Journal of Biomechanical Engineering-Transactions of the Asme最新文献

In electroencephalography (EEG) measurements using dry electrodes, a trade-off between signal stability and user comfort is a critical barrier to long-term, wearable applications. While various approaches have been proposed to address this issue, the mechanical impact of electrode tip geometry has not been adequately quantified, and most existing evaluations predominantly rely on subjective assessments. To address this gap with a quantitative, mechanics-based framework, the current study aimed to identify an optimized electrode tip geometry that minimizes mechanical stress on the scalp even under tilted contact conditions. Finite element analysis was conducted using strain energy density (SED), a mechanical index known to correlate with neural impulse activity, as a quantitative indicator of the mechanical influence of tip geometry on the skin. Six types of electrode tip geometries, ranging from flat to hemispherical, were defined based on the ratio of fillet radius to prong radius. These geometries were analyzed under inclination angles from 0 to 5 degree, and their peak SED values were compared. Additionally, a geometry optimization using an iterative search algorithm was performed to minimize peak SED under the 5 degree tillt. The findings revealed that intermediate fillet geometries with gently rounded edges more effectively reduce peak SED under inclined conditions. Optimization further identified a geometry ratio of Rrate* = 0.61875 as the most effective tip geometry for minimizing mechanical loading under the specified conditions. These results offer a potential geometric design guideline for dry EEG electrodes that can help maintain user comfort across varying inclination angles.

Axial interfragmentary motion is known to stimulate fracture healing. A mechanically compliant fracture fixation plate incorporating flexures is proposed to provide controlled axial micromotion to long bone fractures. To explore the concept's feasibility, computational modeling of general diaphyseal and distal femur fractures treated with both rigid and compliant plates is conducted. In Part I of this study, a diaphyseal fracture finite element model for novel compliant plates is validated against experimental data with good agreement. In Part II, a parametric analysis is conducted using the validated model to characterize the performance of many compliant plate designs with varying geometry and materials. Under axial loading, all compliant plate configurations provided greater (1.03 mm versus 0.22 mm) and more symmetric (270-390%) axial interfragmentary motion than rigid plates. Steel compliant plates with thicker flexures (0.3-0.6 mm) may provide the best performance given their enhanced motion and comparable bending/torsional rigidity. In Part III, compliant plates are adapted for use in treating distal femur fractures. Results demonstrate that compared to a rigid plate, a compliant distal femur plate with increased thickness can effectively modulate interfragmentary motion-that is, increase the insufficient near cortex motion under low loads (from 0.14 mm to 0.23 mm) and reduce the excessive far cortex motion under large loads (from 7.96 mm to 2.54 mm). Flexure-based locking plates represent a promising new approach to treating diaphyseal and/or distal femur fractures. Additional research is needed to investigate in vivo performance.

Computational fluid dynamics (CFD) is commonly used to investigate hemodynamics in the cardiovascular system, particularly in regions prone to cardiovascular disease, such as the carotid artery bifurcation. Despite its potential, significant variability exists across different computational approaches, highlighting the need for systematic solver comparisons. This study provides a comprehensive evaluation of three open-source finite element method (FEM) solvers-SimVascular, FEBio, and FEniCS Oasis-for simulating blood flow in a subject-specific carotid artery model. We conducted a rigorous comparison using a model derived from 4D phase-contrast magnetic resonance imaging (4D Flow MRI), examining solver performance across multiple mesh resolutions. This analysis focused on key hemodynamic metrics, including velocity fields, time-averaged wall shear stress (TAWSS), oscillatory shear index (OSI), and wall shear stress (WSS) topology. By maintaining identical meshes, boundary conditions, and postprocessing methods, we isolated solver-specific characteristics while focusing on high-resolution mesh refinements. All solvers demonstrated similar capability in representing the 4D-Flow MRI data. Notably, all solvers consistently identified critical hemodynamic regions, such as flow disturbance zones in the carotid sinus. Mesh convergence analysis showed the ability of all solvers to achieve converged predictions at relatively lower mesh resolutions. The computational time was also compared across the three solvers. While demonstrating the capabilities of each solver in predicting physiologically relevant hemodynamic patterns, our study underscores the utility of open-source solvers for high-fidelity hemodynamic predictions.

