NAME

       antsMotionCorr - part of ANTS registration suite

DESCRIPTION

   COMMAND:
              antsMotionCorr

              antsMotionCorr = motion correction. This program is a user-level registration application meant to
              utilize  ITKv4-only 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 specified-dimensional image. If not specified, the
              program tries to infer the dimensionality from the input image.

       -l, --use-estimate-learning-rate-once

              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 fine-scale registration.

       -n, --n-images 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 mean-squares). 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  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.

       -a, --average-image

              Average the input time series image.

       -w, --write-displacement

              Write the low-dimensional 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

antsMotionCorr 2.2.0                               August 2017                                 ANTSMOTIONCORR(1)