Biomechanics and Modeling in Mechanobiology最新文献

We propose a robust framework for quantitatively comparing model-predicted and experimentally measured strain fields in the human brain during harmonic skull motion. Traumatic brain injuries (TBIs) are typically caused by skull impact or acceleration, but how skull motion leads to brain deformation and consequent neural injury remains unclear and comparison of model predictions to experimental data remains limited. Magnetic resonance elastography (MRE) provides high-resolution, full-field measurements of dynamic brain deformation induced by harmonic skull motion. In the proposed framework, full-field strain measurements from human brain MRE in vivo are compared to simulated strain fields from models with similar harmonic loading. To enable comparison, the model geometry and subject anatomy, and subsequently, the predicted and measured strain fields are nonlinearly registered to the same standard brain atlas. Strain field correlations (({C}_{v})), both global (over the brain volume) and local (over smaller sub-volumes), are then computed from the inner product of the complex-valued strain tensors from model and experiment at each voxel. To demonstrate our approach, we compare strain fields from MRE in six human subjects to predictions from two previously developed models. Notably, global ({C}_{v}) values are higher when comparing strain fields from different subjects (({C}_{v})~0.6–0.7) than when comparing strain fields from either of the two models to strain fields in any subject. The proposed framework provides a quantitative method to assess similarity (and to identify discrepancies) between model predictions and experimental measurements of brain deformation and thus can aid in the development and evaluation of improved models of brain biomechanics.

Cell migration via autologous chemotaxis in the presence of interstitial fluid flow is important in cancer metastasis and embryonic development. Despite significant recent progress, our understanding of flow-induced autologous chemotaxis of multicellular systems remains poor. The literature presents inconsistent findings regarding the effectiveness of collective autologous chemotaxis of densely packed cells under interstitial fluid flow. Here, we present a high-fidelity computational model to analyze the migration of multicellular systems performing autologous chemotaxis in the presence of interstitial fluid flow. Our simulations show that the details of the complex transport dynamics of the chemoattractant and fluid flow patterns that occur in the extracellular space, previously overlooked, are essential to understand this cell migration mechanism. We find that, although flow-induced autologous chemotaxis is a robust migration mechanism for individual cells, the cell-cell interactions that occur in multicellular systems render autologous chemotaxis an inefficient mechanism of collective cell migration. Our results offer new perspectives on the potential role of autologous chemotaxis in the tumor microenvironment, where fluid flow is an important modulator of transport.

Subject-specific cerebrovascular models predict individual unmeasurable vessel haemodynamics using principles of physics, assumed constitutive laws, and measurement-deduced boundary conditions. However, the process of generating these models can be time-consuming, which is a barrier for use in time-sensitive clinical applications. In this work, we developed a semi-automated pipeline to generate anatomically and functionally personalised 0D cerebrovascular models from vasculature geometry and blood flow data. The pipeline extracts the vessel connectivity and geometric parameters from vessel segmentation to automatically generate a bond graph-based (linear and time-dependent) model of subject vasculature. Then, using a neurofuzzy control scheme, the peripheral resistances of the model are calibrated to minimise the discrepancy between measured and predicted blood flow distributions. We validated the pipeline by generating subject-specific models of the Circle of Willis (CoW) for 10 cases and compared haemodynamic predictions against acquired 4D flow MRI data. The results showed a relative error of (0.25pm 0.66%) for flow and (13.87pm 18.24 %) for pulsatility, with a higher error for smaller vessels. We then demonstrated a use case of the model by simulating the blood flow redistribution during vascular occlusion for different CoW geometries. The results highlighted the benefit of a completely connected CoW to redistribute flow. The modular nature and rapid model generation time of this pipeline make it a promising tool for research and clinical use, where the type and structure of data are variable, and computing resources may be limited.

