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

antsMotionCorr

antsMotionCorr = motion correction. 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. Specialized for
4D time series data: fixed image is 3D, moving image should be the 4D time
series. Fixed image is a reference space or time slice.
OPTIONS:

d, dimensionality 2/3

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

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.

n, nimages 10

 This option sets the number of images to use to construct the template image.

m, metric CC[fixedImage,movingImage,metricWeight,radius,<samplingStrategy={Regular,Random}>,<samplingPercentage=[0,1]>]

 MI[fixedImage,movingImage,metricWeight,numberOfBins,<samplingStrategy={Regular,Random}>,<samplingPercentage=[0,1]>]
Demons[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 Demons: Thirion's
Demons (modified meansquares). Note that the metricWeight is currently not
used. Rather, it is a temporary place holder until multivariate metrics are
available for a single stage. The fixed image should be a single time point (eg
the average of the time series). By default, this image is not used, the fixed
image for correction of each volume is the preceding volume in the time series.
See below for the option to use a fixed reference image for all volumes.

u, useFixedReferenceImage (0)/1

 use a fixed reference image to correct all volumes, instead of correcting each
image to the prior volume in the time series.

e, useScalesEstimator

 use the scale estimator to control optimization.
 t, transform Affine[gradientStep]

Rigid[gradientStep]
GaussianDisplacementField[gradientStep,updateFieldSigmaInPhysicalSpace,totalFieldSigmaInPhysicalSpace]
SyN[gradientStep,updateFieldSigmaInPhysicalSpace,totalFieldSigmaInPhysicalSpace]

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
transformspecific 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.

a, averageimage

 Average the input time series image.

w, writedisplacement

 Write the lowdimensional 3D transforms to a 4D displacement field

v, verbose (0)/1

 Verbose output.

h

 Print the help menu (short version).
<VALUES>: 0

help

 Print the help menu.
<VALUES>: 1, 0