A Novel Free Form Femoral Cutting Guide

Knee arthoplasty is a common procedure that requires the removal of damaged bone and cartilage from the distal femur so that a reconstructive implant may be installed. Traditionally, a five planar resection has been accomplished with a universal cutting box and navigated with either metal jigs or optically tracked computer navigation systems. Free form, or curved, resections have been made possible with surgical robots which control the resection pathway and serve as the navigation system. The free form femoral cutting guide serves as a non powered framework to guide a standard surgical drill along an anatomically defined pathway, resulting in the removal of distal femoral cartilage. It is fixed via attachment to a bone mounted base component, which is positioned with a patient specific jig. To operate, the surgeon slides the surgical drill along a pair of interlocked tracks. One track controls motion in the anteroposterior (AP) direction and one track controls motion in the mediolateral (ML) direction. Combining both motions results in the removal of cartilage from the area of the distal femur for unilateral or total knee arthoplasty

Autonomous Direct 3D Segmentation of Articular Knee Cartilage

The aim of the work presented here, is to speed up the entire evaluation process of articular knee cartilage and the associated medication developments for Osteoarthritis. To enable this, the development of an automated direct 3D segmentation is described that incorporates non-linear diffusion for efficient image denoising. Cartilage specific magnetic resonance imaging is used, which allows acquiring the entire cartilage volume as one 3D image. The segmentation itself is based on level sets for their accuracy, stability and topological flexibility. By using this kind of segmentation, it is hoped to improve the time efficiency and accuracy for quantitative and qualitative integrity evaluation of cartilage and to enable an earlier diagnosis and treatment of Osteoarthritis

Image-based biomechanical models of the musculoskeletal system

Finite element modeling is a precious tool for the investigation of the biomechanics of the musculoskeletal system. A key element for the development of anatomically accurate, state-of-the art finite element models is medical imaging. Indeed, the workflow for the generation of a finite element model includes steps which require the availability of medical images of the subject of interest: segmentation, which is the assignment of each voxel of the images to a specific material such as bone and cartilage, allowing for a three-dimensional reconstruction of the anatomy; meshing, which is the creation of the computational mesh necessary for the approximation of the equations describing the physics of the problem; assignment of the material properties to the various parts of the model, which can be estimated for example from quantitative computed tomography for the bone tissue and with other techniques (elastography, T1rho, and T2 mapping from magnetic resonance imaging) for soft tissues. This paper presents a brief overview of the techniques used for image segmentation, meshing, and assessing the mechanical properties of biological tissues, with focus on finite element models of the musculoskeletal system. Both consolidated methods and recent advances such as those based on artificial intelligence are described

