Craig Bennetts

Normal glenoid vault anatomy and validation of a novel glenoid implant shape

Current glenoid implants are designed to be secured to the articular surface. When the articular surface is compromised, a glenoid component could be implanted if it obtained fixation from the endosteal surface of the glenoid vault. The first step for designing such a glenoid implant is to define the normal three-dimensional anatomy of the glenoid vault. The purpose of this study was to define the variations in glenoid vault shape in a large group of cadaver scapula. Computed tomographic (CT) scans of 61 normal scapulae (mean, 25-34 years) from the Haman-Todd Osteological Collection, with a wide range of sizes, were examined to define the normal glenoid vault anatomy. A custom software program was used to manipulate and measure the scans to determine the morphologic variations among the different glenoid vaults. From these data, we defined a unique glenoid vault shape and empirically developed 5 sizes to represent the study population of the 61 scapulae. A second group of 11 cadaver scapulae were used to validate the shape defined using the other 61. Prototype implants were placed into the real 11 scapulae using standard surgical techniques and then CT-scanned to analyze the shape of the glenoid vault. In the 61 scapulae, 85% of the points defining the endosteal surfaces vary among scapulae by less than 2 mm. For each of the 11 cadaver scapulae, the implant size used in the virtual computer implantation was the same size used for the plastic components placed into the cadaver scapulae. Fifty percent of the measured distances between the outer dimensions of the plastic models was within 2.4 mm of the glenoid endosteal surface. Eighty percent of the surface area of the plastic models was within 3.1 mm of the glenoid endosteal surface. Five percent of the dimensions were less than 1 mm and were considered to be areas of point contact. Before designing implants that can be used in pathologic glenoids, the shape of the normal glenoid vault must first be defined. This study defined a normal glenoid vault shape that can accommodate different sized scapula with 5 sizes. This glenoid shape may be used as a template to design a glenoid implant that obtains fixation within the glenoid vault.

Multiscale cartilage biomechanics: technical challenges in realizing a high-throughput modelling and simulation workflow.

Understanding the mechanical environment of articular cartilage and chondrocytes is of the utmost importance in evaluating tissue damage which is often related to failure of the fibre architecture and mechanical injury to the cells. This knowledge also has significant implications for understanding the mechanobiological response in healthy and diseased cartilage and can drive the development of intervention strategies, ranging from the design of tissue-engineered constructs to the establishment of rehabilitation protocols. Spanning multiple spatial scales, a wide range of biomechanical factors dictate this mechanical environment. Computational modelling and simulation provide descriptive and predictive tools to identify multiscale interactions, and can lead towards a greater comprehension of healthy and diseased cartilage function, possibly in an individualized manner. Cartilage and chondrocyte mechanics can be examined in silico, through post-processing or feed-forward approaches. First, joint–tissue level simulations, typically using the finite-element method, solve boundary value problems representing the joint articulation and underlying tissue, which can differentiate the role of compartmental joint loading in cartilage contact mechanics and macroscale cartilage field mechanics. Subsequently, tissue–cell scale simulations, driven by the macroscale cartilage mechanical field information, can predict chondrocyte deformation metrics along with the mechanics of the surrounding pericellular and extracellular matrices. A high-throughput modelling and simulation framework is necessary to develop models representative of regional and population-wide variations in cartilage and chondrocyte anatomy and mechanical properties, and to conduct large-scale analysis accommodating a multitude of loading scenarios. However, realization of such a framework is a daunting task, with technical difficulties hindering the processes of model development, scale coupling, simulation and interpretation of the results. This study aims to summarize various strategies to address the technical challenges of post-processing-based simulations of cartilage and chondrocyte mechanics with the ultimate goal of establishing the foundations of a high-throughput multiscale analysis framework. At the joint–tissue scale, rapid development of regional models of articular contact is possible by automating the process of generating parametric representations of cartilage boundaries and depth-dependent zonal delineation with associated constitutive relationships. At the tissue–cell scale, models descriptive of multicellular and fibrillar architecture of cartilage zones can also be generated in an automated fashion. Through post-processing, scripts can extract biphasic mechanical metrics at a desired point in the cartilage to assign loading and boundary conditions to models at the lower spatial scale of cells. Cell deformation metrics can be extracted from simulation results to provide a simplified description of individual chondrocyte responses. Simulations at the tissue–cell scale can be parallelized owing to the loosely coupled nature of the feed-forward approach. Verification studies illustrated the necessity of a second-order data passing scheme between scales and evaluated the role that the microscale representative volume size plays in appropriately predicting the mechanical response of the chondrocytes. The tools summarized in this study collectively provide a framework for high-throughput exploration of cartilage biomechanics, which includes minimally supervised model generation, and prediction of multiscale biomechanical metrics across a range of spatial scales, from joint regions and cartilage zones, down to that of the chondrocytes.

A Comprehensive Specimen-Specific Multiscale Data Set for Anatomical and Mechanical Characterization of the Tibiofemoral Joint

Understanding of tibiofemoral joint mechanics at multiple spatial scales is essential for developing effective preventive measures and treatments for both pathology and injury management. Currently, there is a distinct lack of specimen-specific biomechanical data at multiple spatial scales, e.g., joint, tissue, and cell scales. Comprehensive multiscale data may improve the understanding of the relationship between biomechanical and anatomical markers across various scales. Furthermore, specimen-specific multiscale data for the tibiofemoral joint may assist development and validation of specimen-specific computational models that may be useful for more thorough analyses of the biomechanical behavior of the joint. This study describes an aggregation of procedures for acquisition of multiscale anatomical and biomechanical data for the tibiofemoral joint. Magnetic resonance imaging was used to acquire anatomical morphology at the joint scale. A robotic testing system was used to quantify joint level biomechanical response under various loading scenarios. Tissue level material properties were obtained from the same specimen for the femoral and tibial articular cartilage, medial and lateral menisci, anterior and posterior cruciate ligaments, and medial and lateral collateral ligaments. Histology data were also obtained for all tissue types to measure specimen-specific cell scale information, e.g., cellular distribution. This study is the first of its kind to establish a comprehensive multiscale data set for a musculoskeletal joint and the presented data collection approach can be used as a general template to guide acquisition of specimen-specific comprehensive multiscale data for musculoskeletal joints.

