Skip to contents

Map a volume to surface after SDF-based rigid alignment

Source: R/vol_to_surf_sdf.R
vol_to_surf_sdf.Rd

This wrapper estimates a rigid transform from the input volume to the template surface space using a signed distance field (SDF), then reuses vol_to_surf() for the actual sampling in volume space.

Usage

vol_to_surf_sdf(
  surf_wm,
  surf_pial,
  vol,
  sdf_vol,
  mask = NULL,
  n_hull = 2000L,
  thresh = NULL,
  init_par = rep(0, 6),
  outside_penalty = 20,
  method = "L-BFGS-B",
  ...
)

Arguments

surf_wm

White-matter (inner) surface in template space

surf_pial

Pial (outer) surface in template space

vol

NeuroVol in (approximate) template/MNI space

sdf_vol

NeuroVol SDF of the template cortical surface

mask

optional mask for hull extraction and mapping

n_hull

number of hull points to retain (approximate)

thresh

optional intensity threshold for hull computation

init_par

optional initial parameters (tx, ty, tz, rx, ry, rz)

outside_penalty

value used when SDF sampling is outside the grid

method

optimization method passed to roptim (default "L-BFGS-B")

...

passed through to vol_to_surf() (e.g., fun, knn, sigma)

Value

A NeuroSurface with values mapped from vol

Examples

# \donttest{
# Requires surfaces and volumes in template space
# wm <- read_surf_geometry("lh.white")
# pial <- read_surf_geometry("lh.pial")
# vol <- neuroim2::read_vol("mydata.nii")
# sdf <- neuroim2::read_vol("template_sdf.nii")
# result <- vol_to_surf_sdf(wm, pial, vol, sdf)
# }