Introduction. Real-time tracking of surgical tools has applications for assessment of surgical skill and OR workflow. Accordingly, efforts have been devoted to the development of low-cost systems that track the location of surgical tools in real-time without significant augmentation to the tools themselves. Deep learning methodologies have recently shown success in a multitude of computer vision tasks, including object detection, and thus show potential for the application of surgical tool tracking. The objective of the current study was to develop and evaluate a deep learning-based computer vision system using a single camera for the detection and pose estimation of multiple surgical tools routinely used in both knee and hip arthroplasty. Methods. A computer vision system was developed for the detection and 6-DoF pose estimation of two surgical tools (mallet and broach handle) using only RGB camera frames. The deep learning approach consisted of a single convolutional neural network (CNN) for object detection and semantic key point prediction, as well as an optimization step to place prior known geometries into the local camera coordinate system. Inference on a camera frame with size of 256-by-352 took 0.3 seconds. The object detection component of the system was evaluated on a manually-annotated stream of video frames. The accuracy of the system was evaluated by comparing pose (position and orientation) estimation of a tool with the ground truth pose as determined using three retroreflective markers placed on each tool and a 14 camera motion capture system (Vicon, Centennial CO). Markers placed on the tool were transformed into the local camera coordinate system and compared to estimated location. Results. Detection accuracy determined from frame-wise confusion matrices was 82% and 95% for the mallet and broach handle, respectively. Object detection and key point predictions were qualitatively assessed. Marker error resulting from pose estimation was as little as 1.3 cm for the evaluation scenes. Pose estimation of the tools from each evaluation scene was also qualitatively assessed. Discussion. The proposed computer vision system combined CNNs with optimization to estimate the 6-DoF pose of surgical tools from only RGB camera frames. The system's object detection component performed on par with state-of-the-art object detection literature and the pose estimation error was efficiently computed from CNN predictions. The current system has implications for surgical skill assessment and operations based research to improve operating room efficiency. However, future development is needed to make improvements to the object detection and key point prediction components of the system, in order to minimize potential pose error. Nominal marker errors of 1.3 cm demonstrate the potential of this system to yield accurate pose estimates of surgical tools. For any figures or tables, please contact authors directly

A method is proposed to assess risk parameters of anterior cruciate ligament (ACL) injury using human pose estimation (HPE) and a single stereo depth camera. Detectron2 is used to identify key points of a subject performing a single leg jump test. This allows dynamic pivot of the knee to be assessed during landing using four risk parameters: knee valgus, knee translation in the coronal plane, pelvic tilt, and head-ankle alignment (body sway). Results show the model has an accuracy of 7° in angular measurements and 38 mm in linear measurements. Compared to previous studies, which only consider front-on analysis, this method has partially reduced accuracy in linear measurements and half the accuracy in angular measurements. Despite this, coupling information from multiple risk parameters reduces the accuracy required on any one parameter and the use of a single depth camera enables reliable analysis at a subject orientation of ±45° relative to the camera. These factors create a novel solution, proposing the ability for broad evaluation of ACL risk parameters in environments outside a testing laboratory, which has not been done before

Purpose. For 3D kinematic analysis of total knee arthroplasty (TKA), 2D/3D registration techniques which use X-ray fluoroscopic images and computer-aided design model of the knee implants, have been applied to clinical cases. These techniques are highly valuable for dynamic 3D kinematic analysis, but have needed time-consuming and labor-intensive manual operations in some process. In previous study, we reported a robust method to reduce manual operations to remove spurious edges and noises in edge detection process of X-ray images. In this study, we address another manual operations problem occurred when setting initial pose of TKA implants model for 2D/3D registration. To set appropriate initial pose of the model with manual operations for each X-ray image is important to obtain the good registration results. However, the number of X-ray images for a knee performance is very large, and thus to set initial pose with manual operations is very time-consuming and a problem for practical clinical applications. Therefore, this study proposes an initial pose estimation method for automated 3D kinematic analysis of TKA. Methods. 