The glenohumeral joint is an important joint with large mobility of the human upper extremity. In shoulder arthroplasty patients often has an unsatisfactory outcome. In order to understand the biomechanical complexity of the shoulder, a novel computer controlled experimental shoulder simulator with an innovative muscle control were constructed. The main component of the simulator includes the active pneumatic muscles to replicate the deltoid and the rotator-cuff function and two springs as passive muscle. The aim of this study is to evaluate the impact of a variation of shoulder joint geometries on shoulder biomechanics in the basis of motion analysis. The radius of the glenoid cavity varied from 28–33mm with 2.5mm increment while the radius the humeral head are varied from 20.1–25.1 with 2.5mm increment. The “teach-in” function of the simulator allows an operator to assign the movement to the simulator where the lengths of the pneumatic muscles are recorded. Then the simulator repeats the assigned movement according to the recorded muscles length. The daily living activities includes abduction/adduction, internal/external rotation with adducted arm, and circumduction. The results show promising repeatability of the simulator with minor deviation. However, damage on the surface of the humeral head has been found which should be further studied for both shoulder behavior investigation and the shoulder simulator optimisation. Therefore, this study is a decent initial study toward the verification of the simulator and lead to a better understanding of shoulder biomechanical behavior to cope with the clinical problems in the future.

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.

Knee biomechanics after total knee arthroplasty (TKA) has received more attention in recent years. One critical biomechanical aspect involved in the workflow of present TKA strategies is the intraoperative optimisation of ligament balancing. Ligament balancing is usually performed with passive flexion-extension in unloaded situations. Medial and lateral ligaments strains after TKA differ in loaded flexion compared to unloaded passive flexion making the passive unloaded ligament balancing for TKA questionable. To address this problem, the development of detailed and specific knowledge on the biomechanical behaviour of loaded knee structures is essential. Stress MRI techniques were introduced in previous studies to evaluate loaded joint kinematics. Previous studies captured the knee movement either in atypical loading supine positions, or in upright positions with help of inclined supporting backrests being insufficient for movement capture under full body weight-bearing conditions.

In this work, we proposed a combined MR imaging approach for measurement and assessment of knee kinematics under full body weight-bearing in single legged stance as a first step towards the understanding of complex biomechanical aspects of bony structures and soft tissue envelope. The proposed method is based on registration of high resolution static MRI data (supine acquisition) with low resolution data, quasi-static upright-MRI data (loaded flexion positions) and was applied for the measurement of tibio-femoral kinematics in 10 healthy volunteers. The high resolution MRI data were acquired using a 1.5T Philips-Intera system, while the quasi-static MRI data (full bodyweight-bearing) was obtained with a 0.6T Fonar-Upright™ system. Contours of femur, tibia, and patella from both MRI techniques were extracted using expert manual segmentation. Anatomical surface models were then obtained for the high resolution static data.

The upright-MRI acquisition consisted of Multi-2D, quasi-static sagittal scans each including 4 slices for each flexion angle. Starting with full knee extension, the subjects were asked to increase the flexion in 4–5 steps to reach the maximum flexion angle possible under space and force limitations. Knees were softly padded for stabilisation in lateral-medial direction only in order to reduce motion artifacts. During the upright acquisition the subjects were asked to transfer their bodyweight onto the leg being imaged and maintain the predefined flexion position in single legged stance. The acquisition at every flexion angle was obtained near the scanner's isocenter and takes ∼39 seconds.

The anatomical surface models of the static data were each registered to their corresponding contours from the weight-bearing scans using an iterative closest point (ICP) based approach. A reference registration step was carried out to register the surface models to the full extension loaded position. The registered surfaces from this step were then considered as initial conditions for next ICP registration step. This procedure was similarly repeated to ensure successful registrations between subsequent flexion acquisitions.

The tibio-femoral kinematics was calculated using the joint coordinate system (JCS). The combined MR imaging approach allows the non-invasive measurement of kinematics in single legged stance and under physiological full weight-bearing conditions. We believe that this method can provide valuable insights for TKA for the validation of patient-specific biomechanical models.

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.

