Nifti Images
Nifti Images - Related Videos
09:41
A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery
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Cited by 19 •
2016
We describe the steps to use our custom designed software for image integration, visualization and planning in epilepsy surgery.
17:06
Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
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Cited by 17 •
2012
We describe a novel approach for simultaneous analysis of brain function and structure using magnetic resonance imaging (MRI). We assess brain structure with high-resolution diffusion-weighted imaging and white-matter fiber tractography. Unlike standard structural MRI, these techniques allow us to directly relate anatomical connectivity to functional properties of brain networks.
13:05
Functional Magnetic Resonance Imaging (fMRI) with Auditory Stimulation in Songbirds
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Cited by 12 •
2013
This article shows an optimized procedure for imaging of the neural substrates of auditory stimulation in the songbird brain using functional Magnetic Resonance Imaging (fMRI). It describes the preparation of the sound stimuli, the positioning of the subject and the acquisition and subsequent analysis of the fMRI data.
07:57
Scaled Anatomical Model Creation of Biomedical Tomographic Imaging Data and Associated Labels for Subsequent Sub-surface Laser Engraving (SSLE) of Glass Crystals
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2017
A methodology is described herein for representing anatomical imaging data within crystals. We create scaled three-dimensional models of biomedical imaging data for use in Sub-Surface Laser Engraving (SSLE) of crystal glass. This tool offers a useful complement to computational display or three-dimensionally printed models used within clinical or educational settings.
13:45
Hybrid µCT-FMT imaging and image analysis
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Cited by 34 •
2015
We describe a protocol for hybrid imaging, combining fluorescence-mediated tomography (FMT) with micro computed tomography (µCT). After fusion and reconstruction, we perform interactive organ segmentation to extract quantitative measurements of the fluorescence distribution.
FAQs
The affine transformation links voxel indices to physical coordinates, allowing software to determine where each voxel lies in space. This spatial mapping is essential when images must be aligned, registered, or compared across scans. If the transformation is interpreted incorrectly, anatomical locations may appear displaced, making visualization and downstream analysis unreliable even when the intensity data remain unchanged.
Header information tells analysis software how to read and interpret the stored array. Dimensions describe the image structure, the data type specifies how voxel values are represented, and orientation information establishes spatial relationships. Together, these fields determine whether a dataset is treated correctly during loading, visualization, preprocessing, and statistical analysis rather than merely displaying numerical values without meaningful anatomical context.
A four-dimensional dataset can organize a sequence of three-dimensional brain volumes, making it suitable for representing measurements that change over time. This structure is particularly relevant to functional MRI workflows, where repeated volumes support analysis of brain function. The time-series organization distinguishes these files from single-volume structural images and affects how software reads and processes the data.
A typical workflow begins by loading the image and interpreting its header, dimensions, orientation, and coordinate mapping. Researchers can then apply preprocessing, register images to one another, visualize the results, and perform statistical analysis. Because the same file structure can support structural and functional measurements, the workflow can be adapted to different neuroimaging study designs while retaining consistent data handling.
Registration uses the spatial information associated with the images to align scans or datasets within a common coordinate framework. Once aligned, researchers can compare brain anatomy or functional measurements across images more consistently. The combination of voxel values and coordinate mapping supports this process, while compatible software enables the resulting datasets to move through visualization and statistical analysis workflows.
A standardized file structure gives researchers a consistent way to exchange neuroimaging data and preserve the metadata needed for interpretation. Broad compatibility with established software supports shared preprocessing, visualization, registration, and statistical workflows. These features improve reproducibility by making analyses easier to repeat and support comparisons between structural and functional brain measurements from different imaging studies.