bionic (1) antsSliceRegularizedRegistration.1.gz
NAME
antsSliceRegularizedRegistration - part of ANTS registration suite
DESCRIPTION
COMMAND:
antsSliceRegularizedRegistration
antsSliceRegularizedRegistration This program is a user-level application for slice-by-slice
translation registration. Results are regularized in z using polynomial regression. The program is
targeted at spinal cord MRI. Only one stage is supported where a stage consists of a transform; an
image metric; and iterations, shrink factors, and smoothing sigmas for each level. Specialized for
3D data: fixed image is 3D, moving image is 3D. Registration is performed slice-by-slice then
regularized in z. The parameter -p controls the polynomial degree. -p 0 means no
regularization.Implemented by B. Avants and conceived by Julien Cohen-Adad.
Outputs:
OutputPrefixTxTy_poly.csv: polynomial fit to Tx &
Ty
OutputPrefix.nii.gz: transformed image
Example call:
antsSliceRegularizedRegistration -p 4 --output [OutputPrefix,OutputPrefix.nii.gz] --transform
Translation[0.1] --metric MI[ fixed.nii.gz, moving.nii.gz , 1 , 16 , Regular , 0.2 ] --iterations
20 --shrinkFactors 1 --smoothingSigmas 0
OPTIONS:
-m, --metric
CC[fixedImage,movingImage,metricWeight,radius,<samplingStrategy={Regular,Random}>,<samplingPercentage=[0,1]>]
MI[fixedImage,movingImage,metricWeight,numberOfBins,<samplingStrategy={Regular,Random}>,<samplingPercentage=[0,1]>]
MeanSquares[fixedImage,movingImage,metricWeight,radius,<samplingStrategy={Regular,Random}>,<samplingPercentage=[0,1]>]
GC[fixedImage,movingImage,metricWeight,radius,<samplingStrategy={Regular,Random}>,<samplingPercentage=[0,1]>]
Four image metrics are available--- GC : global correlation, CC: ANTS neighborhood cross
correlation, MI: Mutual information, and MeanSquares: mean-squares intensity difference. Note that
the metricWeight is currently not used. Rather, it is a temporary place holder until multivariate
metrics are available for a single stage.
-x, --mask mask-in-fixed-image-space.nii.gz
Fixed image mask to limit voxels considered by the metric.
-n, --interpolation Linear
NearestNeighbor MultiLabel[<sigma=imageSpacing>,<alpha=4.0>]
Gaussian[<sigma=imageSpacing>,<alpha=1.0>] BSpline[<order=3>] CosineWindowedSinc WelchWindowedSinc
HammingWindowedSinc LanczosWindowedSinc GenericLabel[<interpolator=Linear>]
Several interpolation options are available in ITK. These have all been made available.
-t, --transform Translation[gradientStep]
Rigid[gradientStep] Similarity[gradientStep]
Several transform options are available. The gradientStep orlearningRate characterizes the
gradient descent optimization and is scaled appropriately for each transform using the shift
scales estimator. Subsequent parameters are transform-specific and can be determined from the
usage.
-i, --iterations MxNx0...
Specify the number of iterations at each level.
-s, --smoothingSigmas MxNx0...
Specify the amount of smoothing at each level.
-f, --shrinkFactors MxNx0...
Specify the shrink factor for the virtual domain (typically the fixed image) at each level.
-o, --output [outputTransformPrefix,<outputWarpedImage>,<outputAverageImage>]
Specify the output transform prefix (output format is .nii.gz ).Optionally, one can choose to warp
the moving image to the fixed space and, if the inverse transform exists, one can also output the
warped fixed image.
-h, --help
Print the help menu (short version). <VALUES>: 1, 0
-v, --verbose
verbose option <VALUES>: 0
-p, --polydegree
degree of polynomial - up to zDimension-2. Controls the polynomial degree. 0 means no
regularization. This may be a vector denoted by 2x2x1 for a 3-parameter transform ( e.g. rigid ).
This would regularize the translation by 2nd degree polynomial and the rotation by a linear
function.