International Journal for Numerical Methods in Biomedical Engineering最新文献

We present our continuous efforts from a modeling and numerical viewpoint to develop a powerful and flexible mathematical and computational framework called Ocular Mathematical Virtual Simulator (OMVS). The OMVS aims to solve problems arising in biomechanics and hemodynamics within the human eye. We discuss our contribution towards improving the reliability and reproducibility of computational studies by performing a thorough validation of the numerical predictions against experimental data. The OMVS proved capable of simulating complex multiphysics and multiscale scenarios motivated by the study of glaucoma. Furthermore, its modular design allows the continuous integration of new models and methods as the research moves forward, and supports the utilization of the OMVS as a promising non-invasive clinical investigation tool for personalized research in ophthalmology.

The study was carried out to investigate the effect of the artery with different pulse values and stenosis rates on the pressure drop, the peristaltic pump outlet pressure, fractional flow reserve (FFR) and most importantly the amount of power consumed by the peristaltic pump. For this purpose, images taken from the clinical environment were produced as models (10 mm inlet diameter) with 0% and 70% percent areal stenosis rates (PSR) on a three-dimensional (3D) printer. In the experimental system, pure water was used as the fluid at 54, 84, 114, 132, and 168 bpm pulse values. In addition, computational fluid dynamics (CFD) analyzes of the test region were performed using experimental boundary conditions with the help of ANSYS-Fluent software. The findings showed that as PSR increases in the arteries, the pressure drop in the stenosis region increases and this amount increases dramatically with increasing effort. An increase of approximately 40% was observed in the pump outlet pressure value from 54 bpm to 168 bpm in the PSR 0% model and 51% increase in the PSR 70% model. It has been observed that the pump does more work to overcome the increased pressure difference due to increased pulse rate and PSR. With the effect of contraction, the power consumption of the pump increased from 9.2% for 54 bpm to 13.8% for 168 bpm. In both models, the Wall Shear Stress (WSS) increased significantly. WSS increased abruptly in the stenosis and arcuate regions, while sudden decreases were observed in the flow separation region.

In silico trials are a promising way to increase the efficiency of the development, and the time to market of cardiovascular implantable devices. The development of transcatheter aortic valve implantation (TAVI) devices, could benefit from in silico trials to overcome frequently occurring complications such as paravalvular leakage and conduction problems. To be able to perform in silico TAVI trials virtual cohorts of TAVI patients are required. In a virtual cohort, individual patients are represented by computer models that usually require patient-specific aortic valve geometries. This study aimed to develop a virtual cohort generator that generates anatomically plausible, synthetic aortic valve stenosis geometries for in silico TAVI trials and allows for the selection of specific anatomical features that influence the occurrence of complications. To build the generator, a combination of non-parametrical statistical shape modeling and sampling from a copula distribution was used. The developed virtual cohort generator successfully generated synthetic aortic valve stenosis geometries that are comparable with a real cohort, and therefore, are considered as being anatomically plausible. Furthermore, we were able to select specific anatomical features with a sensitivity of around 90%. The virtual cohort generator has the potential to be used by TAVI manufacturers to test their devices. Future work will involve including calcifications to the synthetic geometries, and applying high-fidelity fluid–structure-interaction models to perform in silico trials.

Breast cancer is a significant public health issue affecting women worldwide. While advancements in treatment options have led to improved survival rates, the impact of breast cancer and its treatments on bone health cannot be overlooked. Bone remodeling is a complex process regulated by the delicate balance between bone formation and resorption. Any disruption to this balance can lead to decreased bone density, increased fracture risk, and compromised physical function. To investigate the effects of breast cancer and its treatments on bone remodeling, a finite element model was developed in this study. This model incorporated bone remodeling equations to simulate the mechanical behavior of bone under different conditions. The ABAQUS/UMAT software was used to simulate the behavior of bone tissue under the influence of breast cancer and treatments. Our findings suggest that bone loss is more pronounced after secondary breast cancer and treatment, leading to bone loss (6%–19% decrease in BV/TV), reduced bone stimulation, and decreased effectiveness of physical activity on recovery. These results highlight the importance of early intervention and management of bone health in breast cancer patients to mitigate the negative impact of cancer and treatment on bone remodeling.

We present a reduced order model for efficient nonlinear homogenization of bones, accounting for strength difference effects and containing some well-known plasticity models (like von Mises or Drucker-Prager) as special cases. The reduced order homogenization is done by using a cluster-based model order reduction technique, called cluster-based nonuniform transformation field analysis. For an offline phase, a space–time decomposition is performed on the mesoscopic plastic strain fields, while a clustering analysis is employed for a spatial decomposition of the mesoscale RVE model. A volumetric-deviatoric split is additionally introduced to capture the enriched characteristics of the mesoscopic plastic strain fields. For an online analysis, the reduced order model is formulated in a unified minimization problem, which is compatible with a large variety of material models. Both cortical and trabecular bones are considered for numerical experiments. Compared to conventional FE-based RVE computations, the developed reduced order model renders a considerable acceleration rate beyond $��{10}^3�$, while maintaining a sufficient accuracy level.

