---
title: Working with 3D Image Volumes
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---

```{r, echo = FALSE, message = FALSE}
knitr::opts_chunk$set(collapse = T, comment = "#>")
```

## Reading a NIFTI formatted image volume

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


```{r}
    library(neuroim2)
    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:


```{r}
    print(vol)
```



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

```{r}
    class(vol)
    
    is.array(vol)
    
    dim(vol)
    
    vol[1,1,1]
    
    vol[64,64,24]
    
```
    
Arithmetic can be performed on images as if they were ordinary `array`s:


```{r}
    
    vol2 <- vol + vol
    sum(vol2) == 2 * sum(vol)
    
    vol3 <- vol2 - 2*vol
    all(vol3 == 0)
```

## Inspecting geometry and spatial metadata

Each `NeuroVol` has an associated `NeuroSpace` describing its geometry (dimensions, spacing, origin, axes/orientation).

```{r}
    sp <- space(vol)
    sp                 # human-readable summary
    dim(vol)           # spatial dimensions (x, y, z)
    spacing(vol)       # voxel size in mm
    origin(vol)        # image origin
```

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

```{r}
    idx <- 1:5
    g   <- index_to_grid(vol, idx)     # 1D index -> (i,j,k)
    w   <- index_to_coord(vol, idx)    # 1D index -> world coords
    idx2 <- coord_to_index(vol, w)     # back to indices
    all.equal(idx, idx2)
```

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

```{r}
    
    vol2 <- as.logical(vol)
    class(vol2)
    print(vol2[1,1,1])
```

## Masks and LogicalNeuroVol

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

```{r}
    # Threshold-based mask
    mask1 <- as.mask(vol > 0.5)
    mask1

    # Index-based mask
    idx_hi <- which(vol > 0.8)
    mask2 <- as.mask(vol, idx_hi)
    sum(mask2) == length(idx_hi)

    # Use a mask to compute a summary
    mean_in_mask <- mean(vol[mask1@.Data])
    mean_in_mask
```

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


```{r}
    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.

```{r}
    bspace <- NeuroSpace(dim=c(64,64,64), spacing=c(1,1,1))
    vol <- NeuroVol(x, bspace)
    vol
```

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:


```{r}
    vol2 <- NeuroVol((vol+1)*25, space(vol))
    max(vol2)
    space(vol2)
```

## Slicing and quick visualization

Extract a single 2D slice for display using standard array indexing:

```{r slice_mid, fig.alt='Mid-slice of example volume (grayscale image).', fig.cap='Mid-slice of example volume'}
    z <- ceiling(dim(vol)[3] / 2)
    image(vol[,,z], main = paste("Slice z=", z))
```

## 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.

```{r}
    # 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`:

```{r eval=FALSE}
    # 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.

```{r}
    # Downsample by target spacing
    vol_ds1 <- downsample(vol, spacing = spacing(vol)[1:3] * 2)
    dim(vol_ds1)

    # Or by factor
    vol_ds2 <- downsample(vol, factor = 0.5)
    dim(vol_ds2)
```

## Writing a NIFTI formatted image volume

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

```{r eval=FALSE}
    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:

```{r}
    tmp <- tempfile(fileext = ".nii.gz")
    write_vol(vol2, tmp)
    file.exists(tmp)
    unlink(tmp)
```