-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.
-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]>,<useGradientFilter=false>]
- MI[fixedImage,movingImage,metricWeight,numberOfBins,<samplingStrategy={Regular,Random}>,<samplingPercentage=[0,1]>,<useGradientFilter=false>]
Demons[fixedImage,movingImage,metricWeight,radius,<samplingStrategy={Regular,Random}>,<samplingPercentage=[0,1]>,<useGradientFilter=false>]
GC[fixedImage,movingImage,metricWeight,radius,<samplingStrategy={Regular,Random}>,<samplingPercentage=[0,1]>,<useGradientFilter=false>]
- 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. useGradientFilter
specifies whether a smoothingfilter is applied when estimating the metric
gradient.
-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 sigma for smoothing at each level. Smoothing may be specified
in mm units or voxels with "AxBxCmm" or "AxBxCvox". No
units implies voxels.
-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.
--use-histogram-matching 0/(1)
- Histogram match the moving images to the reference image.
--random-seed seedValue
- Use a fixed seed for random number generation. By default, the system
clock is used to initialize the seeding. The fixed seed can be any nonzero
int value.
- -p, --interpolation
Linear
- NearestNeighbor BSpline[<order=3>] BlackmanWindowedSinc
CosineWindowedSinc WelchWindowedSinc HammingWindowedSinc
LanczosWindowedSinc
- Several interpolation options are available in ITK. The above are
available (default Linear).
-v, --verbose (0)/1
- Verbose output.
-h
- Print the help menu (short version). <VALUES>: 0
--help
- Print the help menu. <VALUES>: 1, 0