Voxel Array Reconstruction
Voxel Array Reconstruction - 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...
09:16
Array Comparative Genomic Hybridization (Array CGH) for Detection of Genomic Copy Number Variants
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Cited by 7 •
2015
Array CGH for the detection of genomic copy number variants has replaced G-banded karyotype analysis. This paper describes the technology and its application in a diagnostic service laboratory.
10:59
Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands
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Cited by 10 •
2014
We present an image registration approach for 3-dimensional (3D) histology volume reconstruction, which facilitates the study of the changes of an organ at the level of macrostructures made up of cells . Using this approach, we studied the 3D changes between wild-type and Igfbp7-null mammary glands.
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
The reconstruction process integrates measurements acquired from multiple viewing angles or from successive image slices, then applies mathematical reconstruction to estimate values throughout the volume. These estimated values are assigned to specific spatial positions, allowing neighboring measurements to form a continuous three-dimensional representation rather than remaining as separate two-dimensional images.
Interpolation estimates voxel values in locations that are not directly represented by an individual measurement or slice. This step helps fill spatial gaps and organize the dataset into a regular three-dimensional grid. The resulting arrangement supports smoother cross-sectional viewing and more consistent multiplanar or three-dimensional visualization of internal structures.
Voxel intensity, spatial location, and tissue structure must remain correctly associated throughout reconstruction. Intensity provides the measured value assigned to a position, while location preserves the arrangement of anatomy within the volume. Maintaining both relationships allows the dataset to support spatial interpretation and quantitative analysis without separating measurements from the structures they represent.
Individual slices show anatomy within separate two-dimensional planes, whereas an addressable voxel array preserves those measurements within a shared three-dimensional coordinate arrangement. This organization permits cross-sectional review in additional planes and supports three-dimensional rendering. Consequently, users can examine the spatial relationships among structures rather than interpreting each slice in isolation.
A typical workflow begins with imaging measurements obtained from multiple projections or sequential slices. Mathematical reconstruction then estimates values throughout the sampled region, and interpolation helps populate the volume between measured locations. Finally, the estimated values are organized into an addressable voxel array that can be visualized or analyzed using cross-sectional, multiplanar, or three-dimensional methods.
The resulting dataset can provide cross-sectional views, multiplanar displays, and three-dimensional renderings of anatomy or pathology. Because each value remains tied to a spatial location, the volume also supports quantitative analysis. These outputs help users examine internal structure from different orientations and assess measured patterns within a consistent three-dimensional framework.
Medical researchers and clinicians can use reconstructed volumes when internal anatomy or pathology must be examined beyond a single imaging plane. The datasets support visualization, quantitative analysis, and computer-assisted assessment. Their value lies in preserving spatial relationships while enabling different views of the same specimen or body region, which can improve interpretation of complex internal structures.