Quantification of the normal patellofemoral shape and its clinical applications

Thesis (MScEng)--Stellenbosch University, 2013.ENGLISH ABSTRACT: The shape of the knee’s trochlear groove is a very important factor in the overall stability of the knee. However, a quantitative description of the normal three-dimensional geometry of the trochlea is not available in the literature. This is also reflected in the poor outcomes of patellofemoral arthroplasty (PFA). In this study, a standardised method for femoral parameter measurements on three-dimensional femur models was established. Using software tools, virtual femur models were aligned with the mechanical and the posterior condylar planes and this framework was used to measure the femoral parameters in a repeatable way. An artificial neural network (ANN), incorporating the femoral parameter measurements and classifications done by experienced surgeons, was used to classify knees into normal and abnormal categories. As a result, 15 knees in the database were classified by the ANN as being normal. Furthermore, the geometry of the normal knees was analysed by fitting B-spline curves and circular arcs on their sagittal surface curves to prove and reconfirm that the groove has a circular shape on a sagittal plane. Self-organising maps (SOM), which is a type of ANN, was trained with the acquired data of the normal knees and in this way the normal trochlear geometry could be predicted. The prediction of the anterior-posterior (AP) distance and the trochlear heights showed an average agreement of 97 % between the actual and the predicted normal geometries. A case study was conducted on four types of trochlear dysplasia to determine a normal geometry for these knees, and a virtual surface reconstruction was performed on them. The study showed that the trochlea was deepened after the surface reconstruction, having an average trochlea depth of 5.5 mm compared to the original average value of 2.9 mm. In summary, this research proposed a quantitative method for describing and predicting the normal geometry of a knee by making use of ANN and the femoral parameters that are unaffected by trochlear dysplasia.AFRIKAANSE OPSOMMING: Die vorm van die trogleêre keep is ’n belangrike faktor in patella-stabiliteit. Tog is ’n kwantitatiewe beskrywing van die normale driedimensionele geometrie van die troglea nog nie beskikbaar nie, wat duidelik blyk uit die swak uitkomste van patellofemorale artroplastie (PFA). In hierdie studie is ’n gestandaardiseerde metode vir die meting van femorale parameters op grond van driedimensionele femurmodelle ontwikkel. Die femurmodel is in lyn gebring met die meganiese en posterior kondilêre vlak, welke raamwerk gebruik is om die femorale parameters op ’n herhaalbare wyse te meet. Die normale knieë is geklassifiseer met ’n kunsmatige neurale netwerk (ANN), wat die femorale parameter-mate sowel as die chirurgiese klassifikasie ingesluit het, en 15 knieë is gevolglik as normaal aangewys. Die normaleknie-geometrie is ontleed deur B-latkrommes en sirkelboë op die sagittale oppervlak-kurwes aan te bring om te bewys en te herbevestig dat die keep uit ’n sirkelvorm op ’n sagittale vlak bestaan. Die ingesamelde data van die normale knieë is ingevoer by selfreëlende kaarte (SOM), synde ’n soort ANN, wat die navorser in staat gestel het om die normale trogleêre geometrie te voorspel. Die voorspelling van die anterior-posterior (AP) afstand en die trogleêre hoogtes toon ’n gemiddelde ooreenkoms van meer as 97 % tussen die werklike en voorspelde normale geometrie. ’n Gevallestudie is op vier soorte trogleêre displasie uitgevoer om die normale geometrie te voorspel en ’n oppervlakrekonstruksie daarop uit te voer. Hierdie studie het getoon dat die troglea ná oppervlakrekonstruksie verdiep was, met ’n gemiddelde trogleadiepte van 5.5 mm in vergelyking met die aanvanklike gemiddelde waarde van 2.9 mm. Hierdie navorsing het dus ’n metode aan die hand gedoen vir die kwantitatiewe beskrywing en voorspelling van normale geometrie met behulp van ANN sowel as met die femorale parameters wat nie deur die trogleêre displasie geraak word nie

Three Dimensional Nonlinear Statistical Modeling Framework for Morphological Analysis

This dissertation describes a novel three-dimensional (3D) morphometric analysis framework for building statistical shape models and identifying shape differences between populations. This research generalizes the use of anatomical atlases on more complex anatomy as in case of irregular, flat bones, and bones with deformity and irregular bone growth. The foundations for this framework are: 1) Anatomical atlases which allow the creation of homologues anatomical models across populations; 2) Statistical representation for output models in a compact form to capture both local and global shape variation across populations; 3) Shape Analysis using automated 3D landmarking and surface matching. The proposed framework has various applications in clinical, forensic and physical anthropology fields. Extensive research has been published in peer-reviewed image processing, forensic anthropology, physical anthropology, biomedical engineering, and clinical orthopedics conferences and journals. The forthcoming discussion of existing methods for morphometric analysis, including manual and semi-automatic methods, addresses the need for automation of morphometric analysis and statistical atlases. Explanations of these existing methods for the construction of statistical shape models, including benefits and limitations of each method, provide evidence of the necessity for such a novel algorithm. A novel approach was taken to achieve accurate point correspondence in case of irregular and deformed anatomy. This was achieved using a scale space approach to detect prominent scale invariant features. These features were then matched and registered using a novel multi-scale method, utilizing both coordinate data as well as shape descriptors, followed by an overall surface deformation using a new constrained free-form deformation. Applications of output statistical atlases are discussed, including forensic applications for the skull sexing, as well as physical anthropology applications, such as asymmetry in clavicles. Clinical applications in pelvis reconstruction and studying of lumbar kinematics and studying thickness of bone and soft tissue are also discussed

