SparseNeuroVol Class

This class represents a three-dimensional brain image using a sparse data representation. It is particularly useful for large brain images with a high proportion of zero or missing values, offering efficient storage and processing.

Construct a SparseNeuroVol instance

Usage

SparseNeuroVol(data, space, indices = NULL, label = "")

Arguments

data: a numeric vector or ROIVol
space: an instance of class NeuroSpace
indices: a index vector indicating the 1-d coordinates of the data values
label: a character string

Value

SparseNeuroVol instance

Details

The SparseNeuroVol class extends the NeuroVol class and implements the ArrayLike3D interface. It uses a sparseVector from the Matrix package to store the image data, which allows for memory-efficient representation of sparse 3D neuroimaging data.

Image data is backed by Matrix::sparseVector.

Slots

data: A sparseVector object from the Matrix package, storing the image volume data in a sparse format.

References

Bates, D., & Maechler, M. (2019). Matrix: Sparse and Dense Matrix Classes and Methods. R package version 1.2-18. https://CRAN.R-project.org/package=Matrix

Examples


# Create a sparse 3D brain image
dim <- c(64L, 64L, 64L)
space <- NeuroSpace(dim = dim, origin = c(0, 0, 0), spacing = c(1, 1, 1))
sparse_data <- Matrix::sparseVector(x = c(1, 2, 3),
                                    i = c(100, 1000, 10000),
                                    length = prod(dim))
sparse_vol <- new("SparseNeuroVol", space = space, data = sparse_data)
sparse_vol[1000] == 1
#> sparse vector (nnz/length = 0/1) of class "lsparseVector"
#> [1] .

data <- 1:10
indices <- seq(1,1000, length.out=10)
bspace <- NeuroSpace(c(64,64,64), spacing=c(1,1,1))
sparsevol <- SparseNeuroVol(data,bspace,indices=indices)
densevol <- NeuroVol(data,bspace,indices=indices)
sum(sparsevol) == sum(densevol)
#> [1] TRUE