International Journal for Numerical Methods in Biomedical Engineering最新文献

We develop a cluster-based model order reduction (called C-pRBMOR) approach for efficient homogenization of bones, compatible with a large variety of generalized standard material (GSM) models. To this end, the pRBMOR approach based on a mixed incremental potential formulation is extended to a clustered version for a significantly improved computational efficiency. The microscopic modeling of bones falls into a mixed incremental class of the GSM framework, originating from two potentials. An offline phase of the C-pRBMOR approach includes both a clustering analysis spatially decomposing the micro-domain within an RVE and a space–time decomposition of the microscopic plastic strain fields. A comparative study on two different clustering approaches and two algorithms for mode identification is additionally conducted. For an online analysis, a cluster-enhanced version of evolution equations for the reduced variables is derived from an effective incremental variational formulation, rendering a very small set of nonlinear equations to be numerically solved. Several numerical examples show the effectiveness of the C-pRBMOR approach. A striking acceleration rate beyond 10⁴ against conventional FE computations and that beyond 10³ against the original pRBMOR approach are observed.

Healing of tibia demonstrates a complex mechanobiological process as it is stimulated by the major factor of strains applied by body weight. The effect of screw heads and bodies as well as their pressure distribution is often overlooked. Hence, effective mechanical conditions of the healing process of tibia can be categorized into the material of the plate and screws, post-operation loadings, and screw type and pressure. In this paper, a mathematical biodegradation model was used to simulate the PGF/PLA plate-screw device over 8 weeks. The effect of different post-operation loading patterns was studied for both locking and non-locking screws. The aim was to reach the best configuration for the most achievable healing using FEA by computing the healing pattern, trend, and efficiency with the mechano-regulation theory based on deviatoric strain. The biodegradation process of the plate and screws resulted in 82% molecular weight loss and 1.05 GPa decrease in Young's modulus during 8 weeks. The healing efficiency of the cases ranged from 4.72% to 14.75% in the first week and 18.64% to 63.05% in the eighth week. Finally, an optimal case was achieved by considering the prevention of muscle erosion, bone density reduction, and nonunion, according to the obtained results.

The electromagnetic middle-ear implant (MEI) is a new type of hearing device for addressing sensorineural and mixed hearing loss. The hearing compensation effect of the MEI varies depending on the transducer stimulation sites. This paper investigates the impact of transducer stimulation sites on MEI performance by analyzing stapes spatial motion. Firstly, we constructed a human-ear finite element model based on micro-CT scanning and inverse molding techniques. This model was validated by comparing its predictions of stapes spatial motion and cochlear response with experimental data. Then, stimulation force was applied at four common sites: umbo, incus body, incus long process and stapes to simulate the electromagnetic transducer. Results show that at low and middle frequencies, stapes-stimulating and incus-long-process-stimulating produce similar spatial motion to normal hearing; at high frequencies, incus-body-stimulating produces similar results to normal hearing. The equivalent sound pressure level generated by the stapes piston motion is less sensitive to the stimulation direction than that deduced by the stapes rocking motion.

In the last decades, many computational models have been developed to predict soft tissue growth and remodeling (G&R). The constrained mixture theory describes fundamental mechanobiological processes in soft tissue G&R and has been widely adopted in cardiovascular models of G&R. However, even after two decades of work, large organ-scale models are rare, mainly due to high computational costs (model evaluation and memory consumption), especially in long-range simulations. We propose two strategies to adaptively integrate history variables in constrained mixture models to enable large organ-scale simulations of G&R. Both strategies exploit that the influence of deposited tissue on the current mixture decreases over time through degradation. One strategy is independent of external loading, allowing the estimation of the computational resources ahead of the simulation. The other adapts the history snapshots based on the local mechanobiological environment so that the additional integration errors can be controlled and kept negligibly small, even in G&R scenarios with severe perturbations. We analyze the adaptively integrated constrained mixture model on a tissue patch for a parameter study and show the performance under different G&R scenarios. To confirm that adaptive strategies enable large organ-scale examples, we show simulations of different hypertension conditions with a real-world example of a biventricular heart discretized with a finite element mesh. In our example, adaptive integrations sped up simulations by a factor of three and reduced memory requirements to one-sixth. The reduction of the computational costs gets even more pronounced for simulations over longer periods. Adaptive integration of the history variables allows studying more finely resolved models and longer G&R periods while computational costs are drastically reduced and largely constant in time.

