Radiostereometric analysis using clinical radiographic views: Development of a universal calibration object

Radiostereometric analysis (RSA) is a highly accurate technique used to provide three-dimensional (3D) measurements of orthopaedic implant migration for clinical research applications, yet its implementation in routine clinical examinations has been limited. Previous studies have introduced a modified RSA procedure that separates the calibration examinations from the patient examinations, allowing routine clinical radiographs to be analyzed using RSA. However, in order to calibrate the wide range of clinical views, a new calibration object is required. In this study, a universal, isotropic calibration object was designed to calibrate any pair of radiographic views used in the clinic for RSA. A numerical simulation technique was used to design the calibration object, followed by a phantom validation test of a prototype to verify the performance of the novel object, and to compare the measurement reliability to the conventional calibration cage. The 3D bias for the modified calibration method using the new calibration object was 0.032 ± 0.006 mm, the 3D repeatability standard deviation was 0.015 mm, and the 3D repeatability limit was 0.042 mm. Although statistical differences were present between the universal calibration object and the conventional cage, the differences were considered to be not clinically meaningful. The 3D bias and repeatability values obtained using the universal calibration object were well under the threshold acceptable for RSA, therefore it was successfully validated. The universal calibration object will help further the adoption of RSA into a more routine practice, providing the opportunity to generate quantitative databases on joint replacement performance.     

Improved RSA accuracy with DLT and balanced calibration marker distributions with an assessment of initial-calibration

Roentgen stereophotogrammetric analysis (RSA) has been used for over 25 years for accurate micromotion measurement in a wide variety of orthopaedic applications. This study investigated two possible improvements to the method. First, direct linear transformation (DLT) was compared to the traditional RSA reconstruction algorithm. The two methods were considered with respect to standard extrapolation and interpolation calibration cages. Matlab simulations showed that reconstruction accuracy was greatly improved (>60%) by combining DLT with an even distribution of enclosing calibration markers. Second, a benchtop study using phantoms translated at 0.0254-mm intervals showed initial-calibration, followed by removal of the interpolation cage for subsequent exposures, was potentially twice as accurate as self-calibration with an extrapolation cage. These results showed optimizations for the application of RSA when unobstructed space is required.     

A new model-based RSA method validated using CAD models and models from reversed engineering

Roentgen stereophotogrammetric analysis (RSA) was developed to measure micromotion of an orthopaedic implant with respect to its surrounding bone. A disadvantage of conventional RSA is that it requires the implant to be marked with tantalum beads. This disadvantage can potentially be resolved with model-based RSA, whereby a 3D model of the implant is used for matching with the actual images and the assessment of position and rotation of the implant. In this study, a model-based RSA algorithm is presented and validated in phantom experiments. To investigate the influence of the accuracy of the implant models that were used for model-based RSA, we studied both computer aided design (CAD) models as well as models obtained by means of reversed engineering (RE) of the actual implant. The results demonstrate that the RE models provide more accurate results than the CAD models. If these RE models are derived from the very same implant, it is possible to achieve a maximum standard deviation of the error in the migration calculation of 0.06 mm for translations in x- and y-direction and 0.14 mm for the out of plane z-direction, respectively. For rotations about the y-axis, the standard deviation was about 0.1 degrees and for rotations about the x- and z-axis 0.05 degrees. Studies with clinical RSA-radiographs must prove that these results can also be reached in a clinical setting, making model-based RSA a possible alternative for marker-based RSA.     

Precision measurements of the RSA method using a phantom model of hip prosthesis

Radiostereometric analysis (RSA) has become one of the recommended techniques for pre-market evaluation of new joint implant designs. In this study we evaluated the effect of repositioning of X-ray tubes and phantom model on the precision of the RSA method. In precision measurements, we utilized mean error of rigid body fitting (ME) values as an internal control for examinations. ME value characterizes relative motion among the markers within each rigid body and is conventionally used to detect loosening of a bone marker. Three experiments, each consisting of 10 double examinations, were performed. In the first experiment, the X-ray tubes and the phantom model were not repositioned between one double examination. In experiments two and three, the X-ray tubes were repositioned between one double examination. In addition, the position of the phantom model was changed in experiment three. Results showed that significant differences could be found in 2 of 12 comparisons when evaluating the translation and rotation of the prosthetic components. Repositioning procedures increased ME values mimicking deformation of rigid body segments. Thus, ME value seemed to be a more sensitive parameter than migration values in this study design. These results confirmed the importance of standardized radiographic technique and accurate patient positioning for RSA measurements. Standardization and calibration procedures should be performed with phantom models in order to avoid unnecessary radiation dose of the patients. The present model gives the means to establish and to follow the intra-laboratory precision of the RSA method. The model is easily applicable in any research unit and allows the comparison of the precision values in different laboratories of multi-center trials.     

