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Selected Publication:
SHR Neuro Cancer Cardio Lipid Metab Microb
Wu, J; Augustin, CM; Shadden, SC.
Reconstructing vascular homeostasis by growth-based prestretch and optimal fiber deposition.
J Mech Behav Biomed Mater. 2021; 114:104161
Doi: 10.1016/j.jmbbm.2020.104161
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- Leading authors Med Uni Graz
- Augustin Christoph
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- Abstract:
- Computational modeling of cardiovascular biomechanics should generally start from a homeostatic state. This is particularly relevant for image-based modeling, where the reference configuration is the loaded in vivo state obtained from imaging. This state includes residual stress of the vascular constituents, as well as anisotropy from the spatially varying orientation of collagen and smooth muscle fibers. Estimation of the residual stress and fiber orientation fields is a formidable challenge in realistic applications. To help address this challenge, we herein develop a growth based Algorithm to recover a residual stress distribution in vascular domains such that the stress state in the loaded configuration is equal to a prescribed homeostatic stress distribution at physiologic pressure. A stress-driven fiber deposition process is included in the framework, which defines the distribution of the fiber alignments in the vascular homeostatic state based on a minimization procedure. Numerical simulations are conducted to test this two-stage homeostasis generation algorithm in both idealized and non-idealized geometries, yielding results that agree favorably with prior numerical and experimental data.
- Find related publications in this database (using NLM MeSH Indexing)
- Anisotropy - administration & dosage
- Biomechanical Phenomena - administration & dosage
- Cardiovascular System - administration & dosage
- Collagen - administration & dosage
- Computer Simulation - administration & dosage
- Homeostasis - administration & dosage
- Stress, Mechanical - administration & dosage
- Find related publications in this database (Keywords)
- Vascular tissue mechanics
- Residual stress
- Constrained mixture
- Image-based modeling