Degeneration of the shoulder joint is a frequent problem. There are two main types of shoulder degeneration: Osteoarthritis and cuff tear arthropathy (CTA) which is characterized by a large rotator cuff tear and progressive articular damage. It is largely unknown why only some patients with large rotator cuff tears develop CTA. In this project, we investigated CT data from ‘healthy’ persons and patients with CTA with the help of 3D imaging technology and statistical shape models (SSM). We tried to define a native scapular anatomy that predesignate patients to develop CTA. Statistical shape modeling and reconstruction:
A collection of 110 CT images from patients without glenohumeral arthropathy or large cuff tears was segmented and meshed uniformly to construct a SSM. Point-to-point correspondence between the shapes in the dataset was obtained using non-rigid template registration. Principal component analysis was used to obtain the mean shape and shape variation of the scapula model. Bias towards the template shape was minimized by repeating the non-rigid template registration with the resulting mean shape of the first iteration. Eighty-six CT images from patients with different severities of CTA were analyzed by an experienced shoulder surgeon and classified. CT images were segmented and inspected for signs of glenoid erosion. Remaining healthy parts of the eroded scapulae were partitioned and used as input of the iterative reconstruction algorithm. During an iteration of this algorithm, 30 shape components of the shape model are optimized and the reconstructed shape is aligned with the healthy parts. The algorithm stops when convergence is reached.Background
Methods
Joint laxity assessments have been a valuable resource in order to understand the biomechanics and pathologies of the knee. Clinical laxity tests like the Lachman test, Pivot-shift test and Drawer test are, however, subjective of nature and will often only provide basic information of the joint. Stress radiography is another option for assessing knee laxity; however, this method is also limited in terms of quantifiability and one-dimensionality. This study proposes a novel non-invasive low-dose radiation method to accurately measure knee joint laxity in 3D. A method that combines a force controlled parallel manipulator device, a medical image and a biplanar x-ray system. As proof-of-concept, a cadaveric knee was CT scanned and subsequently mounted at 30 degrees of flexion in the device and placed inside a biplanar x-ray scanner. Biplanar x-rays were obtained for eleven static load cases. The preliminary results from this study display that the device is capable of measuring primary knee laxity kinematics similar to what have been reported in previous studies. Additionally, the results also display that the method is capable of capturing coupled motions like internal/external rotation when anteroposterior loads are applied. We have displayed that the presented method is capable of obtaining knee joint laxity in 3D. The method is combining concepts from robotic arthrometry and stress radiography into one unified solution that potentially enables unprecedented 3D joint laxity measurements non-invasively. The method potentially eliminates limitations present in previous methods and significantly reduces the radiation exposure of the patient compared to conventional stress radiography.
In patients with neural disorders such as cerebral palsy, three-dimensional marker-based motion analysis has evolved to become a well standardized procedure with a large impact on the clinical decision-making process. On the other hand, in knee arthroplasty research, motion analysis has been little used as a standard tool for objective evaluation of knee joint function. Furthermore, in the available literature, applied methodologies are diverse, resulting in inconsistent findings [1]. Therefore we developed and evaluated a new motion analysis framework to enable standardized quantitative assessment of knee joint function. The proposed framework integrates a custom-defined motion analysis protocol with associated reference database and a standardized post-processing step including statistical analysis. Kinematics are collected using a custom-made marker set defined by merging two existing protocols and combine them with a knee alignment device. Following a standing trial, a star-arc hip motion pattern and a set of knee flexion/extension cycles allowing functional, subject-specific calibration of the underlying kinematic model, marker trajectories are acquired for three trials of a set of twelve motor tasks: walking, walking with crossover turn, walking with sidestep turn, stair ascent, stair descent, stair descent with crossover turn, stair descent with sidestep turn, trunk rotations, chair rise, mild squat, deep squat and lunge. This specific set of motor tasks was selected to cover as much as possible common daily life activities. Furthermore, some of these induce greater motion at the knee joint, thus improving the measurement-to-error ratio. Kinetics are acquired by integrating two forceplates in the walkway. Bilateral muscle activity of 8 major muscles is monitored with a 16 channel wireless electromyography (EMG) system. Finally, custom-built software with an associated graphical user interface was created for automated and flexible analysis of gait lab data, including repeatability analysis, analysis of specific kinematic, kinetic and spatiotemporal parameters and statistical comparisons.INTRODUCTION
MATERIALS AND METHODS