3D pose of an implant model is estimated using a 2D/3D registration technique based on a robust feature-based algorithm. To reduce labor-intensive manual operations of initial pose setting for large number of X-ray images, we utilize an interpolation technique with an approximate function. First, for some X-ray images (key frames), initial poses are manually adjusted to be as close as possible, and 3D poses of the model are accurately estimated for each key frame. These key frames were appropriately selected from the 2D feature point of knee motion in the X-ray images. Next, the 3D pose data estimated for each key frame are interpolated with an approximate function. In this study, we employed a multilevel B-spline function. Thus, we semi-automatically estimate the initial 3D pose of the implant model in X-ray images except for key frames. Fig. 1 shows the algorithm of initial pose estimation, and Fig. 2 shows the scheme of the data interpolation with an approximate function. Experimental results. To validate the feasibility of the proposed initial pose estimation method, experiments using X-ray fluoroscopic images of 8 TKA patients during knee motions were performed. For the experiments, we prepared two sorts of contour images, and applied the proposed method to the one image contained spurious edges and noises. The other image which spurious edges and noises didn't exist was used for determination of correct poses (reference data) using 2D/3D registration. In order to assess the performance of the proposed method, automation rate was calculated, and the rate was defined as the X-ray frame number of satisfying clinical required accuracy (error within 1 mm, 1 degree) relative to all X-ray frame number. As results of the experiments, the automation rate of the femoral and tibial component were about 79 % and 73 %, respectively. Conclusions. This study presented an initial pose estimation method for automated 3D kinematic analysis of TKA using X-ray fluoroscopic images. The method without labor-intensive operations is thought to be very useful for practical clinical applications

Purpose. To achieve 3D kinematic analysis of total knee arthroplasty (TKA), 2D/3D registration techniques, which use X-ray fluoroscopic images and computer aided design model of the knee implants, have been applied to clinical cases. However, most conventional methods have needed time-consuming and labor-intensive manual operations in some process. In particular, for the 3D pose estimation of tibial component model from X-ray images, these manual operations were carefully performed because the pose estimation of symmetrical tibial component get severe local minima rather than that of unsymmetrical femoral component. In this study, therefore, we propose an automated 3D kinematic estimation method of tibial component based on statistical motion model, which is created from previous analyzed 3D kinematic data of TKA. Methods. The used 2D/3D registration technique is based on a robust feature-based (contour-based) algorithm. In our proposed method, a statistical motion model which represents average and variability of joint motion is incorporated into the robust feature-based algorithm, particularly for the pose estimation of tibial component. The statistical motion model is created from previous a lot of analyzed 3D kinematic data of TKA. In this study, a statistical motion model for relative knee motion of the tibial component with respect to the femoral component was created and utilized. Fig. 1 shows each relative knee motion model for six degree of freedom (three translations and three rotations parameter). Thus, after the pose estimation of the femoral component model, 3D pose of the tibial component model is determined by maximum a posteriori (MAP) estimation using the new cost function introduced the statistical motion model. Experimental results. To validate the feasibility and effectiveness of 3D pose estimation for the tibial component using the proposed method, experiments using X-ray fluoroscopic images of 20 TKA patients under the squatting knee motion were performed. For the creation of correct pose (reference data) and the statistical motion model, we used the 3D pose data which were got by carefully applying previous method to the contour images which spurious edges and noises were removed manually. In order to ensure the validity for the statistical motion model of the proposed method, leave-one-out cross validation method was applied. In the 3D pose estimation of tibial component model, for the only first frame, initial guess pose of the model was manually given. For all images except for the first frame, the 3D pose of the model was automatically estimated without manual initial guess pose of the model. To assess the automation performance, the automation rate was calculated, and the rate was defined as the X-ray frame number of satisfying clinical required accuracy (error within 1mm, 1 degree) relative to all X-ray frame number. As results of the experiments, 3D pose of the tibial component model for all X-ray images except for the first frame was full-automatically stably-estimated, and the automation rate was 80.1 %. Conclusions. The proposed method by MAP estimation introduced the statistical motion model was successfully performed, and did not need labor-intensive manual operations for 3D pose estimation of tibial component. For any figures or tables, please contact authors directly (see Info & Metrics tab above).

