py_nipy_coreg: Register a computerized tomography (CT) image to MRI via `'nipy'`

Description

Align 'CT' using nipy.algorithms.registration.histogram_registration.

Usage

py_nipy_coreg(
  ct_path,
  mri_path,
  clean_source = TRUE,
  inverse_target = TRUE,
  precenter_source = TRUE,
  smooth = 0,
  reg_type = c("rigid", "affine"),
  interp = c("pv", "tri"),
  similarity = c("crl1", "cc", "cr", "mi", "nmi", "slr"),
  optimizer = c("powell", "steepest", "cg", "bfgs", "simplex"),
  tol = 1e-04,
  dry_run = FALSE
)
cmd_run_nipy_coreg(
  subject,
  ct_path,
  mri_path,
  clean_source = TRUE,
  inverse_target = TRUE,
  precenter_source = TRUE,
  reg_type = c("rigid", "affine"),
  interp = c("pv", "tri"),
  similarity = c("crl1", "cc", "cr", "mi", "nmi", "slr"),
  optimizer = c("powell", "steepest", "cg", "bfgs", "simplex"),
  dry_run = FALSE,
  verbose = FALSE
)

Value

Nothing is returned from the function. However, several files will be generated at the 'CT' path:

'ct_in_t1.nii': aligned 'CT' image; the image is also re-sampled into 'MRI' space
'CT_IJK_to_MR_RAS.txt': transform matrix from volume 'IJK' space in the original 'CT' to the 'RAS' anatomical coordinate in 'MR' scanner
'CT_RAS_to_MR_RAS.txt': transform matrix from scanner 'RAS' space in the original 'CT' to 'RAS' in 'MR' scanner space

Arguments

ct_path, mri_path: absolute paths to 'CT' and 'MR' image files
clean_source: whether to replace negative 'CT' values with zeros; default is true
inverse_target: whether to inverse 'MRI' color intensity; default is true
precenter_source: whether to adjust the 'CT' transform matrix before alignment, such that the origin of 'CT' is at the center of the volume; default is true. This option may avoid the case that 'CT' is too far-away from the 'MR' volume at the beginning of the optimization
smooth, interp, optimizer, tol: optimization parameters, see 'nipy' documentation for details.
reg_type: registration type, choices are 'rigid' or 'affine'
similarity: the cost function of the alignment; choices are 'crl1' ('L1' regularized correlation), 'cc' (correlation coefficient), 'cr' (correlation), 'mi' (mutual information), 'nmi' (normalized mutual information), 'slr' (likelihood ratio). In reality I personally find 'crl1' works best in most cases, though many tutorials suggest 'nmi'.
dry_run: whether to dry-run the script and to print out the command instead of executing the code; default is false
subject: 'RAVE' subject
verbose: whether to verbose command; default is false