Tomogram Reconstruction
Tomogram Reconstruction - Related Videos
10:25
Deep Learning-Based Segmentation of Cryo-Electron Tomograms
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Cited by 53 •
2022
This is a method for training a multi-slice U-Net for multi-class segmentation of cryo-electron tomograms using a portion of one tomogram as a training input. We describe how to infer this network to other tomograms and how to extract segmentations for further analyses, such as subtomogram averaging and filament tracing.
08:01
Designing CAD/CAM Surgical Guides for Maxillary Reconstruction Using an In-house Approach
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Cited by 7 •
2018
Methods for designing a computer-aided design/computer-aided manufacturing (CAD/CAM) surgical guide are shown. Cutting planes are separated, united, and thickened to easily visualize the necessary bone transfer. These designs can be three-dimensional printed and checked for accuracy.
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.
06:32
Coronoid-Temporalis Pedicled Flap for Orbital Floor Defect Reconstruction
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2025
This protocol presents a novel coronoid-temporalis pedicled flap (CTPF) technique for reconstructing small-to-medium orbital floor defects. CTPF avoids microvascular risks while maintaining biomechanical stability. Clinical cases demonstrate effective structural restoration, reduced complications, and preserved facial symmetry, offering a viable alternative to traditional grafts or alloplastic materials.
10:42
A Postoperative Evaluation Guideline for Computer-Assisted Reconstruction of the Mandible
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Cited by 11 •
2020
Here, we propose a practical, feasible and reproducible evaluation guideline for computer-assisted reconstruction of the mandible in order to create uniformity between studies regarding postoperative accuracy evaluation. This protocol continues and specifies an earlier publication of this evaluation guideline.
FAQs
Accurate alignment is central because each projection records the specimen from a different viewing angle. The algorithm places these two-dimensional images into a consistent geometric relationship before estimating internal structure. If projections do not correspond correctly, the resulting three-dimensional representation can misplace features and weaken structural analysis.
Tomogram reconstruction addresses an inverse problem: it starts with measured projections and estimates the structure that could account for them. This means internal organization is not observed directly in a single image but inferred computationally from combined views. The resulting estimate provides access to cross-sectional structure and three-dimensional organization.
Projections from different viewing angles provide complementary information about the specimen. Their combination lets the computational method estimate internal structure and spatial relationships that are not available from one viewing direction alone. In biological imaging, this broader view helps researchers examine cellular architecture, organelles, and macromolecular assemblies within the same three-dimensional context.
Once the projections have been aligned and the internal structure estimated, researchers can examine the result in cross-section or render it as a three-dimensional representation. Cross-sectional views support inspection of internal features, while three-dimensional rendering helps reveal the arrangement and spatial relationships of structures throughout the specimen.
A basic workflow begins with collecting multiple two-dimensional projections of the specimen at different viewing angles. Computational algorithms then align those projections and solve the inverse problem to estimate internal structure. The completed tomogram can be inspected through cross-sectional views or rendered in three dimensions for structural analysis.
Researchers use this approach when they need information about three-dimensional organization rather than a single two-dimensional view. It can support studies of cellular architecture, organelles, macromolecular assemblies, and their spatial relationships. The method is particularly valuable for examining biological organization in near-native contexts while retaining access to internal structural detail.
In biology, tomogram reconstruction provides the computational foundation for interpreting projection data in cryo-electron tomography. The same general approach also supports medical imaging and other forms of microscopy. Across these settings, it converts multiple views into structural information that can be examined in cross-section or as a three-dimensional representation.