Assessment of the humeral head translation with respect to the glenoid joint, termed humeral head migration (HHM), is crucial in total shoulder arthroplasty pre-operative planning. Its assessment informs current classification systems for shoulder osteoarthritis as well as the evaluation of surgical correction. In current clinical practice, HHM assessment relies on computed-tomography (CT) imaging. However, the associated supine position might undermine its functional relevance as it does not reflect the weight-bearing condition with active muscle engagement associated with the upright standing position of most daily activities. Therefore, we assessed to what extent HHM in a supine position is associated with HHM in a range of functional arm positions. 26 shoulder osteoarthritis patients and 12 healthy volunteers were recruited. 3D shapes of the humerus and scapula were reconstructed from their respective CT scans using an image processing softwareIntroduction
Method
A septic revision of an artificial joint is routinely split up in a so-called Nine fresh frozen cadaveric hips were used and primary THA was undertaken via a direct anterior approach. Before implantation of the components varying amounts of fluorescent powder (GloGerm) were deposited, simulating bacterial infection. Second, a one-step exchange was performed via a posterolateral approach. After implant removal, debridement, and lavage, randomization determined which clean phase protocol was followed, i.e. no, some or full additional measures. Finally, the new prosthesis was re-implanted (fig. 1). In order to determine the effect of different clean phase protocols on contamination of the sterile field standardized UV light-enhanced photographs were obtained of 1) the gloves, 2) the instrument table, 3) the drapes, and 4) the wound and these were ranked on cleanliness by a blind panel of hip surgeons. In order to determine whether or not it is possible to re-implant the prosthesis completely clean, the implant was taken out again at the end of the one-step exchange and inspected for contamination under UV light.Aim
Method
During shoulder arthroplasty the native functionality of the diseased shoulder joint is restored, this functionality is strongly dependent upon the native anatomy of the pre-diseased shoulder joint. Therefore, surgeons often use the healthy contralateral scapula to plan the surgery, however in bilateral diseases such as osteoarthritis this is not always feasible. Virtual reconstructions are then used to reconstruct the pre-diseased anatomy and plan surgery or subject-specific implants. In this project, we develop and validate a statistical shape modeling method to reconstruct the pre-diseased anatomy of eroded scapulae with the aim to investigate the existence of predisposing anatomy for certain shoulder conditions. The training dataset for the statistical shape model consisted of 110 CT images from patients without observable scapulae pathologies as judged by an experienced shoulder surgeon. 3D scapulae models were constructed from the segmented images. An open-source non-rigid B-spline-based registration algorithm was used to obtain point-to-point correspondences between the models. The statistical shape model was then constructed from the dataset using principle component analysis. The cross-validation was performed similarly to the procedure described by Plessers et al. Virtual defects were created on each of the training set models, which closely resemble the morphology of glenoid defects according to the Wallace classification method. The statistical shape model was reconstructed using the leave-one-out method, so the corresponding training set model is no longer incorporated in the shape model. Scapula reconstruction was performed using a Monte Carlo Markov chain algorithm, random walk proposals included both shape and pose parameters, the closest fitting proposal was selected for the virtual reconstruction. Automatic 3D measurements were performed on both the training and reconstructed 3D models, including glenoid version, critical shoulder angle, glenoid offset and glenoid center position. The root-mean-square error between the measurements of the training data and reconstructed models was calculated for the different severities of glenoid defects. For the least severe defect, the mean error on the inclination, version and critical shoulder angle (°) was 2.22 (± 1.60 SD), 2.59 (± 1.86 SD) and 1.92 (± 1.44 SD) respectively. The reconstructed models predicted the native glenoid offset and centre position (mm) an accuracy of 0.87 (± 0.96 SD) and 0.88 (± 0.57 SD) respectively. The overall reconstruction error was 0.71 mm for the reconstructed part. For larger defects each error measurement increased significantly. A virtual reconstruction methodology was developed which can predict glenoid parameters with high accuracy. This tool will be used in the planning of shoulder surgeries and investigation of predisposing scapular morphologies.
