The Neuroimaging Informatics Technology Initiative (NIFTI) format is a standard file type for storing and sharing brain imaging data, such as Magnetic Resonance Imaging (MRI) and functional MRI (fMRI). The format packages complex, multi-dimensional data into a single structure that different software programs can process. This standardization allows researchers from various institutions to collaborate more effectively, ensuring a brain scan from one lab can be accurately analyzed in another.
The Genesis of NIFTI Format
Before NIFTI, neuroimaging research relied on formats like ANALYZE 7.5. This format had a significant limitation in its handling of spatial orientation, as it lacked a standard method for defining the left-right and front-back orientation of a brain scan. Researchers often had to guess if an image was in a “radiological” view (where left is right) or “neurological” view (where left is left).
This ambiguity created problems for data sharing and the comparability of research findings, as different software could display the same file differently. To address these issues, the National Institutes of Health (NIH) convened a working group in 2003 to develop an improved format. The result was NIFTI, which was built upon the ANALYZE 7.5 foundation to maintain some backward compatibility while ensuring spatial information could be interpreted without ambiguity.
Deconstructing NIFTI Files
NIFTI files are structured to hold both raw image data and the metadata needed to interpret it correctly. These files most commonly appear with a `.nii` extension, signifying a single file containing both the header (metadata) and image data. For efficiency in storage and transfer, these files are often compressed, resulting in a `.nii.gz` extension.
NIFTI data can also be stored as a pair of files to reflect its heritage from the older ANALYZE format. In this structure, a `.hdr` file contains the header information, and a separate `.img` file holds the raw image data, though the single `.nii` file is more common.
The image data itself is composed of voxels, which are three-dimensional pixels. Each voxel has a specific value representing signal intensity and a precise location in 3D space. NIFTI files organize this volumetric data into a grid, capturing the full three-dimensional structure of the scanned object, such as a human brain.
Essential Data Within NIFTI
The utility of the NIFTI format comes from its 348-byte header, which stores metadata that describes the image data. The header contains fields that define the data’s dimensions, such as the number of spatial dimensions (x, y, z) and time points in a functional scan. It also specifies the size of each voxel through the `pixdim` field and the numerical type of the data in the `datatype` field.
A primary feature of the NIFTI header is its system for storing spatial orientation information. It includes two independent methods for specifying the position and orientation of the voxel grid. The first method, `qform`, uses a quaternion to define the alignment. The second method, `sform`, uses an affine transformation matrix to map voxel coordinates to a standardized anatomical space.
These orientation codes ensure that a neuroimaging file can be displayed correctly and consistently across different software platforms. This precision is important for accurate analysis, especially when comparing or averaging scans from multiple subjects.
NIFTI in Practice: Uses and Tools
The NIFTI format is the standard for many neuroimaging research applications, including structural MRI, functional MRI (fMRI), and Diffusion Tensor Imaging (DTI). Its adoption has streamlined sharing large datasets and facilitated the development of analysis pipelines. Research consortiums and open data initiatives rely on NIFTI to distribute brain imaging data to scientists worldwide, enabling reproducible research.
This widespread use is supported by an ecosystem of software tools designed to view, process, and analyze NIFTI files. These packages provide capabilities for tasks ranging from preprocessing raw fMRI data to performing complex statistical analyses. A wide range of software supports the NIFTI format, including:
- FSL (FMRIB Software Library)
- SPM (Statistical Parametric Mapping)
- AFNI (Analysis of Functional NeuroImages)
- 3D Slicer
- ITK-SNAP
- Mango
- NiBabel for Python
This broad support allows researchers to select the best tool for their specific scientific question while working within a common data framework.