Reducing the computational time required by high-fidelity, full-order models (FOMs) for the solution of problems in cardiac mechanics is crucial to allow the translation of patient-specific simulations into clinical practice. Indeed, while FOMs, such as those based on the finite element method, provide valuable information on the cardiac mechanical function, accurate numerical results can be obtained at the price of very fine spatio-temporal discretizations. As a matter of fact, simulating even just a few heartbeats can require up to hours of wall time on high-performance computing architectures. In addition, cardiac models usually depend on a set of input parameters that are calibrated in order to explore multiple virtual scenarios. To compute reliable solutions at a greatly reduced computational cost, we rely on a reduced basis method empowered with a new deep learning-based operator approximation, which we refer to as Deep-HyROMnet technique. Our strategy combines a projection-based POD-Galerkin method with deep neural networks for the approximation of (reduced) nonlinear operators, overcoming the typical computational bottleneck associated with standard hyper-reduction techniques employed in reduced-order models (ROMs) for nonlinear parametrized systems. This method can provide extremely accurate approximations to parametrized cardiac mechanics problems, such as in the case of the complete cardiac cycle in a patient-specific left ventricle geometry. In this respect, a 3D model for tissue mechanics is coupled with a 0D model for external blood circulation; active force generation is provided through an adjustable parameter-dependent surrogate model as input to the tissue 3D model. The proposed strategy is shown to outperform classical projection-based ROMs, in terms of orders of magnitude of computational speed-up, and to return accurate pressure-volume loops in both physiological and pathological cases. Finally, an application to a forward uncertainty quantification analysis, unaffordable if relying on a FOM, is considered, involving output quantities of interest such as, for example, the ejection fraction or the maximal rate of change in pressure in the left ventricle.

The incidence of oral malocclusion is increasing and is seriously damaging the oral health of human beings. The Kitchon root-controlled auxiliary archwire is an individualized orthodontic arch. It is used clinically for the treatment of tooth-lingual tilt/root-lip tilt phenomenon of the central incisors. However, the bending parameters of the Kitchon root-controlled auxiliary archwire used in different patients are based on the clinical experience of the dentists. Therefore, this orthodontic treatment has a high risk and unpredictability. In this paper, the loading performance and orthodontic process of Kitchon root-controlled auxiliary archwire are analyzed. And the prediction model of support resistance and correction torque are established. The bending parameters of the Kitchon root-controlled auxiliary archwire, as well as the effect of the bending parameters on the support resistance and the correction torque, are all quantified. And the prediction models for the support resistance and the correction torque are calculated separately. The correlation coefficients of calculated data and experimental data are ξ_T1 > .97 and ξ_A1 > .96, respectively; the correlation coefficients of simulated data and experimental data are ξ_T2 > .96 and ξ_A2 > .96, respectively. The accuracy and reliability of the established prediction models are verified. It provides an effective theoretical guide for dentists to safely and efficiently perform root-controlled rotation orthodontic treatment on patients' central incisors.

Comminuted fractures are orthopedic traumas with greater surgical difficulty. In clinical treatment, a great challenge is precise reduction of multiple broken bone fragments; Another great challenge is personalized and precise internal fixation after reduction. For these two issues, we designed an automated method framework for precise reduction and internal fixation of comminuted fractures. First, the Gaussian mixture model (GMM) is used to distinguish section points and noise points in a broken bone model; Second, ellipse fitting is carried out to achieve section points matching and a descriptor is proposed to describe the section features; Then, the Convolution Auto-Encoder (CAE) and genetic algorithm are used to extract feature vectors; Finally, after broken bone models registration, internal fixed plate can be reconstructed. Three verification experiments for comminuted bone fracture show this method has high accuracy and good efficiency. It can provide support for minimally invasive treatment for comminuted fractures.

The reconstruction of blood velocity in a vessel from contrast enhanced x-ray computed tomography projections is a complex inverse problem. It can be formulated as reconstruction problem with a partial differential equation constraint. A solution can be estimated with the a variational adjoint method and proper orthogonal decomposition (POD) basis. In this work, we investigate new inversion approaches based on PODs coupled with deep learning methods. The effectiveness of the reconstruction methods is shown with simulated realistic stationary blood flows in a vessel. The methods outperform the reduced adjoint method and show large speed-up at the online stage.

Anaemia is common in end-stage heart failure patients supported with continuous flow left ventricular assist device (CF-LVAD) and is associated with adverse outcomes such as heart failure readmission. This study evaluates the haemodynamic effects of anaemia on cardiac function and cerebral blood flow in heart failure patients supported with CF-LVAD using computational simulations. A dynamic model simulating cardiac function, systemic, pulmonary and cerebral circulations, cerebral flow autoregulatory mechanisms and gas contents in blood was used to evaluate the effects of anaemia and iron deficiency in heart failure and during CF-LVAD support. CF-LVAD therapy was simulated by a model describing HeartMate 3. Anaemia and iron deficiency were simulated by reducing the haemoglobin level from 15 to 9 g/dL and modifying scaling coefficients in the models simulating heart chamber volumes. Reduced haemoglobin levels decreased the arterial O₂ content, which increased cerebral blood flow rate by more than 50% in heart failure and during CF-LVAD assistance. Reduced haemoglobin levels simulating anaemia had minimal effect on the arterial and atrial blood pressures and ventricular volumes. In contrast, iron deficiency increased end-diastolic left and right ventricular diameters in heart failure from 6.6 cm to 7 cm and 2.9 cm to 3.1 cm and during CF-LVAD support from 6.1 to 6.4 cm and 3.1 to 3.3 cm. The developed numerical model simulates the effects of anaemia in failing heart and during CF-LVAD therapy. It is in good agreement with clinical data and can be utilised to assess CF-LVAD therapy.