The identification of femoral landmarks is a common procedure in multiple academic fields. Femoral bone coordinate systems are used particularly in orthopedics and biomechanics, and are defined by landmarks, axes and planes. A fully automatic detection overcomes the drawbacks of a labor-intensive manual identification. In this paper, a new automatic atlas- and a priori knowledge-based approach that processes femoral surface models, called the A&A method, was evaluated. The A&A method is divided in two stages. Firstly, a single atlas-based registration maps landmarks and areas from a template surface to the subject. In the second stage, landmarks, axes and planes that are used to construct several femoral bone coordinate systems are refined using a priori knowledge. Three common femoral coordinate systems are defined by the landmarks detected. The A&A method proved to be very robust against a variation of the spatial alignment of the surface models. The results of the A&A method and a manual identification were compared. No significant rotational differences existed for the bone coordinate system recommended by the International Society of Biomechanics. Minor significant differences of maximally 0.5° were observed for the two other coordinate systems. This might be clinically irrelevant, depending on the context of use and should, therefore, be evaluated by the potential user regarding the specific application. The entire source code of the A&A method and the data used in the study is open source and can be accessed via https://github.com/RWTHmediTEC/FemoralCoordinateSystem.
The native femoral J-Curve is known to be a relevant determinant of knee biomechanics. Similarly, after total knee arthroplasty, the J-Curve of the femoral implant component is reported to have a high impact on knee kinematics. The shape of the native femoral J-Curve has previously been analyzed in 2D, however, the knee motion is not planar. In this study, we investigated the J-Curve in 3D by principal component analysis (PCA) and the resulting mean shapes and modes by geometric parameter analysis. Surface models of 90 cadaveric femora were available, 56 male, 32 female and two without respective information. After the translation to a bone-specific coordinate system, relevant contours of the femoral condyles were derived using virtual rotating cutting planes. For each derived contour, an extremum search was performed. The extremum points were used to define the 3D J-Curve of each condyle. Afterwards a PCA and a geometric parameter analysis were performed on the medial and lateral 3D J-Curves. The normalized measures of the mean shapes and the aspects of shape variation of the male and female 3D J-Curves were found to be similar. When considering both female and male J-Curves in a combined analysis, the first mode of the PCA primarily consisted of changes in size, highlighting size differences between female and male femora. Apart from changes in size, variation regarding aspect ratio, arc lengths, orientation, circularity, as well as regarding relative location of the 3D J-Curves was found. The results of this study are in agreement with those of previous 2D analyses on shape and shape variation of the femoral J-Curves. The presented 3D analysis highlights new aspects of shape variability, e.g., regarding curvature and relative location in the transversal plane. Finally, the analysis presented may support the design of (patient-specific) femoral implant components for TKA.
The J-Curve in the native knee as well as the femoral component’s J-Curve after total knee arthroplasty are known to have a high influence on kinematics. Furthermore, the J-Curve’s shape affects ligament strain and tension and consequently already slight changes may strongly alter knee forces and stability. To optimize current implants’ J-Curve design with regard to the population’s morphology, information about the main sources of contour variation is necessary.In this study, a principal component analysis (PCA) was performed on the medial and lateral femoral J-Curves of 90 cadavers without history of osteoarthritis. The J-Curves’ mean shapes were further investigated by geometric parameter analysis and effect sizes were calculated for the first three principal components (PCs). In addition, a combined PCA for both sides was performed and evaluated qualitatively. The results were compared with the variation in standard implants’ J-Curve shape.The isolated PCA of medial and lateral J-Curves resulted in PCs involving changes in contour orientation, arc length, scaling and circularity. The combined PCA of both sides resulted in PCs comprising combinations of the individual variations together with changes in relative position. In contrast, the qualitative evaluation of J-Curves from 2 different standard implant systems revealed no visible changes in shape but only changes in size.Limitations of this study were the restriction to a 2-dimensional contour derivation and the sole consideration of the femoral contours. Nevertheless, the sagittal variability in the medial, lateral and combined femoral J-Curves should be considered in implant design.
In total knee arthroplasty, the femoral implant size is chosen mainly based on the femoral anteroposterior (AP) height and mediolateral (ML) width. This choice often is a compromise, due to limited size availability. Inadequate AP fit is expected to alter flexion laxity and thus knee function. Inadequate ML fit entails underhang or overhang, which is linked to worse clinical outcomes. Hence, we aimed to find implant size distributions, which maximize population coverage, and to evaluate the sensitivity regarding error bounds and the number of implant sizes for a database of 85,143 cases. All patients in the database have been provided with a patient‐specific implant in the past. For a subset of 1049 cases, three‐dimensional preoperative bone surface models were available. These were used to validate whether the implant dimensions were representative of the bone dimensions. Particle Swarm Optimization was used for optimizing the implant size distribution. The deviations between implant and bone measures in the subset were found to be clinically irrelevant. Therefore, the full database of 85,143 cases was used for further analyses. A higher sensitivity of the population coverage regarding the error bounds compared to the number of implant sizes was found. For an exemplary setup of 12 optimized implant sizes and error bounds of ±1.5 mm for AP and ±3 mm for ML, a population coverage of almost 85% was achieved. In contrast, even with 30 implant sizes, a full population coverage could not be reached. Hence, remaining cases should be provided with patient‐specific implants.
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