Release 1.5

Homepage: http://www.picsl.upenn.edu/ANTS/

Introduction -- ANTS is mostly a tool for computing smooth warps or mappings between
images.  It reads any image type that can be read by ITK, that is, jpg, tiff, hdr,
nii,  nii.gz and probably more image types as well.   For the most part, ANTS will
output float images which you can convert to JPG or Byte with the ANTS ConvertToJpg
or ImageMath tools.  ImageMath has a bunch of basic utilities such as multiplication,
inversion and many more advanced tools such as computation of the Lipschitz norm
of a deformation field.   ANTS programs may be called from the command line on
almost any platform .... you can compile the code yourself or use the precompiled
binaries for Windows (Vista), OSX (Darwin) or linux (32 bit or 64 bit).  Download
the binaries that are correct for you.   If you are a naive user (not from a medical
imaging background) then you might still find the tools here useful.   Many of the
operations available, for instance, in PhotoShop are available in ANTS and many
more are available as well.   For instance, ANTS tools may be useful in face
mapping / morphing and also in generating animations from two different images,
for instance, interpolating between frames in a movie.  But, mainly, ANTS is useful
for brain mapping, segmentation, measuring cortical thickness and in generating
automated or semi-automated labeling of three-dimensional imagery (e.g. labeling
hippocampus or cortical regions or lobes of the lung).  Many prior-based
segmentation possibilities are available in the Apocrita tool.

# directory guide:
Documentation -- pdf / tex describing ANTS
Examples    -- the executable programs and test data in Examples/Data
Scripts -- user-friendly scripts for template building and running studies
Utilities --- basic utilities
ImageRegistration	-- base code for ImageRegistration
Temporary  -- where temporary code lives
Tensor  -- base code for diffusion tensor operations


Use cmake (cmake.org) to set up compilation.
To build ANTS, do the following:

1.  get ANTS

cvs -d:pserver:[email protected]:/cvsroot/advants co ANTS


2.  get itk :

 cvs -d :pserver:anoncvs@www.itk.org:/cvsroot/Insight co Insight

3.  compile itk and ANTS -- link ANTS to itk build directory

  ccmake ANTS/Examples/

4.   call  ctest in the compile directory and verify that the tests pass

5.  in general, to perform a mapping :

# include the mask, if desired.  mask in inclusive.

ANTS 3 -m PR[tp22_s1.nii,template.nii.gz,1,4] -i 50x20x10 -o tp22map   -t SyN[0.25]  -x mask.nii.gz  -r Gauss[3,0]


# The ANTS executable reflects the variational optimization problem
# which balances regularization of the transformation model's parameters
# and the quality of matchins as driven by a similarity (or data) term
#
# explanation :   -m  PR  -- the similarity metric =>  PR[fixed.nii,moving,nii,weight,metric-radius]
#             :   -i 50x20 --  the number of iterations and number of resolution levels
#             :   -o  tp22map --  the output naming convention (can add an extension)
#             :   -t  SyN/Elast/Exp/Syn[time] --- transformation model
#             :   -r  Gauss/Bspline  -- the regularization models
#                                       Gauss[gradient-regularize,deformation-regularize]
#             :   -x  mask -- an inclusive mask -- dictates what information to use in registration
#                          -- defined in the fixed domain but works on both domains
#             :    -m  other metrics :  PSE MSQ MI  etc -- some are label-image (or point-set) metrics
#                                       and some are intensity metrics
#
# Call ANTS with no params to get detailed help
#
# warp the tp22 to template image

WarpImageMultiTransform 3 tp22_s1.nii tp22totemplate.nii  -R template.nii.gz -i  tp22mapAffine.txt tp22mapInverseWarp.nii

# warp the template image to tp22 -- note reversal of order from above

WarpImageMultiTransform 3 template.nii.gz templatetotp22.nii  -R tp22_s1.nii   tp22mapWarp.nii tp22mapAffine.txt

# or call ants.sh for a standard approach.

#
#  use CreateJacobianDeterminantImage to get log-Jacobian (volumetric change) images
#  and programs StudentsTestOnImages or  GLM to peform a statistical study
#  one might also use SurfaceCurvature to do a curvature study
#  or LaplacianThickness to do a thickness study
#