X-ray Computed Tomography
X-ray Computed Tomography - Related Videos
06:56
Tree Core Analysis with X-ray Computed Tomography
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Cited by 10 •
2023
Here we show how to process tree cores with an X-ray computed tomography toolchain. Except for chemical extraction for some purposes, no further physical lab treatment is needed. The toolchain can be used for biomass estimations, for obtaining MXD/tree-ring width data as well as for obtaining quantitative wood anatomy data.
09:37
Extracting Metrics for Three-dimensional Root Systems: Volume and Surface Analysis from In-soil X-ray Computed Tomography Data
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Cited by 2 •
2016
A methodology for obtaining visual and quantitative root structure information from X-ray computed tomography data acquired in-soil is presented.
13:09
Segmentation and Measurement of Fat Volumes in Murine Obesity Models Using X-ray Computed Tomography
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Cited by 26 •
2012
Fat content analysis is routinely conducted in studies utilizing murine obesity models. Emerging methods in small animal CT imaging and analysis are providing for longitudinal detail rich fat content analysis. Here we detail step by step procedures for performing small animal CT imaging, analysis, and visualization.
11:06
3D Printing of Preclinical X-ray Computed Tomographic Data Sets
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Cited by 16 •
2013
Using modern plastic extrusion and printing technologies, it is now possible to quickly and inexpensively produce physical models of X-ray CT data taken in a laboratory. The three -dimensional printing of tomographic data is a powerful visualization, research, and educational tool that may now be accessed by the preclinical imaging community.
12:24
Computed Tomography-guided Time-domain Diffuse Fluorescence Tomography in Small Animals for Localization of Cancer Biomarkers
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Cited by 18 •
2012
Diffuse fluorescence tomography offers a relatively low-cost and potentially high-throughout approach to preclinical in vivo tumor imaging. The methodology of optical data collection, calibration, and image reconstruction is presented for a computed tomography-guided non-contact time-domain system using fluorescent targeting of the tumor biomarker epidermal growth factor receptor in a mouse glioma model.
FAQs
X-ray CT distinguishes regions through differences in X-ray attenuation, meaning how strongly materials reduce the beam as it passes through them. Each detector reading captures the combined effect of material along a particular path. Repeating measurements from many angles supplies the variation needed to separate internal features in the reconstructed result, rather than treating the object as a single undifferentiated image.
Rotation is essential because one projection cannot locate an internal feature reliably in three dimensions. As the source and detector move around the specimen, they collect measurements from changing directions. Reconstruction algorithms combine these projections into cross-sectional slices and a volumetric representation. This angular sampling makes the internal arrangement interpretable and supports later inspection of component geometry and defects.
CT findings depend on contrasts in attenuation between regions, so voids, inclusions, porosity, and cracks can appear when they produce distinguishable measurement patterns from surrounding material. The reconstructed volume lets an investigator examine where such features occur inside a part. This capability is especially valuable for enclosed structures, where internal conditions must be assessed without opening or cutting the specimen.
An examination begins with the specimen positioned so the X-ray source and detector can rotate around it. Measurements are collected as the beam passes through the object from multiple directions, recording attenuation differences. Computational reconstruction then converts the resulting projections into cross-sectional and three-dimensional views. Engineers can inspect these views for internal features, geometry, and assembly-related problems.
The essential measurement system contains an X-ray source and a detector arranged to record how the beam changes as it passes through the specimen. Their coordinated rotation provides projections from multiple directions. Computational reconstruction algorithms form the other critical element by processing those measurements into a volumetric image that engineers can examine for internal structure and defects.
X-ray CT allows components, composites, and manufactured parts to be examined internally without cutting them open. Engineers can use the reconstructed volume to identify voids, cracks, inclusions, porosity, and assembly errors. Because the part remains available for further evaluation, this approach supports nondestructive testing while revealing conditions that may be hidden within the completed structure.
The reconstructed images provide information for dimensional inspection and failure analysis, linking internal structure with the condition or performance of an engineered part. In manufacturing and design work, these results can support quality control and design validation. Examination of defects and assembly errors also helps engineers understand material performance and investigate why a component may not meet expectations.