Purpose:. To materialize 3D kinematic analysis of total knee arthroplasty (TKA), 2D/3D registration techniques, which use X-ray fluoroscopic images and the knee implants CAD, have been applied to clinical cases. However, most conventional methods have needed time-consuming and labor-intensive manual operations in some process. In previous study, we addressed a manual operations problem when setting initial pose of implants model for 2D/3D registration, and reported a semi-automated initial pose estimation method based on an interpolation technique. However, this method still required appropriate initial pose estimation of the model with manual operations for some X-ray images (key frames). Additionally, in the situation like fast knee motion and use of low frame rate, good registration results were not obtained because of the large displacement between each frame silhouette. To overcome these problems, this study proposes an improved semi-automated 3D kinematic estimation method. Methods:. Our 2D/3D registration technique is based on a robust feature-based algorithm. In improved initial pose estimation method, for the only first frame, the initial pose is manually adjusted as close as possible. That is, we automatically estimate appropriate initial pose of the model for X-ray images except for the first frame. To automatically estimate the initial pose of the model, we utilize a transformation with feature points extracted from the previous and next frames. A transform matrix which has three DOF (translations parallel to the image, and a rotation perpendicular to the image) is calculated by registration of corresponding feature points between the previous and next frame extracted with SURF algorithm. While, the corresponding point sets extracted by SURF sometimes include some error sets. Therefore, in this study, LmedS method was employed to detect the error corresponding sets and calculate a transform matrix accurately. In Fig. 1(a) and (b), the orange square shows the region defined with the boundary box of the model, and some lines show the combined corresponding point sets. The blue lines are correct corresponding point sets, and the pink lines are error corresponding point sets detected with LmedS method. Finally, 3D pose of the model estimated in previous frame is transformed with accurately calculated transform matrix, and the transformed pose is used as an initial 3D pose of the model in next frame. Experimental results:. To validate the feasibility of the improved semi-automated 3D kinematic estimation method, experiments using X-ray fluoroscopic images of 4 TKA patients during knee motions were performed. In order to assess the performance of the improved method, automation rate was calculated, and the rate was defined as the X-ray frame number of satisfying clinical required accuracy (error within 1 mm, 1 degree) relative to all X-ray frame number. As results of the experiments, 3D pose of the model for all X-ray images except for the first frame is automatically stably-estimated, the automation rate of the femoral and tibial component were 83.7% and 73.5%, respectively. Conclusions:. The present method doesn't need labor-intensive manual operations for 3D kinematic estimation of TKA, and is thought to be very helpful for practical clinical applications

Purpose. To achieve 3D kinematic analysis of total knee arthroplasty (TKA), 2D/3D registration techniques, which use X-ray fluoroscopic images and computer-aided design model of the knee implants, have been applied to clinical cases. In previous feature-based registration methods, only edge contours originated from knee implants are assumed to be extracted from X-ray images before 2D/3D registration. Due to the influence of bone and bone-cement close to knee implants, however, edge detection methods extract unwanted spurious edges and noises in clinical images. Thus, time-consuming and labor-intensive manual operations are often necessary to remove the unwanted edges. It has been a serious problem for clinical applications, and there is a strong demand for development of improved method. The purpose of this study was to develop a pose estimation method to perform accurate 2D/3D registration even if spurious edges and noises exist in knee images. Methods. Our 2D/3D registration technique is based on a feature-based algorithm, and contour points from X-ray images are extracted by Gaussian Laplacian filter and zero crossing methods. The basic principle of the algorithm is that the 3D pose of a model can be determined by projecting rays from contour points in an image back to the X-ray focus and noting that all of these rays are tangential to the model surface. Therefore, 3D poses are estimated by minimizing the sum of Euclidean distances between all projected rays and the model surface. Additionally, we introduce robust statistics into the 3D pose estimation method to perform accurate 2D/3D registration even if spurious edges and noises exist in knee images. The robust estimation method employs weight functions to reduce the influence of spurious edges and noises. The weight functions are defined for each contour point, and optimization is performed after the weight functions are multiplied to a cost function. Experimental results. The accuracy and stability validation were performed using in vivo images. The effects of robust estimation were evaluated by comparison with non-robust estimation. One image contained