Fascicular elastic fibers have recently been shown to play a significant role in tendon mechanics despite relatively low abundance, leading to increased linear modulus in ramp to failure mechanical testing with elastin knockdown. Despite elastin providing fatigue and recoil properties in a variety of tissues such as vasculature and lungs, its role in tendon fatigue mechanics is largely unknown. Therefore, this study validated and leveraged a novel murine model of local elastin knockout in the limbs (Prx1Cre+; Elnfl/fl) to study the impacts of elastin on tendon stress relaxation, ramp to failure, and fatigue mechanics for functionally distinct Achilles tendons (ATs) and tibialis anterior tendons (TBs). Elastin knockout was confirmed via gene expression analysis, biochemical protein quantification, immunofluorescence confocal imaging, and analysis of 3D two-photon image stacks. Consistent with previous results of elastin reduction or disruption in ramp to failure mechanical testing, Prx1Cre+; Elnfl/fl ATs exhibited increased linear modulus; in contrast, TBs exhibited decreased linear modulus, demonstrating tendon-specific effects. In agreement with previous results, mechanical changes corresponded to alterations in dynamic alignment of collagen fibers, suggesting elastin can mediate collagen fiber orientation and recruitment. Furthermore, elastin knockdown led to increased strain during fatigue testing in ATs but increased early hysteresis and decreased fatigue-life in both tendon types. Damage metrics showed opposite trends for collagen denaturation by tendon type, but consistent results for kinking after fatigue loading. Results suggest different mechanisms underly each type of damage and that the impact of elastic fiber knockdown is tendon-dependent.

To quantify the contributions of specific ligaments to overall joint biomechanics, the principle of superposition has been used for nearly 30 years. This principle relies on a robotic test system to move a biological joint to the same pose before and after transecting a ligament. The difference in joint forces before and after transecting the ligament is assumed to be the transected ligament's tension. However, the robotic test system's ability to accurately return the joint to the commanded pose is dependent on the compliance of the system's various components, which is often neglected. An alternative approach to superposition testing is to use additional sensors attached directly to the joint to inform robot motion. Accordingly, there are two objectives: (1) describe a testing methodology with 6DOF position sensors to correct for system compliance and (2) demonstrate the effectiveness of this methodology to reduce uncertainty of in situ forces determined using superposition. A Sensor Fusion algorithm fuses 6DOF position sensors with robot pose measurements to compensate for system compliance. For the equipment, loading condition, and surrogate knee joint used in this study, the Traditional control method underestimated ligament tension by 23% while the Sensor Fusion control method brought that error down to 3%. Thus, this Sensor Fusion algorithm is a promising approach to minimize errors in superposition testing caused by compliance in a robotic test system.

This study aimed to integrate deeplabcut (dlc) with our Automatic Gait Analysis Through Hues and Areas (AGATHA) algorithm. Prior work with AGATHA shows that it can be used to understand spatiotemporal gait adaptations in multiple disease models. However, AGATHA cannot detect kinematic variables, like joint angles, which dlc was designed to measure. Here, these two approaches are integrated, and the gait variables that can be achieved with both methods are compared. To train dlc, hand digitization of high-speed videos was conducted to estimate the location of several key anatomical markers; then a neural network within dlc was used to automate the digitization of these same points in subsequent videos. A matlab pipeline was developed to calculate average stride profiles for toe height, back angle, and midfoot angle from dlc coordinates. Then, 418 videos of naïve Sprague-Dawley rats (12 w.o., n = 18) walking unprompted across an arena were collected. These videos were analyzed using dlc and AGATHA. For velocity, hind limb duty factor, stride length, and step width, variability was larger in dlc than in AGATHA. However, when used in conjunction, dlc and AGATHA have strong complementary datasets, where AGATHA can provide spatiotemporal and dynamic measures, and dlc can provide kinematic measures that AGATHA cannot currently measure. Decisions on whether to use AGATHA alone, dlc alone, or AGATHA-dlc in tandem are thus dependent on the gait variables being evaluated.