Heat transfer in the human eyeball, a complex organ, is significantly influenced by various pathophysiological and external parameters. Particularly, heat transfer critically affects fluid behavior within the eye and ocular drug delivery processes. Overcoming the challenges of experimental analysis, this study introduces a comprehensive three-dimensional mathematical and computational model to simulate the heat transfer in a realistic geometry. Our work includes an extensive sensitivity analysis to address uncertainties and delineate the impact of different variables on heat distribution in ocular tissues. To manage the model's complexity, we employed a very fast model reduction technique with certified sharp error bounds, ensuring computational efficiency without compromising accuracy. Our results demonstrate remarkable consistency with experimental observations and align closely with existing numerical findings in the literature. Crucially, our findings underscore the significant role of blood flow and environmental conditions, particularly in the eye's internal tissues. Clinically, this model offers a promising tool for examining the temperature-related effects of various therapeutic interventions on the eye. Such insights are invaluable for optimizing treatment strategies in ophthalmology.

Comminuted proximal humerus fractures are often repaired by metal plates, but potentially still experience bone refracture, bone “stress shielding,” screw perforation, delayed healing, and so forth. This “proof of principle” investigation is the initial step towards the design of a new plate using alternative materials to address some of these problems. Finite element modeling was used to create design graphs for bone stress, plate stress, screw stress, and interfragmentary motion via three different fixations (no, 1, or 2 “kickstand” [KS] screws across the fracture) using a wide range of plate elastic moduli (E_P = 5–200 GPa). Well-known design optimization criteria were used that could minimize bone, plate, and screw failure (i.e., peak stress < ultimate tensile strength), reduce bone “stress shielding” (i.e., bone stress under the new plate ≥ bone stress for an intact humerus, titanium plate, and/or steel plate “control”), and encourage callus growth leading to early healing (i.e., 0.2 mm ≤ axial interfragmentary motion ≤ 1 mm; shear/axial interfragmentary motion ratio <1.6). The findings suggest that a potentially optimal configuration involves the new plate being manufactured from a material with an E_P of 5–41.5 GPa with 1 KS screw; but, using no KS screws would cause immediate bone fracture and 2 KS screws would almost certainly lead to delayed healing. A prototype plate might be fabricated using alternative materials suggested for orthopedics and other industries, like fiber-metal laminates, fiber-reinforced polymers, metal foams, pure polymers, shape memory alloys, or 3D-printed porous metals.

The opening and closing dynamics of the aortic valve (AV) has a strong influence on haemodynamics in the aortic root, and both play a pivotal role in maintaining normal physiological functions of the valve. The aim of this study was to establish a subject-specific fluid–structure interaction (FSI) workflow capable of simulating the motion of a tricuspid healthy valve and the surrounding haemodynamics under physiologically realistic conditions. A subject-specific aortic root was reconstructed from magnetic resonance (MR) images acquired from a healthy volunteer, whilst the valve leaflets were built using a parametric model fitted to the subject-specific aortic root geometry. The material behaviour of the leaflets was described using the isotropic hyperelastic Ogden model, and subject-specific boundary conditions were derived from 4D-flow MR imaging (4D-MRI). Strongly coupled FSI simulations were performed using a finite volume-based boundary conforming method implemented in FlowVision. Our FSI model was able to simulate the opening and closing of the AV throughout the entire cardiac cycle. Comparisons of simulation results with 4D-MRI showed a good agreement in key haemodynamic parameters, with stroke volume differing by 7.5% and the maximum jet velocity differing by less than 1%. Detailed analysis of wall shear stress (WSS) on the leaflets revealed much higher WSS on the ventricular side than the aortic side and different spatial patterns amongst the three leaflets.

