Voxel-based Sampling
Voxel-based Sampling - Related Videos
07:12
Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
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Cited by 23 •
2014
Informational connectivity measures the correspondence between time courses of multi-voxel information across different brain regions. Multi-voxel pattern discriminability time series are extracted from regions and compared, revealing networks that are not identified in a typical functional connectivity approach.
09:17
Measuring Grey Matter Differences with Voxel-based Morphometry: The Musical Brain
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2023
Source: Laboratories of Jonas T. Kaplan and Sarah I. Gimbel—University of Southern California Experience shapes the brain. It is well understood that our brains are different as a result of learning. While many experience-related changes manifest themselves at the microscopic level, for example by neurochemical adjustments in the behavior of individual neurons, we may also examine anatomical changes to the structure of the brain at a macroscopic level. One famous example of this kind of change...
07:55
An All-in-one Sample Holder for Macromolecular X-ray Crystallography with Minimal Background Scattering
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Cited by 4 •
2019
A novel sample holder for macromolecular X-ray crystallography along with a suitable handling protocol is presented. The system allows crystal growth, crystal soaking and in situ diffraction data collection at both, ambient and cryogenic temperature without the need of any crystal manipulation or mounting.
12:08
From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
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Cited by 14 •
2014
The bottleneck for cellular 3D electron microscopy is feature extraction (segmentation) in highly complex 3D density maps. We have developed a set of criteria, which provides guidance regarding which segmentation approach (manual, semi-automated, or automated) is best suited for different data types, thus providing a starting point for effective segmentation.
11:36
Voxel Printing Anatomy: Design and Fabrication of Realistic, Presurgical Planning Models through Bitmap Printing
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Cited by 1 •
2022
This method demonstrates a voxel-based 3D printing workflow, which prints directly from medical images with exact spatial fidelity and spatial/contrast resolution. This enables the precise, graduated control of material distributions through morphologically complex, graduated materials correlated to radiodensity without loss or alteration of data.
FAQs
Voxel size sets the spatial scale of the analysis, while image resolution limits the detail that can be retained at that scale. A finer sampling arrangement can preserve more anatomical or cellular detail, whereas a coarser arrangement represents the volume with less spatial specificity. Selecting these parameters therefore affects whether measurements emphasize broad tissue organization or smaller biological structures.
Defined spatial coordinates make measurements reproducible across a volume. When each sampled location is associated with an intensity or another measured property, researchers can compare corresponding regions rather than relying only on visual inspection. This coordinate-based organization supports quantitative assessment of spatial patterns and helps maintain consistent measurements when biological samples or datasets are compared.
Each voxel can serve as a localized data element in a three-dimensional representation. Organizing these elements according to their positions allows the measured properties throughout an imaged volume to be assembled into a spatial model. That model can support reconstruction of biological structures and reveal how anatomical, cellular, molecular, or functional signals are distributed.
The workflow begins with an imaged three-dimensional volume and a chosen sampling arrangement. The volume is partitioned into voxels, data are assigned at defined spatial coordinates, and the resulting values are analyzed across the volume. Researchers can then use the organized measurements for spatial comparisons, structural reconstruction, or quantitative characterization of the biological sample.
The approach applies to volumetric data from microscopy, magnetic resonance imaging, and other three-dimensional imaging sources. Its usefulness extends across signal types because sampled values may represent intensity or another molecular or functional property. This flexibility lets investigators examine tissue architecture, cell distributions, or spatially varying signals within the same analytical framework.
By assigning data to defined spatial locations, the method creates a consistent basis for comparing biological samples. Researchers can evaluate differences in tissue architecture, cell distributions, or molecular and functional signals across corresponding portions of volumetric datasets. The resulting comparisons are quantitative and spatially explicit, making them more informative than descriptions that lack organized location-specific measurements.