spurious edges and noises, and the other image didn't (they were erased manually). We applied robust and non-robust methods to each image (300 frames). As correct poses, we used the poses which were got by applying previous method to the contour images which spurious edges and noises didn't exist. The root mean square errors (RMSE) and success rate were calculated, and the success rate was defined as the rate of satisfying clinical required accuracy (error is less than 1mm, 1 degree). As results of the experiments, when non-robust method was applied to contour images in which spurious edges and noises exist, RMSE was too large and success rate was 0 %. However, when robust method was applied to the same images, RMSE was less than 1 mm, 1 degree, and the success rate was about 60 percent. Fig. 1 shows typical result of the experiment. Conclusions. We have developed a robust 3D kinematic estimation method of TKA from X-ray images, and the method was found to be helpful for analyzing TKA kinematics without labor-intensive operations

Despite of the high success of TKA, 20% of recipients remain dissatisfied with their surgery. There is an increasing discordance in the literature on what is an optimal goal for component alignment. Furthermore, the unique patient specific anatomical characteristics will also play a role. The dynamic characteristic of a TKR is a product of the complex interaction between a patient's individual anatomical characteristics and the specific alignment of the components in that patient knee joint. These interactions can be better understood with computational models. Our objective was to characterise ligament characteristics by measuring knee joint laxity with functional radiograph and with the aid of a computational model and an optimisation study to estimate the subject specific free length of the ligaments. Pre-operative CT and functional radiographs, varus and valgus stressed X-rays assessing the collateral ligaments, were captured for 10 patients. CT scan was segmented and 3D–2D pose estimation was performed against the radiographs. Patient specific tibio-femoral joint computational model was created. The model was virtually positioned to the functional radiograph positions to simulate the boundary conditions when the knee is stressed. The model was simulated to achieve static equilibrium. Optimisation was done on ligament free length and a scaling coefficient, flexion factor, to consider the ligaments wrapping behaviour. Our findings show the generic values for reference strain differ significantly from reference strains calculated from the optimised ligament parameters, up to 35% as percentage strain. There was also a wide variation in the reference strain values between subjects and ligaments, with a range of 37% strain between subjects. Additionally, the knee laxity recorded clinically shows a large variation between patients and it appears to be divorced from coronal alignment measured in CT. This suggests the ligaments characteristics vary widely between subjects and non-functional imaging is insufficient to determine its characteristics. These large variations necessitate a subject-specific approach when creating knee computational models and functional radiographs may be a viable method to characterise patient specific ligaments

Despite being demonstrably better than conventional surgical techniques with regards to implant alignment and outlier reduction, computer navigation systems have not faced widespread adoption in surgical operating rooms. We believe that one of the reasons for the low uptake stems from the bulky design of the optical tracker assemblies. These trackers must be rigidly fixed to a patient's bone and they occupy a significant portion of the surgical workspace, which makes them difficult to use. In this study we introduce the design for a new optical tracker system, and subsequently we evaluate the tracker's performance. The novel tracker consists of a set of low-profile flexible pins that can be placed into a rigid body and individually deflect without greatly affecting the pose estimation. By relying on a pin's stiff axial direction while neglecting lateral deviations, we can gain sufficient constraint over the underlying body. We used an unscented Kalman filter based algorithm as a recursive body pose estimator that can account for relative marker displacements. We assessed our tracker's performance through a series of simulations and experiments inspired by a total knee arthroplasty. We found that the flexible tracker performs comparably to conventional trackers with regards to accuracy and precision, with tracking errors under 0.3mm for typical operating conditions. The tracking error remained below 0.5mm during pin deflections of up to 40mm. Our algorithm ran at computation speeds greater than real-time at 30Hz which means that it would be suitable for use in real-time applications. We conclude that this flexible pin concept provides sufficient accuracy to be used as a replacement for rigid trackers in applications where its lower profile, its reduced invasiveness and its robustness to deflection are desirable characteristics

