International Journal for Numerical Methods in Biomedical Engineering最新文献

Reduced order modelling (ROMs) methods, such as proper orthogonal decomposition (POD), systematically reduce the dimensionality of high-fidelity computational models and potentially achieve large gains in execution speed. Machine learning (ML) using neural networks has been used to overcome limitations of traditional ROM techniques when applied to nonlinear problems, which has led to the recent development of reduced order models augmented by machine learning (ML-ROMs). However, the performance of ML-ROMs is yet to be widely evaluated in realistic applications and questions remain regarding the optimal design of ML-ROMs. In this study, we investigate the application of a non-intrusive parametric ML-ROM to a nonlinear, time-dependent fluid dynamics problem in a complex 3D geometry. We construct the ML-ROM using POD for dimensionality reduction and neural networks for interpolation of the ROM coefficients. We compare three different network designs in terms of approximation accuracy and performance. We test our ML-ROM on a flow problem in intracranial aneurysms, where flow variability effects are important when evaluating rupture risk and simulating treatment outcomes. The best-performing network design in our comparison used a two-stage POD reduction, a technique rarely used in previous studies. The best-performing ROM achieved mean test accuracies of 98.6% and 97.6% in the parent vessel and the aneurysm, respectively, while providing speed-up factors of the order $��{10}^5�$.

Surgery of jawbones has a high potential risk of causing complications associated with temporomandibular joint disorder (TMD). The objective of this study was to investigate the effects of two drive modeling methods on the biomechanical behavior of the temporomandibular joint (TMJ) including articular disc during mandibular movements. A finite element (FE) model from a healthy human computed tomography was used to evaluate TMJ dynamic using two methods, namely, a conventional spatial-oriented method (displacement-driven) and a compliant muscle-initiated method (masticatory muscle-driven). The same virtual FE model was 3D printed and a custom designed experimental platform was established to validate the accuracy of experimental and theoretical results of the TMJ biomechanics during mandibular movements. The results show that stress distributed to TMJ and articular disc from mandibular movements provided better representation from the muscle-driving approach than those of the displacement-driven modeling. The simulation and experimental data exhibited significant strong correlations during opening, protrusion, and laterotrusion (with canonical correlation coefficients of 0.994, 0.993, and 0.932, respectively). The use of muscle-driven modeling holds promise for more accurate forecasting of stress analysis of TMJ and articular disc during mandibular movements. The compliant approach to analyze TMJ dynamics would potentially contribute to clinic diagnosis and prediction of TMD resulting from occlusal disease and jawbone surgery such as orthognathic surgery or tumor resection.

Improper osteotomy during total knee arthroplasty (TKA) can lead to anterior femoral notching, which increases the risk of periprosthetic fractures due to stress concentration. One potential solution is the addition of an intramedullary stem to the femoral component. However, the optimal stem length remains unclear. In this study, we aimed to determine the optimal stem length using finite element models. Finite element models of femurs were developed with unstemmed prostheses and prostheses with stem lengths of 50, 75, and 100 mm. Under squat loading conditions, the von Mises stress at the notch and stress distribution on four transversal sections of the femur were analyzed. Additionally, micromotion of the prosthesis–bone interface was evaluated to assess initial stability. The unstemmed prosthesis exhibited a von Mises stress of 191.8 MPa at the notch, which decreased to 43.1, 8.8, and 23.5 MPa for stem lengths of 50, 75, and 100 mm, respectively. The stress reduction on four selected femoral transversal sections compared with the unstemmed prosthesis was 40.0%, 84.4%, and 67.1% for stem lengths of 50, 75, and 100 mm, respectively. Micromotion analysis showed a maximum of 118.8 μm for the unstemmed prosthesis, which decreased significantly with the application of stems, particularly at the anterior flange. Intramedullary stems effectively reduced stress concentration at the femoral notch. The 50-mm stem length provided the optimal combination of reduced notch stress, minimized stress-shielding effect, and decreased micromotion at the anterior flange.

