Register two images using 'ANTs' — ants

Usage

ants_registration(
  fixed,
  moving,
  type_of_transform = "SyN",
  initial_transform = NULL,
  outprefix = tempfile(),
  mask = NULL,
  grad_step = 0.2,
  flow_sigma = 3,
  total_sigma = 0,
  aff_metric = c("mattes", "GC", "meansquares"),
  aff_sampling = 32,
  aff_random_sampling_rate = 0.2,
  syn_metric = c("mattes", "CC", "meansquares", "demons"),
  syn_sampling = 32,
  reg_iterations = c(40, 20, 0),
  aff_iterations = c(2100, 1200, 1200, 10),
  aff_shrink_factors = c(6, 4, 2, 1),
  aff_smoothing_sigmas = c(3, 2, 1, 0),
  write_composite_transform = FALSE,
  verbose = FALSE,
  smoothing_in_mm = FALSE,
  ...
)

Arguments

fixed: fixed image to which we register the moving image, can be character path to 'NIfTI' image, or 'ANTsImage' instance, 'oro.nifti' object, 'niftiImage' from package 'RNifti', or 'threeBrain.nii' from package 'threeBrain'; see also as_ANTsImage
moving: moving image to be mapped to fixed space; see also as_ANTsImage
type_of_transform: a linear or non-linear registration type; print ants$registration to see details
initial_transform: optional list of strings; transforms to apply prior to registration
outprefix: output file to save results
mask: image mask; see also as_ANTsImage
grad_step, flow_sigma, total_sigma: optimization parameters
aff_metric: the metric for the 'affine' transformation, choices are 'GC', 'mattes', 'meansquares'
aff_sampling, aff_random_sampling_rate, aff_iterations, aff_shrink_factors, aff_smoothing_sigmas: controls 'affine' transform
syn_metric: the metric for the 'SyN' transformation, choices are 'GC', 'mattes', 'meansquares', 'demons'
syn_sampling, reg_iterations: controls the 'SyN' transform
write_composite_transform: whether the composite transform (and its inverse, if it exists) should be written to an 'HDF5' composite file; default is false
verbose: verbose the progress
smoothing_in_mm: logical, currently only impacts low dimensional registration
...: others passed to ants$registration

Value

A 'Python' dictionary of aligned images and transform files.

Details

Function family ants_registration* align images (specified by moving) to fixed. Here are descriptions of the variations:

ants_registration: Simple wrapper function for 'Python' implementation ants.registration, providing various of registration options
ants_registration_halpern1: Rigid-body registration designed for 'Casey-Halpern' lab, mainly used for aligning 'MRI' to 'CT' (or the other way around)

Examples


if(interactive() && ants_available()) {

  ants <- load_ants()

  # check the python documentation here for detailed explanation
  print(ants$registration)

  # example to register
  fi <- ants$image_read(ants$get_ants_data('r16'))
  mo <- ants$image_read(ants$get_ants_data('r64'))

  # resample to speed up this example
  fi <- ants$resample_image(fi, list(60L,60L), TRUE, 0L)
  mo <- ants$resample_image(mo, list(60L,60L), TRUE, 0L)

  # SDR transform
  transform <- ants_registration(
    fixed=fi, moving=mo, type_of_transform = 'SyN' )

  ants$plot(fi, overlay = transform$warpedmovout, overlay_alpha = 0.3)


}