International Journal for Numerical Methods in Biomedical Engineering最新文献

The Windkessel (WK) model is a simplified mathematical model used to represent the systemic arterial circulation. While the WK model is useful for studying blood flow dynamics, it suffers from inaccuracies or uncertainties that should be considered when using it to make physiological predictions. This paper aims to develop an efficient and easy-to-implement uncertainty quantification method based on a local gradient-based formulation to quantify the uncertainty of the pressure waveform resulting from aleatory uncertainties of the WK parameters and flow waveform. The proposed methodology, tested against Monte Carlo simulations, demonstrates good agreement in estimating blood pressure uncertainties due to uncertain Windkessel parameters, but less agreement considering uncertain blood-flow waveforms. To illustrate our methodology's applicability, we assessed the aortic pressure uncertainty generated by Windkessel parameters-sets from an available in silico database representing healthy adults. The results from the proposed formulation align qualitatively with those in the database and in vivo data. Furthermore, we investigated how changes in the uncertainty of the Windkessel parameters affect the uncertainty of systolic, diastolic, and pulse pressures. We found that peripheral resistance uncertainty produces the most significant change in the systolic and diastolic blood pressure uncertainties. On the other hand, compliance uncertainty considerably modifies the pulse pressure standard deviation. The presented expansion-based method is a tool for efficiently propagating the Windkessel parameters' uncertainty to the pressure waveform. The Windkessel model's clinical use depends on the reliability of the pressure in the presence of input uncertainties, which can be efficiently investigated with the proposed methodology. For instance, in wearable technology that uses sensor data and the Windkessel model to estimate systolic and diastolic blood pressures, it is important to check the confidence level in these calculations to ensure that the pressures accurately reflect the patient's cardiovascular condition.

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.

Laser corneal reshaping is a safe and effective technique utilized to treat common vision disorders. An advanced laser delivery system equipped with a pulsed UV laser with specific parameters is used to ablate parts of the cornea surface to correct the existing refractive error. The argon fluoride (ArF) excimer pulsed gas laser at 193 nm is the most employed type in the commercial devices for such treatments. This laser is generated using a mixture of Argon, Fluorine, and a significant amount of Neon gases. However, due to the ongoing Russian-Ukraine war, the availability of Neon gas is currently very limited, as this region is considered the primary supplier of pure Neon gas. Consequently we suggest replacing the common ArF laser source in the commercial devices with a solid-state (forth harmonic neodymium-doped yttrium aluminum garnet laser at 266 nm). This replacement uses the same operation parameters, optics, and scanning algorithm. Parameters from five commercial devices (Zeiss MEL 90, Technolas TENEO 317, Alcon Wave Light EX 500, Schwind Amaris 750 s, OptoSystems MICROSCAN VISUM) were compared with those of the i-ablation device, a research device that uses a 266 nm laser source. Our goal is to reduce production costs through a simple modification that has a significant impact. Consequently, the present study aims to find an alternative laser source for the current ArF laser without exchanging the complete system's design. This recommendation is based on a numerical simulation study. The thermal effect on a human cornea model was numerically evaluated using finite-element solutions of Pennes' bioheat equation on the COMSOL platform by applying two laser wavelengths. The results demonstrated that changing the laser source significantly impacts the thermal effect, even with the same laser settings. All studied devices showed a reduction in the thermal effect to below 40°C, compared with nearly 100°C under ordinary conditions.

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.