Automatic atlas-based three-label cartilage segmentation from MR knee images

Osteoarthritis (OA) is the most common form of joint disease and often characterized by cartilage changes. Accurate quantitative methods are needed to rapidly screen large image databases to assess changes in cartilage morphology. We therefore propose a new automatic atlas-based cartilage segmentation method for future automatic OA studies

Automatic 3D extraction of pleural plaques and diffuse pleural thickening from lung MDCT images

Pleural plaques (PPs) and diffuse pleural thickening (DPT) are very common asbestos related pleural diseases (ARPD). They are currently identified non-invasively using medical imaging techniques. A fully automatic algorithm for 3D detection of calcified pleura in the diaphragmatic area and thickened pleura on the costal surfaces from multi detector computed tomography (MDCT) images has been developed and tested. The algorithm for detecting diaphragmatic pleura includes estimation of the diaphragm top surface in 3D and identifying those voxels at a certain vertical distance from the estimated diaphragm, and with intensities close to that of bone, as calcified pleura. The algorithm for detecting thickened pleura on the costal surfaces includes: estimation of the pleural costal surface in 3D, estimation of the centrelines of ribs and costal cartilages and the surfaces that they lie on, calculating the mean distance between the two surfaces, and identifying any space between the two surfaces whose distance exceeds the mean distance as thickened pleura. The accuracy and performance of the proposed algorithm was tested on 20 MDCT datasets from patients diagnosed with existing PPs and/or DPT and the results were compared against the ground truth provided by an experienced radiologist. Several metrics were employed and evaluations indicate high performance of both calcified pleura detection in the diaphragmatic area and thickened pleura on the costal surfaces. This work has made significant contributions to both medical image analysis and medicine. For the first time in medical image analysis, the approach uses other stable organs such as the ribs and costal cartilage, besides the lungs themselves, for referencing and landmarking in 3D. It also estimates fat thickness between the rib surface and pleura (which is usually very thin) and excludes it from the detected areas, when identifying the thickened pleura. It also distinguishes the calcified pleura attached to the rib(s), separates them in 3D and detects calcified pleura on the lung diaphragmatic surfaces. The key contribution to medicine is effective detection of pleural thickening of any size and recognition of any changes, however small. This could have a significant impact on managing patient risks

Subject Specific Computational Models of the Knee to Predict Anterior Cruciate Ligament Injury

Knee joint is a complex joint involving multiple interactions between cartilage, bone, muscles, ligaments, tendons and neural control. Anterior Cruciate Ligament (ACL) is one ligament in the knee joint that frequently gets injured during various sports or recreational activities. ACL injuries are common in college level and professional athletes especially in females and the injury rate is growing in epidemic proportions despite significant increase in the research focusing on neuromuscular and proprioceptive training programs. Most ACL injuries lead to surgical reconstruction followed by a lengthy rehabilitation program impacting the health and performance of the athlete. Furthermore, the athlete is still at the risk of early onset of osteoarthritis. Regardless of the gender disparity in the ACL injury rates, a clear understanding of the underlying injury mechanisms is required in order to reduce the incidence of these injuries. Computational modeling is a resourceful and cost effective tool to investigate the biomechanics of the knee. The aim of this study was twofold. The first aim was to develop subject specific computational models of the knee joint and the second aim to gain an improved understanding of the ACL injury mechanisms using the subject specific models. We used a quasi-static, multi-body modeling approach and developed MRI based tibio-femoral computational knee joint models. Experimental joint laxity and combined loading data was obtained using five cadaveric knee specimens and a state-of-the-art robotic system. Ligament zero strain lengths and insertion points were optimized using joint laxity data. Combined loading and ACL strain data were used for model validations. ACL injury simulations were performed using factorial design approach comprising of multiple factors and levels to replicate a large and rich set of loading states. This thesis is an extensive work covering all the details of the ACL injury project explained above and highlighting the importance of 1) computational modeling in inj