Search PubMed⌕ Search
Home/Archive/PubMed

PubMed · 8992879

A digital model of trabecular bone.

K Engelke·S M Song·C C Glüer·H K Genant
View original source ↗Export metadata ↓

Abstract

A 3D microCT dataset of bovine bone was used to create a digital 3D model simulating trabecular bone. The model serves a dual purpose: It allows for standard quantitative histomorphometric analysis and it approximates the reality e.g. of high resolution CT in vivo datasets of trabecular bone. Thus the model can potentially be used as a reference to develop 2D and 3D structural analysis algorithms applicable in vivo while it simultaneously allows verification of the results of these algorithms by standard histomorphometry. The model can be used as a standard to evaluate the impact of image processing techniques and of restrictions of imaging systems on the quantitative analysis of structural parameters describing a trabecular network. The model can be used for a comparison of 2D and 3D structural analysis methods and for an analysis of decreasing spatial resolution. The effects of segmentation and filtration can be studied separately and grayscale analysis is possible. As examples standard 2D histomorphometry and the analysis of topological parameters like node number and trabecular network length were applied to the model. The influence of spatial resolution was investigated by decreasing the spatial resolution of the digital model. The bone surface area determined by 3D surface triangulation was only 4% smaller than the surface area determined from the traditional 2D bone histomorphometric parameter bone surface/tissue volume (BS/TV) when 2D results were averaged over all slices of the 3D volume. However, BS/TV showed large (10%) variations among slices within the volume. Both histomorphometric and topological parameters were heavily influenced by spatial resolution and image segmentation. Our initial experience with the digital model indicates a need to investigate bone microstructure based on volume data or to average the 2D results of many slices.

Explore related subjects

AlgorithmsAnimalsAnisotropyBone and BonesCattleComputer SimulationImage Processing, Computer-AssistedModels, AnatomicModels, StructuralTomography, X-Ray ComputedComparative StudyJournal ArticleResearch Support, Non-U.S. Gov't

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

K Engelke, S M Song, C C Glüer, H K Genant. 1996. A digital model of trabecular bone.. https://doi.org/10.1002/jbmr.5650110409

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2026

A note on a generalized single step theory for any number of hierarchical genomic matrices.

BACKGROUND: The Single Step algorithm allows combining information from genotyped and un-genotyped individuals, provided they are connected by a pedigree. However, current single step theory is limited to a single list of markers. RESULTS: We present a generalized single step (GSS) method that can accommodate any number of hierarchical molecular datasets (e.g. sequence, high and low density arrays) and pedigree, avoiding imputation. We prove that a similar efficient inversion algorithm exists. The method is recursive, starting with the highest marker density scenario. We illustrate the method with simulation and show that GSS can increase predictive accuracy compared to standard single step. R code is provided so that custom scenarios can be easily compared, either with simulated or real data. CONCLUSION: The method developed generalizes extant single step theory to any number of hierarchical molecular relationship matrices, broadening the scenarios where single step can be applied. A topic of particular interest can be ecology field data or human populations where pedigree is not available, but where samples sequenced and genotyped at different densities can exist. GSS can also be a useful tool to optimize allocation of genotyping and / or sequencing resources.

Algorithms↗
PubMed2026

cgDist: Nucleotide-level distance calculation from cgMLST allelic profiles.

Bacterial genomic surveillance requires balancing computational efficiency with genetic resolution for effective cluster investigation. cgMLST distance calculations treat all allelic differences as equivalent units, obscuring nucleotide-level variation. Furthermore, single nucleotide polymorphism-based pipelines provide finer resolution at substantially higher computational cost, which limits their routine deployment in surveillance laboratories. We present cgDist, an algorithm that calculates nucleotide-level distances directly from cgMLST allelic profiles, providing finer resolution than allele-count distances by leveraging within-allele nucleotide variation. The cache architecture stores alignment statistics, enabling distance calculation modes without computation and supporting both dataset-specific and schema-complete cache generation. This design enables incremental surveillance analysis, with performance benefits as laboratories accumulate alignment data. cgDist functions as a precision 'zoom lens' for the investigation of clusters identified through initial cgMLST screening. Rather than restructuring population relationships, this targeted approach concentrates enhanced resolution where it is most informative. The algorithm ensures that cgDist distances are greater than or equal to corresponding cgMLST distances, preserving epidemiological interpretability while adding genetic discrimination. By increasing resolution within identified clusters, cgDist may also support outbreak investigation, a potential application that remains to be evaluated on outbreak-derived data.

Algorithms↗
PubMed2026

Theseus: fast and optimal affine-gap sequence-to-graph alignment.

MOTIVATION: Sequence-to-graph alignment is a central problem in bioinformatics, with applications in multiple sequence alignment (MSA) and pangenome analysis, among others. However, current algorithms for optimal affine-gap alignment impose high memory and computational requirements, limiting their scalability to aligning long sequences to complex graphs. Practical solutions partially address this problem using heuristic strategies that ultimately trade off optimality for speed. RESULTS: This work presents Theseus, a novel, fast, and optimal affine-gap sequence-to-graph alignment algorithm. Theseus leverages similarities between genomic sequences to accelerate the alignment computation and reduces the overall memory requirements without compromising optimality. To that end, Theseus processes only a subset of the dynamic programming cells, using a sparse-data strategy that enables efficient sequence-to-graph alignment. Moreover, our algorithm supports optimal affine-gap alignment on arbitrary directed graphs, including those with cycles. We evaluate Theseus on two key problems: MSA and pangenome read mapping. For MSA, we compare it against SPOA, abPOA, and POASTA. Theseus is 1.6× to 17.6× faster than POASTA, and 7.3× faster, on average, than SPOA, both optimal aligners. Compared with abPOA, Theseus ensures optimality and scales to the largest problems. For pangenome read mapping, we benchmark Theseus against the alignment stage of the mapping tool vg map, along with the alignment kernels of SPOA, abPOA, and POASTA. Theseus outperforms the other methods, showing a 1.9× to 16.9× speedup on short reads. Moreover, Theseus is 1.5× to 36.3× faster than vg when aligning against synthetic cyclic graphs. AVAILABILITY AND IMPLEMENTATION: Theseus code and documentation are publicly available at https://github.com/albertjimenezbl/theseus-lib.

Algorithms↗