3d Reconstruction Ct
3d Reconstruction Ct - Related Videos
08:57
High Resolution 3D Imaging of Ex-Vivo Biological Samples by Micro CT
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Cited by 13 •
2011
Non-destructive volume visualization can be achieved only by tomographic techniques, of which the most efficient is the x-ray micro computerized tomography ( CT).
08:00
Imaging and 3D Reconstruction of Cerebrovascular Structures in Embryonic Zebrafish
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Cited by 1 •
2014
Imaging of cerebrovascular development in larval zebrafish is described. Techniques to facilitate 3D imaging and modify cerebrovascular development using chemical treatments are also provided.
11:18
Micro-CT Imaging of a Mouse Spinal Cord
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2023
Source: Peiman Shahbeigi-Roodposhti and Sina Shahbazmohamadi, Biomedical Engineering Department, University of Connecticut, Storrs, Connecticut It's a little-known fact that the discovery and (inadvertent) use of X-rays garnered the first ever Nobel Prize in Physics. The famous X-ray image of Dr. Röntgen's wife's hand from 1895 that sent shock waves through the scientific community looks like most modern day 2D medical X-ray images. Though it is not the newest technology, X-ray absorption...
09:08
Combined SPECT and CT Imaging to Visualize Cardiac Functionality
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2023
Source: Alycia G. Berman, James A. Schaber, and Craig J. Goergen, Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana Here we will demonstrate the fundamentals of single-photon emission computed tomography/computed tomography (SPECT/CT) imaging using mice. The technique involves injecting a radionuclide into a mouse, imaging the animal after it is distributed throughout the body, and then reconstructing the produced images to create a volumetric dataset. This can...
05:05
Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
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Cited by 11 •
2019
We analyzed joint kinematics from four-dimensional computed tomography data. The sequential 3D-3D registration method semiautomatically provides the kinematics of the moving bone with respect to the subject bone from four-dimensional computed tomography data.
FAQs
Reconstruction algorithms combine X-ray attenuation measurements acquired from multiple angles and assign the resulting information to three-dimensional voxels. Each voxel contributes to a volumetric representation that can be examined quantitatively rather than only as separate cross-sectional images. This conversion allows internal anatomy, pores, tissues, and device components to be analyzed according to their spatial arrangement.
Acquisition settings and spatial resolution strongly influence how accurately the reconstructed volume represents physical structure. They determine the level of spatial detail available for distinguishing neighboring tissues, pores, or implant components. When the available resolution is inadequate, small or closely positioned features may not be represented reliably, limiting subsequent segmentation, geometric measurements, and engineering analysis.
Segmentation separates structures within the reconstructed voxel volume so that tissues, pores, or device components can be treated as distinct regions. This step converts visual information into identifiable geometry for measurement, rendering, and modeling. Accurate segmentation is therefore essential because errors in boundary assignment can affect anatomical analysis, scaffold or implant design, and downstream simulations.
Individual CT slices show cross-sectional information one plane at a time, whereas a reconstructed volume supports examination of spatial relationships across the complete dataset. After segmentation, the volume can be rendered to distinguish selected structures and provide measurable geometry. This broader representation is useful when shape, continuity, or three-dimensional interfaces matter to bioengineering analysis.
A typical workflow begins with CT acquisition, in which detectors record X-ray attenuation from multiple angles. Reconstruction algorithms then generate a voxel-based volume, followed by segmentation of the structures of interest. The selected regions can be rendered or converted into quantitative geometry for anatomical analysis, implant or scaffold design, finite-element simulations, or image-guided planning.
Bioengineering researchers use CT-based models to connect measured internal structure with engineering design and analysis. Anatomical models can inform implant development and image-guided planning, while representations of pores and scaffold geometry support scaffold design. The reconstructed geometry can also provide the structural basis for finite-element simulations, allowing computational analyses to reflect observed anatomy or material architecture.