To achieve 3D kinematic analysis of total knee arthroplasty (TKA), 2D/3D registration techniques which use X-ray fluoroscopic images and computer-aided design (CAD) model of the knee implants, have been applied to clinical cases. These techniques are highly valuable for dynamic 3D kinematic measurement of TKA implants, but have needed time-consuming and labor-intensive manual operations in some process. To overcome a manual operations problem of initial pose estimation for 2D/3D registration, this study proposes an improvement method for semi-automated 3D kinematic measurement of TKA using X-ray fluoroscopic images. To automatically estimate the initial pose of the implant CAD model, we utilise a transformation with feature points extracted from the previous and next frames. A transform matrix which has three degree of freedom (translations parallel to the image, and a rotation perpendicular to the image) is calculated by registration of corresponding feature points between the previous and next frame extracted with speeded up robust features (SURF) algorithm. While, the corresponding point sets extracted by SURF sometimes include some error sets. Therefore, in this study, least median of squares method is employed to detect the error corresponding sets and calculate a transform matrix accurately. Finally, the 3D pose of the model estimated (by the 2D/3D registration) in previous frame is transformed with the accurately calculated transform matrix, and the transformed pose is used as an initial 3D pose of the model (for the 2D/3D registration) in next frame. To validate the feasibility of the improved semi-automated 3D kinematic measurement method, experiments using X-ray fluoroscopic images of four TKA patients during knee motions were performed. In order to assess the performance of the improved method, automation rate was calculated, and the rate was defined as the X-ray frame number of satisfying clinical required accuracy (error within 1mm, 1 degree) relative to all X-ray frame number. As results of the experiments, 3D pose of the model for all X-ray images except for the first frame is automatically stably-estimated, the automation rate of the femoral and tibial component were 83.7 % and 73.5 %, respectively. The improved method doesn't need labor-intensive manual operations for 3D kinematic measurement of TKA, and is thought to be very helpful for actual clinical practice

Introduction. Surgical planning for Patient Specific Instrumentation (PSI) in total knee arthroplasty (TKA) is based on static non-functional imaging (CT or MRI). Component alignment is determined prior to any assessment of clinical soft tissue laxity. This leads to surgical planning where assumptions of correctability of preoperative deformity are false and a need for intraoperative variation or abandonment of the PSI blocks occurs. The aim of this study is to determine whether functional radiology complements pre-surgical planning by identifying non-predictable patient variation in laxity. Method. Pre-operative CT's, standing radiographs and functional radiographs assessing coronal laxity at 20° flexion were collected for 20 patients. Varus/valgus laxity was assessed using the TELOS stress device (TELOS GmbH, Marburg, Germany, see Figure 1). The varus/valgus load was incrementally increased to either a maximum load of 150N or until the patient could not tolerate the discomfort. Radiographs were taken whilst the knee was held in the stressed position. CT scans were segmented and anatomical points landmarked. 2D–3D pose estimations were performed using the femur and tibia against the radiographs to determine knee alignment with each functional radiograph and so characterise the varus/valgus laxity. Results. The mean coronal alignment on CT and standing radiographs were 3.8° varus (SD, 5.6°) and 4.3° varus (SD, 6.7°) respectively. Of these, 5 of the knees were valgus aligned and 15 varus aligned in both standing and CT positions. The varus group had a mean of 5.9° in CT and 6.9° varus standing, while the valgus group had means of 4.4° valgus and 5.4° valgus in standing, indicating a collapse into further coronal malalignment while weightbearing. Each knee in the group had a laxity envelope calculated from the varus and valgus stressed radiographs. In the varus knees, the envelope ranged from 11.0° to 1.0° degree, with a mean of 5.1° (SD, 2.4°). In the valgus knees, the envelope ranged from 10.0° to 5.0° degrees, with a mean of 6.6° (SD, 2.3°), though this difference did not reach statistical significance. Using ±3° of neutral alignment as an indicator of correctable deformity, 7 of the 15 varus knees did not have a correctable deformity, while all of the valgus did. As determined by laxity limits, the CT and standing alignments were not well centered within their functional radiology groups. Specifically, for the valgus knees, 2 were near the valgus limit (lower quartile) of their laxity envelope, while for the varus knees, 9 were near their varus limit (upper quartile) and 2 at the valgus limit. In total, 65% of the knees did not have their standing alignment well centered on their functional laxity imits. Conclusions. Varus/valgus laxity in TKA appears to be subject specific and divorced from static radiological parameters. Surgical planning without reproducible clinical assessments of coronal laxity may not be sufficient to obtain a balanced TKA while avoiding ligament releases. Functional radiographs may be a viable method to individualise and refine the surgical plan in TKA on a per patient basis, incorporating objective information normally only available during the surgery itself. For any figures or tables, please contact authors directly (see Info & Metrics tab above).

