NeuroSpace-class: NeuroSpace Class

In bbuchsbaum/neuroim2: Data Structures and IO for Neuroimaging Data with an Emphasis on Functional Magnetic Resonance Imaging (FMRI)

NeuroSpace Class

Description

The NeuroSpace class represents the geometric properties of a brain image, including its dimensions, origin, spacing, axes, and coordinate transformations. It provides a comprehensive framework for handling spatial information in neuroimaging data analysis.

Slots

dim

An integer vector representing the grid dimensions of the image.

origin

A numeric vector representing the coordinates of the spatial origin.

spacing

A numeric vector representing the dimensions (in mm) of the grid units (voxels).

axes

A named AxisSet object representing the set of spatial axes in the untransformed native grid space.

trans

A matrix representing an affine transformation that converts grid coordinates to real-world coordinates.

inverse

A matrix representing an inverse transformation that converts real-world coordinates to grid coordinates.

Validity

A NeuroSpace object is considered valid if:

• The length of the dim slot is equal to the lengths of the spacing, origin, and number of axes in the axes slots.

• The dim slot contains only non-negative values.

Methods

The following methods are available for NeuroSpace objects:

• dim: Get the dimensions of the space.

• origin: Get or set the origin of the space.

• spacing: Get or set the spacing of the space.

• axes: Get the axes of the space.

• transform: Apply the affine transformation to coordinates.

• inverse_transform: Apply the inverse transformation to coordinates.

Usage

The NeuroSpace class is fundamental in representing and manipulating the spatial properties of neuroimaging data. It is used extensively throughout the package for operations that require spatial information, such as image registration, resampling, and coordinate transformations.

References

For more information on spatial transformations in neuroimaging: Brett, M., Johnsrude, I. S., & Owen, A. M. (2002). The problem of functional localization in the human brain. Nature Reviews Neuroscience, 3(3), 243-249.

See Also

AxisSet-class for details on the axis set representation. NeuroVol-class and NeuroVec-class for classes that use NeuroSpace.

Examples

# Create a NeuroSpace object
space <- NeuroSpace(dim = c(64L, 64L, 64L),
                    origin = c(0, 0, 0),
                    spacing = c(1, 1, 1))

# Get the dimensions
dim(space)

# Set a new origin
origin(space) <- c(-32, -32, -32)

# Apply a transformation to a set of coordinates
coords <- matrix(c(10, 20, 30), ncol = 3)
transformed_coords <- transform(space, coords)

bbuchsbaum/neuroim2 documentation built on Jan. 2, 2025, 3:38 p.m.