International Journal for Numerical Methods in Biomedical Engineering最新文献

Coronary artery disease (CAD) is one of the leading causes of mortality worldwide. Fractional flow reserve (FFR) is a diagnostic metric for evaluating ischemic coronary stenoses, necessitating invasive pressure measurements using a guidewire during maximal hyperemia. The stenosis morphology and the presence of a guidewire influence coronary hemodynamics, warranting further investigation to improve FFR accuracy. This study systematically examines the effect of a pressure guidewire on FFR across different stenosis morphologies under clinically relevant boundary conditions (BCs). Six idealized models of coronary stenosis were developed, representing area stenoses (AS: percentage reduction in the cross-sectional area) of 64%, 75%, 84%, and 91%, based on the dimensions of the left anterior descending (LAD) artery. Computational fluid dynamics (CFD) simulations were conducted using coronary BCs validated against both in vivo and in silico data in the literature. Guidewire-induced FFR deviation (dFFR) exhibited a linear correlation with the blockage ratio—guidewire area relative to minimum lumen area—with deviations exceeding 0.04 for AS greater than 80%. dFFR values were comparable for AS of 64% and 75% across different shapes, but shape-related variation increased (> 0.02) at AS of 84% and 91%. Lesion length (LL) significantly influenced FFR based on morphology: a threefold increase in LL reduced FFR by 0.06 in crescent-shaped stenosis, while having minimal impact in the fully eccentric circular case (AS 84%). However, dFFR remained largely unaffected by LL. Finally, the effects of guidewire malposition on dFFR were negligible in non-circular stenoses (< 0.01) but considerable in circular stenoses (> 0.04 for AS 84%).

We consider the numerical simulation of blood flows in a patient-specific kidney including the renal artery, the renal vein, and the kidney tissue using a coupled system of unsteady Stokes–Darcy equations. The Stokes equations and the Darcy equations are implicitly coupled on the interfaces by enforcing three conditions, namely the conservation of mass, the balance of the normal force and the Beavers–Joseph–Saffman condition. To discretize the system we introduce a stabilized P1–P1–P1 finite element method for the spatial variables and an implicit backward Euler method for the temporal variable. A mathematical theory is developed to guarantee the stability and the convergence of the proposed discretization method. To efficiently solve the large, sparse and highly ill-conditioned algebraic systems, we further propose a Krylov subspace method preconditioned by a robust two-scale additive Schwarz method consisting of a mixed-dimensional coarse preconditioner with a 1D central-line preconditioner in the vascular region and a 3D preconditioner for the kidney tissue with some compatibility conditions imposed on the 1D and 3D interfaces. Some numerical experiments for a benchmark problem and a patient-specific kidney with physiologic parameters are presented to verify the accuracy, the robustness, and the effectiveness of the proposed method.

Pelvic organ prolapse (POP) affects many women and involves the displacement of pelvic organs due to weakened support structures. While synthetic meshes are used in surgeries to reinforce these structures, they can lead to complications due to poor biocompatibility and mechanical properties. Biodegradable meshes offer an innovative solution, providing flexible and strong support that enhances tissue reinforcement and reduces the risk of injuries. The main objective of this study is to enhance our understanding and provide valuable insights into the performance of vaginal tissue and mesh implants, which may contribute to the advancement of POP treatment methodologies. This study utilized melt electrowriting to 3D print biodegradable mesh implants with quadratic and cross-shaped geometries, each with a thickness of 240 μm. Uniaxial and ball burst tests were performed on these meshes and sow's vaginal tissue to determine their mechanical properties, and a numerical simulation of the ball burst test was validated, allowing for accurate material representation. The results indicate that the placement of biodegradable PCL meshes in sow's vaginal tissue increases the maximum force by 14% to 20% during ball burst tests. Furthermore, the simulation effectively mimicked the experimental analysis, demonstrating a strong correlation with the experimental data for both the meshes and the tissue. The computational analysis for the quadratic-shaped mesh revealed a maximum difference of 7%, while the vaginal tissue simulation exhibited a difference of approximately 6%. However, discrepancies were observed in the tissue reinforced with the mesh, where the simulation yielded a maximum error of 14%, which may be attributed to the complex interactions between the mesh and the tissue. This multifaceted approach, integrating simulation, material testing, and experimental validation, forms the foundation of in-depth research into the mechanical behavior of vaginal tissue and mesh implants, making significant contributions to the field of POP treatment. Utilizing experimental analysis to validate numerical simulations is essential, as it allows for reducing the need for extensive randomized controlled trials (RCTs) and minimizing the use of animal testing once the simulations are validated.

Trunnionosis can be defined as the wear of the femoral head–neck contact. This phenomenon is increasingly recognized as a major factor in hip arthroplasty failure. The selection of a highly compatible head has become crucial to reduce the trunnionosis effects in orthopedic surgery. In this study, the biomechanical behavior and wear corrosion of the trunnion of hip implants with 28, 32, 36, 40, and 44 mm head sizes and ±3 mm head offset gap configurations have been investigated using finite element methods. The hip implant configurations were modeled in Autodesk Fusion 360, and the simulations of the static, modal, and dynamic finite element analyses were performed using ANSYS. The effects of different head sizes and offset gap configurations, offset angles, fixation models, and trunnion lengths on wear corrosion are analyzed based on stress, deformation, penetration, sliding distance, frequency vibrational effects, and moment reactions. The results show that the configuration with −3 mm offset, 28 mm head size, 132° offset angle, and type 3 fixation results in higher stability; the −3 mm offset, 28 mm head size, 127° offset angle, and type 2 fixation configuration leads to lower moment reaction.