The biomechanical environment around implants plays a crucial role in the stability and success of dental implants. In our previous studies, laboratory-based micro X-ray computed tomography (micro-CT) was used to conduct in situ biomechanical experiments and finite element method (FEM) analyses of bone-implant biomechanics. Compared to micro-CT, cone beam computed tomography (CBCT) is more commonly used in dental clinics. This study uses CBCT to investigate peri-implant bone biomechanics. Voxel-based finite element models were constructed from CBCT images of five human cadaveric bone-tooth specimens. The three-dimensional strain distribution in bone surrounding immediately loaded implants was computed and quantitatively compared with experimental results. Our findings revealed significant strain concentration at bone-implant contact (BIC) areas (greater than 0.8%), extending to both buccal and lingual bone plates. Notably, the thinner buccal plate exhibited greater strain concentration (greater than 0.8%) than the thicker lingual plate (approximately 0.6%). The comparison of FEM-computed averaged maximum principal strain values and experimental results showed good agreement for both buccal (slope 0.892, R-squared 0.9607) and lingual plates (slope 1.0965, R-squared 0.9633). However, CBCT-based FEM overestimated strain at BIC locations by a factor of 1.7. CBCT-based FEM is effective in predicting strain in both buccal and lingual plates. This strain concentration in the buccal plate may contribute to observed buccal bone resorption. Insights from this work could inform development of biomechanics-guided preclinical assessments and CBCT-based implant planning.

Lung injuries lead to heterogeneous ventilation behavior in the lung parenchyma, and conventional methods used to assess lung health, such as spirometry, fall short of providing regional information about lung function. Dynamic medical imaging and image registration offer a powerful tool for estimating the kinematic behavior of lung parenchyma in vivo. However, the difficulty of validating lung deformation estimated by image registration has curbed widespread adoption in the clinic. In-silico images, reconstructed from finite element (FE) simulations, provide a method to verify the results estimated through image registration (IR). Our objective in this study was to use in-silico computed tomography (CT) images, reconstructed from FE simulations, to assess the accuracy of an image registration method. In this study, we used dynamic CT (4DCT) images from human patients to reconstruct the lungs and generate an FE mesh. In-silico simulations were performed using the lung FE mesh, and the results were used to generate in-silico dynamic CT images matching the resolution of the actual 4DCT images. Image registration was performed on the actual and in-silico images, and the results were compared to those from the FE simulation. Results indicated good agreement in displacement estimated by the FE simulations and the image registration of the actual and in-silico CT images. The difference in predicted displacement image registration of the actual CT images and the FE simulations was greatest at the main bronchi, with a value of 2.7 mm. This result highlighted the effectiveness of the FE simulation-based method to generate in-silico CT images. The volumetric strain comparisons between actual 4DCT and the in-silico images were used to assess the method's accuracy. A new set of in-silico images was generated at a higher spatial resolution, resulting in improved agreement for the volumetric strain contours. We expect the method reported in this study to be applied to optimize medical imaging methods and investigate the behavior of various lung diseases under medical imaging.

Obstructive sleep apnea (OSA) is characterized by recurrent upper airway collapse during sleep, resulting from interactions between aerodynamic forces, and neuromuscular activation of structures surrounding the airway. This study introduces a novel methodology for inferring the neuromuscular activity noninvasively from airway wall acceleration. The results will allow identification of the triggers for movements such as genioglossus advancement and to assess why they fail in patients with OSA. A patient with OSA underwent magnetic resonance imaging (MRI) to capture airway anatomy and motion under sleeplike sedation. A virtual airway model was segmented from high-resolution MRI and animated by registering dynamic cine MRI sequences. Computational fluid dynamics (CFD) simulations with this prescribed wall motion were used to compute airflow pressure forces acting on the airway wall. By quantifying airway wall acceleration and comparing it to airflow pressure forces, we inferred the contribution of internal forces, consisting of neuromuscular activation and tissue elasticity. Pressure-acceleration analysis at the soft palate, tongue, and epiglottis revealed distinct force imbalances leading to airway collapse and dilation. During inhalation, airway collapse started before peak negative pressure, suggesting insufficient neuromuscular activation. During exhalation, substantial neuromuscular-driven motion occurred. The relationship between airway pressure and acceleration was nonlinear, indicating that internal forces vary dynamically throughout the respiratory cycle. This study demonstrates a novel approach for assessing neuromuscular activation in OSA using airway wall acceleration. By analyzing pressure-acceleration relationships, passive collapse was distinguished from active neuromuscular motion, enabling more precise phenotyping of OSA patients.