The NIfTI-1 format is a popular file format for storing medical imaging data, widely used in medical research and related fields. Conceptually, a NIfTI-1 file incorporates multidimensional numeric data, like an R array, but with additional metadata describing the real-space resolution of the image, the physical orientation of the image, and how the image should be interpreted.
There are several packages available for reading and writing NIfTI-1 files in R, and these are summarised in the Medical Imaging task view. However, RNifti is distinguished by its
The latest development version of the package can always be installed from GitHub using the devtools package.
The primary role of RNifti is in reading and writing NIfTI-1 files, either gzip-compressed or uncompressed, and providing access to image data and metadata. An image may be read into R using the readNifti function.
This image is an R array with some additional attributes containing information such as its dimensions and the size of its pixels (or voxels, in this case, since it is a 3D image). There are auxiliary functions for extracting this information: the standard dim(), plus pixdim() and pixunits().
So this image is of size 96 x 96 x 60 voxels, with each voxel representing 2.5 x 2.5 x 2.5 mm in real space. (The temporal unit, seconds here, only applies to the fourth dimension, if it is present.) Replacement versions of the latter functions are also available, for modifying the metadata.
A fuller list of the raw metadata stored in the file can be obtained using the niftiHeader function.
niftiHeader(image)
# NIfTI-1 header
# sizeof_hdr: 348
# dim_info: 0
# dim: 3 96 96 60 1 1 1 1
# intent_p1: 0
# intent_p2: 0
# intent_p3: 0
# intent_code: 0
# datatype: 8
# bitpix: 32
# slice_start: 0
# pixdim: -1.0 2.5 2.5 2.5 0.0 0.0 0.0 0.0
# vox_offset: 352
# scl_slope: 0
# scl_inter: 0
# slice_end: 0
# slice_code: 0
# xyzt_units: 10
# cal_max: 2503
# cal_min: 0
# slice_duration: 0
# toffset: 0
# descrip: TractoR NIfTI writer v3.0.0
# aux_file:
# qform_code: 2
# sform_code: 2
# quatern_b: 0
# quatern_c: 1
# quatern_d: 0
# qoffset_x: 122.0339
# qoffset_y: -95.18523
# qoffset_z: -55.03814
# srow_x: -2.5000 0.0000 0.0000 122.0339
# srow_y: 0.00000 2.50000 0.00000 -95.18523
# srow_z: 0.00000 0.00000 2.50000 -55.03814
# intent_name:
# magic: n+1Advanced users who know the NIfTI format well may want to alter elements of this metadata directly, and this can be performed using the $ operator shorthand, as in
If you need to modify multiple metadata elements at once, or replace metadata wholesale with new information from another image, the updateNifti function provides a more efficient interface. See ?updateNifti for details.
An image can be written back to NIfTI-1 format using the writeNifti function. gzip compression will be used if the specified file name ends with “.gz”.
The NIfTI-1 format has a mechanism for indicating the physical orientation and location of the image volume in real space. The reference orientation has the left–right direction aligned with the x-axis, the posterior–anterior (back–front) direction aligned with the y-axis, and the inferior–superior (bottom–top) direction aligned with the z-axis; but “xform” information stored with an image can describe a transformation from that coordinate system to the one used by that particular image, in the form of an affine matrix. To obtain the full xform matrix for an image, call the xform function:
xform(image)
# [,1] [,2] [,3] [,4]
# [1,] -2.5 0.0 0.0 122.03390
# [2,] 0.0 2.5 0.0 -95.18523
# [3,] 0.0 0.0 2.5 -55.03814
# [4,] 0.0 0.0 0.0 1.00000Just the rotation with respect to the canonical axes can be obtained with the rotation function:
In this case, the image is flipped along the x-axis relative to the canonical axes, so the positive x-direction points towards the left rather than the right. This is compactly represented by the output of the orientation function, which indicates the approximate real-world directions of the positive axes in each dimension.
