if(identical(Sys.getenv("IEEGIO_PKGDOWN", unset = ""), "")) { knitr::opts_chunk$set( collapse = TRUE, comment = "#>", eval = FALSE ) } else { options("rgl.useNULL" = TRUE) library(ieegio) library(rgl) knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) rgl::setupKnitr(autoprint = FALSE) cache_dir <- tools::R_user_dir("ieegio", "cache") dir.create(cache_dir, showWarnings = FALSE, recursive = TRUE) cache_dir <- normalizePath(cache_dir, mustWork = TRUE) options("ieegio.extract_path", cache_dir) }
ieegio
supports reading from and writing to multiple imaging formats:
NIfTI
& FreeSurfer MGH/MGZ
GIfTI
& FreeSurfer
geometry, annotation, curvature/measurement, w
formatTo start, please load ieegio
. This vignette uses sample data which requires extra download.
library(ieegio) # volume file nifti_file <- ieegio_sample_data("brain.demosubject.nii.gz") # geometry geom_file <- ieegio_sample_data( "gifti/icosahedron3d/geometry.gii") # measurements shape_file <- ieegio_sample_data( "gifti/icosahedron3d/rand.gii" ) # time series ts_file <- ieegio_sample_data( "gifti/icosahedron3d/ts.gii")
ieegio::read_volume
and ieegio::write_volume
provides high-level interfaces for reading and writing volume data such as MRI
, CT
. fMRI
, etc.
Each volume data (NIfTI
, MGH
, ...) contains a header
, a data
, and a transforms
list.
volume <- read_volume(nifti_file) volume
The transforms contain transforms from volume (column, row, slice) index to other coordinate systems. The most commonly used one is vox2ras
, which is a 4x4
matrix mapping the voxels to scanner (usually T1-weighted
) RAS
(right-anterior-superior) system.
Accessing the image values via [
operator. For example,
volume[128, , ]
Plotting the anatomical slices:
par(mfrow = c(1, 3), mar = c(0, 0, 3.1, 0)) ras_position <- c(-50, -10, 15) ras_str <- paste(sprintf("%.0f", ras_position), collapse = ",") for(which in c("coronal", "axial", "sagittal")) { plot(x = volume, position = ras_position, crosshair_gap = 10, crosshair_lty = 2, zoom = 3, which = which, main = sprintf("%s T1RAS=[%s]", which, ras_str)) }
Reading surface file using read_surface
supports multiple data types
library(ieegio) # geometry geometry <- read_surface(geom_file) # measurements measurement <- read_surface(shape_file) # time series time_series <- read_surface(ts_file)
You can merge them to a single object, making an object with multiple embedding data-sets:
merged <- merge(geometry, measurement, time_series) print(merged)
Plot the surfaces in 3D
viewer, colored by shape measurement
# plot the first column in measurements section plot(merged, name = list("measurements", 1))
Plot the normalized time-series data
ts_demean <- apply( merged$time_series$value, MARGIN = 1L, FUN = function(x) { x - mean(x) } ) merged$time_series$value <- t(ts_demean) plot( merged, name = "time_series", col = c( "#053061", "#2166ac", "#4393c3", "#92c5de", "#d1e5f0", "#ffffff", "#fddbc7", "#f4a582", "#d6604d", "#b2182b", "#67001f" ) )
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