Pre-operative 3D glenoid planning improves component placement in terms of version, inclination, offset and orientation. Version and inclination measurements require the position of the inferior angle. As a consequence, current planning tools require a 3D model of the full scapula to accurately determine the glenoid parameters. Statistical shape models (SSMs) can be used to reconstruct the missing anatomy of bones. Therefore, the objective of this study is to develop and validate an SSM for the reconstruction of the inferior scapula, hereby reducing the irradiation exposure for patients. The training dataset for the statistical shape consisted of 110 CT images from patients without observable scapulae pathologies as judged by an experienced shoulder surgeon. 3D scapulae models were constructed from the segmented images. An open-source non-rigid B-spline-based registration algorithm was used to obtain point-to-point correspondences between the models. A statistical shape model was then constructed from the dataset using principal component analysis. Leave-one-out cross-validation was performed to evaluate the accuracy of the predicted glenoid parameters from virtual partial scans. Five types of virtual partial scans were created on each of the training set models, where an increasing amount of scapular body was removed to mimic a partial CT scan. The statistical shape model was reconstructed using the leave-one-out method, so the corresponding training set model is no longer incorporated in the shape model. Reconstruction was performed using a Monte Carlo Markov chain algorithm, random walk proposals included both shape and pose parameters, the closest fitting proposal was selected for the virtual reconstruction. Automatic 3D measurements were performed on both the training and reconstructed 3D models, including glenoid version, inclination, glenoid centre point position and glenoid offset. In terms of inclination and version we found a mean absolute difference between the complete model and the different virtual partial scan models of 0.5° (SD 0.4°). The maximum difference between models was 3° for inclination and 2° for version. For offset and centre point position the mean absolute difference was 0 mm with an absolute maximum of 1 mm. The magnitude of the mean and maximum differences for all anatomic measurements between the partial scan and complete models is smaller than the current surgical accuracy. Considering these findings, we believe a SSM based reconstruction technique can be used to accurately reconstruct the glenoid parameters from partial CT scans.
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
In this study, excellent positioning of custom-made glenoid components was achieved using patient-specific guides. Achieving the preoperatively planned orientation of the component improved significantly and more screws were located inside the scapular bone compared to implantations without such guide. Today's techniques for total or reverse shoulder arthroplasty are limited when dealing with severe glenoid defects. The available procedures, for instance the use of bone allografts in combination with available standard implants, are technically difficult and tend to give uncertain outcomes (Hill et al. 2001; Elhassan et al. 2008; Sears et al. 2012). A durable fixation between bone and implant with optimal fit and implant positioning needs to be achieved. Custom-made defect-filling glenoid components are a new treatment option for severe glenoid defects. Despite that the patient-specific implants are uniquely designed to fit the patient's bone, it can be difficult to achieve the preoperatively planned position of the component, resulting in less optimal screw fixation. We hypothesised that the use of a patient-specific guide would improve implant and screw positioning. The aim of this study was to evaluate the added value of a newly developed patient-specific guide for implant and screw positioning, by comparing glenoid implantations with and without such guide.Summary Statement
Introduction
Glenosphere disengagement can be a potential serious default in reverse shoulder arthroplasty [1]. To ensure a good clinical outcome, it is important for the surgeon to obtain an optimal assembly of the glenosphere - base plate system during surgery. However interpositioning of material particles (bone, soft tissue) between the contact surface of the glenosphere and the base plate and/or a misalignment of the glenosphere relative to the base plate can result in a suboptimal assembly of the glenosphere – base plate system [2]. This misalignment is typically caused by unwanted contact between the glenosphere and the scapula due to inadequate reaming. Both defects prevent the Morse taper from fully engaging, leading to a system configuration for which the assembly was not designed to be loaded in vivo. This study quantifies the influence these defects have on the relative movement between the glenosphere and metaglene. A biaxial test setup [Fig. 1] was developed to mechanically load the glenoidal assembly (base plate + glenosphere) of 5 Depuy® Delta Xtend 38 prostheses. The setup allows applying a cyclic loading pattern to the glenoidal component with a constant actuator load of 750 N. Each of the 5 samples was tested for 5000 cycles on 3 defects: an interpositioning of 150 µm thick (0.48 mm3) and two local underreaming defects, pushing one side of the glenosphere up 0.5 mm and 1 mm respectively, hence causing a misalignment. The relative movement was recorded using 4 Linear Variable Differential Transducers (LVDTs). The cycling frequency is 1 Hz.INTRODUCTION
MATERIALS AND METHODS
