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