Working with Image Volumes

2025-02-21

Reading a NIFTI formatted image volume

The way to read an volumetric image file is to use read_vol:

    library(neuroim2)
#> Loading required package: Matrix
#> 
#> Attaching package: 'neuroim2'
#> The following object is masked from 'package:base':
#> 
#>     scale
    file_name <- system.file("extdata", "global_mask2.nii.gz", package="neuroim2")
    vol <- read_vol(file_name)

Working with image volumes

Information about the geometry of the image volume is shown here:

    print(vol)
#> 
#>  === NeuroVol Object === 
#> 
#> * Basic Information 
#>   Type:      DenseNeuroVol
#>   Dimensions: 64 x 64 x 25 (406.6 Kb)
#>   Total Voxels: 102,400
#> 
#> * Data Properties
#>   Value Range: [0.00, 1.00]
#> 
#> * Spatial Properties
#>   Spacing: 3.50 x 3.50 x 3.70 mm
#>   Origin:  112.0, -108.5, -46.2 mm
#>   Axes:    Right-to-Left x Posterior-to-Anterior x Inferior-to-Superior
#> 
#> ======================================
#> 
#>  Access Methods: 
#>   .  Get Slice:   slice(object, zlevel=10) 
#>   .  Get Value:   object[i, j, k] 
#>   .  Plot:       plot(object)  # shows multiple slices

read_vol returns an object of class NeuroVol object which extends an R array and has 3 dimensions (x,y,z).

    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

Arithmetic can be performed on images as if they were ordinary arrays:

    
    vol2 <- vol + vol
    sum(vol2) == 2 * sum(vol)
#> [1] TRUE
    
    vol3 <- vol2 - 2*vol
    all(vol3 == 0)
#> [1] TRUE

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

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

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

Now we reate 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
#> 
#>  === NeuroVol Object === 
#> 
#> * Basic Information 
#>   Type:      DenseNeuroVol
#>   Dimensions: 64 x 64 x 64 (2 Mb)
#>   Total Voxels: 262,144
#> 
#> * Data Properties
#>   Value Range: [0.00, 0.00]
#> 
#> * Spatial Properties
#>   Spacing: 1.00 x 1.00 x 1.00 mm
#>   Origin:  0.0, 0.0, 0.0 mm
#>   Axes:    Left-to-Right x Posterior-to-Anterior x Inferior-to-Superior
#> 
#> ======================================
#> 
#>  Access Methods: 
#>   .  Get Slice:   slice(object, zlevel=10) 
#>   .  Get Value:   object[i, j, k] 
#>   .  Plot:       plot(object)  # shows multiple slices

We do not usually have to create NeuroSpace objects, because geometric information about an image is automatically determined from information stored in the image file header. Thus, NeuroSpace objects are usually copied from existing images using the space extractor function when needed:

    vol2 <- NeuroVol((vol+1)*25, space(vol))
    max(vol2)
#> [1] 25
    space(vol2)
#> 
#>  NeuroSpace Object 
#> 
#>  >> Dimensions 
#>   Grid Size: 64 x 64 x 64
#>   Memory:   5.9 KB
#> 
#>  >> Spatial Properties 
#>   Spacing:   1.00 x 1.00 x 1.00 mm
#>   Origin:    0.00 x 0.00 x 0.00 mm
#> 
#>  >> Anatomical Orientation 
#>   X: Left-to-Right  |  Y: Posterior-to-Anterior  |  Z: Inferior-to-Superior 
#> 
#>  >> World Transformation 
#>   Forward (Voxel to World): 
#>     1.000  0.000  0.000  0.000
#> 0.000  1.000  0.000  0.000
#> 0.000  0.000  1.000  0.000
#> 0.000  0.000  0.000  1.000 
#>   Inverse (World to Voxel): 
#>     1.000  0.000  0.000  0.000
#> 0.000  1.000  0.000  0.000
#> 0.000  0.000  1.000  0.000
#> 0.000  0.000  0.000  1.000 
#> 
#>  >> Bounding Box 
#>   Min Corner: 0.0, 0.0, 0.0 mm
#>   Max Corner: 63.0, 63.0, 63.0 mm
#> 
#> ==================================================

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")