This function downsamples a neuroimaging object, reducing its spatial resolution while preserving the temporal dimension.
Usage
downsample(x, ...)
# S4 method for class 'DenseNeuroVec'
downsample(x, spacing = NULL, factor = NULL, outdim = NULL, method = "box")
# S4 method for class 'NeuroVec'
downsample(x, spacing = NULL, factor = NULL, outdim = NULL, method = "box")
# S4 method for class 'DenseNeuroVol'
downsample(x, spacing = NULL, factor = NULL, outdim = NULL, method = "box")
# S4 method for class 'NeuroVol'
downsample(x, spacing = NULL, factor = NULL, outdim = NULL, method = "box")Arguments
- x
A DenseNeuroVol object to downsample
- ...
Additional arguments passed to specific downsample methods.
- spacing
Target voxel spacing (numeric vector of length 3)
- factor
Downsampling factor (single value or vector of length 3, between 0 and 1)
- outdim
Target output dimensions (numeric vector of length 3)
- method
Downsampling method (currently only "box" for box averaging)
Examples
# Create a sample 4D image
data <- array(rnorm(64*64*32*10), dim = c(64, 64, 32, 10))
space <- NeuroSpace(dim = c(64, 64, 32, 10),
origin = c(0, 0, 0),
spacing = c(2, 2, 2))
nvec <- DenseNeuroVec(data, space)
# Downsample by factor
nvec_down1 <- downsample(nvec, factor = 0.5)
# Downsample to target spacing
nvec_down2 <- downsample(nvec, spacing = c(4, 4, 4))
# Downsample to target dimensions
nvec_down3 <- downsample(nvec, outdim = c(32, 32, 16))
# Create a sample 3D volume
data <- array(rnorm(64*64*32), dim = c(64, 64, 32))
space <- NeuroSpace(dim = c(64, 64, 32),
origin = c(0, 0, 0),
spacing = c(2, 2, 2))
vol <- DenseNeuroVol(data, space)
# Downsample by factor
vol_down1 <- downsample(vol, factor = 0.5)
# Downsample to target spacing
vol_down2 <- downsample(vol, spacing = c(4, 4, 4))
# Downsample to target dimensions
vol_down3 <- downsample(vol, outdim = c(32, 32, 16))