antsMotionCorr(1) part of ANTS registration suite



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.


-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.
Print the help menu (short version). <VALUES>: 0
Print the help menu. <VALUES>: 1, 0