Appropriate transverse rotation of the tibial component is critical to achieving a balance of tibial coverage and proper tibio-femoral kinematics in total knee replacement (TKR), yet no consensus exists on the best anatomic references to determine rotation. Historically, surgeons have aligned the tibial component to the medial third of the tibial tubercle1, but recent literature suggests this may externally rotate the tibial component relative to the femoral epicondylar axis (ECA) and that the medial border of the tubercle is more reliable2. Meanwhile, some TKR components are designed with asymmetry of the tibial tray assuming that maximizing component coverage of the resected tibia will result in proper alignment. The purpose of this study was to determine how different rotational landmarks and natural variation in osteoarthritic patient anatomy may affect asymmetry of the resected tibial plateau. Pre-operative computed-tomography scans were collected from 14,791 TKR patients. The tibia and femur were segmented and anatomic landmarks identified: tibial mechanical axis, medial third and medial border of the tibial tubercle, PCL attachment site, and the surgical ECA of the femur. Virtual surgery was performed with an 8-mm resection (referencing the high side) made perpendicular to the tibial mechanical axis in the frontal plane, with 3° posterior slope, and transversely aligned with three different landmarks: the ECA, the medial border, and medial third of the tubercle. In each of these rotational alignments, the relative asymmetry of the medial and lateral plateaus was calculated (Medial AP/Lateral AP) (Fig. 1).Introduction:
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Adequate coverage of the resected tibial plateau with the tibial tray is necessary to reduce the theoretical risk of tibial subsidence after primary total knee arthroplasty (TKA). Maximizing tibial coverage is balanced against avoiding excessive overhang of the tray causing soft tissue irritation, and establishing proper tray alignment improving implant longevity and patella function1. Implant design factors, including the number of tray sizes, tray shape, and tray asymmetry influence the ability to cover the tibial plateau2. Furthermore, rotating platform (RP) tray designs decouple restoring proper tibial rotation from maximizing tibial coverage, which may enhance the ability to maximize coverage. The purpose of the current study was to assess the ability of five modern tray designs (Fig. 1), including symmetric, asymmetric, fixed-bearing, and RP designs, to maximize coverage of the tibial plateau across a large patient population. Lower limb computed-tomography scans were collected from 14,791 TKA patients and the tibia was segmented. Virtual surgery was performed with an 8-mm tibial resection (referencing the high side) made perpendicular to the tibial mechanical axis in the frontal plane, with 3° posterior slope, and aligned transversely to the medial third of the tibial tubercle. An automated algorithm placed the largest possible tray on the plateau, optimizing the ML and AP placement (and I-E rotation for the RP tray), to minimize overhang. The largest sized tray that fit the plateau with less than 2-mm of tray overhang was identified for each of the five implant systems. The surface area of the tibial tray was divided by the area of the resected plateau and the percentage of patients with greater than 85% plateau coverage was calculated.Introduction:
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The low-cost, no-harm conditions associated with vibroarthography, the study of listening to the vibrations and sound patterns of interaction at the human joints, has made this method a promising tool for diagnosing joint pathologies. This current study focuses on the knee joint and aims to synchronize computational models with vibroarthographic signals via a comprehensive graphical user interface (GUI) to find correlations between kinematics, vibration signals, and joint pathologies. This GUI is the first of its kind to synchronize computational models with vibroarthographic signals and gives researchers a new advantage of analyzing kinematics, vibration signals, and pathologies simultaneously in an easy-to-use software environment. The GUI (Figure 1) has the option to view live or previously captured fluoroscopic videos, the corresponding computational model, and/or the pre- or post-processed vibration signals. Having more than one signal axes available allows for comparison of different filtering techniques to the same signal, or comparison of signals coming from different sensor placements (ex: medial vs. lateral femoral condyle). Using computational models derived using fluoroscopic data synchronized with the vibration signals, the areas of contact between articulating surfaces can be mapped for the in vivo signal (figure 2). This new method gives the opportunity to find correlations between the different sensor signals and contact maps with the diagnosis and cartilage degeneration map, provided by a surgeon, during arthroscopy or TKA implantation (figure 3).Introduction
Methods
In-vivo data pertaining to the actual cam-post engagement mechanism in PS and Bi-Cruciate Stabilized (BCS) knees is still very limited. Therefore, the objective of this study was to determine the cam-post mechanism interaction under in-vivo, weight-bearing conditions for subjects implanted with either a Rotating Platform (RP) PS TKA, a Fixed Bearing (FB) PS TKA or a FB BCS TKA. In-vivo, weight-bearing, 3D knee kinematics were determined for eight subjects (9 knees) having a RP-PS TKA (DePuy Inc.), four subjects (4 knees) with FB-PS TKA (Zimmer Inc.), and eight subjects (10 knees) having BCS TKA (Smith&Nephew Inc.), while performing a deep knee bend. 3D-kinematics was recreated from fluoroscopic images using a previously published 3D-to-2D registration technique (Figure 1). Images from full extension to maximum flexion were analyzed at 10° intervals. Once the 3D-kinematics of implant components was recreated, the cam-post mechanism was scrutinized. The distance between the interacting surfaces was monitored throughout flexion and the predicted contact map was calculated.INTRODUCTION
METHODS
