# Advanced 3D Volume Patterns

#### 2026-04-16

This article is now the detailed follow-on to `vignette("VolumesAndVectors")`. Read that vignette first if you want the shortest introduction to `read_vol()`, `read_vec()`, and the basic `NeuroVol` / `NeuroVec` mental model.

Use this article when you specifically want deeper 3D-volume details: masks, coordinate conversion, manual construction, and slice-level inspection.

## Read one volume to establish context

    file_name <- system.file("extdata", "global_mask2.nii.gz", package = "neuroim2")
    vol <- read_vol(file_name)

This article assumes you already know the basic `NeuroVol` story from `vignette("VolumesAndVectors")`. The remaining sections focus on patterns that are specific to 3D work.

    class(vol)
    #> [1] "DenseNeuroVol"
    #> attr(,"package")
    #> [1] "neuroim2"
    is.array(vol)
    #> [1] TRUE
    dim(vol)
    #> [1] 64 64 25
    vol[1, 1, 1]
    #> [1] 0
    vol[64, 64, 24]
    #> [1] 0

## Coordinate conversion and spatial metadata

    sp <- space(vol)
    sp
    #> <NeuroSpace> [3D]
    #> ── Geometry ────────────────────────────────────────────────────────────────────
    #>   Dimensions    : 64 x 64 x 25
    #>   Spacing       : 3.5 x 3.5 x 3.7 mm
    #>   Origin        : 112, -108.5, -46.25
    #>   Orientation   : LAS
    #>   Voxels        : 102,400
    dim(vol)
    #> [1] 64 64 25
    spacing(vol)
    #> [1] 3.5 3.5 3.7
    origin(vol)
    #> [1]  112.00 -108.50  -46.25

You can convert between indices, voxel grid coordinates, and real-world coordinates:

    idx <- 1:5
    g <- index_to_grid(vol, idx)
    w <- index_to_coord(vol, idx)
    idx2 <- coord_to_index(vol, w)
    all.equal(idx, idx2)
    #> [1] "Mean relative difference: 0.3333333"

A numeric image volume can be converted to a binary image as follows:

    vol2 <- as.logical(vol)
    class(vol2)
    #> [1] "LogicalNeuroVol"
    #> attr(,"package")
    #> [1] "neuroim2"
    print(vol2[1, 1, 1])
    #> [1] FALSE

## Masks and LogicalNeuroVol

Create a mask from a threshold or an explicit set of indices. Masks are `LogicalNeuroVol` and align with the 3D space.

    mask1 <- as.mask(vol > 0.5)
    mask1
    #> <DenseNeuroVol> [406.6 Kb]
    #> ── Spatial ─────────────────────────────────────────────────────────────────────
    #>   Dimensions    : 64 x 64 x 25
    #>   Spacing       : 3.5 x 3.5 x 3.7 mm
    #>   Origin        : 112, -108.5, -46.25
    #>   Orientation   : LAS
    #> ── Data ────────────────────────────────────────────────────────────────────────
    #>   Range         : [0.000, 1.000]

    idx_hi <- which(vol > 0.8)
    mask2 <- as.mask(vol, idx_hi)
    sum(mask2) == length(idx_hi)
    #> [1] TRUE

    mean_in_mask <- mean(vol[mask1@.Data])
    mean_in_mask
    #> [1] 1

## Constructing volumes manually

We can also create a `NeuroVol` instance from an `array` or `numeric` vector. First we construct a standard R `array`:

        x <- array(0, c(64,64,64))

Now we create a `NeuroSpace` instance that describes the geometry of the image, including at minimum its dimensions and voxel spacing.

        bspace <- NeuroSpace(dim=c(64,64,64), spacing=c(1,1,1))
        vol <- NeuroVol(x, bspace)
        vol
    #> <DenseNeuroVol> [2 Mb]
    #> ── Spatial ─────────────────────────────────────────────────────────────────────
    #>   Dimensions    : 64 x 64 x 64
    #>   Spacing       : 1 x 1 x 1 mm
    #>   Origin        : 0, 0, 0
    #>   Orientation   : RAS
    #> ── Data ────────────────────────────────────────────────────────────────────────
    #>   Range         : [0.000, 0.000]

We do not usually have to create `NeuroSpace` objects by hand because real image files carry this information in their headers. In practice you usually copy an existing space:

        vol2 <- NeuroVol((vol+1)*25, space(vol))
        max(vol2)
    #> [1] 25
        space(vol2)
    #> <NeuroSpace> [3D]
    #> ── Geometry ────────────────────────────────────────────────────────────────────
    #>   Dimensions    : 64 x 64 x 64
    #>   Spacing       : 1 x 1 x 1 mm
    #>   Origin        : 0, 0, 0
    #>   Orientation   : RAS
    #>   Voxels        : 262,144

## Slicing and quick visualization

The easiest way to view a volume is with `plot()`, which shows a 3 x 3 montage of evenly-spaced axial slices:

    plot(vol)

Default plot() montage

You can also extract a single 2D slice for display using standard array indexing:

        z <- ceiling(dim(vol)[3] / 2)
        image(vol[,,z], main = paste("Slice z=", z))

Mid-slice of example volume

## Reorienting and resampling

You can change an image’s orientation and voxel spacing. Use `reorient()` to remap axes (e.g., to RAS) and `resample_to()` to match a target space.

        # Reorient the space (LPI -> RAS) and compare coordinate mappings
        sp_lpi <- space(vol)
        sp_ras <- reorient(sp_lpi, c("R","A","S"))
        g     <- t(matrix(c(10, 10, 10)))
        world_lpi <- grid_to_coord(sp_lpi, g)
        world_ras <- grid_to_coord(sp_ras, g)
        # world_lpi and world_ras differ due to axis remapping

Resample to a new spacing or match a target `NeuroSpace`:

        # Create a target space with 2x finer resolution
        sp  <- space(vol)
        sp2 <- NeuroSpace(sp@dim * c(2,2,2), sp@spacing/2, origin=sp@origin, trans=trans(vol))

        # Resample (trilinear)
        vol_resamp <- resample_to(vol, sp2, method = "linear")
        dim(vol_resamp)

## Downsampling

Reduce spatial resolution to speed up downstream operations.

        # Downsample by target spacing
        vol_ds1 <- downsample(vol, spacing = spacing(vol)[1:3] * 2)
        dim(vol_ds1)
    #> [1] 32 32 32

        # Or by factor
        vol_ds2 <- downsample(vol, factor = 0.5)
        dim(vol_ds2)
    #> [1] 32 32 32

## Writing a NIFTI formatted image volume

When we’re ready to write an image volume to disk, we use `write_vol`

        write_vol(vol2, "output.nii")

        ## adding a '.gz' extension results ina gzipped file.
        write_vol(vol2, "output.nii.gz")

You can also write to a temporary file during workflows:

        tmp <- tempfile(fileext = ".nii.gz")
        write_vol(vol2, tmp)
        file.exists(tmp)
    #> [1] TRUE
        unlink(tmp)

For reorientation, resampling, and downsampling, use `vignette("Resampling")`, which now owns that topic directly.