antsRegistration(1)
part of ANTS registration suite
DESCRIPTION
COMMAND:

antsRegistration

This program is a userlevel registration application meant to utilize
ITKv4only classes. The user can specify any number of "stages" where a stage
consists of a transform; an image metric; and iterations, shrink factors, and
smoothing sigmas for each level. Note that explicitly setting the
dimensionality, metric, transform, output, convergence, shrinkfactors, and
smoothingsigmas parameters is mandatory.
OPTIONS:

version

 Get Version Information.

d, dimensionality 2/3

 This option forces the image to be treated as a specifieddimensional image. If
not specified, we try to infer the dimensionality from the input image.

o, output outputTransformPrefix

[outputTransformPrefix,<outputWarpedImage>,<outputInverseWarpedImage>]

 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. Note that only the
images specified in the first metric call are warped. Use antsApplyTransforms to
warp other images using the resultant transform(s).

j, savestate saveSateAsTransform

 Specify the output file for the current state of the registration. The state
file is written to an hdf5 composite file. It is specially usefull if we want to
save the current state of a SyN registration to the disk, so we can load and
restore that later to continue the next registration process directly started
from the last saved state. The output file of this flag is the same as the
writecompositetransform, unless the last transform is a SyN transform. In that
case, the inverse displacement field of the SyN transform is also added to the
output composite transform. Again notice that this file cannot be treated as a
transform, and restorestate option must be used to load the written file by
this flag.

k, restorestate restoreStateAsATransform

 Specify the initial state of the registration which get immediately used to
directly initialize the registration process. The flag is mutually exclusive
with other intialization flags.If this flag is used, none of the
initialmovingtransform and initialfixedtransform cannot be used.

a, writecompositetransform 1/(0)

 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.
<VALUES>: 0

p, printsimilaritymeasureinterval <unsignedIntegerValue>

 Prints out the CC similarity metric measure between the fullsize input fixed
and the transformed moving images at each iteration a value of 0 (the default)
indicates that the full scale computation should not take placeany value greater
than 0 represents the interval of full scale metric computation.
<VALUES>: 0

writeintervalvolumes <unsignedIntegerValue>

 Writes out the output volume at each iteration. It helps to present the
registration process as a short movie a value of 0 (the default) indicates that
this option should not take placeany value greater than 0 represents the
interval between the iterations which outputs are written to the disk.
<VALUES>: 0

z, collapseoutputtransforms (1)/0

 Collapse output transforms. Specifically, enabling this option combines all
adjacent transforms wherepossible. All adjacent linear transforms are written to
disk in the forman itk affine transform (called xxxGenericAffine.mat).
Similarly, all adjacent displacement field transforms are combined when written
to disk (e.g. xxxWarp.nii.gz and xxxInverseWarp.nii.gz (if available)).Also, an
output composite transform including the collapsed transforms is written to the
disk (called outputCollapsed(Inverse)Composite).
<VALUES>: 1

i, initializetransformsperstage (1)/0

 Initialize linear transforms from the previous stage. By enabling this option,
the current linear stage transform is directly intialized from the previous
stage's 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).
<VALUES>: 0
 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. Currently the interpolator choice is only used to warp (and possibly
inverse warp) the final output image(s).

g, restrictdeformation PxQxR

 This option allows the user to restrict the optimization of the displacement
field, translation, rigid or affine transform on a percomponent basis. For
example, if one wants to limit the deformation or rotation of 3D 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.
Lowdimensional restriction only works if there are no preceding
transformations.
 q, initialfixedtransform initialTransform

[initialTransform,<useInverse>]
[fixedImage,movingImage,initializationFeature]

Specify the initial fixed transform(s) which get immediately incorporated into
the composite transform. The order of the transforms is stackesque in that the
last transform specified on the command line is the first to be applied. In
addition to initialization with ITK transforms, the user can perform an initial
translation alignment by specifying the fixed and moving images and selecting an
initialization feature. These features include using the geometric center of the
images (=0), the image intensities (=1), or the origin of the images (=2).
 r, initialmovingtransform initialTransform

[initialTransform,<useInverse>]
[fixedImage,movingImage,initializationFeature]

Specify the initial moving transform(s) which get immediately incorporated into
the composite transform. The order of the transforms is stackesque in that the
last transform specified on the command line is the first to be applied. In
addition to initialization with ITK transforms, the user can perform an initial
translation alignment by specifying the fixed and moving images and selecting an
initialization feature. These features include using the geometric center of the
images (=0), the image intensities (=1), or the origin of the images (=2).

m, metric CC[fixedImage,movingImage,metricWeight,radius,<samplingStrategy={None,Regular,Random}>,<samplingPercentage=[0,1]>]

