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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