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