Knee biomechanics after total knee arthroplasty (TKA) has received more attention in recent years. One critical biomechanical aspect involved in the workflow of present TKA strategies is the intraoperative optimisation of ligament balancing. Ligament balancing is usually performed with passive flexion-extension in unloaded situations. Medial and lateral ligaments strains after TKA differ in loaded flexion compared to unloaded passive flexion making the passive unloaded ligament balancing for TKA questionable. To address this problem, the development of detailed and specific knowledge on the biomechanical behavior of loaded knee structures is essential. Stress MRI techniques were introduced in previous studies to evaluate loaded joint kinematics. Previous studies captured the knee movement either in atypical loading supine positions, or in upright positions with help of inclined supporting backrests being insufficient for movement capture under full body weight-bearing conditions.

In this work, we proposed a combined MR imaging approach for measurement and assessment of knee kinematics under full body weight-bearing in single legged stance as a first step towards the understanding of complex biomechanical aspects of bony structures and soft tissue envelope. The proposed method is based on registration of high resolution static MRI data (supine acquisition) with low resolution data, quasi-static upright-MRI data (loaded flexion positions) and was applied for the measurement of tibio-femoral kinematics in 10 healthy volunteers. The high resolution MRI data were acquired using a 1.5T Philips-Intera system, while the quasi-static MRI data (full bodyweight-bearing) was obtained with a 0.6T Fonar-Upright™ system. Contours of femur, tibia, and patella from both MRI techniques were extracted using expert manual segmentation. Anatomical surface models were then obtained for the high resolution static data.

The upright-MRI acquisition consisted of Multi-2D, quasi-static sagittal scans each including 4 slices for each flexion angle. Starting with full knee extension, the subjects were asked to increase the flexion in 4–5 steps to reach the maximum flexion angle possible under space and force limitations. Knees were softly padded for stabilisation in lateral-medial direction only in order to reduce motion artifacts. During the upright acquisition the subjects were asked to transfer their bodyweight onto the leg being imaged and maintain the predefined flexion position in single legged stance. The acquisition at every flexion angle was obtained near the scanner's isocenter and takes ∼39 seconds.

The anatomical surface models of the static data were each registered to their corresponding contours from the weight-bearing scans using an iterative closest point (ICP) based approach. A reference registration step was carried out to register the surface models to the full extension loaded position. The registered surfaces from this step were then considered as initial conditions for next ICP registration step. This procedure was similarly repeated to ensure successful registrations between subsequent flexion acquisitions.

The tibio-femoral kinematics was calculated using the joint coordinate system (JCS). The combined MR imaging approach allows the non-invasive measurement of kinematics in single legged stance and under physiological full weight-bearing conditions. We believe that this method can provide valuable insights for TKA for the validation of patient-specific biomechanical 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.

Cup position planning for total hip replacement (THR) is a complex task which is influenced by several factors. Whereas aspects like appropriate implant fixation and bone stock preservation are rather evaluated according to intra-operative findings, functional analyses using biomechanical hip models can rely on pre-operative imaging. Due to the wide availability and cost-efficiency of X-ray imaging technology along with the common restriction of biomechanical evaluations to the frontal plane, pre-operative imaging for such purposes is usually limited to AP radiographs. One example is biomechanical optimisation based on the so-called BLB score, which has already been introduced into clinical practice. In this approach, the assumed suitability of potential hip centres of rotation (CORs) is presented to the surgeon by applying colour-coding within the pre-operative AP radiograph. However, to realise the plan, the surgeon has to transfer the 2D positions presented in the radiograph into the 3D surgical site.

We developed a CT-based simulation tool allowing for the generation of 3D bone surface models as well as standardised digitally reconstructed radiographs (DRRs). Within a 3D view, the cup, which is represented as a hemisphere, can freely be shifted in the coronal plane. The 2D point corresponding to the COR defined by the hemisphere is then automatically computed.

In our study, four CT datasets of hips with large bony defects were used. After segmentation 3D bone surface models were generated. These bone surface models were aligned on the basis of the pelvic coordinate system [3], and standardised AP DRRs were computed. BLB score evaluation in intact hips assumes that the central beam passes through the centroid of both hip CORs. As only the contra-lateral hip COR was available due to the defects, a virtual ipsi-lateral COR was obtained by mirroring the contra-lateral hip across the mid-sagittal plane.

Twelve surgeons (divided into two groups of six each according to their experience) had the task to shift the cup such that its 3D position would best match a predefined 2D target position, which was close to the virtual ipsi-lateral COR and displayed as a cross within the standardised DRR. However, the current 2D position corresponding to the current 3D position was not revealed during positioning. Once the user was satisfied with the 3D position, the corresponding 2D position was recorded.

The following results were obtained (mean ± SD across six surgeons of the respective group) for the four patients:

x-error, more experienced: 2.0 ± 6.1; −3.0 ± 5.9; 4.1 ± 4.8; 2.1 ± 5.2; x-error, less experienced: 4.3 ± 4.2; −3.1 ± 1.8; 1.9 ± 4.0; 5.2 ± 4.1; |x-error|, more experienced: 5.2 ± 3.0; 5.4 ± 3.2; 5.5 ± 2.7; 4.3 ± 3.0;|x-error|, less experienced: 4.3 ± 4.2; 3.1 ± 1.8; 3.3 ± 2.7; 5.7 ± 3.3; y-error, more experienced: 12.0 ± 9.1; 0.3 ± 4.3; 6.2 ± 6.6; 1.9 ± 3.2;

y-error, less experienced: 6.1 ± 3.1; 0.8 ± 4.0; 2.4 ± 5.5; 1.4 ± 4.1;|y-error|, more experienced: 12.0 ± 9.1; 3.2 ± 2.6; 6.2 ± 6.6; 3.0 ± 1.9;|y-error|, less experienced: 6.1 ± 3.1; 3.4 ± 1.6; 4.6 ± 3.3; 3.2 ± 2.6;total error, more experienced: 13.5 ± 8.9; 6.6 ± 3.5; 9.8 ± 4.1; 5.4 ± 3.4;total error, less experienced: 8.5 ± 2.7; 4.9 ± 1.5; 6.5 ± 2.5; 6.7 ± 3.8.

Our experimental results show that mental 2D/3D matching for cup positioning in pelvises with bony defects is a difficult task, and that mental 2D/3D matching cannot be expected to yield the correct 3D cup positions corresponding to positions predefined in radiographs. The largest errors were found in the patient with the lowest image quality suggesting that image quality plays an important role. On contrary, experience was not found to be an important factor.

We believe that in clinical practice mental 2D/3D matching between pre-operative radiographs and the surgical site without the help of 3D imaging or special tools would be more difficult than the task given in this study because only small portions of the pelvis would be exposed. Furthermore, as additional aspects of cup positioning would need to be taken into consideration simultaneously, the mental load could be expected to be higher. We conclude that in hips with large bony defects cup positioning based on pre-operative radiographs is highly unreliable without additional computer-assistance or intra-operative imaging. If pre-operative radiographs are needed for functional analyses, combination with 3D image data seems attractive: Firstly, 3D images can easily be used for navigation; secondly, they allow for the generation of highly standardised views, which is essential for comparability across multiple patients.

Future studies relying on more datasets with a wider range of defects could also investigate whether cranio-caudal or medio-lateral positioning errors prevail. This is an interesting question since the BLB score usually is much more specific in the medio-lateral direction than in the cranio-caudal direction, implying that correct 2D/3D matching for the cranio-caudal direction appears less important. In the current study involving only four hips, however, no clear tendency could be observed.

This work has been funded in part by the German Ministry for Education and Research (BMBF) in the framework of the orthoMIT project under grant No. BMBF 01EQ0802/BMBF 01IBE02C.

Consideration of biomechanical aspects during computer assisted orthopaedic surgery (CAOS) is recommendable in order to obtain satisfactory long-term results in total hip arthroplasty (THA). In addition to the absolute value of the hip joint resultant force R the pre- and post-operative orientation of R is an important aspect in the context of the development of a planning module for computer-assisted THA and furthermore for planning of acetabular orientation in periacetabular osteotomy interventions. It is possible to estimate the orientation of hip joint resultant force R for individual patients based on geometrical and anthropometrical parameters. The aim of this study was to examine how far the choice of the mathematical model influences the computational results for the orientation of R in the frontal plane. A further aspect was the comparison of the results with in-vivo data published in the open access OrthoLoad database (www.orthoload.com).

Our comparative study included the 2D-models suggested by Pauwels, Blumentritt and Debrunner as well as the 3D-model suggested by Iglič and three patient datasets from the Orthoload database. As computation of R according to each model relies on standardized X-ray imaging, three anterior-posterior (a.p.) digitally reconstructed radiographs (DRRs) were generated from CT data (x21_x21, x8_x8, x12_x12). The orientation of R was expressed in terms of the angle δ for these three patient individual datasets. The angle δ is defined as the angle between the longitudinal axis and R. The computation results were also compared with in vivo telemetric measurement data from the OrthoLoad database. The following data were used to evaluate R in the frontal plane: the highest load peak of the single leg stance (static conditions) of three patients (EBL, HSR, KWR) respectively in the same manner for planar gait (dynamic conditions) of one patient (KWR). The mean value of the orientation of R under static conditions in single leg stance was calculated in order to get a reference value. For the orientation of R under dynamic conditions δ was calculated by using only the highest peak of three cycles (heel strike to toe off) determined in one single patient (among the three patients involved in the measurements under static conditions) of the database.

The following values of δ were obtained:

Pauwels: 18.26°/20.34°/17.31° (x21_x21/x8_x8/x12_x12) Debrunner: 12.37°/14.30°/12.59° Blumentritt: 5.18°/6.52°/6.14° Iglič: 9.24°/9.01°/9.20°

OrthoLoad database (in-vivo): 28.41°/17.08°/13.32°-static (EBL/HSR/KWR) 16.44°-dynamic (KWR)

The differences in the computational results appear to depend more on the hip model than on the variability of patient-specific geometrical and anthropometrical parameters. The results obtained with in-vivo measurement data are best approximated by using Pauwels' model. The mean values of Pauwels (18.64°), Debrunner (13.09°) and Iglič (9.15°) are a little bit more vertically orientated than the mean value of the static in-vivo results (19.60°). Only Pauwels' model result has a larger angle δ than the in-vivo dynamic result (KWR = 16.44°). By comparing the in-vivo values obtained under dynamic conditions, i.e. gait, (16.44°) with the static in-vivo values of the same patient (13.32°), it could be recognized that the static values are a little bit more vertically orientated than the dynamic result. But both are in the same range as the mathematical models.

The computational biomechanical hip models try to approximate the physiological conditions of the hip joint and the OrthoLoad database represents the physiological reconstructed (artificial) hip joint. Therefore, we think our validation approach is useful for a comparison of the biomechanical computation models.

In contrast, Blumentritt's model outcomes have the largest deviation from the other models as well as from the in-vivo data (static and dynamic conditions). Blumentritt used the weight bearing surface as a reference. He defined it being perpendicular to the longitudinal axis [3]. He postulated that a valid and optimal orientation of R is approximately perpendicular on the weight bearing surface respectively parallel to the longitudinal axis. This approach for validation is questionable because the results show that in the three included and analysed DDR's the orientation is in the mean value 5.95° to the longitudinal axis. It can be concluded that Blumentritt's model assumptions have to be carefully reviewed due to the deviations from in-vivo measurement data.

Among the limitations of our study is the fact that the OrthoLoad database offers only a small number of patient datasets. There is only one dataset for the direct comparison of static (single leg stance) and dynamic (free planar gait) in-vivo measurement data of the same patient included. Furthermore, the individual anatomic geometry data of the patients included in the database are not revealed. Additionally, a source of errors could be an inaccuracy during the data acquisition from the DRR.

Further research seems to be recommendable in the context of implementing a biomechanical hip model in a planning module for computer-assisted THA or periacetabular osteotomy interventions, respectively. Sensitivity analyses and parameter studies for different mathematical models using a multi-body-simulation system are objectives of our ongoing work.

Biomechanical considerations are relevant to cup positioning in total hip replacement (THR) to optimise the patient-specific post-operative outcome. One goal is to place the hip centre of rotation (COR) such that parameters characterising the biomechanics of the hip joint lie within physiological ranges. Different biomechanical models have been developed and are based on exact knowledge about muscle insertion points whose positions can be estimated on the basis of bony landmarks. Therefore, accurate landmark localisation is necessary to obtain reliable and comparable parameter values.

As most biomechanical considerations are limited to the frontal plane, landmark localisation relying on standardised pre-operative radiographs has been established in clinical practice. One potential drawback of this approach is that user-interactive landmark localisation in radiographs might be more error-prone and subjective than localisation in 3D images. Therefore, we investigated the possibility of increasing the reproducibility of interactive landmark localisation by providing 3D localisation techniques. As the so-called BLB score based on Blumentritt's biomechanical hip model has already been introduced into clinical practice as a criterion for cup position planning, we examined the anatomical landmarks involved in BLB score evaluation. We developed a CT-based simulation tool allowing for the generation of 3D bone surface models and standardised digitally reconstructed radiographs (DRRs). Correspondences between points in the 2D DRR and rays in the 3D bone surface model are automatically established and optionally visualised by the tool.

Two modes of landmark localisation were examined: In the 2D-mode, only AP DRRs were displayed, and the users had to localise the landmarks by clicking within the DRR image. In the 3D-mode, additionally the arbitrarily rotatable bone surface models together with the aforementioned 2D/3D correspondences were visualised. The user could then choose between landmark localisation by clicking either within the DRR image or within the 3D view. In either case, the 2D landmark positions within the DRR were recorded.

The participants were given both an example AP pelvis radiograph with highlighted anatomical landmarks and the following landmark descriptions from the user's manual (v2.06) of the mediCAD software (Hectec GmbH, Landshut, Germany): P4: ca. 3cm distal lesser trochanter minor (in the imagined direction of pull of the rectus femoris muscle towards the medial upper edge of the patella); P5:lateral, most proximal edge of the trochanter major; P6: most cranial edge of the sclerotic area; P7:spina iliaca anterior inferior; P8/P9:most lateral/cranial point of the wing of the ilium.

(P1 and P2 are only needed to define the position of the mid-sagittal plane, and P3 is the pre-operative COR. Due to correct radiograph standardisation, we assumed this plane and P3 to be known prior to landmark localisation.)

Thirteen surgeons repeated the experiments on four hips (CT datasets of two male patients).

The following results were obtained (SD of relevant coordinates obtained with 2D localisation vs. SD of those obtained with 3D localisation) in the first patient (left hip: 1L; right hip: 1R) and the second patient (left hip: 2L; right hip: 2R):P4: 6.3 vs. 9.0 (1L); 6.7 vs. 5.6 (1R); 9.0 vs. 11.1 (2L); 7.1 vs. 8.6 (2R); P5: 4.4 vs. 2.8 (1L); 3.1 vs. 3.1 (1R); 4.3 vs. 2.4 (2L); 4.7 vs. 4.1 (2R); P6: 4.8 vs. 3.8 (1L); 2.9 vs. 2.8 (1R); 3.7 vs. 5.2 (2L); 6.9 vs. 3.5 (2R); P7: 12.2 vs. 6.1 (1L); 12.1 vs. 3.7 (1R); 7.6 vs. 4.6 (2L); 6.2 vs. 4.5 (2R); P8: 1.2 vs. 2.8 (1L); 2.0 vs. 2.6 (1R); 1.5 vs. 2.1 (2L); 2.0 vs. 1.6 (2R);P8: 4.1 vs. 2.1 (1L); 7.3 vs. 3.9 (1R); 1.6 vs. 2.6 (2L); 4.1 vs. 3.2 (2R).

The greatest differences in reproducibility were observed in P7, which was barely distinguishable in the radiographs and, hence, showed very low reproducibility only for the 2D-mode. P4 showed low reproducibility in both modes due to its vague description and the relatively small portions of the femurs contained in the CT-scanned volume. In P9 the low reproducibility obtained with the 2D-mode might be partly explained by truncation artefacts present in the DRRs.

Although our study needs to be extended to more datasets, we conclude that the availability of 3D data allows for higher landmark localisation reproducibility when compared with the conventional X-ray-based approach, which has additional drawbacks: Standardisation of X-ray imaging, which is necessary to retain comparability of biomechanical parameter values determined in different patients, is hard to achieve; specifications e.g. concerning the central beam may be met only after acquiring several radiographs. Moreover, once a 2D target cup position is defined based on the 2D biomechanical analyses, the transfer of this position into the 3D surgical site is difficult without additional 3D imaging.

Hence, the use of 3D imaging and 3D landmark localisation techniques seems more promising for cup positioning based on biomechanical models, which, however, need validation.