Wiley: International Journal for Numerical Methods in Biomedical Engineering: Table of Contents

Influence of Fiber Dispersion Representation on the Accuracy of the Mechanical Response of Healthy and Aneurysmal Aortic Wall Tissue

Omid Ghorbani, Victorien Prot, Gerhard A. Holzapfel, Bjørn Skallerud — Fri, 05 Jun 2026 05:36:54 -0700

This study evaluates five strategies for quantifying fiber dispersion in the constitutive modeling of the human aortic wall and identifies a two-layer model as the optimal balance between accuracy and complexity. Furthermore, the separate anisotropic contribution of elastin proves advantageous for healthy tissue but offers only minor benefits for aneurysm samples, thus supporting its inclusion in the ground matrix.

ABSTRACT

This study investigates the influence of fiber dispersion representation on the accuracy of the mechanical response of anisotropic hyperelastic material models for healthy and aneurysmal human aortic wall tissue under planar biaxial loading. Five fitting strategies were compared: (i) detailed integration of fiber orientation and dispersion data across the thickness; (ii) a single, thickness-averaged fiber distribution; (iii) two representative layers (media and adventitia); (iv) multiple layers discretized in the radial direction; and (v) a single layer with a symmetrical in-plane distribution of collagen fibers. The goodness of fit was calculated using the coefficient of determination averaged over all experimental tests. The most accurate fits were achieved with strategy (i), with a goodness of fit Ravg2≈0.96±0.02$$ {R}_{\mathrm{avg}}^2\approx 0.96\pm 0.02 $$ for the combined cohort of aneurysm patients and the healthy cohort; comparable accuracy was achieved by the two-layer model (iii), Ravg2≈0.91±0.06$$ {R}_{\mathrm{avg}}^2\approx 0.91\pm 0.06 $$, but with significantly less complexity in model implementation. As a secondary objective of this study, regional parameters that correlate with the two-layer model (iii) and are intended for finite element analyses are presented. The method using a symmetrical single layer in model (v) yielded the lowest accuracy, Ravg2≈0.73±0.14$$ {R}_{\mathrm{avg}}^2\approx 0.73\pm 0.14 $$, and highlighted the need to capture non-symmetric fiber families. The modeling of elastin as a separate anisotropic material was also investigated by comparing two different constitutive modeling approaches. While the healthy cohort showed the best fit by including elastin in model (i) (Ravg2≈0.99±0.01$$ {R}_{\mathrm{avg}}^2\approx 0.99\pm 0.01 $$), separate treatment of elastin in the combined cohorts resulted in only a slight increase of Ravg2$$ {R}_{\mathrm{avg}}^2 $$ of ≈0.02$$ \approx 0.02 $$. This marginal improvement is offset by the risk of overfitting due to additional parameters, which supports the inclusion of elastin in the ground matrix.

A Novel Decomposition‐Based Growth Model for Simulating Stress‐Modulated Spinal Growth

Serhat Onur Çakmak, Ercan Gürses — Thu, 04 Jun 2026 22:33:16 -0700

This study introduces the first decomposition-based finite-strain growth model for a simplified Functional Spinal Unit, where growth occurs simultaneously with mechanical loading. The approach eliminates the major physical inconsistency of sequential methods—growth in an artificial stress-free configuration—while preventing mesh distortion and providing smoother, more stable growth predictions with improved computational efficiency.

ABSTRACT

Understanding the mechanobiological mechanisms of spinal growth is essential for modeling progressive deformities, particularly adolescent idiopathic scoliosis (AIS). In this study, we present a novel finite element methodology for simulating mechanobiological growth governed by the Hueter-Volkmann law. The approach is developed in the large deformation framework and uses the Hueter-Volkmann law for the first time in a decomposition-based framework in which the growth is incorporated into the constitutive model via multiplicative decomposition of the deformation gradient. For comparison purposes, we also implement the sequential method widely used in the literature. Although both approaches are based on the Hueter-Volkmann law, which relates the normal compressive stress on the growth plate to local growth inhibition, they differ significantly in how this principle is integrated into the finite element framework. Unlike the sequential method, which requires successive growth computation and mechanical solution steps, along with repeated updates of the growth direction, the proposed decomposition-based approach solves stress-induced deformations and growth simultaneously. It is formulated in the Lagrangian configuration, eliminating the need to update the growth direction throughout the simulation. The proposed formulation not only offers superior numerical stability but also exhibits lower computational complexity compared to the sequential method, particularly under large deformations. The method is evaluated using a finite element model of a simplified Functional Spinal Unit (FSU) under symmetric and asymmetric compressive loading over a two-year growth period. While both approaches produce comparable wedge angle progression, the decomposition-based formulation, which more realistically resembles the actual growth process, results in smoother deformations, less element distortion, and substantially reduced computational cost. Additionally, a robust wedge angle calculation technique is introduced using least plane fitting to full endplate nodes of the vertebral bodies, improving geometric accuracy and reducing mesh sensitivity. Together, these features establish a reliable and efficient framework for long-term spinal growth simulations and offer a promising foundation for future patient-specific modeling and clinical applications.