The article presents a semi-automatic approach to generating structured hexahedral meshes of patient-specific aortas ailed by aortic dissection. The condition manifests itself as a formation of two blood flow channels in the aorta, as a result of a tear in the inner layers of the aortic wall. Subsequently, the morphology of the aorta is greatly impacted, making the task of domain discretization highly challenging. The meshing algorithm presented herein is automatic for the individual lumina, whereas the tears require user interaction. Starting from an input (triangle) surface mesh, we construct an implicit surface representation as well as a topological skeleton, which provides a basis for the generation of a block-structure. Thereafter, the mesh generation is performed via transfinite maps. The meshes are structured and fully hexahedral, exhibit good quality and reliably match the original surface. As they are generated with computational fluid dynamics in mind, a fluid flow simulation is performed to verify their usefulness. Moreover, since the approach is based on valid block-structures, the meshes can be made very coarse (around 1000 elements for an entire aortic dissection domain), and thus promote using solvers based on the geometric multigrid method, which is typically reliant on the presence of a hierarchy of coarser meshes.

Experimental blood flow measurement techniques are invaluable for a better understanding of cardiovascular disease formation, progression, and treatment. One of the emerging methods is time-resolved three-dimensional phase-contrast magnetic resonance imaging (4D flow MRI), which enables noninvasive time-dependent velocity measurements within large vessels. However, several limitations hinder the usability of 4D flow MRI and other experimental methods for quantitative hemodynamics analysis. These mainly include measurement noise, corrupt or missing data, low spatiotemporal resolution, and other artifacts. Traditional filtering is routinely applied for denoising experimental blood flow data without any detailed discussion on why it is preferred over other methods. In this study, filtering is compared to different singular value decomposition (SVD)-based machine learning and autoencoder-type deep learning methods for denoising and filling in missing data (imputation). An artificially corrupted and voxelized computational fluid dynamics (CFD) simulation as well as in vitro 4D flow MRI data are used to test the methods. SVD-based algorithms achieve excellent results for the idealized case but severely struggle when applied to in vitro data. The autoencoders are shown to be versatile and applicable to all investigated cases. For denoising, the in vitro 4D flow MRI data, the denoising autoencoder (DAE), and the Noise2Noise (N2N) autoencoder produced better reconstructions than filtering both qualitatively and quantitatively. Deep learning methods such as N2N can result in noise-free velocity fields even though they did not use clean data during training. This work presents one of the first comprehensive assessments and comparisons of various classical and modern machine-learning methods for enhancing corrupt cardiovascular flow data in diseased arteries for both synthetic and experimental test cases.

Tumor treating fields (TTFields) is a novel therapeutic approach for the treatment of glioblastoma. The electric field intensity is a critical factor in the therapeutic efficacy of TTFields, as stronger electric field can more effectively impede the proliferation and survival of tumor cells. In this study, we aimed to improve the therapeutic effectiveness of TTFields by optimizing the position of electrode arrays, resulting in an increased electric field intensity at the tumor. Three representative head models of real glioblastoma patients were used as the research subjects in this study. The improved subtraction-average-based optimization (ISABO) algorithm based on circle chaos mapping, opposition-based learning and golden sine strategy, was employed to optimize the positions of the four sets of electrode arrays on the scalp. The electrode positions are dynamically adjusted through iterative search to maximize the electric field intensity at the tumor. The experimental results indicate that, in comparison to the conventional layout, the positions of the electrode arrays obtained by the ISABO algorithm can achieve average electric field intensity of 1.7887, 2.0058, and 1.3497 V/cm at the tumor of three glioblastoma patients, which are 23.6%, 29.4%, and 8.5% higher than the conventional layout, respectively. This study demonstrates that optimizing the location of the TTFields electrode array using the ISABO algorithm can effectively enhance the electric field intensity and treatment coverage in the tumor area, offering a more effective approach for personalized TTFields treatment.