Implementation and validation of an implant-based coordinate system for RSA migration calculation

An in vitro radiostereometric analysis (RSA) phantom study of a total knee replacement was carried out to evaluate the effect of implementing two new modifications to the conventional RSA procedure: (i) adding a landmark of the tibial component as an implant marker and (ii) defining an implant-based coordinate system constructed from implant landmarks for the calculation of migration results. The motivation for these two modifications were (i) to improve the representation of the implant by the markers by including the stem tip marker which increases the marker distribution (ii) to recover clinical RSA study cases with insufficient numbers of markers visible in the implant polyethylene and (iii) to eliminate errors in migration calculations due to misalignment of the anatomical axes with the RSA global coordinate system. The translational and rotational phantom studies showed no loss of accuracy with the two new measurement methods. The RSA system employing these methods has a precision of better than 0.05 mm for translations and 0.03° for rotations, and an accuracy of 0.05 mm for translations and 0.15° for rotations. These results indicate that the new methods to improve the interpretability, relevance, and standardization of the results do not compromise precision and accuracy, and are suitable for application to clinical data.     

Three-dimensional computer simulation of radiostereometric analysis (RSA) in distal radius fractures

Physical phantom models have conventionally been used to determine the accuracy and precision of radiostereometric analysis (RSA) in various orthopaedic applications. Using a phantom model of a fracture of the distal radius it has previously been shown that RSA is a highly accurate and precise method for measuring both translation and rotation in three-dimensions (3-D). The main shortcoming of a physical phantom model is its inability to mimic complex 3-D motion. The goal of this study was to create a realistic computer model for preoperative planning of RSA studies and to test the accuracy of RSA in measuring complex movements in fractures of the distal radius using this new model. The 3-D computer model was created from a set of tomographic scans. The simulation of the radiographic imaging was performed using ray-tracing software (POV-Ray). RSA measurements were performed according to standard protocol. Using a two-part fracture model (AO/ASIF type A2), it was found that for simple movements in one axis, translations in the range of 25microm-2mm could be measured with an accuracy of +/-2microm. Rotations ranging from 16 degrees to 2 degrees could be measured with an accuracy of +/-0.015 degrees . Using a three-part fracture model the corresponding values of accuracy were found to be +/-4microm and +/-0.031 degrees for translation and rotation, respectively. For complex 3-D motion in a three-part fracture model (AO/ASIF type C1) the accuracy was +/-6microm for translation and +/-0.120 degrees for rotation. The use of 3-D computer modelling can provide a method for preoperative planning of RSA studies in complex fractures of the distal radius and in other clinical situations in which the RSA method is applicable.     

Comparative accuracy of radiostereometric and optical tracking systems

This study aims to quantify and compare the accuracy of traditional radiostereometric analysis (RSA), fluoroscopic RSA (fRSA), and optical tracking systems. Three phantoms were constructed, each having three stainless steel spheres and three reflective markers. One phantom was mounted to the base of a precision cross-slide table, one to the base of a precision rotation table, and the third was mounted to each moveable tabletop. Two dial-gauges, rigidly mounted to the cross-slide table and rotation table, quantified translations and rotations. Two fluoroscopy units placed orthogonally tracked the steel spheres while a four-camera optical motion capture system tracked the reflective markers in three-dimensional space. RSA was performed with both digital radiography and fluoroscopy. Three axes of translation were tested: parallel to one fluoroscopy image, parallel to the other fluoroscopy image, and at approximately 45° to each image. One axis of rotation was tested. Intraclass correlation coefficients indicated excellent agreement between the actual (dial-gauge) and measured translations for all modalities (ICCs>0.99) and excellent agreement between actual and measured rotations for RSA and fRSA (ICCs>0.99). Standard errors of measurement ranged from 0.032 mm and 0.121° for RSA, to 0.040 mm and 0.229° for fRSA, and to 0.109 mm and 0.613° for optical tracking. Differences between actual and measured translations along the 45° axis were significantly smaller than the two parallel axes. These findings suggest that under ideal conditions, accuracy of fRSA is comparable to traditional RSA, and superior to optical tracking. Accuracy is highest when measured at 45° to the fluoroscopy units.     