The effectiveness of various stroke treatments depends on the anatomical variability of the cerebral vasculature, particularly the collateral blood vessel network. Collaterals at the level of the Circle of Willis and distal collaterals, such as the leptomeningeal arteries, serve as alternative avenues of flow when the primary pathway is obstructed during an ischemic stroke. Stroke treatment typically involves catheterization of the primary pathway, and the potential risk of further flow reduction to the affected brain area during this treatment has not been previously investigated. To address this clinical question, we derived the lumped parameters for catheterized blood vessels and implemented a corresponding distributed compartment (0D) model. This 0D model was validated against an experimental model and benchmark test cases solved using a 1D model. Additionally, we compared various off-center catheter trajectories modeled using a 3D solver to this 0D model. The differences between them were minimal, validating the simplifying assumption of the central catheter placement in the 0D model. The 0D model was then used to simulate blood flows in realistic cerebral arterial networks with different collateralization characteristics. Ischemic strokes were modeled by occlusion of the M1 segment of the middle cerebral artery in these networks. Catheters of different diameters were inserted up to the obstructed segment and flow alterations in the network were calculated. Results showed up to 45% maximum blood flow reduction in the affected brain region. These findings suggest that catheterization during stroke treatment may have a further detrimental effect for some patients with poor collateralization.

Autologous arteriovenous fistula (AVF) is a commonly used vascular access (VA) for hemodialysis, and hemodynamic changes are one of the main factors for its failure. To explore the effect of geometry on the hemodynamics in the AVF, a modified model is built with a gradual and smooth turn at the anastomosis and is compared with the traditional model, which has an abrupt sharp turn at the anastomisis. Transient computational fluid dynamics (CFD) simulations were performed for the comparison and analysis of the hemodynamic fields of the two models at different stages of the pulse cycle. The results showed that the low shear stress region and high oscillatory shear stress region in the modified AVF model coincided with regions of intimal hyperplasia that have been identified by previous studies. A comparison with the blood flow velocities measured in vivo was performed, and the error between the simulation results and the medical data was reduced by 22% in the modified model, which verifies the rationality and utility of the modified model.

Renal anisotropy is a complex property of the kidney and often poses a challenge in obtaining consistent measurements when using shear wave elastography to detect chronic kidney disease. To circumvent the challenge posed by renal anisotropy in clinical settings, a dimensionless biomarker termed the ‘anisotropic ratio’ was introduced to establish a correlation between changes in degree of renal anisotropy and progression of chronic kidney disease through an in silico perspective. To achieve this, an efficient model reduction approach was developed to model the anisotropic property of kidneys. Good agreement between the numerical and experimental data were obtained, as percentage errors of less than 5.5% were reported when compared against experimental phantom measurement from the literature. To demonstrate the applicability of the model to clinical measurements, the anisotropic ratio of sheep kidneys was quantified, with both numerical and derived experimental results reporting a value of .667. Analysis of the anisotropic ratio with progression of chronic kidney disease demonstrated that patients with normal kidneys would have a lower anisotropic ratio of .872 as opposed to patients suffering from renal impairment, in which the anisotropic ratio may increase to .904, as determined from this study. The findings demonstrate the potential of the anisotropic ratio in improving the detection of chronic kidney disease using shear wave elastography.

Anticancer treatment is performed in various ways, and photothermal therapy (PTT) is gaining traction from a noninvasive treatment perspective. PTT is a treatment technique based on the photothermal effect that kills tumors by increasing their temperature. In this study, gold nanoparticles (AuNPs), which are photothermal agents, were used in numerical simulations to determine the PTT effect by considering diffusion induced changes in the distribution area of the AuNPs. The treatment effect was confirmed by varying the initial injection radius of AuNPs represented by the injection volume, the elapsed time after injection of AuNPs, and the laser intensity. The degree of maintenance of the apoptotic temperature band in the tumor was quantitatively analyzed by the apoptotic variable. Ultimately, if the initial injection radius of AuNPs is 0.7 mm or less, the optimal time to start treatment is 240 min after injection, and for 1.0 and 1.2 mm, it is optimal to start treatment when the elapsed time after injection is 90 and 30 min, respectively. This study identified the optimal treatment conditions for dosage of AuNPs and treatment start time in PTT using AuNPs, which will serve as a reference point for future PTT studies.