Automated generation of tissue-specific three-dimensional finite element meshes containing ellipsoidal cellular inclusions

Finite element analysis provides a means of describing cellular mechanics in tissue, which can be useful in understanding and predicting physiological and pathological changes. Many prior studies have been limited to simulations of models containing single cells, which may not accurately describe the influence of mechanical interactions between cells. It is desirable to generate models that more accurately reflect the cellular organisation in tissue in order to evaluate the mechanical function of cells. However, as the model geometry becomes more complicated, manual model generation can become laborious. This can be prohibitive if a large number of distinct cell-scale models are required, for example, in multiscale modelling or probabilistic analysis. Therefore, a method was developed to automatically generate tissue-specific cellular models of arbitrary complexity, with minimal user intervention. This was achieved through a set of scripts, which are capable of generating both sample-specific models, with explicitly defined geometry, and tissue-specific models, with geometry derived implicitly from normal statistical distributions. Models are meshed with tetrahedral (TET) elements of variable size to sufficiently discretise model geometries at different spatial scales while reducing model complexity. The ability of TET meshes to appropriately simulate the biphasic mechanical response of a single-cell model is established against that of a corresponding hexahedral mesh for an illustrative use case. To further demonstrate the flexibility of this tool, an explicit model was developed from three-dimensional confocal laser scanning image data, and a set of models were generated from a statistical cellular distribution of the articular femoral cartilage. The tools presented herein are free and openly accessible to the community at large.

A comparison of hexahedral and tetrahedral finite elements for biphasic analysis of cartilage micromechanics

Articular cartilage is a hydrated composite structure comprised of collagen type II extracellular matrix (ECM), collagen type VI pericellular matrix (PCM), and chondrocytes, each with low hydraulic permeability resulting in long viscoelastic stress relaxation times. Therefore, when subjected to fast loading, cartilage behaves as a nearly incompressible material [1]. When finite element analysis (FEA) is employed to solve mechanical problems involving nearly incompressible materials, a hexahedral finite element mesh is desirable [2]. Unfortunately, hexahedral mesh generation is difficult for complex geometries and often requires painstaking user-assisted construction which is often not feasible. Realistic chondron shapes and distributions within cartilage exhibit this complexity [3]. In these scenarios, a tetrahedral finite element discretization may need to be employed (Figure 1).Copyright

Surface Stiffness of Patient-Specific Arterial Segments With Varying Plaque Compositions

Peripheral arterial disease (PAD), resulting from the accumulation of plaque, causes obstruction of blood flow in the large arteries in the arm and leg. In the United States, approximately 8.4 million people over the age of 40 have PAD [1]. If not treated, PAD can cause ischemic ulcerations and gangrene, which could eventually lead to amputation. Approximately, 25% of patients with PAD have worsening limb symptoms over 5 years, 7% requiring revascularization, and 4% requiring amputation [2].Copyright

Comprehensive identification of tibiofemoral joint anatomy and mechanical response: Pathway to multiscale characterization

The human knee joint is a complex multi-body structure, whose substructures greatly affect its mechanical response. An understanding of the multiscale mechanics of the joint is essential for the prevention and treatment of knee joint injuries and pathologies. Due to the limitations associated with in vivo experimentation, mechanical characterization of the knee joint has commonly relied on in vitro experimentation [1,2]. Predictive and descriptive studies of the mechanical function of the knee and its substructures have commonly employed computational modeling, in particular finite element (FE) analysis, which can be driven by experimental data. With the recent focus on the use of FE models of the knee joint for scientific and clinical purposes [3–5], data for model development, verification, and validation became increasingly important, especially when relying on FE analysis for decision making. An adequate representation of a joint not only depends on the specimen-specific anatomy but may also need to be informed by specimen-specific tissue properties for model development, and specimen-specific joint/tissue response to confirm model response.Copyright

Automated Generation of Tissue-Specific Finite Element Models Containing Ellipsoidal Cellular Inclusions

Microstructural cellular finite element (FE) models provide a means of describing tissue micromechanics and cell mechanobiology. For the cartilage, for example, the mechanical environment of chondrocytes has been explored with models such as those from the pioneering work of Guilak and Mow [1]. However, most cellular FE models typically include a single cell. These models do not provide the capacity to explore mechanobiological function and micromechanical effects caused by intercellular interactions. Therefore, it is desirable to develop models that more closely represent cell distribution and shape within the tissue of interest.Copyright

2D Spatial Analysis of Chondrocyte Distribution: Implications for Identifying Representative Volume Elements for Multiscale Knee Modeling

Finite element analysis (FEA) of the knee including joint, tissue and cell representations can be used to predict chondrocyte stresses in the cartilage given the joint level loading. This is of clinical significance since excessive loading on the joint may cause elevated cartilage stresses, potentially inducing chondrocyte apoptosis [1]. However, the level of complexity of a model that fully represents all cells within the cartilage is prohibitive given the current state of computational technology if results are desired in a timely fashion.© 2011 ASME