The Development And Application Of A Statistical Shape Model Of The Human Craniofacial Skeleton

Biomechanical investigations involving the characterization of biomaterials or improvement of implant design often employ finite element (FE) analysis. However, the contemporary method of developing a FE mesh from computed tomography scans involves much manual intervention and can be a tedious process. Researchers will often focus their efforts on creating a single highly validated FE model at the expense of incorporating variability of anatomical geometry and material properties, thus limiting the applicability of their findings. The goal of this thesis was to address this issue through the use of a statistical shape model (SSM). A SSM is a probabilistic description of the variation in the shape of a given class of object. (Additional scalar data, such as an elastic constant, can also be incorporated into the model.) By discretizing a sample (i.e. training set) of unique objects of the same class using a set of corresponding nodes, the main modes of shape variation within that shape class are discovered via principal component analysis. By combining the principal components using different linear combinations, new shape instances are created, each with its own unique geometry while retaining the characteristics of its shape class. In this thesis, FE models of the human craniofacial skeleton (CFS) were first validated to establish their viability. A mesh morphing procedure was then developed to map one mesh onto the geometry of 22 other CFS models forming a training set for a SSM of the CFS. After verifying that FE results derived from morphed meshes were no different from those obtained using meshes created with contemporary methods, a SSM of the human CFS was created, and 1000 CFS FE meshes produced. It was found that these meshes accurately described the geometric variation in human population, and were used in a Monte Carlo analysis of facial fracture, finding past studies attempting to characterize the fracture probability of the zygomatic bone are overly conservative

Analysis of uncertainty and variability in finite element computational models for biomedical engineering: characterization and propagation

Computational modeling has become a powerful tool in biomedical engineering thanks to its potential to simulate coupled systems. However, real parameters are usually not accurately known, and variability is inherent in living organisms. To cope with this, probabilistic tools, statistical analysis and stochastic approaches have been used. This article aims to review the analysis of uncertainty and variability in the context of finite element modeling in biomedical engineering. Characterization techniques and propagation methods are presented, as well as examples of their applications in biomedical finite element simulations. Uncertainty propagation methods, both non-intrusive and intrusive, are described. Finally, pros and cons of the different approaches and their use in the scientific community are presented. This leads us to identify future directions for research and methodological development of uncertainty modeling in biomedical engineering

An Automated Process for 2D and 3D Finite Element Overclosure and Gap Adjustment using Radial Basis Function Networks

In biomechanics, geometries representing complicated organic structures are consistently segmented from sparse volumetric data or morphed from template geometries resulting in initial overclosure between adjacent geometries. In FEA, these overclosures result in numerical instability and inaccuracy as part of contact analysis. Several techniques exist to fix overclosures, but most suffer from several drawbacks. This work introduces a novel automated algorithm in an iterative process to remove overclosure and create a desired minimum gap for 2D and 3D finite element models. The RBF Network algorithm was introduced by its four major steps to remove the initial overclosure. Additionally, the algorithm was validated using two test cases against conventional nodal adjustment. The first case compared the ability of each algorithm to remove differing levels of overclosure between two deformable muscles and the effects on mesh quality. The second case used a non-deformable femur and deformable distal femoral cartilage geometry with initial overclosure to test both algorithms and observe the effects on the resulting contact FEA. The RBF Network in the first case study was successfully able to remove all overclosures. In the second case, the nodal adjustment method failed to create a usable FEA model, while the RBF Network had no such issue. This work proposed an algorithm to remove initial overclosures prior to FEA that has improved performance over conventional nodal adjustment, especially in complicated situations and those involving 3D elements. The work can be included in existing FEA modeling workflows to improve FEA results in situations involving sparse volumetric segmentation and mesh morphing. This algorithm has been implemented in MATLAB, and the source code is publicly available to download at the following GitHub repository: https://github.com/thor-andreassen/femorsComment: 26 Pages, 5 Figures, 2 Table

Explicit Finite Element Modeling of Knee Mechanics During Simulated Dynamic Activities