Posterior stabilized (PS) total knee arthroplasty (TKA) provides posterior stability with the use of a cam-post mechanism which performs the function of the posterior cruciate ligament. The tibial post engages with the femoral cam, prevents the femur from sliding anteriorly and provides the posterior femoral rollback necessary for achieving deep flexion of the knee. However, these designs do not substitute the resection of the anterior cruciate ligament. In order to overcome this deficit, other TKA designs have been recently introduced to provide dual support, with the help of dual cam-post engagement mechanism. Various studies conducted on the PS TKA have suggested that the cam-post mechanism does not engage as designed, resulting in tibial post wear and increased stresses resulting in backside wear of the polyethylene insert component. Also, the in vivo data pertaining to the actual cam-post engagement mechanism in bi-cruciate stabilized knees is still very limited. Therefore, the objective of this study was to determine the cam-post mechanism interaction under in vivo, weight bearing conditions for subjects implanted with either a Rotating Platform (RP) Posterior Stabilized (PS) TKA or a bi-cruciate stabilizing TKA (BCS). In-vivo, weight-bearing, 3D knee kinematics were determined for eight subjects (9 knees) having a RP-PS TKA (DePuy Inc.) and eight subjects (10 knees) having BCS TKA (Smith&Nephew Inc.), while performing a deep knee bend. 3D kinematics was recreated from the fluoroscopic images using a previously published 3D-to-2D registration technique (Figure 1). Images from full extension to maximum flexion were analyzed at 10° intervals. Once the 3D kinematics of all implant components was recreated, the cam-post mechanism was scrutinized. The distance between the interacting surfaces was monitored throughout the flexion and the predicted contact map was calculated. The instances, when the minimum distance between the cam and post surfaces dropped to zero was considered to indicate the engagement of the mechanism. This analysis was carried out for both the, anterior and posterior cam-post engagement sites.INTRODUCTION
METHODS
Knee simulators are being used to evaluate wear. The current international standards have been developed from clinical investigations of the normal knee [1, 2] or from a single TKA patient [3, 4]. However, the forces and motions in a TKA patient differ from a normal knee and, furthermore, the resulting kinematic outcomes after TKA will depend on the design of the device [5]. Consequently, these standard tests may not recreate in-vivo conditions; therefore, the goal of this study was to perform a novel wear simulation using design-specific inputs that have been derived from fluoroscopic images of a deep knee bend. A wear simulation was developed using fluoroscopic data from a pool of eighteen TKA patients performing a deep knee bend. All patients had a Sigma CR Fixed Bearing implant (DePuy) and were well functioning (Knee Society Score > 90). A single patient was selected that represented the typical motions, which was characterized by early rollback followed by anterior motion with an overall modest internal tibial rotation (Figure 1). The relative motion between the femoral and tibial components was transformed to match the coordinate system of an AMTI knee wear simulator [6] and a compressive load input was derived using inverse dynamics [7]. The resulting force and motions (Figure 2) were then applied in a wear simulation with 5 MRad crosslinked and remelted polyethylene for 3 Mcyc at 1 Hz. Components were carefully positioned and each joint (n=3) was tested in 25% bovine calf serum (Hyclone Laboratories), which was recirculated at 37±2°C [3]. Serum was supplemented with sodium azide and EDTA. Wear was quantified gravimetrically every 0.5 Mcyc using a digital balance (XP250, Mettler-Toledo) with load soak compensation.INTRODUCTION
METHODS
Anterior knee pain is one of the most frequently reported musculoskeletal complaints in all age groups. However, patient's complaints are often nonspecific, leading to difficulty in properly diagnosing the condition. One of the causes of pain is the degeneration of the articular cartilage. As the cartilage deteriorates, its ability to distribute the joint reaction forces decreases and the stresses may exceed the pain threshold. Unfortunately, the assessment of the cartilage condition is often limited to a detailed interview with the patient, careful physical examination and x-ray imaging. The X-ray screening may reveal bone degeneration, but does not carry sufficient information of the soft tissues' conditions. More advanced imaging tools such as MRI or CT are available, but these are expensive, time consuming and are only suitable for detection of advanced arthritis. Arthroscopic surgery is often the only reliable option, however due to its semi-invasive nature, it cannot be considered as a practical diagnostic tool. However, as the articular cartilage degenerates, the surfaces become rougher, they produce higher vibrations than smooth surfaces due to higher friction during the interaction. Therefore, it was proposed to detect vibrations non-invasively using accelerometers, and evaluate the signals for their potential diagnostic applications. Vibration data was collected for 75 subjects; 23 healthy and 52 subjects suffering from knee arthritis. The study was approved by the IRB and an Informed Consent was obtained prior to data collection. Five accelerometers were attached to skin around the knee joint (at the patella, medial and lateral femoral condyles, tibial tuberosity and medial tibial plateau). Each subject performed 5 activities; (1) flexion-extension, (2) deep knee bend, (3) chair rising, (4) stair climbing and (5) stair descent. The vibration and motion components of the signals were separated by a high pass filter. Next, 33 parameters of the signals were calculated and evaluated for their discrimination effectiveness (Figure 1). Finally the pattern recognition method based on Baysian classification theorem was used for classify each signal to either healthy or arthritic group, assuming equal prior probabilities. The variance and mean of the vibration signals were significantly higher in the arthritic group (p=2.8e-7 and p=3.7e-14, respectively), which confirms the general hypothesis that the vibration magnitudes increase as the cartilage degenerates. Other signal features providing good discrimination included the 99th quantile, the integral of the vibration signal envelope, and the product of the signal envelope and the activity duration. The pattern classification yielded excellent results with the success rate of up to 92.2% using only 2 features, up to 94.8% using 3 (Figure 2), and 96.1% using 4 features. The current study proved that the vibrations can be studied non-invasively using a low-cost technology. The results confirmed the hypothesis that the degeneration of the cartilage increases the vibration of the articulating bones. The classification rate obtained in the study is very encouraging, providing over 96% accuracy. The presented technology has certainly a potential of being used as an additional screening methodology enhancing the assessment of the articular cartilage condition.