Combining prediction of secondary structure and solvent accessibility in proteins

Owing to the use of evolutionary information and advanced machine learning protocols, secondary structures of amino acid residues in proteins can be predicted from the primary sequence with more than 75% per-residue accuracy for the 3-state (i.e., helix, beta-strand, and coil) classification problem. In this work we investigate whether further progress may be achieved by incorporating the relative solvent accessibility (RSA) of an amino acid residue as a fingerprint of the overall topology of the protein. Toward that goal, we developed a novel method for secondary structure prediction that uses predicted RSA in addition to attributes derived from evolutionary profiles. Our general approach follows the 2-stage protocol of Rost and Sander, with a number of Elman-type recurrent neural networks (NNs) combined into a consensus predictor. The RSA is predicted using our recently developed regression-based method that provides real-valued RSA, with the overall correlation coefficients between the actual and predicted RSA of about 0.66 in rigorous tests on independent control sets. Using the predicted RSA, we were able to improve the performance of our secondary structure prediction by up to 1.4% and achieved the overall per-residue accuracy between 77.0% and 78.4% for the 3-state classification problem on different control sets comprising, together, 603 proteins without homology to proteins included in the training. The effects of including solvent accessibility depend on the quality of RSA prediction. In the limit of perfect prediction (i.e., when using the actual RSA values derived from known protein structures), the accuracy of secondary structure prediction increases by up to 4%. We also observed that projecting real-valued RSA into 2 discrete classes with the commonly used threshold of 25% RSA decreases the classification accuracy for secondary structure prediction. While the level of improvement of secondary structure prediction may be different for prediction protocols that implicitly account for RSA in other ways, we conclude that an increase in the 3-state classification accuracy may be achieved when combining RSA with a state-of-the-art protocol utilizing evolutionary profiles. The new method is available through a Web server at http://sable.cchmc.org.     

The robustness and accuracy of in vivo linear wear measurements for knee prostheses based on model-based RSA

Accurate in vivo measurements methods of wear in total knee arthroplasty are required for a timely detection of excessive wear and to assess new implant designs. Component separation measurements based on model-based Roentgen stereophotogrammetric analysis (RSA), in which 3-dimensional reconstruction methods are used, have shown promising results, yet the robustness of these measurements is unknown. In this study, the accuracy and robustness of this measurement for clinical usage was assessed. The validation experiments were conducted in an RSA setup with a phantom setup of a knee in a vertical orientation. 72 RSA images were created using different variables for knee orientations, two prosthesis types (fixed-bearing Duracon knee and fixed-bearing Triathlon knee) and accuracies of the reconstruction models. The measurement error was determined for absolute and relative measurements and the effect of knee positioning and true seperation distance was determined. The measurement method overestimated the separation distance with 0.1mm on average. The precision of the method was 0.10mm (2*SD) for the Duracon prosthesis and 0.20mm for the Triathlon prosthesis. A slight difference in error was found between the measurements with 0° and 10° anterior tilt. (difference=0.08mm, p=0.04). The accuracy of 0.1mm and precision of 0.2mm can be achieved for linear wear measurements based on model-based RSA, which is more than adequate for clinical applications. The measurement is robust in clinical settings. Although anterior tilt seems to influence the measurement, the size of this influence is low and clinically irrelevant.     

Model-based Roentgen stereophotogrammetry of orthopaedic implants

Attaching tantalum markers to prostheses for Roentgen stereophotogrammetry (RSA) may be difficult and is sometimes even impossible. In this study, a model-based RSA method that avoids the attachment of markers to prostheses is presented and validated. This model-based RSA method uses a triangulated surface model of the implant. A projected contour of this model is calculated and this calculated model contour is matched onto the detected contour of the actual implant in the RSA radiograph. The difference between the two contours is minimized by variation of the position and orientation of the model. When a minimal difference between the contours is found, an optimal position and orientation of the model has been obtained. The method was validated by means of a phantom experiment. Three prosthesis components were used in this experiment: the femoral and tibial component of an Interax total knee prosthesis (Stryker Howmedica Osteonics Corp., Rutherfort, USA) and the femoral component of a Profix total knee prosthesis (Smith & Nephew, Memphis, USA). For the prosthesis components used in this study, the accuracy of the model-based method is lower than the accuracy of traditional RSA. For the Interax femoral and tibial components, significant dimensional tolerances were found that were probably caused by the casting process and manual polishing of the components surfaces. The largest standard deviation for any translation was 0.19mm and for any rotation it was 0.52 degrees. For the Profix femoral component that had no large dimensional tolerances, the largest standard deviation for any translation was 0.22mm and for any rotation it was 0.22 degrees. From this study we may conclude that the accuracy of the current model-based RSA method is sensitive to dimensional tolerances of the implant. Research is now being conducted to make model-based RSA less sensitive to dimensional tolerances and thereby improving its accuracy.     

