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ANTS

ANTS

NAME

ANTS - part of ANTS registration suite

DESCRIPTION

Example usage:

./ANTS ImageDimension -m MI[fixedimage.nii.gz,movingimage.nii.gz,1,32] -o Outputfname.nii.gz -i 30x20x0 -r Gauss[3,1] -t Elast[3]

Compulsory arguments:
ImageDimension: 2 or 3 (for 2 or 3 Dimensional registration)
-m:
Type of similarity model used for registration.
For intramodal image registration, use:
CC = cross-correlation MI = mutual information PR = probability mapping MSQ = mean square difference
For intermodal image registration, use:
MI = mutual information PR = probability mapping
-o
Outputfname.nii.gz: the name of the resulting image.
-i
Max-iterations in format: JxKxL, where:
J = max iterations at coarsest resolution (here, reduce by power of 2^2) K = middle resolution iterations (here,reduce by power of 2) L = fine resolution iterations (here, full resolution). This level takes much more time per iteration!
Adding an extra value before JxKxL (i.e. resulting in IxJxKxL) would add another iteration level.
-r
Regularization
-t
Type of transformation model used for registration
For elastic image registration, use:
Elast = elastic transformation model (less deformation possible)
For diffeomorphic image registration, use:
Syn[GradStep,TimePoints,IntegrationStep] --geodesic 2 = SyN with time with arbitrary number of time points in time discretization SyN[GradStep,2,IntegrationStep] = SyN with time optimized specifically for 2 time points in the time discretization SyN[GradStep] = Greedy SyN, typicall GradStep=0.25 Exp[GradStep,TimePoints] = Exponential GreedyExp = Diffeomorphic Demons style exponential mapping
Please use the `ANTS -h ` call or refer to the ANTS.pdf manual or antsIntroduction.sh script for additional information and typical values for transformation models

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