 MI[fixedImage,movingImage,metricWeight,numberOfBins,<samplingStrategy={None,Regular,Random}>,<samplingPercentage=[0,1]>]
Mattes[fixedImage,movingImage,metricWeight,numberOfBins,<samplingStrategy={None,Regular,Random}>,<samplingPercentage=[0,1]>]
MeanSquares[fixedImage,movingImage,metricWeight,radius=NA,<samplingStrategy={None,Regular,Random}>,<samplingPercentage=[0,1]>]
Demons[fixedImage,movingImage,metricWeight,radius=NA,<samplingStrategy={None,Regular,Random}>,<samplingPercentage=[0,1]>]
GC[fixedImage,movingImage,metricWeight,radius=NA,<samplingStrategy={None,Regular,Random}>,<samplingPercentage=[0,1]>]
ICP[fixedPointSet,movingPointSet,metricWeight,<samplingPercentage=[0,1]>,<boundaryPointsOnly=0>]
PSE[fixedPointSet,movingPointSet,metricWeight,<samplingPercentage=[0,1]>,<boundaryPointsOnly=0>,<pointSetSigma=1>,<kNeighborhood=50>]
JHCT[fixedPointSet,movingPointSet,metricWeight,<samplingPercentage=[0,1]>,<boundaryPointsOnly=0>,<pointSetSigma=1>,<kNeighborhood=50>,<alpha=1.1>,<useAnisotropicCovariances=1>]
IGDM[fixedImage,movingImage,metricWeight,fixedMask,movingMask,<neighborhoodRadius=0x0>,<intensitySigma=0>,<distanceSigma=0>,<kNeighborhood=1>,<gradientSigma=1>]

These image metrics are available CC: ANTS neighborhood cross correlation,
MI: Mutual information, Demons: (Thirion), MeanSquares, and GC: Global
Correlation. The "metricWeight" variable is used to modulate the per stage
weighting of the metrics. The metrics can also employ a sampling strategy
defined by a sampling percentage. The sampling strategy defaults to 'None' (aka
a dense sampling of one sample per voxel), otherwise it defines a point set over
which to optimize the metric. The point set can be on a regular lattice or a
random lattice of points slightly perturbed to minimize aliasing artifacts.
samplingPercentage defines the fraction of points to select from the domain. In
addition, three point set metrics are available: Euclidean (ICP), Pointset
expectation (PSE), and JensenHavrdaCharvetTsallis (JHCT).
 t, transform Rigid[gradientStep]

Affine[gradientStep]
CompositeAffine[gradientStep]
Similarity[gradientStep]
Translation[gradientStep]
BSpline[gradientStep,meshSizeAtBaseLevel]
GaussianDisplacementField[gradientStep,updateFieldVarianceInVoxelSpace,totalFieldVarianceInVoxelSpace]
BSplineDisplacementField[gradientStep,updateFieldMeshSizeAtBaseLevel,totalFieldMeshSizeAtBaseLevel,<splineOrder=3>]
TimeVaryingVelocityField[gradientStep,numberOfTimeIndices,updateFieldVarianceInVoxelSpace,updateFieldTimeVariance,totalFieldVarianceInVoxelSpace,totalFieldTimeVariance]
TimeVaryingBSplineVelocityField[gradientStep,velocityFieldMeshSize,<numberOfTimePointSamples=4>,<splineOrder=3>]
SyN[gradientStep,updateFieldVarianceInVoxelSpace,totalFieldVarianceInVoxelSpace]
BSplineSyN[gradientStep,updateFieldMeshSizeAtBaseLevel,totalFieldMeshSizeAtBaseLevel,<splineOrder=3>]
Exponential[gradientStep,updateFieldVarianceInVoxelSpace,velocityFieldVarianceInVoxelSpace,<numberOfIntegrationSteps>]
BSplineExponential[gradientStep,updateFieldMeshSizeAtBaseLevel,velocityFieldMeshSizeAtBaseLevel,<numberOfIntegrationSteps>,<splineOrder=3>]

Several transform options are available. The gradientStep or learningRate
characterizes the gradient descent optimization and is scaled appropriately for
each transform using the shift scales estimator. Subsequent parameters are
transformspecific and can be determined from the usage. For the Bspline
transforms one can also specify the smoothing in terms of spline distance (i.e.
knot spacing).
 c, convergence MxNxO

[MxNxO,<convergenceThreshold=1e6>,<convergenceWindowSize=10>]

Convergence is determined from the number of iterations per level and is
determined by fitting a line to the normalized energy profile of the last N
iterations (where N is specified by the window size) and determining the slope
which is then compared with the convergence threshold.

s, smoothingsigmas MxNxO...

 Specify the sigma of gaussian smoothing at each level. Units are given in terms
of voxels ('vox') or physical spacing ('mm'). Example usage is '4x2x1mm' and
'4x2x1vox' where no units implies voxel spacing.

f, shrinkfactors MxNxO...

 Specify the shrink factor for the virtual domain (typically the fixed image) at
each level.

u, usehistogrammatching

 Histogram match the images before registration.

l, useestimatelearningrateonce

 turn on the option that lets you estimate the learning rate step size only at
the beginning of each level. * useful as a second stage of finescale
registration.

w, winsorizeimageintensities [lowerQuantile,upperQuantile]

 Winsorize data based on specified quantiles.

x, masks [fixedImageMask,movingImageMask]

 Image masks to limit voxels considered by the metric. Two options are allowed
for mask specification: 1) Either the user specifies a single mask to be used
for all stages or 2) the user specifies a mask for each stage. With the latter
one can select to which stages masks are applied by supplying valid file names.
If the file does not exist, a mask will not be used for that stage. Note that we
handle the fixed and moving masks separately to enforce this constraint.

float

 Use 'float' instead of 'double' for computations.
<VALUES>: 0

minc

 Use MINC file formats for transformations.
<VALUES>: 0

v, verbose (0)/1

 Verbose output.

h

 Print the help menu (short version).

help

 Print the help menu. Will also print values used on the current command line
call.
<VALUES>: 1