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neuroim2

neuroim2

Data structures and I/O for volumetric brain imaging with a focus on fMRI. This is the successor to neuroim with improved S4 classes, sparse/dense 3D–4D representations, and fast resampling/filtering.

Website: https://bbuchsbaum.github.io/neuroim2/ CRAN: https://cran.r-project.org/package=neuroim2

Key Features

  • NIfTI / AFNI support — read and write .nii, .nii.gz, and AFNI .BRIK/.HEAD files
  • S4 class hierarchy — NeuroVol (3D volumes) and NeuroVec (4D time-series) with dense, sparse, memory-mapped, and clustered variants
  • Coordinate systems — full affine-aware transforms between voxel, grid, and world coordinates
  • Searchlights & ROIs — spherical, cuboid, and random searchlight iterators plus flexible ROI construction
  • Spatial filtering — Gaussian blur, guided filter, bilateral filter, and connected-component labelling
  • Resampling — volume-to-volume resampling with configurable interpolation
  • Visualization — slice montages, orthographic views, and overlay plotting via ggplot2

Installation

CRAN 

install.packages("neuroim2")

R-universe (daily builds) 

install.packages("neuroim2",
  repos = c("https://bbuchsbaum.r-universe.dev", "https://cloud.r-project.org"))

Development version (GitHub) 

# install.packages("remotes")
remotes::install_github("bbuchsbaum/neuroim2")

Usage

Read a NIFTI image and perform simple operations:

library(neuroim2)
fname <- system.file("extdata", "global_mask_v4.nii", package="neuroim2")
vol <- read_vol(fname)

v1 <- vol[1,1,1]
vol2 <- vol + vol
all(vol == (vol2 - vol))
## [1] TRUE

Create a 4D image from volumes:

vec <- vec_from_vols(list(vol, vol, vol2))
series1 <- vec[1,1,1,]
length(series1)
## [1] 3

Vignettes

Browse the full set of articles on the pkgdown site:

Getting Started Analysis Workflows Advanced
Overview Analysis Workflows Image Volumes
Choosing Backends Slice Visualization NeuroVector
Coordinate Systems Cookbook Regions of Interest
Volumes & Vectors Smoothing Clustered NeuroVec
Resampling Pipelines