Investigation of Contrast Retention in PCOM Aneurysms as a Possible Marker for Instability and Rupture

Thu, 04 Jun 2026 21:12:46 -0700

Flow stagnation, as seen here in a growing and a ruptured aneurysm, may be helpful to identify aneurysms with future potential for instability or rupture.

ABSTRACT

Identification of cerebral aneurysms at risk of destabilization and rupture remains challenging. Although flow stagnation has been recognized as a risk factor, the underlying mechanisms and its predictive power are poorly understood. The purpose was to investigate possible associations between flow stagnation determined from computational fluid dynamics (CFD)-based virtual angiograms and aneurysm instability and rupture. A total of 312 aneurysms at the posterior communicating (PCOM) artery were analyzed with image-based CFD. Virtual angiograms were constructed by simulating contrast injections, and contrast retention metrics were calculated from time density curves of the aneurysm and their parent artery. These metrics were compared between 23 stable and 18 unstable aneurysms from a longitudinal dataset and between 129 unruptured and 142 ruptured aneurysms from a cross-sectional dataset. Aneurysmal contrast retention was significantly larger (p < 0.05) in ruptured compared to unruptured aneurysms as well as when considering only small aneurysms (size < 7 mm). Similar associations were obtained when comparing stable and growing aneurysms. Multivariate analysis showed that aneurysm instability can be forecasted from flow stagnation with good accuracy (AUC = 0.88), while aneurysm rupture with only moderate accuracy (AUC = 0.72) or marginal accuracy for small aneurysms (AUC = 0.66). Mean residence time was identified as a good predictor of aneurysm instability and rupture. Flow stagnation, determined by contrast retention in angiographies (virtual or real), is associated with PCOM aneurysm instability (growth or symptoms development) and can be used to identify aneurysms prone to further growth and potentially rupture. However, aneurysms can also rupture without exhibiting signs of flow stagnation.

Automated Centerline Extraction From Meshed Vascular Models

Gala Sanchez Van Moer, Shawn C. Shadden — Tue, 02 Jun 2026 06:34:32 -0700

A method to extract centerlines from image-based vascular meshes is presented, which automatically identifies vascular endpoints and traces centerlines by solving Eikonal equations. Compared to a widely used method, the proposed approach requires less user interaction and demonstrates better robustness on models that contain segmentation artifacts from limited image resolution.

ABSTRACT

Centerlines of vascular structures are essential for the analysis of vascular anatomy as well as the development of computational models for simulating biomechanics. In this paper, we present a centerline extraction framework for vascular geometries implemented on piece-wise linear surface meshes, from which tetrahedral volumetric meshes are generated. Our approach involves solving the Eikonal equation using the finite element method to describe wave propagation for automatic endpoint identification and centerline tracing. We evaluated the framework on 19 vascular meshes with varying anatomies and construction methods, comparing our results to those obtained using the widely used Vascular Modeling Toolkit (VMTK) as a benchmark. The proposed method produces well-centered centerlines within a reasonable time frame without the need for tedious outlet labeling required by VMTK. Furthermore, it considerably outperforms VMTK when extracting centerlines from less smooth surface meshes, such as those generated using machine learning-based segmentation techniques.

Evaluation of Hemodynamic Environment of Intracranial Aneurysms After Flow Disruption Based on Angiographic Signatures

Sun, 31 May 2026 19:34:46 -0700

A strategy based on the concept of angiographic signatures to evaluate local hemodynamic environments created immediately after treatment of cerebral aneurysms and connect them to subsequent outcomes is presented. The methodology is demonstrated on aneurysms treated with intrasaccular flow disruptors and endoluminal flow diverters, showing noticeably different angiographic signatures between completely and incompletely occluded aneurysms.

ABSTRACT

Incomplete aneurysm occlusion after flow disruption treatment strongly depends on the persistence of strong flow into the aneurysm, which in turn depends on the aneurysm and parent artery geometry as well as the device placement. However, at the time of treatment it remains challenging to predict future outcomes (complete or incomplete occlusion). This paper describes a strategy to evaluate the local hemodynamic environment created immediately after flow-modifying device deployment to treat cerebral aneurysms and connect it to the subsequent long-term outcomes. The approach is based on the concept of angiographic signatures that can be extracted from DSA sequences or computational models and represented as images that can be used to compare different aneurysmal environments. The methodology is demonstrated on two small series of four experimental rabbit aneurysms each, treated with either intrasaccular flow disruptors or endoluminal flow diverters. Noticeable differences in the angiographic signatures of completely and incompletely occluded aneurysms at follow-up were observed. Specifically, larger values of mean transit times and longer time-to-peaks were observed deeper into the aneurysms that remained incompletely occluded in both series. The findings suggest that angiographic signatures can be used to assess the underlying hemodynamic environment immediately after device implantation and prognosticate the likelihood of future complete or incomplete occlusion.

Leaflets Morphology Optimization of Valved Pulmonary Arterial Conduit Based on Experimental Design

Sun, 31 May 2026 18:05:18 -0700

This study built in vitro and numerical models of valved pulmonary arterial conduit, revealed correlations between hemodynamics and leaflet morphology through experiments and FSI simulations, and used DOE to optimize leaflet morphology, yielding an optimized leaflet design.

