Once you've compiled the binaries, you can copy the bin folder and the lib folder here: /usr/local/ants/.

In case you have previously installed ANTs binaries into /usr/bin and you would like to remove them without manually do each for each file, here is a python script that does it.

To copy ants binaries for SCT, go to your antsbin folder. Then, copy the script found in SCT_DIR/bin/osx or linux and then execute it.

Parameters for antsRegistration

  1. -transform SyN[gradientStep, updateFieldVarianceInVoxelSpace, totalFieldVarianceInVoxelSpace]
  • gradientStep: the higher, the more high frequency deformations (and less stable)
  • updateFieldVarianceInVoxelSpace (default=3) –> the higher, the less high frequency deformations.
  • totalFieldVarianceInVoxelSpace: default=0.
  • does not seem to move a lot
  • gradientStep: 0.5 –> the smaller, the smaller the distortion
  • updateFieldMeshSizeAtBaseLevel:
  • totalFieldMeshSizeAtBaseLevel: The larger, the larger hte deform.
  • splineOrder=3
  1. -metric CC[x, x, weight, radius]
  • weight: 1. This param has no influence if there is only one transfo.
  • radius: 4
  • CC is much slower than MI on large images.
  • nbBins: 32
  • fast! however, does not work if using small mask (not enough values).
  1. -smoothing-sigmas
  • Can have a HUGE impact on the result. Don’t hesite to try without smoothing, i.e., 0x0 (if you have 2 iterations).
  1. -convergence 20×3 –shrink-factors 2×1 –smoothing-sigmas 1x1mm –Restrict-Deformation 1x1x0 –output [tmp_reg,tmp.src_pad_reg.nii] –collapse-output-transforms 1 –interpolation BSpline[3] –winsorize-image-intensities [0.005,0.995]

Visualizing warping field

ANTS saves the warping field as a component image (5D), not in vector format (4D). Therefore, it cannot be interpreted by FSL. To convert the warping field in component, you can use c3d:

c3d -mcs warp_comp.nii -oo warp_vecx.nii warp_vecy.nii warp_vecz.nii

To convert vector-based warping field into component image readable by ANTS, use the following command

c3d warp_vecx.nii warp_vecy.nii warp_vecz.nii -omc 3 warp_comp.nii


-t Translation[GradientStep]


antsRegistration -d $ImageDimension -r [${FILE_DEST}.${EXT},${FILE_SRC}.${EXT} ,1] -m ${MetricType}
${FILE_DEST}.${EXT},${FILE_SRC}.${EXT},1,4] -o $OutPrefix -t Translation -c [10000x10000x10000,1.e-8,20] -s 4x2x1vox -f 3x2x1

Restrict gradient deformation

–Restrict-Deformation XxYxZ

Example: restrict the gradient in two dimensions (axial)

ants $ImageDimension -m ${MetricType}[${FILE_DEST}.${EXT_ANAT},${FILE_SRC}.${EXT},1,4] --Restrict-Deformation 1x1x0 -t SyN -r Gauss[6,3] -o ${PATH_OUTPUT}/ -i ${MaxIteration}

Note: this restriction does not apply to the affine transformation

Point-Set Expectation (PSE)

From the Ants Manual PDF:

-m PSE [fixedImage,movingImage,fixedPoints,movingPoints,weight, pointSetPercentage,pointSetSigma,boundaryPointsOnly, kNeighborhood,PartialMatchingIterations=100000]

– fixedImage: defines the space domain of the fixed point set.

– movingImage: defines the space domain of the moving point set.

– fixedPoints/Image: defines the coordinates of the fixed point set or label image. It can be an image with discrete positive labels, a VTK format point set file, or a text file. Details can be found in I/O section (TODO).

– movingPoints/Image: defines the coordinates of the moving point set or label image.

– weight: weight for this metric. 1 weights are relative to the weights on the N other metrics passed to ANTS — N is unlimited.

– pointSetPercentage: the percentage of points to be randomly sampled used in the registration.

– pointSetSigma: the standard deviation of the Parzen window used to estimate the expectation.

– boundaryPointsOnly: 1 (or “true”) means only the boundary points in the label image is used to drive registration.

– kNeighborhood is a positive discrete number. The first k neighbors are used to compute the deformation during the registration.

– PartialMatchingIterations controls the symmtry in the matching. This option assumes the complete labeling is in the first set of label parameters … more iterations leads to more symmetry in the matching - 0 iterations means full asymmetry


ants 3 -m PSE[destination.nii,source.nii,mask_destination.nii,mask_source.nii,0.8,100,1,0,1,100000] -o source_reg -i 0 --rigid-affine true --number-of-affine-iterations 100x10x5 -m MI[destination.nii,source.nii,0.2,4] --use-all-metrics-for-convergence 1

This line does an affine registration of the moving to the fixed image. There are 2 metrics used: PSE (point-set expectation) and MI (mutual information). The weight of each of them is the first “non-string” argument (0.8 for PSE and 0.2 for MI here). To do a diffeomorphic transformation, change the -i paramater to something like 50x50x50 (number of iteration to do for the diffeomorphic transformation). The MARKER files can be done with the ITK-snap labelling utility

 #apply the transformation
 WarpImageMultiTransform $DIM ${FILE_SRC}.${EXT} ${FILE_SRC}_reg_diffeo.${EXT} -R ${FILE_DEST}.${EXT} --use-BSpline
 ${FILE_SRC}Warp.nii.gz ${FILE_SRC}Affine.txt

This line applies the affine transformation to the moving image. For a diffeomorphic case, simply add ${FILE_SRC}Warp.nii.gz in the transformation parameter (end of the line)

Affine-only point-match transformation

Cannot be done with ANTS. Use the following command:

ANTSUseLandmarkImagesToGetAffineTransform fixed_image moving_image affine regAffine.txt

Non-affine point-match transformation

Alternatively to ANTS, you can use the following command:


Note: Images should be in the same space! Otherwise you receive a Segmentation 11 error. Suggestion is to first run ANTSUseLandmarkImagesToGetAffineTransform, then this command.

ITK Transform File

The FixedCenterOfRotationAffineTransform performs the following computation:

X′ = R·(S·X −C)+C+V

Where R is the rotation matrix, S is a scaling factor, C is the center of rotation and V is a translation vector or offset. Therefore the affine matrix M and the affine offset T are defined as:

T =C+V−R·C

NOTES ON ITK Transform Files:

T2d = [
a b o
c d p
0 0 1

T3d = [
a b c o
d e f p
g h i q
0 0 0 1

The “o p q” are the “offset”, which actually is not given in the file. The offset is computed from the 3×3 matrix (the first 9 parameters), from the translation “l m n” (the last three parameters), and from the center “x y z” (the three fixed parameters). To see how this is done, look at the code for ComputeOffset() itkMatrixOffsetTransformBase.hxx.

How centered transforms work: p' = R (p - C) + C + T where p' = transformed point, p = original point, C = Center, T= Translation. R= Rotation matrix.

Format for 2d affine transfo:

#Insight Transform File V1.0
#Transform 0
Transform: AffineTransform_double_2_2
Parameters: a b c d o p
FixedParameters: 0 0

Format for 3d affine transfo:

#Insight Transform File V1.0
#Transform 0
Transform: AffineTransform_double_3_3
Parameters: a b c d e f g h i o p q
FixedParameters: 0 0


  1. ITK Documentation (700 pages):

Conversion world-image coordinate:

Discussions about ITK world coordinates: