Total knee arthroplasty (TKA) is widely accepted as a successful surgical intervention to treat osteoarthritis and other degenerative diseases of the knee. However, present statistics on limited survivorship and patient-satisfaction emphasise the need for an optimal endoprosthetic care. Although, the implant design is directly associated with the clinical outcome comprehensive knowledge on the complex relationship between implant design (morphology) and function is still lacking.

The goal of this study was to experimentally analyse the relationship between the trochlear groove design of the femoral component (iTotal CR, ConforMIS, Inc., Bedford, MA, USA) and kinematics in an in vitro test setup based on rapid prototyping of polymer-based replica knee implants.

The orientation of the trochlear groove was modified in five different variations in a self-developed computational framework. On the basis of the reference design, one was medially tilted (−2°) and four were laterally tilted (+2°, +4°, +6°, +8°). For manufacturing, we used rapid prototyping to produce synthetic replicates made of Acrylnitril-Butadien-Styrol (ABS) and subsequent post-processing with acetone vapor. The morpho-functional analysis of the replicates was performed in our experimental knee simulator. Tibiofemoral and patellofemoral kinematics were recorded with an optical tracking system during a semi-active flexion/extension (∼10° to 90°) motion.

Looking at the results, the patellofemoral kinematics, especially the medial/lateral translation and internal/external rotation were mainly affected. During low flexion, the patella had a more laterally position relative to the femur with increasing lateral trochlear orientation. The internal/external rotation initially differentiated and converged with flexion. Regarding the tibiofemoral kinematics, only the tibial internal/external rotation showed notable differences between the modified replica implants.

We presented a workflow for an experimental morpho-functional analysis of the knee and demonstrated its feasibility on the example of the trochlear groove orientation which might be used in the future for comprehensive implant design parameter optimisation, especially in terms of image based computer assisted patient-specific implants.

In total knee arthroplasty (TKA) the implant design is one key factor for a proper functional restoration of the diseased knee. Therefore, detailed knowledge on the shape (morphology) is essential to guide the design process. In literature, the morphology has been extensively studied revealing differences, e.g. between ethnicity and gender. However, it is still unclear in which way gender-specific morphological differences are sexual dimorphism or explained by differences in size.

The aim of this study was to investigate the morphology of the distal femur under gender-specific aspects for a large group of patients. Statistical analysis was used to reveal significant differences and subsequent correlation analysis to normalise the morphology.

A dataset of n=363 segmented distal femoral bone surface reconstructions (229 female, 134 male) were randomly collected from a database of patients which underwent TKA. In total, 34 morphological features (distances, angles), quantifying the distal femoral geometry, were determined full automatically. Subsequently, graphs and descriptive statistics were used to check normality and gender-specific differences were analysed by calculating the 95% confidence intervals for women and men separately. Finally, significant differences were normalised by dividing each feature by appropriate distance measurements and confidence intervals were recalculated.

Looking at the confidence 95% intervals, 6 of 34 features did not show any significant differences between genders. Remarkably, this primarily involves angular (relative) features whereas distance (absolute) measurements were mostly gender dependent. Then, we normalised all distance measurements and radii according to their direction of measurement: Features defined in medial/lateral (ML) direction were divided by the overall ML width and those following the anterior/posterior direction were normalised based on the overall AP length. The results demonstrated that gender-specific differences mostly disappear by using an adequate normalisation term.

In conclusion, implant sizes (femoral components) should not be linearly scaled according to one dimension. Instead, ML and AP directions should be regarded separately (non-isotropic scaling). Taking this into consideration, gender- specific differences might be neglected.

The consideration of the individual knee ligament attachments is crucial for the application of patient specific musculoskeletal models in the clinical routine, e.g. in knee arthroplasty. Commonly, the pre-operative planning is based on CT images, where no soft tissue information is available. The goal of this study was to evaluate the accuracy of a full automatic and robust mesh morphing method that estimates locations of cruciate ligament attachments on the basis of training data.

The cruciate ligament attachments from 6 (n=6) different healthy male subjects (BH 184±6cm, BW 90±10kg) were identified in MRI-datasets by a clinical expert. The insertion areas were exported as point clouds and the centres of gravitation served as approximations of the attachments. These insertion points were used to annotate mean shapes of femur and tibia.

The mean shapes were built up from 332 training data sets each. The surface data were obtained from CT scans by performing an automatic segmentation followed by manual cleaning steps. The mean shapes were computed by selecting a data set randomly and aligning this reference rigidly to each of the remaining data sets. The data were fitted using the non-rigid ICP variant (N-ICP-A). Due to this morphing step, point correspondences were established.

By morphing a mean shape to the target geometries, including the cruciate ligament attachments, the distribution of the insertions on the original mean shape was obtained. Subsequently, a statistical mean was computed (annotated mean). The annotated mean shape was again morphed to the target data sets and the deviations of the respective predicted insertion points from the measured insertion points were computed.

The training data was successfully morphed to all 6 subjects in an automatic manner with virtually no distance error (10-5 mm). The mean distance between the measured and morphed ligament attachments was highest for the ACL in the femur (4.26±1.48 mm) and lowest for PCL in the tibia (1.63±0.36 mm). The highest deviation was observed for femoral ACL (6.93 mm).

In this study, a morphing based approach was presented to predict origins and insertions of the knee ligaments on the basis of CT-data, exemplarily shown for the cruciate ligaments. It has been demonstrated, that the N-ICP-A is applicable to predict the attachments automatic and robust with a high accuracy. This might help to improve patient-specific biomechanical models and their integration in the clinical routine.

For a proper functional restoration of the knee following knee arthroplasty, a comprehensive understanding of bony and soft tissue structures and their effects on biomechanics of the individual patient is essential. A systematic description of morphological knee joint parameters and a study of their effects could beneficial for an optimal patient-specific implant design.

The goal of this study was the development of a full parametric model for a comprehensive analysis of the distal femoral morphology also enabling a systematic parameter variation in the context of a patient specific multi-parameter optimisation of the knee implant shape.

The computational framework was implemented in MATLAB and tested on 20 CT-models which originated from pathological right knees. The femora were segmented semi-automatically and exported in STL-format.

First, a 3D surface model was imported, visualised and reference landmarks were defined. Cutting planes were rotated around the transepicondylar axis and ellipses were fitted in the cutting contour using pattern recognition. The portions between the ellipses were approximated by using a piecewise cubic hermite interpolation polynom such that a closed contour was obtained following the characteristics of the real bone model. At this point the user could change the parameters of the ellipses in order to manipulate the approximated contour for e.g. higher-level biomechanical analyses. A 3D surface was generated by using the lofting technique. Finally, the parameter model was exported in STL-format and compared against the original 3D surface model to evaluate the accuracy of the framework

The presented framework could be successfully applied for automatic parameterisation of all 20 distal femur surface data-sets. The mean global accuracy was 0.09±0.62 mm with optimal program settings which is more accurate than the optimal resolution of the CT based data acquisition. A systematic variation of the femoral morphology could be proofed based on several examples such as the manipulation of the medial/lateral curvature in the frontal plane, contact width of the condyles, J-Curve and trochlear groove orientation.

In our opinion, this novel approach might offer the opportunity to study the effect of femoral morphology on knee biomechanics in combination with validated biomechanical simulation models or experimental setups. New insights could directly be used for patient-specific implant design and optimisation.

For a proper rehabilitation of the knee following knee arthroplasty, a comprehensive understanding of bony and soft tissue structures and their effects on biomechanics of the individual patient is essential. Musculoskeletal models have the potential, however, to predict dynamic interactions of the knee joint and provide knowledge to the understanding of knee biomechanics. Our goal was to develop a generic musculoskeletal knee model which is adaptable to subject-specific situations and to use in-vivo kinematic measurements obtained under full-weight bearing condition in a previous Upright-MRI study of our group for a proper validation of the simulation results.

The simulation model has been developed and adapted to subject-specific cases in the multi-body simulation software AnyBody. For the implementation of the knee model a reference model from the AnyBody Repository was adapted for the present issue. The standard hinge joint was replaced with a new complex knee joint with 6DoF. The 3D bone geometries were obtained from an optimized MRI scan and then post-processed in the mesh processing software MeshLab. A homogenous dilation of 3 mm was generated for each bone and used as articulating surfaces.

The anatomical locations of viscoelastic ligaments and muscle attachments were determined based on literature data. Ligament parameters, such as elongation and slack length, were adjusted in a calibration study in two leg stance as reference position.

For the subject-specific adaptation a general scaling law, taking segment length, mass and fat into account, was used for a global scaling. The scaling law was further modified to allow a detailed adaption of the knee region, e.g. align the subject-specific knee morphology (including ligament and muscle attachments) in the reference model.

The boundary conditions were solely described by analytical methods since body motion (apart from the knee region) or force data were not recorded in the Upright-MRI study. Ground reaction forces have been predicted and a single leg deep knee bend was simulated by kinematic constraints, such as that the centre of mass is positioned above the ankle joint. The contact forces in the knee joint were computed using the force dependent kinematic algorithm.

Finally, the simulation model was adapted to three subjects, a single leg deep knee bend was simulated, subject-specific kinematics were recorded and then compared to their corresponding in-vivo kinematic measurements data.

We were able to simulate the whole group of subjects over the complete range of motion. The tibiofemoral kinematics of three subjects could be simulated showing the overall trend correctly, whereas absolute values partially differ.

In conclusion, the presented simulation model is highly adaptable to an individual situation and seems to be suitable to approximate subject-specific knee kinematics without consideration of cartilage and menisci. The model enables sensitivity analyses regarding changes in patient specific knee kinematics following e.g. surgical interventions on bone or soft tissue as well as related to the design and placement of partial or total knee joint replacement. However, model optimisation, a higher case number, sensitivity analyses of selected parameters and a semi-automation of the workflow are parts of our ongoing work.

Currently, standard total knee arthroplasty (TKA) procedures focus on axial and rotational alignment of the prosthesis components and ligament balancing. Even though TKA has been constantly improved, TKA patients still experience a significantly poorer functional outcome than total hip arthroplasty patients.

Among others, complications can occur when knee kinematics (active/passive) after TKA do not correspond with the physiological conditions. We hypothesised that the Q-angle has a substantial impact on active joint kinematics and should be taken into account in TKA. The Q-angle can be influenced by the position of the tibial tuberosity (TT). A pathological position of the TT is commonly related to patellofemoral pain and knee instability. A clinically well accepted surgical treatment is the TT medialisation which causes a change in the orientation of the patella tendon and thus alters the biomechanics of the knee. If active and passive knee kinematics differs, this aspect should be considered for implant design and positioning. Therefore we investigated the sensitivity of active knee kinematics related to the position of the TT by using a complex multi-body model with a dynamic simulation of an entire gait cycle.

The validated model has been implemented in the multi-body simulation software AnyBody and was adapted for the present issue. The knee joint is represented by articulating surfaces of a standard prosthesis and contains 6 degrees of freedom. Intra-articular passive structures are implemented and the muscular apparatus consists of 159 muscles per leg. As input parameter for the sensitivity analysis, the TT was translated medially 9 mm and laterally 15 mm from the initial position in equidistant steps of 3 mm.

The Q-angle was about 10° in the initial position, which lies in the physiological range. It changed approximately 2.5° with a gradual shift of 3 mm, confirming the impact of the individual TT position on active knee kinematics. The tibiofemoral kinematics, particularly the internal/external rotation of the tibia was significantly affected. Lateralisation of the TT decreased the external rotation of the tibia, whereas a medialisation caused an increase. During contralateral toe off the external rotation was +7.5° for a medial transfer of 9 mm and −1.4° for a lateral transfer of 15 mm, respectively. The differences in external rotation were almost zero for low flexion angles, correlating with the activation pattern of the quadriceps muscle: the higher the activation of the quadriceps, the greater were the changes in kinematics.

In conclusion, knee kinematics are strongly affected by the Q-angle which is directly associated with the position of the TT. As active kinematics may show significant differences to passive kinematics, intraoperative ligament balancing may result in a suboptimal ligament situation during active motion. Since the Q-angle varies widely between gender and patients, the individual situation should be considered. The optimisation of the model and further experimental validation is one aspect of our ongoing work.

Musculoskeletal loading plays an important role in the primary stability of THA. There are about 210,000 primary THA interventions p.a. in Germany. Consideration of biomechanical aspects during computer-assisted orthopaedic surgery is recommendable in order to obtain satisfactory long-term results. For this purpose simulation of the pre- and post-operative magnitude of the resultant hip joint force R and its orientation is of interest. By means of simple 2D-models (Pauwels, Debrunner, Blumentritt) or more complex 3D-models (Iglič), the magnitude and orientation of R can be computed patient-individually depending on their geometrical and anthropometrical parameters. In the context of developing a planning module for computer-assisted THA, the objective of this study was to evaluate the mathematical models. Therefore, mathematical model computations were directly compared to in-vivo measurements obtained from instrumented hip implants.

With patient-specific parameters the magnitude and orientation of R were model-based computed for three patients (EBL, HSR, KWR) of the OrthoLoad-database. Their patient-specific parameters were acquired from the original patient X-rays. Subsequently, the computational results were compared with the corresponding in-vivo telemetric measurements published in the OrthoLoad-database. To obtain the maximum hip joint load, the static single-leg-stance was considered. A reference value for each patient for the maximum hip load under static conditions was calculated from OrthoLoad-data and related to the respective body weights (BW).

On average there are large deviations of the results for the magnitude (Ø=147%) and orientation (Ø=14.35° too low) of R obtained by using Blumentritt's model from the in-vivo results/measurements. The differences might be partly explained by the supplemental load of 20% BW within Blumentritt's model which is added to the input parameter BW in order to consider dynamic gait influences. Such a dynamic supplemental load is not applied within the other static single-leg-stance models. Blumentritt's model assumptions have to be carefully reviewed due to the deviations from the in-vivo measurement data.

Iglič's 3D-model calculates the magnitude (Ø17%) and the orientation (Ø49%) of R slightly too low. For the magnitude one explanation could be that his model considers nine individual 3D-sets of muscle origins and insertion points taken from literature. This is different from other mathematical models. The patient-individual muscle origin and insertion points should be used.

Pauwels and Debrunner's models showed the best results. They are in the same range compared to in-vivo data. Pauwels's model calculates the magnitude (Ø5%) and the orientation (Ø28%) of R slightly higher. Debrunner's model calculates the magnitude (Ø1%) and the orientation (Ø14%) of R slightly lower.

In conclusion, for the orientation of R, all the computational results showed variations which tend to depend on the used model.

There are limitations coming along with our study: as our previous studies showed, an unambiguous identification of most landmarks in an X-ray (2D) image is hardly possible. Among the study limitations there is the fact that the OrthoLoad-database currently offers only three datasets for direct comparison of static single leg stance with in-vivo measurement data of the same patient. Our ongoing work is focusing on further validation of the different mathematical models.

The alignment of prostheses components has a major impact on the longevity of total knee protheses as it significantly influences the biomechanics and thus also the load distribution in the knee joint.

Knee joint loads depend on three factors: (1) geometrical conditions such as bone geometry and implant position/orientation, (2) passive structures such as ligaments and tendons as well as passive mechanical properties of muscles, and (3) active structures that are muscles. The complex correlation between implant position and clinical outcome of TKA and later in vivo joint loading after TKA has been investigated since 1977. These investigations predominantly focused on component alignment relative to the mechanical leg axis (Mikulicz-line) and more recently on rotational alignment perpendicular to the mechanical axis. In general four different approaches can be used to study the relationship between implant position and knee joint loads: In anatomical studies (1), the influence of the geometrical conditions and passive structures can be analyzed under the constraint that the properties of vital tissue are only approximated. This could be overcome with an intraoperative load measurement approach (2). Though, this set up does not consider the influence of active structures. Although post-operative in vivo load measurements (3) provide information about the actual loading condition including the influence of active structures, this method is not applicable to investigate the influence of different implant positions. Using mathematical approaches (4) including finite element analysis and multi-body-modeling, prostheses positions can be varied freely. However, there exists no systematical analysis of the influence of prosthesis alignment on knee loading conditions not only in axial alignment along and rotational alignment perpendicular to the mechanical axis but in all six degrees of freedom (DOF) with a validated mathematical model. Our goal was therefore to investigate the correlation between implant position and joint load in all six DOF using an adaptable biomechanical multi-body model.

A model for the simulation of static single leg stance was implemented as an approximation of the phase with the highest load during walking cycle. This model is based on the AnyBody simulation software (AnyBody Technology A/S, Denmark). As an initial approach, with regard to the simulation of purely static loading the knee joint was implemented as hinge joint. The patella was realised as a deflection point, a so called “ViaNode,” for the quadriceps femoris muscle. All muscles were implemented based on Hill's muscle model. The knee model was indirectly validated by comparison of the simulation results for single and also double leg stance with in-vivo measurements from the Orthoload database (www.orthoload.de). For the investigation of the correlation between implant position and knee load, major boundary conditions were chosen as follows:•

Flexion angle was set to 20° corresponding to the position with the highest muscle activity during gait cycle.

Comparing the simulation results of single and double leg stance with the in-vivo measurements from the Orthoload database, changes in knee joint forces showed similar trends and the slope of changes in torques transmitted by the joint was equal. Against the background of unknown geometrical conditions in the Orthoload measurements and the simplification (hinge joint) of the initial multi-body-model compared to real knee joints, the developed model provides a reasonable basis for further investigations already – and will be refined in future works.

As influencing parameters are very complex, a non-ambiguous interpretation of force/torque changes in the knee joint as a function of changes in component positions was in many cases hardly possible. Changes in patella force on the other hand could be traced back to geometrical and force changes in the quadriceps femoris muscle. Positional changes mostly were in good agreement with our hypotheses based on literature data when knee load and patellar forces respectively were primarily influenced by active structures, e.g. with regard to the danger of patella luxation in case of increased internal rotation of the tibial component. Whereas simulations also showed results contradicting our expectations for positional changes mainly affecting passive structures, e.g. cranial/caudal translation of the femoral component. This shows the major drawback of the implemented model: Intra-articular passive structures such as cruciate and collateral ligaments were not represented. Additionally kinematic influences on knee and patella loading were not taken into account as the simulations were made under static conditions. Implementation of relative movements of femoral, tibial and patella components and simulation under dynamic conditions might overcome this limitation. Furthermore, the boundary condition of complete muscle adaptations might be critical, as joint loads might be significantly higher shortly after operation. This could lead to a much longer and possibly ineffective rehabilitation process.