rpyANTs (version 0.0.4)

ants_registration: Register two images using 'ANTs'

Description

Register two images using '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,
  ...
)

Value

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

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

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)


}