Surgical navigation systems enable surgeons to carry out surgical interventions more accurately and less invasively, by tracking the surgical instruments inside human body with respect to the target anatomy. Currently, optical tracking (OPT) is the gold standard in surgical instrument tracking because of its sub-millimeter accuracy, but is constrained by direct line of sight (LOS) between camera sensors and active or passive markers. Electromagnetic tracking (EMT) is an alternative without the requirement of LOS, but subject to environmental ferromagnetic distortion. An intuitive idea is to integrate respective strengths of them to overcome respective weakness and we aim to develop a tightly-coupled method emphasising the interactive coupled sensor fusion from magnetic and optical tracking data. In order to get real-time position and orientation of surgical instruments in the surgical field, we developed a new tracking system, which is aiming to overcome the constraints of line-of-sight and paired-point interference in surgical environment. The primary contribution of this study is that the LOS and point correspondence problems can be mitigated using the initial measurements of EMT, and in turn the OPT result can provide initial value for non-linear iterative solver of EMT sensing module. We developed an integrated optical and electromagnetic tracker comprised of custom multiple infrared cameras, optical marker, field generator and sensing coils, because the current commercial optical or magnetic tracker typically consists of unchangeable lower level proprietary hardware and firmware. For the instrument-affixed markers, the relative pose between passive optical markers and magnetic coils is calibrated. The pose of magnetic sensing coils calculated by electromagnetic sensing module, can speed up the extraction of fiducial points and the point correspondences due to the reduced search space. Moreover, the magnetic tracking can compensate the missing information when the optical markers are temporarily occluded. For magnetic sensing subsystem comprised of 3-axis transmitters and 3-axis receiving coils, the objective function for nonlinear pose estimator is given by the summation of the square difference between the measured sensing data and theoretical data from the dipole model. Non-linear optimisation is computational intensive and requires initial pose estimation value. Traditionally, the initial value is calculated by equation-based algorithm, which is sensitive to noise. Instead, we get the initial value from the measurement of optical tracking subsystem. The real-time integrated tracking system was validated to have tracking errors about 0.87mm. The proposed interactive and tightly coupled sensor-fusion of magnetic-optical tracking method is efficient and applicable for both general surgeries as well as intracorporeal surgeries

The verification of the alignment of the lower limb is critical for reconstructive surgery as well as trauma surgery in order to prevent osteoarthritis. The mechanical axis is a straight line defined by the center of the femoral head and the center of the ankle joint, ideally passing the knee joint in its center. Whereas the usual preoperative method to determine the mechanical axis of the lower limbs is still the long standing radiograph, common intra-operative methods are the use of an electrocautery cord or an X-ray grid consisting of wire lines underneath the patient. Both methods require the surgeon to bring the femoral head and the ankle joint exactly to overlay with a radiopaque line that passes through both points. The distance of the knee center from this line is defined as the mechanical axis deviation (MAD). In order to reduce the errors introduced by perspective projection effects, the joint centers must be placed in the center of the c-arm images, which definitely requires time, experience and additional radiation. We propose a computer aided X-ray stitching method that puts individual X-ray images into a panoramic image frame combining the Camera Augmented Mobile C-arm (CamC) system, which features a video camera with its optical center virtually coinciding with the origin of the X-rays, with an optical tracking marker pattern underneath the operating table. The camera image of the marker pattern is used to perform pose estimation of the C-arm, allowing the calculation of the x-ray source motion between the positions in which the individual X-rays were taken. By estimating the homography, the different X-rays can be registered into