Nifty Data Format
Nifty Data Format - Related Videos
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.
14:27
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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Cited by 73 •
2013
Multivariate techniques including principal component analysis (PCA) have been used to identify signature patterns of regional change in functional brain images. We have developed an algorithm to identify reproducible network biomarkers for the diagnosis of neurodegenerative disorders, assessment of disease progression, and objective evaluation of treatment effects in patient populations.
02:16
Solution Formation
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2020
There is no one solvent that can dissolve every type of solute. Some substances that readily dissolve in a certain solvent might be insoluble in a different solvent. A simple way to predict which substances dissolve in which solvent is the phrase "like dissolves like". This means that polar substances, such as salt and sugar, dissolve in a polar substance like water. In contrast, non-polar substances are more soluble in non-polar solvents such as carbon tetrachloride. This selective solubility...
02:37
Standard Enthalpy of Formation
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2020
Enthalpy changes are typically tabulated for reactions in which both the reactants and products are at the same conditions. A standard state is a commonly accepted set of conditions used as a reference point for the determination of properties under other different conditions. For chemists, the IUPAC standard state refers to materials under a pressure of 1 bar and solutions at 1 M and does not specify a temperature. Many thermochemical tables list values with a standard state of 1 atm. Because...
01:30
Data Interpretation
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2026
Research papers often present findings in the form of numerical tables, statistical outputs, and graphical representations. However, numerical results alone do not convey meaning unless researchers examine and place them in context. Data interpretation is the systematic process through which researchers transform raw information into clear, meaningful insights. It begins only after researchers have collected data through experiments, surveys, or observational studies. In their unprocessed form,...
FAQs
The affine transformation links voxel indices to a meaningful anatomical coordinate system. Because image data are stored as an array, the same voxel position is not inherently an anatomical location until this spatial relationship is applied. Registration, segmentation, and quantitative analysis can therefore use coordinates that correspond more meaningfully to brain anatomy rather than relying only on array positions.
Image dimensions, voxel sizes, data type, and orientation determine how software reads and interprets the stored intensities. Dimensions describe the array structure, voxel sizes establish spatial scale, data type controls how values are represented, and orientation helps preserve spatial meaning. Errors or inconsistencies in these fields can compromise downstream neuroimaging analysis.
Voxel intensities provide the measured image values, while spatial metadata explains where those values belong and how large each voxel is. Either component alone is insufficient for reliable interpretation: intensities without location cannot support anatomical mapping, and spatial information without values cannot describe the image. Together, they enable measurements from MRI, functional MRI, and diffusion imaging.
A typical workflow uses the stored image and its spatial information during preprocessing, followed by operations such as registration, segmentation, and quantitative analysis. Preprocessing prepares the data for subsequent analysis, registration aligns images within a meaningful coordinate system, and segmentation separates relevant structures or regions. The resulting measurements can support clinical and biological investigations.
NIfTI supports several neuroimaging modalities, including structural MRI, functional MRI, and diffusion imaging. These modalities produce data that can be processed within related analysis workflows, although their measurements address different aspects of the brain. Using a shared format helps place modality-specific results into compatible software pipelines for preprocessing, registration, segmentation, and quantitative evaluation.
Compatibility with widely used neuroimaging software allows investigators to exchange files and apply consistent analysis workflows across studies. The header preserves dimensions, voxel sizes, data type, orientation, and affine spatial information alongside the voxel array. Retaining these details reduces ambiguity when data move between tools and helps connect image-derived measurements with clinical and biological research.