Digital automated RSA compared to manually operated RSA

The accuracy of digital Roentgen stereophotogrammetric analysis (RSA) was compared to the accuracy of a manually operated RSA system. For this purpose, we used radiographs of a phantom and radiographs of patients. The radiographs of the patients consisted of double examinations of 12 patients that had a tibial osteotomy and of double examinations of 12 patients that received a total hip prosthesis. First, the radiographs were measured manually with an accurate measurement table. Subsequently, the images were digitized by a film scanner at 150 DPI and 300 DPI resolutions and analyzed with the RSA-CMS software. In the phantom experiment, the manually operated system produced significantly better results than the digital system, although the maximum difference between the median values of the manually operated system and the digital system was as low as 0.013 mm for translations and 0.033° for rotations. In the radiographs of the patients, the manually operated system and the digital system produced equally accurate results: no significant differences in translations and rotations were found. We conclude that digital RSA is an accurate, fast, and user friendly alternative for manually operated RSA. Currently, digital RSA systems are being used in a growing number of clinical RSA-studies.     

Dose calculation validation of a convolution algorithm in a solid water phantom

PurposeThe dose calculated using a convolution algorithm should be validated in a simple homogeneous water-equivalent phantom before clinical use. The dose calculation accuracy within a solid water phantom was investigated.MethodsThe specific Gamma knife design requires a dose rate calibration within a spherical solid water phantom. The TMR10 algorithm, which approximates the phantom material as liquid water, correctly computes the absolute dose in water. The convolution algorithm, which considers electron density miscalculates the dose in water as the phantom Hounsfield units were converted into higher electron density when the original CT calibration curve was used. To address this issue, the electron density of liquid water was affected by modifying the CT calibration curve. The absolute dose calculated using the convolution algorithm was compared with that computed by the TMR10. The measured depth dose profiles were also compared to those computed by the convolution and TMR10 algorithms. A patient treatment was recalculated in the solid-water phantom and the delivery quality assurance was checked.ResultsThe convolution algorithm and the TMR10 calculate an absolute dose within 1% when using the modified CT calibration curve. The dose depth profile calculated using the convolution algorithms was superimposed on the TMR10 and measured dose profiles when the modified CT calibration curve was applied. The Gamma index was better than 93%.ConclusionsDose calculation algorithms, which consider electron density, require a CT calibration curve adapted to the phantom material to correctly compute the dose in water.     

Handling modular hip implants in model-based RSA: combined stem-head models

Migration measurements of hip prostheses using marker-based Roentgen stereophotogrammetric analysis (RSA) require the attachment of markers to the prostheses. The model-based approach, which does not require these markers, is, however, less precise. One of the reasons may be the fact that the spherical head has not been modelled. Therefore, we added a 3D surface model of the spherical head and estimated the position and orientation of the combined stem-head model. The new method using a combined stem-head model was compared in a phantom study on five prostheses (of different types) and in a clinical study using double examinations of implanted hip prostheses, with two existing methods: a standard model-based approach and one using elementary geometrical shapes. The combined model showed the highest precision for the rotation about the longitudinal axis in the phantom experiments. With a standard deviation of 0.69 degrees it showed a significant improvement (p=0.02) over the model-based approach (0.96 degrees ) on the phantom data, but no improvement on the clinical data. Overall, the use of elementary geometrical shapes was worse with respect to the model-based approach, with a standard deviation of 1.02 degrees on the phantom data and 0.79 degrees on the clinical data. This decrease in precision was significant (p<0.01) on the clinical data. With relatively small differences in the other migration directions, these results demonstrate that the new method with a combined stem-head model can be a useful alternative to the standard model-based approach.     