a panoramic frame, enabling perfect alignment and metric measurements. In order to reduce parallax effects that lead to axis and metric measurement errors, we applied a method requiring two constraints: The bone plane has to be roughly parallel to the planar marker pattern and the distance between the marker plane and the bone plane has to be estimated. In order to evaluate the method, we used a life-size synthetic skeleton leg. After tightening a straight wire between the centers of the hip and ankle joint, the knee joint was bent into a MAD of 55 mm, which was confirmed by measuring the distance between the knee center and the wire with a ruler. The leg phantom was then placed on a radiolucent operating table, parallel to the pattern plane 130 mm underneath. The operating table was moved through the C-arm while acquiring the three desired X-ray images. which were registered into a panoramic image frame. The centers of the femoral head, the ankle, and the knee were manually determined on the generated panoramic image by a surgeon. The mechanical axis was automatically displayed and the MAD was visualised in the image and computed as 55.23 mm. We presented a new solution to intra-operatively verify alignment of the lower extremity. When using the CamC system, only a marker pattern has to be used for tracking. No additional tracking devices and calibration procedures are needed. Furthermore, the presented method only requires three x-rays that cover the femoral head, the knee and the ankle and marking of the three spots. Due to the parallax correction, these spots do not have to be exactly in the center of the picture. For this reason, compared to using an X-ray grid or an electrocautery cord, our method allows the procedure to be much faster and reduces the number of x-ray images. However, for clinical evaluation, a patient study will be conducted in the future

Purpose. The purpose of this study was to assess the biomechanical stability of the a total ankle arthroplasty system using longitudinal migration (LM) and inducible displacement (ID) measures. This study is the first study of its kind to assess total ankle arthroplasty (TAA) implant micromotion using model-based radiostereometric analysis (MBRSA). Method. Twenty patients underwent TAA that implanted the Mobility(TM) (DePuy, Warsaw IN). The mean (SD) age was 60.4 (12.5) and BMI was 29.1 (2.8) kg/m. 2. One surgeon performed all surgeries. All patients included in this study had given informed consent. Capital Health Research Ethics Board had approved this study. Uniplanar medial-lateral RSA X-ray exams were taken postop (double exam), at six wk, three mth, six mth, one yr and two yr followup times using a supine, unloaded position. Standing medial-lateral exams were taken at three mth, six mth, one yr and two yr followup intervals. LM and ID micromotions were assessed using Model-based RSA 3.2 software (Medis specials, Leiden, The Netherlands). Implant micromotions (x, y, z, Rx, Ry, Rz, MTPM) were determined and assessed for each subject using model-based pose estimation, and the implant-based coordinate system. The Elementary Geometric Shapes module from the Model-based RSA 3.2 software was used to assess the micromotion of the tibial component spherical tip due to implant symmetry. Results. The median (range) maximum total point motion (MTPM) for the implants at 2 year followup were 1.23 mm (0.39–1.95 mm) for the talar implant and 0.96 mm (0.17–2.28 mm) for the spherical tip of the tibia implant. Generally for each subject and implant component, the slopes of the migration curves decreased over time. The talar and tibial implants mean LM showed initial subsidence in the y-direction (migration into the bone) followed by stabilization patterns at one year followup. The median (range) of two year MTPM ID for the talar component was 0.39 (0.27–1.06) mm. At the one year and two year followup times the ID were almost all below the detection limit of 0.85 mm. The highest measured displacement for any one talar component at either of these times was 1.06 mm. Hence, the implant was displaced at least 0.21 mm under loading. The median (range) of one year and two year MTPM ID for the tibial component spherical tip was 0.08 (0.03–0.19) mm. The tibial component spherical tip demonstrates no ID in terms of MTPM greater than the 0.22 mm detection limit. Conclusion. The implant subsides directly into the bone in the line of primary loading during standing or walking. For most of the patients the two year LM for the Mobility(TM) demonstrates a typical subsidence-stabilization behaviour seen in many RSA studies of orthopaedic implants. Based on the results of this study the Mobility(TM) components show no measurable ID. This is the first study of its kind internationally for total ankle arthroplasty and offers novel insight into the need for prosthetic design change

