Apply a 4D bilateral filter to a NeuroVec

This function applies a full 4D bilateral filter to a NeuroVec, smoothing jointly across space (x, y, z) and time (t). The filter uses spatial, temporal, and intensity kernels to preserve edges while reducing noise, leveraging a parallel C++ backend for performance.

Usage

bilateral_filter_4d(
  vec,
  mask,
  spatial_sigma = 2,
  intensity_sigma = 1,
  temporal_sigma = 1,
  spatial_window = 1,
  temporal_window = 1,
  temporal_spacing = 1
)

Arguments

vec

A NeuroVec object (4D image).

mask

An optional LogicalNeuroVol or NeuroVol specifying the spatial region to process. If omitted, the entire spatial extent is processed.

spatial_sigma

Numeric; standard deviation of the spatial Gaussian (default 2).

intensity_sigma

Numeric; standard deviation of the intensity Gaussian (default 1).

temporal_sigma

Numeric; standard deviation of the temporal Gaussian (default 1).

spatial_window

Integer; half-width of the spatial window in voxels (default 1), e.g., 1 => 3x3x3 spatial neighborhood.

temporal_window

Integer; half-width of the temporal window in frames (default 1), e.g., 1 => 3 timepoints (t-1, t, t+1).

temporal_spacing

Numeric; spacing of the temporal dimension (e.g., TR in seconds). Default is 1. This sets the temporal scale used for the temporal kernel.

Value

A NeuroVec with filtered data.

Details

Parameter guidance and units: - spatial_sigma: Measured in physical units (millimeters). Distances are computed using spacing(vec)[1:3], so choose spatial_sigma relative to voxel size. As a rule of thumb, set it to about 1-2 voxel sizes (e.g., 2-4 mm for 2 mm isotropic data) for moderate smoothing. - intensity_sigma: Dimensionless multiplier of the global intensity standard deviation. Internally, the filter uses exp(-(dI)^2 / (2 * (intensity_sigma * sigma_I)^2)), where sigma_I is the standard deviation of all finite voxel intensities within the mask across time. Start with 1.0 for moderate smoothing; use 0.5-0.8 to preserve more edges, or 1.5-2.0 for stronger smoothing. - temporal_sigma: Measured in temporal_spacing units (e.g., seconds). Typical values are 0.5-2 x TR. Larger values blend more across time.

Choosing the neighborhood window sizes: - spatial_window controls the discrete spatial support. A common choice is ceiling(2 * spatial_sigma / min(spacing(vec)[1:3])), which covers ~95 - temporal_window similarly can be set to ceiling(2 * temporal_sigma / temporal_spacing).

Quick presets (typical fMRI with 2-3 mm voxels and TR~2s): - Light: spatial_sigma = 1 x min(spacing), intensity_sigma = 0.8, temporal_sigma = 0.5 x TR, windows = 1 - Moderate (default-ish): spatial_sigma = 1.5 x min(spacing), intensity_sigma = 1.0, temporal_sigma = 1 x TR, windows = 1-2 - Strong: spatial_sigma = 2 x min(spacing), intensity_sigma = 1.5, temporal_sigma = 1.5 x TR, windows = 2

Tip: If your time axis has known TR, pass it via temporal_spacing. For NIfTI inputs, you can get TR via:

  hdr <- read_header(nifti_path)
  tr  <- hdr@header$pixdim[5]
  out <- bilateral_filter_4d(vec, mask, temporal_spacing = tr)

See also

bilateral_filter, NeuroVec-class, NeuroVol-class

Examples

# \donttest{
vec <- read_vec(system.file("extdata", "global_mask_v4.nii", package = "neuroim2"))
mask <- read_vol(system.file("extdata", "global_mask_v4.nii", package = "neuroim2"))
out  <- bilateral_filter_4d(vec, mask,
                            spatial_sigma = 2, intensity_sigma = 1,
                            temporal_sigma = 1, spatial_window = 1,
                            temporal_window = 1, temporal_spacing = 1)
# }