So, here, “LAS” means that the positive x-axis points left, the positive y-axis anterior and the positive z-axis superior. There is also a replacement version of the orientation function, which will reorient the image to align with the requested directions. This is a relatively complex operation, affecting the xform and the storage order of the data.
image[30,30,20]
# [1] 457
orientation(image) <- "RAS"
xform(image)
# [,1] [,2] [,3] [,4]
# [1,] 2.5 0.0 0.0 -115.46609
# [2,] 0.0 2.5 0.0 -95.18523
# [3,] 0.0 0.0 2.5 -55.03814
# [4,] 0.0 0.0 0.0 1.00000
image[30,30,20]
# [1] 409
image[67,30,20]
# [1] 457Notice that the sign of the top-left element of the xform has now flipped, and the value of the image at location (30,30,20) has changed because the data has been reordered. The equivalent x-location is now 67, which is the 30th element counting in the other direction (96 - 30 + 1 = 67).
This latter operation can be useful to ensure that indexing into several images with different native storage conventions will end up always having approximately the same meaning. It is non-destructive, because no interpolation of the data is performed. This means that the axes will not exactly align with the requested directions if the original image was oblique to the canonical axes, but conversely it ensures that no degradation in the image will result. (The RNiftyReg package can be used to apply an arbitrary rotation to an image and interpolate the data onto the new grid, if required.)
The RNifti package uses the robust NIfTI-1 reference implementation, which is written in C, to read and write NIfTI files. It also uses the standard NIfTI-1 data structure as its canonical representation of an image in memory. Together, these make the package extremely fast, as the following benchmark against packages AnalyzeFMRI, ANTsRCore, neuroim, oro.nifti and tractor.base shows.
installed.packages()[c("AnalyzeFMRI","ANTsRCore","neuroim","oro.nifti","RNifti",
"tractor.base"), "Version"]
# AnalyzeFMRI ANTsRCore neuroim oro.nifti RNifti tractor.base
# "1.1-17" "0.5.6.3" "0.0.6" "0.9.1" "0.9.0" "3.2.2"
library(microbenchmark)
microbenchmark(AnalyzeFMRI::f.read.volume("example.nii"),
ANTsRCore::antsImageRead("example.nii"),
neuroim::loadVolume("example.nii"),
oro.nifti::readNIfTI("example.nii"),
RNifti::readNifti("example.nii"),
tractor.base::readImageFile("example.nii"), unit="ms")
# Unit: milliseconds
# expr min lq mean
# AnalyzeFMRI::f.read.volume("example.nii") 26.312881 26.769244 29.685981
# ANTsRCore::antsImageRead("example.nii") 1.150787 1.626918 2.149145
# neuroim::loadVolume("example.nii") 34.596506 37.732245 54.065227
# oro.nifti::readNIfTI("example.nii") 57.059828 61.953430 89.386706
# RNifti::readNifti("example.nii") 0.986773 1.136562 1.683821
# tractor.base::readImageFile("example.nii") 33.380407 34.096574 34.961812
# median uq max neval
# 27.301693 28.214908 185.441664 100
# 2.210696 2.481675 3.376350 100
# 40.714061 45.425047 192.978225 100
# 65.064212 71.425561 220.709246 100
# 1.501528 1.856601 7.961566 100
# 34.617259 35.257633 42.108584 100With a median runtime of less than 2 ms, RNifti is typically at least ten times as fast as the alternatives to read this image into R. The exception is ANTsRCore, which uses a similar low-level pointer-based arrangement as RNifti, and is therefore comparable in speed. However, ANTsR has substantial dependencies, which may affect its suitability in some applications.
The package does not fully duplicate the NIfTI-1 structure’s contents in R-visible objects. Instead, it passes key metadata back to R, such as the image dimensions and pixel dimensions, and it also passes back the pixel values where they are needed. It also creates an external pointer to the native data structure, which is stored in an attribute. This pointer is dereferenced whenever the object is passed back to the C++ code, thereby avoiding unnecessary duplication and ensuring that all metadata remains intact. The full NIfTI-1 header can be obtained using the niftiHeader R function, if it is needed.
This arrangement is efficient and generally works well, but many R operations strip attributes—in which case the external pointer will be removed. The internal structure will be built again when necessary, but using default metadata. In these cases, if it is important to keep the original metadata, the updateNifti function should be called explicitly, with a template object. This reconstructs the NIfTI-1 data structure, using the template as a starting point.