ABSTRACT

This study investigated the influence of leaflet geometry on the hemodynamic performance of pulmonary valved conduits and optimized leaflet trimming parameters using a design of experiments (DOE) approach combined with in vitro hydrodynamic testing. In vitro hydrodynamic experiments were performed to validate the feasibility of the valved conduit. A computational model was then established based on the experimental setup. DOE was applied to systematically assess and optimize leaflet tailoring parameters. Fluid–structure interaction simulations showed that leaflet tailoring significantly affects hemodynamic outcomes. Reducing leaflet height improved conduit performance. At a cardiac output of 3.5 L/min, the optimized valve exhibited a 3.79% decrease in peak velocity, a 45.18% reduction in maximum equivalent strain, and a 66.57% decrease in coaptation area. These findings suggest that scaling down the free edge length relative to graft diameter and moderately reducing leaflet height may help reduce diastolic regurgitation caused by redundant leaflet overlap while lowering leaflet strain, thereby enhancing valve durability.

Numerical Inverse Design of Patient‐Specific Dental Implants: Accelerating FEA‐Based Optimization via Evolutionary Neural Surrogates

María Prados‐Privado — Fri, 29 May 2026 00:36:38 -0700

This framework accelerates patient-specific implant design by coupling high-fidelity FEA neural surrogates with evolutionary optimization. It enables real-time prescription of optimal geometries, significantly reducing biomechanical risk by 44.1% compared with standard clinical protocols.

ABSTRACT

Mechanical complications in dental implantology often arise from a mismatch between standardized geometries and patient-specific anatomical constraints. While high-fidelity finite element analysis (FEA) is the gold standard for identifying these risks, its high computational cost creates a “bottleneck” that prevents its use in routine surgical planning. To develop a numerical inverse design framework that overcomes this limitation by accelerating the generation of optimal implant geometries through a neural-surrogate-assisted optimization engine. A high-fidelity dataset of 3000 high-fidelity 3D FEA simulations was used to train a multilayer perceptron (MLP) regressor (R2=0.9673$$ {R}^2=0.9673 $$). This model served as a real-time inference engine replacing expensive iterative simulations within surrogate-assisted evolutionary optimization framework. The framework's accuracy and clinical validity were tested on a synthetic cohort of 50 virtual patients representing diverse bone qualities and loading conditions. The accelerated inverse design system achieved a statistically significant and substantial reduction in peak von Mises stress at the bone–implant interface compared with standard clinical protocols (p<0.001$$ p<0.001 $$, Cohen's d = 3.22). The optimization successfully identified patient-specific compensatory strategies, such as prescribing wider diameters for low-density bone to maximize load distribution. By integrating deep learning surrogates to accelerate complex numerical optimization, this framework enables real-time, patient-specific implant prescription. This methodology effectively bypasses the computational burden of traditional FEA, offering a scalable numerical solution for personalized surgical planning.

Influence of Input Data Composition and Measurement Errors on Computational Model‐Mediated Assessment of Cardiovascular Properties: An In Silico Study

Zhicheng Zhang, Zhaojun Li, Di Sun, Fuyou Liang — Fri, 29 May 2026 00:30:38 -0700

This study reveals that the composition and measurement errors of the input clinical dataset significantly affect the accuracy of model parameter estimation, which highlights the importance of rigorous clinical data selection and high-precision in vivo measurements for reliable assessment of cardiovascular properties via integration of clinical data with computational model.

ABSTRACT

The integration of computational cardiovascular models with clinical data through parameter estimation algorithms represents a promising approach for quantitative cardiovascular assessment. However, the sensitivity of assessment to variations in the composition of and measurement errors in input clinical data remains insufficiently understood. In this study, we utilized a cardiovascular model to generate in silico datasets representing diverse virtual subjects. From these datasets, hemodynamic data mimicking noninvasive clinical measurements were extracted as inputs for the parameter estimation algorithm (herein the Levenberg–Marquardt algorithm), while the corresponding values of model parameters representing key cardiovascular properties (e.g., arterial stiffness, cardiac function, and vascular resistance) served as the ground truth for evaluating the accuracy of parameter estimation. The obtained results showed that input datasets comprising brachial arterial blood pressures and flow velocities in the ascending aorta and four peripheral arteries (supplying major organs/tissues) enabled accurate parameter estimation. Cardiovascular models reassigned with the estimated parameter values precisely replicated the input data and predicted other important hemodynamic variables (e.g., aortic blood pressure). While these capabilities were largely maintained upon the omission of flow velocity data in a single peripheral artery, they were significantly compromised when flow velocity data in two peripheral arteries were removed. Furthermore, the introduction of measurement errors into the input data substantially increased errors in both parameter estimation and hemodynamic prediction. In summary, this study underscores the importance of rigorous clinical data selection and high-precision in vivo measurements for enhancing the reliability of computational model-mediated cardiovascular assessment.

Issue Information

Fri, 29 May 2026 00:20:15 -0700

International Journal for Numerical Methods in Biomedical Engineering, Volume 42, Issue 6, June 2026.