3d Projection Imaging
3d Projection Imaging - Related Videos
11:34
High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
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Cited by 9 •
2013
This video describes the fundamentals of digital fringe projection techniques, which provide dense 3D measurements of dynamically changing surfaces. It also demonstrates the design and operation of a high-speed binary defocusing system based on these techniques.
16:44
Born Normalization for Fluorescence Optical Projection Tomography for Whole Heart Imaging
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Cited by 15 •
2009
We suggest a Born normalized approach for Optical Projection Tomography (BnOPT) that accounts for the absorption properties of imaged samples to obtain accurate and quantitative fluorescence tomographic reconstructions. We use the proposed algorithm to reconstruct the fluorescence molecular probe distribution within small animal organs.
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).
09:41
A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery
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Cited by 19 •
2016
We describe the steps to use our custom designed software for image integration, visualization and planning in epilepsy surgery.
08:37
Whole-mount Imaging of Mouse Embryo Sensory Axon Projections
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2014
We present here an optimized protocol to genotype, stain and prepare fetal mice for the imaging of peripheral nociceptor axon projections in the whole animal, as an effective method to assess sensory axon growth phenotypes in developing genetically engineered mice.
FAQs
Depth recovery depends on combining image views collected from different angles rather than interpreting one projection in isolation. Computational reconstruction uses the differences among those views to estimate where structures lie relative to one another. This produces spatial information about tissue or cellular organization that cannot be determined reliably from a single two-dimensional image.
Spatial relationships show how tumor regions, cells, and vascular networks are arranged within the same three-dimensional space. That organization helps reveal architectural differences and tumor heterogeneity, meaning variation across tumor regions. Preserving these relationships gives researchers a stronger basis for examining invasion, interactions in the tumor microenvironment, and changes associated with disease progression.
A reconstructed dataset can provide quantitative estimates of a structure’s size, shape, depth, and position. These measurements allow researchers to characterize tumor architecture and cellular distribution in spatial terms rather than relying only on visual appearance. Comparing such features across samples or experimental conditions can support analysis of progression and treatment-related changes.
A typical workflow begins by acquiring projected or two-dimensional images from multiple viewing angles. The collected views are then combined computationally to reconstruct a three-dimensional representation. Researchers can examine the resulting structure and estimate spatial features such as size, shape, depth, and position, using the reconstruction to evaluate biological organization.
In cancer research, the method can visualize the arrangement of tumor regions and vascular networks within a shared three-dimensional context. This helps researchers study how structural organization varies across a tumor and how cellular distribution relates to surrounding features. The resulting spatial characterization supports investigation of tumor heterogeneity and the organization of the tumor microenvironment.
Researchers can compare reconstructed spatial features as disease develops or after treatment. Changes in tumor architecture, cellular distribution, or vascular organization may provide quantitative information about progression and response to drugs. Because the analysis retains depth and position, it can examine changes throughout the structure rather than limiting interpretation to features visible in a conventional image.
Tumor invasion and microenvironmental interactions depend on where cells and tissue features are located relative to one another. Three-dimensional spatial information allows researchers to examine these relationships within tumor architecture, including the association of cellular distributions with vascular networks. This context can clarify how organization within a tumor relates to invasive behavior and other cancer-related processes.