Radiofrequency ablation (RFA) is a widely used minimally invasive technique for solid tumors. Real-time feedback on the thermoelectric effects induced by RFA is very important for precise, personalized treatment. Current computer simulations help predict the electrical and thermal phenomena related to RFA, their high computational cost limits practical clinical use, especially for real- time monitor. In this study, a physics-integrated neural network-based model was proposed to predict the coupled real-time electric and temperature fields during the treatment. This approach, similar to traditional simulation methods, simulates the physical changes during the radiofrequency ablation process with input of treatment parameters. The combined deep learning model consists of a DeepONet network predicting the electrical potential distribution and a coupled ConvLSTM network forecasting the temperature distribution over time. The networks were trained using results from the thermoelectric coupling FEM model, and validated through bio-mimic phantom experiments. The DeepONet network achieves a mean absolute error (MAE) of 0.0241 and a mean maximum relative error (MRE) of 1.44%. The coupled ConvLSTM network achieves an MAE of 0.0286, an MRE of 3.46%, and a Dice score of 0.9334 for areas above 45°C. The model developed can provide coupled temperature and electric field predictions for a 120-s RFA process with varying properties in less than 1 s. This rapid prediction method is expected to be integrated with control calibration algorithms in the future, enabling the acquisition of real-time three-dimensional temperature fields and facilitating more precise temperature control.

Reducing heart rate has been advocated as a pivotal therapeutic approach for patients with aortic aneurysm (AA), aiming to mitigate the risk of rupture. This study aims to investigate the effect of heart rate on the biomechanical characteristics of aneurysms. A patient-specific thoracic aortic aneurysm (TAA) model has been built, and the biomechanical characteristics of the aortic wall under different heart rate conditions, including atrial fibrillation (AF) condition, have been simulated by applying the blood pressure on the inner wall. The viscoelasticity of the aorta has been considered to reflect the cumulative mechanical response under pulsatile blood pressure. Results showed that the heart rate has a weak influence on the stress of the aneurysm wall but has a considerable influence on the deformation and vibration of the aneurysm. The increase in heart rate will increase the deformation of the aneurysm wall, and vice versa, especially at the top site of the aneurysm. Additionally, elevated heart rates were found to increase both the frequency and amplitude of vibrations within the aneurysm wall, especially when AF happened, thereby augmenting the risk of rupture. This research would advance our comprehension of aneurysm biomechanics and furnish valuable biomechanical insights for the formulation of aneurysm treatment strategies.

Recent high-fidelity fluid–structure interaction (FSI) simulations of cerebral aneurysms have revealed flow-induced wall vibrations. However, those simulations were conducted under simplified conditions, and the robustness of the predicted vibrations remains unknown. This study aimed to advance the physiological accuracy of previous models and to investigate the sensitivity to parameter uncertainty. We compared the previously used near-linear St. Venant–Kirchhoff wall model with a three-term hyperelastic Mooney–Rivlin (MR3) model fitted to experimental data and also modeled effects of surrounding cerebrospinal fluid (CSF). We then varied flow rate (1.83 mL/s $��\pm �$ 25%), wall stiffness (soft, medium, stiff), and wall thickness (0.25 $��\pm �$ 0.1 mm). Our main findings for the four aneurysms considered were as follows: the MR3 model led to an average increase of 35% in pulsation and 240% in vibration amplitude, along with an 18% decrease in frequency. Viscous damping by the CSF reduced the vibration amplitude by 68% but did not affect the frequency or pulsation. Changes in flow rate had no effect on pulsation but increased vibration amplitude by 246%. Wall stiffness and thickness had a comparatively smaller impact on vibration, altering amplitude by 36% and 82% and frequency by 20% and 8%. In conclusion, the more advanced models led to a decrease of vibration amplitude and frequency during the cardiac cycle, consistent with clinical observations. Like computational fluid dynamics, FSI simulations can be sensitive to flow rates but are otherwise robust and can provide a fundamental understanding of aneurysm wall vibration without precise knowledge of wall properties.

Anterior cruciate ligament (ACL) reconstruction is a complex surgical procedure with high precision requirements. One of the most critical steps during the operation is the accurate localization of the insertion center and the proper placement of the graft. However, under limited visibility of the arthroscope, surgeons often face challenges in determining the correct position and angle. In this paper, we propose a novel framework for insertion center localization, incorporating two reconstruction approaches. First, an isometric reconstruction method based on 3D geometric processing algorithms is proposed, which leverages the feature lines and points of the geometric morphology of the actual bone model. This method ensures accurate alignment with bones of varying shapes. Second, a novel U-shaped network (EP-UNet), which integrates axial edge and coordinate features, is proposed for medical image segmentation to facilitate anatomical reconstruction. Experimental results demonstrate that EP-UNet exhibits superior performance in ACL segmentation tasks, achieving high accuracy and robustness. Compared to the baseline network, it improves the mean Intersection over Union (mIoU) by 8.16%, providing strong support for ligament image segmentation. This framework allows surgeons to efficiently and automatically determine the position of the patient's insertion center, addressing the challenge of clinical localization with high reliability and improving surgical success rates.