The natural knee is one of the most commonly injured joints in the body due to relatively high loads and motions that can lead to debilitating degenerative diseases such as osteoarthritis. Total knee arthroplasty is a clinically successful method for eliminating pain in the osteoarthritic knee, but is subject to complications that can affect patient satisfaction and long-term implant performance. The work presented in this dissertation is a demonstration of how anatomic three-dimensional (3D) computational knee models can be an effective alternative for investigating knee mechanics when compared to the cost and time prohibitive nature of in-vivo and in-vitro methods. The studies described in this work utilized the explicit finite element (FE) method to investigate varying aspects of soft tissue constraint, implant alignment, and applied dynamic loading on knee mechanics in 3D natural and implanted partial or whole joint knee models. Combined probabilistic and FE methods were used to successfully identify the most important parameters affecting joint laxity in the natural knee and patellar component alignment in the implanted knee. Two model verification studies demonstrated strong agreement between model-predicted and experimental 3D kinematics of specimen-specific isolated patellofemoral and whole joint cadaveric knee models under simulated dynamic loading (deep knee bend and gait) collected in a mechanical simulator. Using one of the single specimen whole joint models, an additional study successfully identified the most important anatomic and implant alignment parameters related to a clinically-relevant complication associated with a particular implant design. Lastly, a new method of efficiently generating 3D natural articular knee surfaces for FE analysis was developed through a combined mesh morphing and statistical shape modeling approach. These studies included several novel methods for investigating knee mechanics under dynamic loading and specimen-specific soft tissue constraint using the explicit FE method that could be used to better reproduce the complex in-vivo knee environment in forward or muscle-driven models and to assist design-phase implant performance evaluation

Developing parametric human models representing various vulnerable populations in motor vehicle crashes

Children, small female, elderly, and obese occupants are vulnerable populations and may sustain increased risk of death and serious injury in motor-vehicle crashes compared with mid-size young male occupants. Unfortunately, current injury assessment tools do not account for immature and growing body structures for children, nor the body shape and composition changes that are thought make female/aging/obese adults more vulnerable. The greatest opportunity to broaden crash protection to encompass all vehicle occupants lies in improved, parametric human models that can represent a wide range of human attributes. In this study, a novel approach to develop such models is proposed. The method includes 1) developing statistical skeleton and human body surface contour models based on medical images and body scan data using Mimics and a series of statistical methods, and 2) linking the statistical geometry model to a baseline human finite element (FE) model through an automated mesh morphing algorithm using radial basis functions, so that the FE model can represent population variability. Examples of using this approach to develop parametric pediatric head model, adult thorax and lower extremity models, and whole-body human models representing various populations were represented. The method proposed in this study enables future safety design optimizations targeting at various vulnerable populations that cannot be considered with current injury assessment tools.http://deepblue.lib.umich.edu/bitstream/2027.42/113667/1/103204.pdf-

Statistical Modeling to Investigate Anatomy and Function of the Knee

The natural knee is a hinge joint with significant functional requirements during activities of daily living; as a result, acute and chronic injuries can occur. Pathologies are influenced by joint anatomy and may include patellar maltracking, cartilage degeneration (e.g. osteoarthritis), or acute injuries such as meniscal or ligamentous tears. Population variability makes broadly applicable conclusions about etiology of these conditions from small-scale investigations challenging. The work presented in this dissertation is a demonstration of statistical modeling approaches to evaluate population variability in anatomy of the knee and function of its tibiofemoral (TF) and patellofemoral (PF) joints. Three-dimensional (3D) computational models of the bone and cartilage in the knee were characterized using a principal component analysis (PCA) algorithm to understand the primary sources of variability in shape and motion and make predictions from sparse data. Statistical models were used to investigate relationships between natural knee anatomy and kinematics and make predictions of both shape and function from sparse data. A whole-joint characterization study identified key correlations between shape and function of the TF and PF joints, successfully recreating results from multiple studies and introducing new relationships under one unified approach. Results from this study were used in a subsequent investigation to build a statistical model of two-dimensional (2D) shape and alignment measures and 6 degree-of-freedom (DOF) kinematics to identify the key measures capable of predicting PF joint motion. The ability to reconstruct the 3D implanted patellar bone of a subject with a total knee replacement (TKR) was evaluated by a statistical shape model of the patella and simulated 2D edge profiles in a custom optimization algorithm. Lastly, a validated predictive algorithm was employed to assess the accuracy of subject-specific knee articular cartilage predictions from bony geometry. The utility of statistical modeling is elucidated by the population-based evaluations of the musculoskeletal system described in this work and could continue to inform characteristics related to pathological conditions and large-scale computational evaluations of implant performance

Patient-specific modelling in orthopedics: from image to surgery

In orthopedic surgery, to decide upon intervention and how it can be optimized, surgeons usually rely on subjective analysis of medical images of the patient, obtained from computed tomography, magnetic resonance imaging, ultrasound or other techniques. Recent advancements in computational performance, image analysis and in silico modeling techniques have started to revolutionize clinical practice through the development of quantitative tools, including patient#specific models aiming at improving clinical diagnosis and surgical treatment. Anatomical and surgical landmarks as well as features extraction can be automated allowing for the creation of general or patient-specific models based on statistical shape models. Preoperative virtual planning and rapid prototyping tools allow the implementation of customized surgical solutions in real clinical environments. In the present chapter we discuss the applications of some of these techniques in orthopedics and present new computer-aided tools that can take us from image analysis to customized surgical treatment