[n]”: time-varying diffeomorphism with where ‘n’ indicates number of time points in velocity field discretization. The initial transform should be computed, if needed, in a separate call to ants.registration.
• “TVMSQ”: time-varying diffeomorphism with mean square metric
• “TVMSQC”: time-varying diffeomorphism with mean square metric for very large deformation
• “antsRegistrationSyN[x]”: recreation of the antsRegistrationSyN.sh script in ANTs where ‘x’ is one of the transforms available (e.g.,‘t’, ‘b’, ‘s’)
• “antsRegistrationSyNQuick[x]”: recreation of the antsRegistrationSyNQuick.sh script in ANTs where ‘x’ is one of the transforms available (e.g., ‘t’, ‘b’, ‘s’)

Example

import ants
fi = ants.image_read(ants.get_ants_data('r16'))
mi = ants.image_read(ants.get_ants_data('r64'))
fi = ants.resample_image(fi, (60,60), 1, 0)
mi = ants.resample_image(mi, (60,60), 1, 0)
mytx = ants.registration(fixed=fi, moving=mi, type_of_transform = 'SyN' )

ants.affine_initializer(fixed_image, moving_image, search_factor=20, radian_fraction=0.1,use_principal_axis=False, local_search_iterations=10, mask=None, txfn=None)
A multi-start optimizer for affine registration Searches over the sphere to find a good initialization for further registration refinement, if needed. This is a arapper for the ANTs function antsAffineInitializer.

ANTsR function: affineInitializer
Parameters:

• fixed_image (ANTsImage) – the fixed reference image
• moving_image (ANTsImage) – the moving image to be mapped to the fixed space
• search_factor (scalar) – degree of increments on the sphere to search
• radian_fraction (scalar) – between zero and one, defines the arc to search over
• use_principal_axis (boolean) – boolean to initialize by principal axis
• local_search_iterations (scalar) – gradient descent iterations
• mask (ANTsImage (optional)) – optional mask to restrict registration
• txfn (string (optional)) – filename for the transformation
  Returns:
  transformation matrix
  Return type: ndarray

Example

import ants
fi = ants.image_read(ants.get_ants_data('r16'))
mi = ants.image_read(ants.get_ants_data('r27'))
txfile = ants.affine_initializer( fi, mi )
tx = ants.read_transform(txfile, dimension=2)

详细函数以及参数介绍可见：官网Registration