Voxel Resolution
Voxel Resolution - Related Videos
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...
07:12
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.
10:06
High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain
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Cited by 8 •
2012
This article describes techniques to perform high-resolution functional magnetic resonance imaging with 1.2 mm sampling in human midbrain and subcortical structures using a 3T scanner. Use of these techniques to resolve topographic maps of visual stimulation in the human superior colliculus (SC) is given as an example.
08:16
High-resolution Structural Magnetic Resonance Imaging of the Human Subcortex In Vivo and Postmortem
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Cited by 5 •
2015
Here we present a protocol to determine the minimum number images that needed to be registered and averaged to resolve subcortical structures and test whether the individual layers of the LGN could be resolved in the absence of physiological noise.
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.
FAQs
Resolution must be considered independently along the x, y, and z axes because voxel dimensions define sampling in each direction. If one axis uses larger voxels, features that are narrow in that direction may be represented less precisely even when the other two axes are finely sampled. This directional difference affects geometric fidelity in three-dimensional engineering models.
Smaller voxels capture narrower features, but the benefit comes with practical costs. Finer sampling increases the amount of stored data and can lengthen processing time, while accurate results often depend on higher-quality measurements. Engineers therefore cannot treat maximum fineness as automatically best; the selected setting must balance detail against computational efficiency and measurement capability.
A coarse selection can prevent the model from representing small defects or preserve the true shape of boundaries. Those losses may smooth geometry and introduce measurement error, reducing the accuracy of computed tomography, digital inspection, or other engineering representations. The concern is not only visual detail: missing spatial information can affect the reliability of the resulting model.
Start by identifying the geometric detail that the model or inspection must preserve, then compare that requirement with available storage, processing time, and measurement quality. Choose dimensions small enough to capture the relevant features, but not finer than the application can support efficiently. This procedure makes voxel resolution a design decision rather than a fixed maximum.
In computed tomography, voxel resolution influences how accurately geometry and defects are represented. In additive manufacturing, it affects the detail retained in the model used to describe a part. For digital inspection, it shapes the spatial information available for evaluating geometry. Across these uses, resolution selection determines the balance between geometric fidelity and manageable computation.
In finite-element representations, voxel resolution influences how faithfully the represented geometry is retained. A finer choice can capture narrower features, while a coarser choice may smooth boundaries and reduce geometric accuracy. Engineers can use this context when deciding whether computational efficiency justifies reduced spatial detail in a model.