OASIS Image Preprocessing

This function does the required preprocessing for OASIS for the FLAIR, T2, T1, and PD volumes using FSL through fslr. The preprocessing steps are (1) inhomogeneity correct using fsl_biascorrect and (2) rigid registration using flirt to the T1 space.

oasis_preproc(flair, t1, t2, pd = NULL, brain_mask = NULL, verbose = TRUE,
  cores = 1)

Arguments

flair	FLAIR volume of class nifti
t1	T1 volume of class nifti
t2	T2 volume of class nifti
pd	PD volume of class nifti
brain_mask	binary mask volume of class nifti
verbose	a logical value for printing diagnostic output
cores	numeric indicating the number of cores to be used (no more than 4 is useful for this software implementation)

Value

Returns a list of objects of class nifti, namely the inhomogeneity corrected FLAIR, T1, T2, and PD registered to the space of the T1 volume.

Examples

library(neurobase)
dl_file = function(url) {
   tfile = tempfile(fileext = ".nii.gz")
   req <- httr::GET(url,
   httr::write_disk(path = tfile))
   httr::stop_for_status(req)
   tfile
}
in_ci <- function() {
 nzchar(Sys.getenv("CI"))
}
on_cran = function() {
 identical(Sys.getenv("NOT_CRAN"), "false")
} 
if (in_ci() || on_cran()) {
  if (fslr::have.fsl() && require(httr)) {
    mods = c("FLAIR", "T1W", "T2W", "consensus_gt", "brainmask")
    base_url = file.path(
      "https://raw.githubusercontent.com/muschellij2/open_ms_data", 
      "master/cross_sectional/coregistered/patient01/")
    files = paste0(base_url, mods, ".nii.gz")
    files = sapply(files, dl_file)
    names(files) = mods

    flair <- readnii(files["FLAIR"])
    t1 <- readnii(files["T1W"])
    t2 <- readnii(files["T2W"])
    brain_mask <- readnii(files["brainmask"])
    gold_standard = readnii(files["consensus_gt"])
    oasis_preprocessed_data <- oasis_preproc(flair, t1, t2, 
      brain_mask = brain_mask)
  } 
}