ArticleScientific reports2021
An inverse method for mechanical characterization of heterogeneous diseased arteries using intravascular imaging.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Automated Coronary Optical Coherence Tomography Feature Extraction with Application to Three-Dimensional Reconstruction.Tomography (Ann Arbor, Mich.) · 2022Pooled it
- Prediction of Post-stenting Lumen Dimensions Using High-Definition Intravascular Ultrasound.JACC. Advances · 2026Article
- A Gradient-Based Machine-Learning Inspired Inverse Modeling Approach for Characterization of Nonlinear Tissue Properties.Annals of biomedical engineering · 2026Article
- Computational simulations of coronary artery bifurcation stenting using realistic plaque distribution and materials.Scientific reports · 2025Article
- An Image-Based Computational Framework to Evaluate the Material Stiffness of Arterial Tissue With High-Resolution Magnetic Resonance Imaging.Journal of biomechanical engineering · 2025Article
- FEBio FINESSE: An Open-Source Finite Element Simulation Approach to Estimate In Vivo Heart Valve Strains Using Shape Enforcement.Annals of biomedical engineering · 2025Article
- Drug coated balloons in percutaneous coronary intervention: how can computational modelling help inform evolving clinical practice?Frontiers in medical technology · 2025Review
- Integrating Computational and Biological Hemodynamic Approaches to Improve Modeling of Atherosclerotic Arteries.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Review
- MicroBundleCompute: Automated segmentation, tracking, and analysis of subdomain deformation in cardiac microbundles.PloS one · 2024Article
- Intravascular imaging of angioplasty balloon under-expansion during pre-dilation predicts hyperelastic behavior of coronary artery lesions.Frontiers in bioengineering and biotechnology · 2023Article
- An image-based approach for the estimation of arterial local stiffnessFrontiers in bioengineering and biotechnology · 2023Article
- Inverse material parameter estimation of patient-specific finite element models at the carotid bifurcation: The impact of excluding the zero-pressure configuration and residual stress.International journal for numerical methods in biomedical engineering · 2023Article
- Characterizing atherosclerotic tissues:Frontiers in bioengineering and biotechnology · 2023Article
- Image-Based Finite Element Modeling Approach for Characterizing In Vivo Mechanical Properties of Human Arteries.Journal of functional biomaterials · 2022Review
- Medical Image-Based Computational Fluid Dynamics and Fluid-Structure Interaction Analysis in Vascular Diseases.Frontiers in bioengineering and biotechnology · 2022Review
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5 authors.
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Abstract
The increasing prevalence of finite element (FE) simulations in the study of atherosclerosis has spawned numerous inverse FE methods for the mechanical characterization of diseased tissue in vivo. Current approaches are however limited to either homogenized or simplified material representations. This paper presents a novel method to account for tissue heterogeneity and material nonlinearity in the recovery of constitutive behavior using imaging data acquired at differing intravascular pressures by incorporating interfaces between various intra-plaque tissue types into the objective function definition. Method verification was performed in silico by recovering assigned material parameters from a pair of vessel geometries: one derived from coronary optical coherence tomography (OCT); one generated from in silico-based simulation. In repeated tests, the method consistently recovered 4 linear elastic (0.1 ± 0.1% error) and 8 nonlinear hyperelastic (3.3 ± 3.0% error) material parameters. Method robustness was also highlighted in noise sensitivity analysis, where linear elastic parameters were recovered with average errors of 1.3 ± 1.6% and 8.3 ± 10.5%, at 5% and 20% noise, respectively. Reproducibility was substantiated through the recovery of 9 material parameters in two more models, with mean errors of 3.0 ± 4.7%. The results highlight the potential of this new approach, enabling high-fidelity material parameter recovery for use in complex cardiovascular computational studies.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.