Validation of a low-dose hybrid RSA and fluoroscopy technique: Determination of accuracy, bias and precision

Analyzing skeletal kinematics with radiostereometric analysis (RSA) following corrective orthopedic surgery allows the quantitative comparison of different implant designs. The purpose of this study was to validate a technique for dynamically estimating the relative position and orientation of skeletal segments using RSA and single plane X-ray fluoroscopy. Two micrometer-based in vitro phantom models of the skeletal segments in the hip and knee joints were used. The spatial positions of tantalum markers that were implanted into each skeletal segment were reconstructed using RSA. The position and orientation of each segment were determined in fluoroscopy images by minimizing the difference between the markers measured and projected in the image plane. Accuracy was determined in terms of bias and precision by analyzing the deviation between the applied displacement protocol and measured pose estimates. Measured translational accuracy was less than 100 microm parallel to the image plane and less than 700 microm in the direction orthogonal to the image plane. The measured rotational error was less than 1 degrees . Measured translational and rotational bias was not statistically significant at the 95% level of confidence. The technique allows real-time kinematic skeletal measurements to be performed on human subjects implanted with tantalum markers for quantitatively measuring the motion of normal joints and different implant designs.     

Prediction-based fingerprints of protein-protein interactions

The recognition of protein interaction sites is an important intermediate step toward identification of functionally relevant residues and understanding protein function, facilitating experimental efforts in that regard. Toward that goal, the authors propose a novel representation for the recognition of protein-protein interaction sites that integrates enhanced relative solvent accessibility (RSA) predictions with high resolution structural data. An observation that RSA predictions are biased toward the level of surface exposure consistent with protein complexes led the authors to investigate the difference between the predicted and actual (i.e., observed in an unbound structure) RSA of an amino acid residue as a fingerprint of interaction sites. The authors demonstrate that RSA prediction-based fingerprints of protein interactions significantly improve the discrimination between interacting and noninteracting sites, compared with evolutionary conservation, physicochemical characteristics, structure-derived and other features considered before. On the basis of these observations, the authors developed a new method for the prediction of protein-protein interaction sites, using machine learning approaches to combine the most informative features into the final predictor. For training and validation, the authors used several large sets of protein complexes and derived from them nonredundant representative chains, with interaction sites mapped from multiple complexes. Alternative machine learning techniques are used, including Support Vector Machines and Neural Networks, so as to evaluate the relative effects of the choice of a representation and a specific learning algorithm. The effects of induced fit and uncertainty of the negative (noninteracting) class assignment are also evaluated. Several representative methods from the literature are reimplemented to enable direct comparison of the results. Using rigorous validation protocols, the authors estimated that the new method yields the overall classification accuracy of about 74% and Matthews correlation coefficients of 0.42, as opposed to up to 70% classification accuracy and up to 0.3 Matthews correlation coefficient for methods that do not utilize RSA prediction-based fingerprints. The new method is available at http://sppider.cchmc.org.     

Accurate prediction of solvent accessibility using neural networks-based regression

Accurate prediction of relative solvent accessibilities (RSAs) of amino acid residues in proteins may be used to facilitate protein structure prediction and functional annotation. Toward that goal we developed a novel method for improved prediction of RSAs. Contrary to other machine learning-based methods from the literature, we do not impose a classification problem with arbitrary boundaries between the classes. Instead, we seek a continuous approximation of the real-value RSA using nonlinear regression, with several feed forward and recurrent neural networks, which are then combined into a consensus predictor. A set of 860 protein structures derived from the PFAM database was used for training, whereas validation of the results was carefully performed on several nonredundant control sets comprising a total of 603 structures derived from new Protein Data Bank structures and had no homology to proteins included in the training. Two classes of alternative predictors were developed for comparison with the regression-based approach: one based on the standard classification approach and the other based on a semicontinuous approximation with the so-called thermometer encoding. Furthermore, a weighted approximation, with errors being scaled by the observed levels of variability in RSA for equivalent residues in families of homologous structures, was applied in order to improve the results. The effects of including evolutionary profiles and the growth of sequence databases were assessed. In accord with the observed levels of variability in RSA for different ranges of RSA values, the regression accuracy is higher for buried than for exposed residues, with overall 15.3-15.8% mean absolute errors and correlation coefficients between the predicted and experimental values of 0.64-0.67 on different control sets. The new method outperforms classification-based algorithms when the real value predictions are projected onto two-class classification problems with several commonly used thresholds to separate exposed and buried residues. For example, classification accuracy of about 77% is consistently achieved on all control sets with a threshold of 25% RSA. A web server that enables RSA prediction using the new method and provides customizable graphical representation of the results is available at http://sable.cchmc.org.     

