ArticleComputer methods in biomechanics and biomedical engineering2016
A nonlinear anisotropic inverse method for computational dissection of inhomogeneous planar tissues.
Article in Computer methods in biomechanics and biomedical engineering, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Full-field analysis indicates late reperfusion therapy broadens and mechanically smooths the borderzone during post-infarction inflammation.Acta biomaterialia · 2026Article
- Calcification-neighboring regions of atherosclerotic aortic tissue exhibit elevated stiffness without elevated radiodensity.Journal of the mechanical behavior of biomedical materials · 2025Article
- It's more than the amount that counts: implications of collagen organization on passive muscle tissue properties revealed with micromechanical models and experiments.Journal of the Royal Society, Interface · 2024Article
- Segmenting mechanically heterogeneous domains via unsupervised learning.Biomechanics and modeling in mechanobiology · 2024Article
- The impact of thickness heterogeneity on soft tissue biomechanics: a novel measurement technique and a demonstration on heart valve tissue.Biomechanics and modeling in mechanobiology · 2023Article
- The Lumbar Facet Capsular Ligament Becomes More Anisotropic and the Fibers Become Stiffer With Intervertebral Disc and Facet Joint Degeneration.Journal of biomechanical engineering · 2023Article
- Characterizing Tissue Remodeling and Mechanical Heterogeneity in Cerebral Aneurysms.Journal of vascular research · 2022Article
- Spatial scaling in multiscale models: methods for coupling agent-based and finite-element models of wound healing.Biomechanics and modeling in mechanobiology · 2019Article
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2 authors.
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Abstract
Quantification of the mechanical behavior of soft tissues is challenging due to their anisotropic, heterogeneous, and nonlinear nature. We present a method for the 'computational dissection' of a tissue, by which we mean the use of computational tools both to identify and to analyze regions within a tissue sample that have different mechanical properties. The approach employs an inverse technique applied to a series of planar biaxial experimental protocols. The aggregated data from multiple protocols provide the basis for (1) segmentation of the tissue into regions of similar properties, (2) linear analysis for the small-strain behavior, assuming uniform, linear, anisotropic behavior within each region, (3) subsequent nonlinear analysis following each individual experimental protocol path and using local linear properties, and (4) construction of a strain energy data set W(E) at every point in the material by integrating the differential stress-strain functions along each strain path. The approach has been applied to simulated data and captures not only the general nonlinear behavior but also the regional differences introduced into the simulated tissue sample.
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