An ascending aortic aneurysm is an often asymptomatic localized dilatation of the aorta. Aortic rupture is a life-threatening event that occurs when the stress on the aortic wall exceeds its mechanical strength. Therefore, patient-specific finite element models could play an important role in estimating the risk of rupture. This requires not only the geometry of the aorta but also the nonlinear anisotropic properties of the tissue. In this study, we presented a methodology to estimate the mechanical properties of the aorta from magnetic resonance imaging (MRI). As a theoretical framework, we used finite element models to which we added noise to simulate clinical data from real patient geometry and different properties of healthy and aneurysmal aortic tissues collected from the literature. The proposed methodology considered the nonlinear properties, the zero pressure geometry, the heart motion, and the external tissue support. In addition, we analyzed the aorta as a homogeneous material and as a heterogeneous model with different properties for the ascending and descending parts. The methodology was also applied to pre-surgical,in vivo MRI data of a patient who underwent surgery during which an aortic wall sample was obtained. The results were compared with those obtained from ex vivo biaxial test of the patient’s tissue sample. The methodology showed promising results after successfully recovering the nonlinear anisotropic material properties of all analyzed cases. This study demonstrates that the variable used during the optimization process can affect the result. In particular, variables such as principal strains were found to obtain more realistic materials than the displacement field.

The geometry and mechanical properties of infant skull bones differ significantly from those of adults. Over the past decades, debates surrounding whether fractures in infants come from deliberate abuse or accidents have generated significant impacts in both legal and societal contexts. However, the etiology of infant skull fractures remains unclear, which motivates this study with two main components of work. Firstly, we present and implement a progressive unidirectional fabric composite damage model for infant cranial vaults to represent ductile and anisotropic properties—two typical mechanical characteristics of infant skulls. Secondly, we hypothesize that these intrinsic material properties cause injuries perpendicular to the fiber direction to dominate infant skull fractures, resulting in fracture lines that align with the direction of mineralization in the infant skull. The material model and the finite element (FE) model were verified hierarchically, and this hypothesis was verified by reconstructing two legal cases with known fall heights and implementing the above damage model into CT-based subject-specific infant FE head models. We discovered that the infant skull is more susceptible to injuries within planes perpendicular to the mineralization direction because of the anisotropic mechanical property caused by the direction of mineralization, leading to infant skull fractures aligning with the mineralization direction. Our findings corroborated the several previously reported observations of fractures on cranial vaults, demonstrating that these fractures were closely associated with sutures and oriented along the mineralization direction, and revealed the underlying mechanisms of infant skull fracture pattern. The modeling methods and results of this study will serve as an anchor point for more rigorous investigations of infant skull fractures, ultimately aiming to provide convincing biomechanical evidence to aid forensic diagnoses of abusive head trauma.

The hemodynamic and convective heat transfer effects of a patient-specific endovascular therapeutic agent based on shape-memory polymer foam (SMPf) are evaluated using computational fluid dynamics studies for six patient-specific aneurysm geometries. The SMPf device is modeled as a continuous porous medium with full expansion for the flow studies and with various degrees of expansion for the heat transfer studies. The flow simulation parameters were qualitatively validated based on the existing literature. Further, a mesh independence study was conducted to verify an optimal cell size and reduce the computational costs. For convective heat transfer, a worst-case scenario is evaluated where the minimum volumetric flow rate is applied alongside the zero-flux boundary conditions. In the flow simulations, we found a reduction of the average intra-aneurysmal flow of > 85% and a reduction of the maximum intra-aneurysmal flow of > 45% for all presented geometries. These findings were compared with the literature on numerical simulations of hemodynamic and heat transfer of SMPf devices. The results obtained from this study provide a novel and practical framework for optimizing the design of patient-specific SMPf devices, integrating advanced computational models of hemodynamics and heat transfer. This framework could guide the future development of personalized endovascular embolization solutions for intracranial aneurysms with improved therapeutic outcome.

The mechanical behaviour of peripheral nerves is known to vary between different nerves and nerve regions. As the field of nerve tissue engineering advances, it is vital that we understand the range of mechanical regimes future nerve implants must match to prevent failure. Data on the mechanical behaviour of human peripheral nerves are difficult to obtain due to the need to conduct mechanical testing shortly after removal from the body. In this work, we adapt a 3D multiscale biomechanical model, developed using asymptotic homogenisation, to mimic the micro- and macroscale structure of a peripheral nerve. This model is then parameterised using experimental data from rat peripheral nerves and used to investigate the effect of varying the collagen content, the fibril radius and number density, and the macroscale cross-sectional geometry of the peripheral nerve on the effective axial Young’s moduli of the whole nerve. Our results indicate that the total amount of collagen within a cross section has a greater effect on the axial Young’s moduli compared to other measures of structure. This suggests that the amount of collagen in a cross section of a peripheral nerve, which can be measured through histological and imaging techniques, is one of the key metrics that should be recorded in the future experimental studies on the biomechanical properties of peripheral nerves.