It is possible to use the package’s NIfTI-handling code in other R packages’ compiled code, thereby obviating the need to duplicate the reference implementation. Moreover, RNifti provides a C++ wrapper class, NiftiImage, which simplifies memory management, supports the package’s internal image pointers and persistence, and provides syntactic sugar. Full doxygen documentation for this class is available at http://doxygen.flakery.org/RNifti/, and is also provided with package releases.
A third-party package can use the NiftiImage class by including
LinkingTo: Rcpp, RNiftiin its DESCRIPTION file, and then including the RNifti.h header file. For example,
#include "RNifti.h"
void myfunction ()
{
RNifti::NiftiImage image("example.nii.gz");
// Do something with the image
}If you’re using the sourceCpp function from Rcpp, you may also need to add the attribute line
to the top of your C++ source file.
In addition to the one taking a file path, there are also constructors taking a SEXP (i.e., an R object), another NiftiImage, or a nifti_image structure from the reference implementation. NiftiImage objects can be implicitly cast to pointers to nifti_image structs, meaning that they can be directly used in calls to the reference implementation’s own API. The latter is accessed through the separate RNiftiAPI.h header file.
#include "RNifti.h"
#include "RNiftiAPI.h"
void myfunction (SEXP image_)
{
RNifti::NiftiImage image(image_);
const size_t volsize = nifti_get_volsize(image);
}(RNifti will also have to be added to the Imports list in the package’s DESCRIPTION file, as well as LinkingTo.) The RNiftiAPI.h header should only be included once per package, since it contains function implementations. Multiple includes will lead to duplicate symbol warnings from your linker. Therefore, if multiple source files require access to the NIfTI-1 reference implementation, it is recommended that the API header be included alone in a separate “.c” or “.cpp” file, while others only include the main RNifti.h.
RNifti is not specifically designed to be thread-safe, and R itself is expressly single-threaded. However, some effort has been made to try to minimise problems associated with parallelisation, such as putting R API calls within a critical region if OpenMP is being used. If you are using the API in a package that does use OpenMP or another form of threads, it is wise to preregister the functions exported by RNifti before use, by calling niftilib_register_all(). In single-threaded contexts this is optional, and will be performed when required.
Thanks to contributions from [@soolijoo](https://github.com/soolijoo), it is possible (as of package version 0.7.0) to use the NiftiImage C++ class in standalone C++ projects. You will need the following files:
| Path | Purpose |
|---|---|
inst/include/lib/*.h |
Headers defining the NiftiImage class itself, related functions and macros |
inst/include/niftilib/*.h |
Headers for the NIfTI-1 reference implementation |
inst/include/znzlib/znzlib.h |
Header for I/O functions from the NIfTI-1 reference implementation |
inst/include/zlib/*.h |
zlib headers for reading and writing gzipped files (optional; system zlib can be used instead) |
src/niftilib/nifti1_io.c |
Source file for the NIfTI-1 reference implementation |
src/znzlib/znzlib.c |
Source for I/O functions from the NIfTI-1 reference implementation |
src/zlib/* |
zlib source files for reading and writing gzipped files (optional, as above) |
Note that the NiftiImage class is header-only, but C code from the NIfTI-1 reference implementation will need to be compiled and linked into the project. The print.h header should be included before including NiftiImage.h, so that the R API is not used for printing error messages. The standalone directory provides a minimal example.
The NIfTI-2 format is an evolution of the far more widely-used NIfTI-1. It primarily uses wider types for various fields, to support large datasets and improve precision.
Unfortunately, the NIfTI-1 version of the reference library is not forwards-compatible with NIfTI-2, and does not recognise NIfTI-2 files as valid, while the NIfTI-2 version changes the definition of the nifti_image data structure, and hence the return type of several core functions, rendering it potentially incompatible with software written for the original library. As a result, adding full NIfTI-2 support to RNifti without breaking existing code is not straightforward. Nevertheless, as of RNifti version 0.8.0, R function niftiVersion() and C++ static method NiftiImage::fileVersion() offer a forwards-compatible way to determine the version of the format used by a particular file, so that calling functions can take action accordingly.