The location of the hip joint center (HJC) allows correct prosthesis aligning and positioning in Computer-Assisted Orthopaedic Surgery (CAOS) applications. For the kinematic HJC localisation, the femur is moved around the pelvis with ad hoc motion trials (“pivoting”). The “Pivoting algorithm” [Siston et al., J Biomech 39 (2006) 125–130] is the functional state-of-the-art method for the hip center localisation. A source of systematic error in HJC localisation algorithms is represented by the pelvis motion during the pivoting. In computer assisted total knee arthroplasty applications, the pelvis pose is not acquired during passive movements. In motion capture applications, Kalman Filter (KF) methodology was used to estimate the pose of hidden segment for rigid body pose estimation. The purpose of this study was to validate the accuracy and robustness of a Kalman Filter algorithm, applied to a state space formulation based on two links model of the hip joint, to track the HJC position during passive movements of the articulation in CAOS procedure. The state space model describes femur and pelvis kinematics under the hypothesis of non-laxity of the articulation (ideal spherical joint). The first link models the femoral bone, while the second link models the pelvis. The femur is tracked with a Dynamic Reference Frame (DRF) attached to the distal end, composed by four active markers, while the pelvis is tracked attaching a marker to it. The kinematic relations between the state vector and the observations are non linear function. The state space has been implemented with II order linear dynamics. The position of HJC in the Femur Reference Frame is modeled with non-dynamic state variables. In order to validate the proposed algorithm, a physical model of the hip joint (femur and pelvis) was realised using SawBones models. An active optical localisation system (Certus, NDI, Ontario, Canada) was used in order to track the coordinates of two DRF rigidly connected on each segment and the coordinates of a marker attached to the pelvis segment (on the Anterior Superior Iliac Spine ASIS). The pelvis phantom is locked on a Mass-Spring-Damper platform with 2 DoFs, which mimics soft tissues behaviour. During the pivoting motion, the poses of the femur DRF and the positions of the ASIS marker of the pelvis DRF were collected. The acquired data were the observable outputs to the KF algorithm, which computes an estimation of the state parameters. The accuracy is evaluated as the Euclidean distance between respectively the estimated and Gold Standard HJC positions in FRF. The KF method performances were compared with the “Pivoting” algorithm. The localisation errors computed for both the methodologies were evaluated with respect to the HJC translation, to the Range Of pivoting Motion (ROM) and to the velocity of femur DRF trajectory (Pearson correlation analysis). The positive correlation coefficients between HJC translation and the localization errors result statistically significant (p<0.01) for both “Pivoting” (correlation index equal to 0.838) and KF (correlation index equal to 0.415) algorithms; while a negative (correlation index equal to −0.355) and positive (correlation index equal to 0.263) correlation respectively for ROM and Velocity is computed as statistically significant (p<0.05) only for KF algorithm errors. Statistically significant difference (Kruskal-Wallis, p<0.01) between “Pivoting” [median 26.71 mm and inter-quartile range (24.04, 32.18)mm] and KF [median 11.71mm and inter-quartile range (7.74, 18.82)mm] algorithms was assessed for HJC translation greater than 7 mm. The new method KF proved to be applicable in current CAOS systems. The substantial improvement of KF method is the possibility of reducing the systematical error, caused by pelvis motion during passive movement of the femur, to compute HJC position. On the other hand, tracking the HJC trajectory in real time is a nontrivial task and requires a very accurate filter parameters tuning. Further tests must be made to estimate the in-vivo range of HJC translation during passive pivoting movements and evaluate the performances of KF method with respect to others state-of-the-art methods