Computational fluid dynamics (CFD) simulations have shown great potentials in cardiovascular disease diagnosis and postoperative assessment. Patient-specific and well-tuned boundary conditions are key to obtaining accurate and reliable hemodynamic results. However, CFD simulations are usually performed under non-patient-specific flow conditions due to the absence of in vivo flow and pressure measurements. This study proposes a new method to overcome this challenge by tuning inlet boundary conditions using data extracted from electrocardiogram (ECG). Five patient-specific geometric models of type B aortic dissection were reconstructed from computed tomography (CT) images. Other available data included stoke volume (SV), ECG, and 4D-flow magnetic resonance imaging (MRI). ECG waveforms were processed to extract patient-specific systole to diastole ratio (SDR). Inlet boundary conditions were defined based on a generic aortic flow waveform tuned using (1) SV only, and (2) with ECG and SV (ECG + SV). 4D-flow MRI derived inlet boundary conditions were also used in patient-specific simulations to provide the gold standard for comparison and validation. Simulations using inlet flow waveform tuned with ECG + SV not only successfully reproduced flow distributions in the descending aorta but also provided accurate prediction of time-averaged wall shear stress (TAWSS) in the primary entry tear (PET) and abdominal regions, as well as maximum pressure difference, ∆P_max, from the aortic root to the distal false lumen. Compared with simulations with inlet waveform tuned with SV alone, using ECG + SV in the tuning method significantly reduced the error in false lumen ejection fraction at the PET (from 149.1% to 6.2%), reduced errors in TAWSS at the PET (from 54.1% to 5.7%) and in the abdominal region (from 61.3% to 11.1%), and improved ∆P_max prediction (from 283.1% to 18.8%) However, neither of these inlet waveforms could be used for accurate prediction of TAWSS in the ascending aorta. This study demonstrates the importance of SDR in tailoring inlet flow waveforms for patient-specific hemodynamic simulations. A well-tuned flow waveform is essential for ensuring that the simulation results are patient-specific, thereby enhancing the confidence and fidelity of computational tools in future clinical applications.

In this work, we couple a lumped-parameter closed-loop model of the cardiovascular system with a physiologically-detailed mathematical description of the baroreflex afferent pathway. The model features a classical Hodgkin–Huxley current-type model for the baroreflex afferent limb (primary neuron) and for the second-order neuron in the central nervous system. The pulsatile arterial wall distension triggers a frequency-modulated sequence of action potentials at the afferent neuron. This signal is then integrated at the brainstem neuron model. The efferent limb, representing the sympathetic and parasympathetic nervous system, is described as a transfer function acting on heart and blood vessel model parameters in order to control arterial pressure. Three in silico experiments are shown here: a step increase in the aortic pressure to evaluate the functionality of the reflex arch, a hemorrhagic episode and an infusion simulation. Through this model, it is possible to study the biophysical dynamics of the ionic currents proposed for the afferent limb components of the baroreflex during the cardiac cycle, and the way in which currents dynamics affect the cardiovascular function. Moreover, this system can be further developed to study in detail each baroreflex loop component, helping to unveil the mechanisms involved in the cardiovascular afferent information processing.

Traumatic brain injury is a significant problem worldwide. In the United States of America, around 1.7 million cases are documented annually, displaying the need for a deeper understanding of the effects on the human brain. The tests required for this assessment are very complex. Tests on cadavers may raise serious ethical questions, and in vivo crash tests are not viable. In this context, there is a great need to developing finite element head models (FEHM) to study the biomechanics of the tissues when submitted to a certain impact or acceleration/deceleration scenario. An excellent compromise between accuracy and CPU efficiency is always desirable for a FEHM, For this reason, this work focuses on the improvement of an existing head model, including the study of the behavior of the brain using distinct finite element types. The finite element type and formulation is of utmost importance for the general accuracy and efficiency of the models. Several validations were performed, comparing the simulation results against experimental data. The simulations with hexahedral elements, under specific conditions, obtained more accurate results with a lower computational cost. Using hexahedrals, a comparison was also performed using two material characterizations with more than 10 years apart, using the latest finite element head model validation experiment. Overall, the newer material model displays a less stiff response, although its implementation must always depend on the overall purpose of the model it is being applied to.