Computational models of musculoskeletal joints and limbs can provide useful information about joint mechanics. Validated models can be used as a predictive device for understanding joint function and serve as a clinical tool for predicting the outcome of surgical procedures. A new computational modeling approach was developed for simulating joint kinematics that are dictated by bone/joint anatomy, ligamentous constraints, and applied loading.Three-dimensional computational models of the lower leg were created. Model development involved generating three-dimensional surfaces from CT images, followed by importing these surfaces into SolidWorks and COSMOSMotion. ThroughSolidWorks and COSMOSMotion, each bone surface was created into a solid object and positioned, necessary components added, and simulations executed. Three dimensional contacts inhibited intersection of the bones during motion. Ligaments were represented as linear springs. Model predictions were then validated by comparison to three different previously performed cadaver studies (syndesmotic injury study, inversion stability study, and mechanical laxity study) and one simultaneously performed cadaver study (anterior drawer test).In the syndesmotic injury study, the relative motion between the tibia and fibula in intact, transected, and repaired states was measured under the application of an external rotation of the ankle. The inversion stability study focused on the elongation behavior of lateral ankle ligaments and inversion range of motion during the application of an applied load. The mechanical laxity study focused on differences in anterior/posterior and inversion/eversion movement in intact and transected states. Each computational simulation was placed under the same conditions as its respective cadaver study and revealed a capability to predict behaviors in each case. The syndesmotic injury model was able to predict tibia1 rotation, fibular rotation, and anterior/posterior displacement. In the inversion simulation, calcaneofibular ligament extension and angles of inversion compared well. The laxity study showed increases in anteroposter motion after the transactions of the ATFL and CFL; and diffenences in inversion after the transaction of the CFL. The Anterior Drawer simulation produced similar ligament elongations and loads when compared to cadaver studies.Overall, the computational models were able to predict joint kinematics of the lower leg with particular focus on the ankle complex. Additional parameters can be calculated through such models that are not easily obtained experimentally such as ligament forces, force transmission across joints, and three-dimensional movement of all bones.

The following report contains an evaluation of the use of mathematical simulation programs at the company Isaberg Rapid AB. The work includes booth FE and motion simulations where the results are compared with real life test data. The goal of the report is to evaluate the accuracy of simulations which can be performed by engineers as a part of the design process. By using mathematical simulation tools it is possible to find a good design solution early in the development phase and thereby shorten lead time and reduce costs.

<p>The following report contains an evaluation of the use of mathematical simulation programs at the company Isaberg Rapid AB. The work includes booth FE and motion simulations where the results are compared with real life test data.</p><p>The goal of the report is to evaluate the accuracy of simulations which can be performed by engineers as a part of the design process. By using mathematical simulation tools it is possible to find a good design solution early in the development phase and thereby shorten lead time and reduce costs.</p>

The glenohumeral joint is the most dislocated joint in the body due to the lack of bony constraints and dependence on soft tissue, primarily muscles and ligaments, to stabilize the joint. The goal of this study was to develop a computational model of the glenohumeral joint whereby joint behavior was dictated by articular contact, ligamentous constraints, muscle loading, and external perturbations. Validation of this computational model was achieved by comparing predicted results from the model to the results of a cadaveric experiment in which the relative contribution of muscles and ligaments to anterior joint stability was examined. The results showed the subscapularis to be critical to stabilization in both neutral and external rotations, the biceps stabilized the joint in neutral but not external rotation, and the inferior glenohumeral ligament resisted anterior displacement only in external rotation. Knowledge gained from this model could assist in pre-operative planning or the design of orthopedic implants. Use of this model as a companion to cadaveric testing could save valuable time and resources.

Musculoskeletal computational modeling is a versatile and effective tool which may be used to study joint mechanics, examine muscle and ligament function, and simulate surgical reconstructive procedures. While injury to the elbow joint can be significantly debilitating, questions still remain regarding its normal, pathologic, and repaired behavior. Biomechanical models of the elbow have been developed, but all have assumed fixed joint axes of rotation and ignored the effects of ligaments. Therefore, the objective of this thesis was to develop and validate a computational model of the elbow joint whereby joint kinematics are dictated by three-dimensional bony geometry contact, ligamentous constraints, and muscle loading.Accurate three-dimensional bone geometry was generated by acquiring CT scans, segmenting the images to isolate skeletal features, and fitting surfaces to the segmented data. Ligaments were modeled as tension-only linear springs, and muscle were represented as force vectors with discrete attachment points. Bone contact was modeled by a routine which applied a normal force at points of penetration, with a force magnitude being a function of penetration depth. A rigid body dynamics simulator was used to predict the model's behavior under particular external loading conditions.The computational model was validated by simulating past experimental investigations and comparing results. Passive flexion-extension range of motion predicted by the model correlated exceptionally well with reported values. Bony and ligamentous structures responsible for enforcing motion limits also agreed with past observations. The model's varus stability as a function of elbow flexion and coronoid process resection was also investigated. The trends predicted by the model matched those of the associated cadaver study.This thesis successfully developed an accurate musculoskeletal computational model of the elbow joint complex. While the model may now be used in a predictive manner, further refinements may expand its applicability. These include accounting for the interference between soft tissue and bone, and representing the dynamic behavior of muscles.

Rigid body modeling has historically been used to study various features of the elbow joint including both physical and computational models. Computational modeling provides an inexpensive, easily customizable, and effective method by which to predict and investigate the response of a physiological system to in vivo stresses and applied perturbations. Utilizing computer topography scans of a cadaveric elbow, a virtual representation of the joint was created using the commercially available MIMICS(TM) and SolidWorks(TM) software packages. Accurate 3D articular surfaces, ligamentous constraints, and joint contact parameters dictated motion. The model was validated against two cadaveric studies performed by Chanlalit et al. (2011, 2012) considering monopolar and bipolar circular radial head replacements in their effects on radiocapitellar stability and respective reliance upon lateral soft tissues, as well as a comparison of these with a novel anatomic radial head replacement system in an elbow afflicted with the “terrible triad” injury. Rigid body simulations indicated that the computational model was able to accurately recreate the translation of forces in the joint and demonstrate results similar to those presented in the cadaveric data in both the intact elbow and in unstable injury states. Trends in the resulting data were reflective of the average behavior of the cadaveric specimens while percent changes between states correlated closely with the experimental data. Information on the transposition of forces within the joint and ligament tensions gleaned from the computational model provided further insight into the stability of the elbow with a compromised radial head.

The use of computational modeling is an effective and inexpensive way to predict the response of complex systems to various perturbations. However, not until the early 1990s had this technology been used to predict the behavior of physiological systems, specifically the human skeletal system. To that end, a computational model of the human elbow joint was developed using computed topography (CT) scans of cadaveric donor tissue, as well as the commercially available software package SolidWorks™. The kinematic function of the joint model was then defined through 3D reconstructions of the osteoarticular surfaces and various soft-tissue constraints. The model was validated against cadaveric experiments performed by Hull et al and Fern et al that measured the significance of coronoid process fractures, lateral ulnar collateral ligament ruptures, and radial head resection in elbow joint resistance to varus displacement of the forearm. Kinematic simulations showed that the computational model was able to mimic the physiological movements of the joint throughout various ranges of motion including flexion/extension and pronation/supination. Quantitatively, the model was able to accurately reproduce the trends, as well as the magnitudes, of varus resistance observed in the cadaveric specimens. Additionally, magnitudes of ligament tension and joint contact force predicted by the model were able to further elucidate the complex soft-tissue and osseous contributions to varus elbow stability.