3d Tomographic Model
3d Tomographic Model - Related Videos
11:06
3D Printing of Preclinical X-ray Computed Tomographic Data Sets
0 Views •
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.
07:57
Scaled Anatomical Model Creation of Biomedical Tomographic Imaging Data and Associated Labels for Subsequent Sub-surface Laser Engraving (SSLE) of Glass Crystals
0 Views •
2017
A methodology is described herein for representing anatomical imaging data within crystals. We create scaled three-dimensional models of biomedical imaging data for use in Sub-Surface Laser Engraving (SSLE) of crystal glass. This tool offers a useful complement to computational display or three-dimensionally printed models used within clinical or educational settings.
08:41
Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution
0 Views •
Cited by 11 •
2012
Lensfree optical tomography is a three-dimensional microscopy technique that offers a spatial resolution of <1 μm × <1 μm × <3 μm in x, y and z dimensions, respectively, over a large imaging-volume of 15-100 mm3, which can be particularly useful for integration with lab-on-a-chip platforms.
09:17
3D Printing of Biomolecular Models for Research and Pedagogy
0 Views •
Cited by 29 •
2017
Physical models of biomolecules can facilitate an understanding of their structure-function for the researcher, aid in communication between researchers, and serve as an educational tool in pedagogical endeavors. Here, we provide detailed guidance for the 3D printing of accurate models of biomolecules using fused filament fabrication desktop 3D printers.
03:33
Generating a 3D Co-Culture Model of Human Corneal Fibroblasts and Neurons
0 Views •
2025
This video demonstrates a technique to generate a three-dimensional co-culture model system of primary human corneal fibroblasts and differentiated neuronal cells. This co-culture system can be used to study the stromal-nerve interactions.
FAQs
Reconstruction depends on computational alignment of the available projection data. The algorithm brings separate two-dimensional views into spatial correspondence, then combines them to recover volumetric information. This processing allows the resulting image to represent organization across depth, rather than treating each projection as an isolated view. In cancer research, that depth supports analysis of tumor structure.
Multiple projections provide complementary views of the same specimen, allowing structural relationships to be examined across the reconstructed volume. Because the specimen does not need to be physically sectioned, the analysis can preserve an integrated view of tissue architecture and cellular distribution. This is particularly useful when the research question concerns how tumor components are arranged in relation to one another.
An important strength is the connection between image reconstruction and quantitative measurement. Instead of relying only on visual inspection, investigators can use the volumetric representation as a basis for more precise analysis of tumor biology. This approach supports evaluation of structural features and comparison across disease progression or treatment, although the specific measurements depend on the study design.
Changes in a model can be interpreted comparatively rather than as a single static observation. Researchers can examine tumor morphology, invasion, and vascular organization at different stages of disease or after treatment, then relate those structural patterns to progression or response. The value lies in tracking spatially organized changes, not merely recording that a tumor is present.
A basic workflow begins with collecting multiple two-dimensional projections of the specimen. Computational algorithms then align those projections and reconstruct the spatial information into a three-dimensional volume. Researchers can inspect the resulting representation for tissue architecture, cellular distribution, and structural relationships, and connect the imaging output with quantitative measurements. This sequence turns projection data into an analyzable model.
These models provide a spatial framework for characterizing tumor morphology and assessing invasion. Rather than viewing those features only in separate two-dimensional projections, investigators can examine their organization within the reconstructed volume. That perspective helps relate tumor form to surrounding structural relationships and supports comparisons of how these characteristics change as disease progresses.
Researchers can compare reconstructed models from different treatment conditions or stages to identify changes in tumor morphology, invasion, or vascular organization. Linking those image-based differences with quantitative measurements supports evaluation of therapeutic responses. The same strategy can also contribute to assessment of diagnostic methods by providing structural information for comparison.