Systematic accuracy and precision analysis of video motion capturing systems--exemplified on the Vicon-460 system

With rising demand on highly accurate acquisition of small motion the use of video-based motion capturing becomes more and more popular. However, the performance of these systems strongly depends on a variety of influencing factors. A method was developed in order to systematically assess accuracy and precision of motion capturing systems with regard to influential system parameters. A calibration and measurement robot was designed to perform a repeatable dynamic calibration and to determine the resultant system accuracy and precision in a control volume investigating small motion magnitudes (180x180x150mm(3)). The procedure was exemplified on the Vicon-460 system. Following parameters were analyzed: Camera setup, calibration volume, marker size and lens filter application. Equipped with four cameras the Vicon-460 system provided an overall accuracy of 63+/-5mum and overall precision (noise level) of 15mum for the most favorable parameter setting. Arbitrary changes in camera arrangement revealed variations in mean accuracy between 76 and 129mum. The noise level normal to the cameras' projection plane was found higher compared to the other coordinate directions. Measurements including regions unaffected by the dynamic calibration reflected considerably lower accuracy (221+/-79mum). Lager marker diameters led to higher accuracy and precision. Accuracy dropped significantly when using an optical lens filter. This study revealed significant influence of the system environment on the performance of video-based motion capturing systems. With careful configuration, optical motion capturing provides a powerful measuring opportunity for the majority of biomechanical applications.     

Evaluation of a new body composition phantom for quality control and cross-calibration of DXA devices.

This study evaluated a new body composition phantom and its use for quality control and cross-calibration of dual-energy X-ray absorptiometry (DXA) instruments for measurements of body composition. We imaged the variable composition phantom (Lunar, Madison, WI) on eight different DXA devices. Deviations of up to 7% fat were observed when we compared the percent fat values measured by the different devices with the nominal values provided by the manufacturer. Absolute precision error of percent fat measurements for the phantom ranged from 0.6 to 0.8%. The phantom's percent fat values were also compared with whole body composition measurements from 130 female and male volunteers. The phantom detected differences in percent fat values that were similar to those found by comparing in vivo measurements with values from different DXA scanner models from the same manufacturer. When comparing different models of scanners from different manufacturers, such as the Hologic QDR-4500 and the Lunar DPX-IQ, the phantom showed a different relationship than was seen for patients. Therefore, corrections or comparisons based on the phantom data alone would be incorrect. In conclusion, the Lunar variable composition phantom is capable of accurately measuring the fat calibration of DXA devices and may be suitable for cross-sectional cross-calibration between scanners from the same manufacturer; however, for comparison of DXA scanners from different manufacturers, in vivo cross-calibration is still the only accurate method. The phantom may be used in longitudinal quality control to verify an instrument's temporal stability.     

Accuracy and repeatability of Roentgen stereophotogrammetric analysis (RSA) for measuring knee laxity in longitudinal studies

Roentgen stereophotogrammetric analysis (RSA) can be used to assess temporal changes in anterior-posterior (A-P) knee laxity. However, the accuracy and precision of RSA is dependent on many factors and should be independently evaluated for a particular application. The objective of this study was to evaluate the use of RSA for measuring A-P knee laxity. The specific aims were to assess the variation or "noise" inherent to RSA, to determine the reproducibility of RSA for repeated A-P laxity testing, and to assess the accuracy of these measurements. Two experiments were performed. The first experiment utilized three rigid models of the tibiofemoral joint to assess the noise and to compare digitization errors of two independent examiners. No differences were found in the kinematic outputs of the RSA due to examiner, repeated trials, or the model used. In a second experiment, A-P laxity values between the A-P shear load limits of +/-60 N of five cadaver goat knees were measured to assess the error associated with repeated testing. The RSA laxity values were also compared to those obtained from a custom designed linkage system. The mean A-P laxity values with the knee 30 degrees, 60 degrees, and 90 degrees of flexion for the ACL-intact goat knee (+/-95% confidence interval) were 0.8 (+/-0.25), 0.9 (+/-0.29), and 0.4 (+/-0.22) mm, respectively. In the ACL-deficient knee, the A-P laxity values increased by an order of magnitude to 8.8 (+/-1.39), 7.6 (+/-1.32), and 3.1 (+/-1.20)mm, respectively. No significant differences were found between the A-P laxity values measured by RSA and the independent measurement technique. A highly significant linear relationship (r(2)=0.83) was also found between these techniques. This study suggests that the RSA method is an accurate and precise means to measure A-P knee laxity for repeated testing over time.