Down-sample or super-sample volume

Using nearest-neighbor.

Usage

resample_volume(x, new_dim, na_fill = NA)

Arguments

x

image volume

new_dim

new dimension

na_fill

value to fill if missing

Value

A new volume with desired shape

Examples

# ---- Toy example ----------------------------

dm <- c(6, 6, 6)
arr <- array(seq_len(prod(dm)) + 0.5, dm)
orig <- as_ieegio_volume(
  arr, vox2ras = cbind(diag(1, nrow = 4, ncol = 3), c(-dm / 2, 1)))

# resample
downsampled <- resample_volume(orig, new_dim = c(3, 3, 3))
dim(downsampled)
#> [1] 3 3 3

# up-sample on coronal
upsampled <- resample_volume(orig, new_dim = c(20, 20, 24))
dim(upsampled)
#> [1] 20 20 24

par(mfrow = c(2, 2), mar = c(0, 0, 2.1, 0.1))
plot(orig, pixel_width = 0.5, zoom = 20, main = "Original")
plot(downsampled, pixel_width = 0.5, zoom = 20, main = "Down-sampled")
plot(upsampled, pixel_width = 0.5, zoom = 20, main = "Super-sampled")
plot(
  orig,
  main = "Overlay super-sample (diff)",
  col = c("black", "white"),
  pixel_width = 0.5, zoom = 20
)
plot(
  upsampled,
  add = TRUE,
  col = c("white", "black"),
  pixel_width = 0.5, zoom = 20,
  alpha = 0.5
)


# ---- Real example ---------------------------
nifti_file <- "brain.demosubject.nii.gz"

if( ieegio_sample_data(nifti_file, test = TRUE) ) {

  orig <- read_volume(ieegio_sample_data(nifti_file))
  dim(orig)

  # resample
  downsampled <- resample_volume(orig, new_dim = c(30, 30, 30))
  dim(downsampled)

  # up-sample on coronal
  upsampled <- resample_volume(orig, new_dim = c(300, 300, 64))
  dim(upsampled)

  par(mfrow = c(2, 2), mar = c(0, 0, 2.1, 0.1))
  plot(orig, main = "Original")
  plot(downsampled, main = "Down-sampled")
  plot(upsampled, main = "Super-sampled")
  plot(
    orig,
    main = "Overlay super-sample",
    col = c("black", "white"),
    zoom = 2,
    vlim = c(0, 255)
  )
  plot(
    upsampled,
    add = TRUE,
    col = c("white", "black"),
    zoom = 2,
    alpha = 0.5,
    vlim = c(0, 255)
  )


}