A class representing a four-dimensional brain image, backed by a dense array. This class is designed for neuroimaging data where most voxels contain non-zero values.
This function constructs a DenseNeuroVec object, which represents a dense four-dimensional brain image. It handles various input data formats and ensures proper dimensionality.
Arguments
- data
The image data. This can be:
A 4-dimensional array
A 2-dimensional matrix (either nvoxels x ntime-points or ntime-points x nvoxels)
A vector (which will be reshaped to match the space dimensions)
- space
A
NeuroSpaceobject defining the spatial properties of the image.- label
A character string providing a label for the DenseNeuroVec object. Default is an empty string.
Details
DenseNeuroVec objects store their data in a dense array format, which is efficient
for operations that require frequent access to all voxels. This class inherits from
both NeuroVec and array classes, combining spatial
information with array-based storage.
The function performs several operations based on the input data type:
For matrix input: It determines the correct orientation (voxels x time or time x voxels) and reshapes accordingly. If necessary, it adds a 4th dimension to the space object.
For vector input: It reshapes the data to match the dimensions specified in the space object.
For array input: It ensures the dimensions match those specified in the space object.
Note that the label parameter is currently not used in the object creation, but is included for potential future use or consistency with other constructors.
Validity
A DenseNeuroVec object is considered valid if:
The underlying data is a four-dimensional array.
See also
NeuroVec-class for the parent class.
SparseNeuroVec-class for a sparse representation alternative.
NeuroVec-class for the parent class.
SparseNeuroVec-class for the sparse version of 4D brain images.
NeuroSpace-class for details on spatial properties.
Examples
if (FALSE) { # \dontrun{
# Create a simple 4D brain image
data <- array(rnorm(64*64*32*10), dim = c(64, 64, 32, 10))
space <- NeuroSpace(dim = c(64, 64, 32,10), origin = c(0, 0, 0), spacing = c(3, 3, 4))
dense_vec <- new("DenseNeuroVec", .Data = data, space = space)
# Access dimensions
dim(dense_vec)
# Extract a single 3D volume
first_volume <- dense_vec[[1]]
} # }
# Create a simple 4D brain image
dim <- c(64, 64, 32, 10) # 64x64x32 volume with 10 time points
data <- array(rnorm(prod(dim)), dim)
space <- NeuroSpace(dim, spacing = c(3, 3, 4))
# Create a DenseNeuroVec object
dense_vec <- DenseNeuroVec(data = data, space = space, label = "Example")
print(dense_vec)
#>
#> DenseNeuroVec (10.01 bytes MB)
#>
#> - Spatial Info ---------------------------
#> | Dimensions : 64 x 64 x 32 (10 timepoints)
#> | Total Voxels : 131,072
#> | Spacing : 3 x 3 x 4
#>
#> - Properties ---------------------------
#> | Origin : 0 x 0 x 0
#> | Orientation : Left-to-Right Posterior-to-Anterior Inferior-to-Superior
#>
#> - Statistics ---------------------------
#> Mean +/- SD : 0.002 +/- 1
#>
#> Label: Example
#>
# Create from a matrix (voxels x time)
mat_data <- matrix(rnorm(prod(dim)), nrow = prod(dim[1:3]), ncol = dim[4])
dense_vec_mat <- DenseNeuroVec(data = mat_data, space = space)
print(dense_vec_mat)
#>
#> DenseNeuroVec (10.01 bytes MB)
#>
#> - Spatial Info ---------------------------
#> | Dimensions : 64 x 64 x 32 (10 timepoints)
#> | Total Voxels : 131,072
#> | Spacing : 3 x 3 x 4
#>
#> - Properties ---------------------------
#> | Origin : 0 x 0 x 0
#> | Orientation : Left-to-Right Posterior-to-Anterior Inferior-to-Superior
#>
#> - Statistics ---------------------------
#> Mean +/- SD : -0.005 +/- 0.996
#>
#> Label: none
#>