AUTOMATIC PARAMETERIZATION OF THE PROXIMAL TIBIA BASED ON 3D SURFACE DATA FOR MORPHOLOGICAL ANALYSIS AND IMPLANT OPTIMIZATION Malte Asseln Dipl.-Ing.* , Maximilian C.M. Fischer Dipl.-Ing., Christoph Hänisch Dipl.-Ing., Klaus Radermacher PhD *
Chair of Medical Engineering, Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, Aachen, 52074, Germany,
[email protected] INTRODUCTION Total knee arthroplasty (TKA) can effectively treat end-stage arthritis with the aim of relieving pain, restoring functionality and providing stability and durability. However, longevity and patient-satisfaction still remain a concern. Patient specific parameters such as implant design, alignment and soft-tissue management are major factors under consideration. Patient-specific implants have shown the potential to offer certain, inherent benefits in TKA such as complete coverage of cancellous bone and conformity of the implant with the actual individual anatomy (Palumbo et al. 2012). However, for an optimal implant design, especially in the case of deformities, a systematic description of morphological knee joint parameters and a study of their effects on the individual biomechanical situation are essential. In literature a large number of various parameters and measurement methods have been proposed to characterize the proximal tibial morphology and to find correlations to kinematics, loading situations and resulting pathologies (Hashemi et al. 2008; Giffin et al. 2004). Mahfouz et al. investigated shape differences in ethnic groups by applying a feature extraction methodology to calculate 9 linear and angular measurements of the tibia (Mahfouz et al. 2012). Based on our previous work on the parameterization of the femur (Asseln et al. 2015), the aim of this study was to identify morphological parameters and to develop and evaluate a full parametric model of the proximal tibia for comprehensive morphological analysis of those which also enables systematic variation of certain parameters for multiparameter optimization of the knee shape. MATERIALS AND METHODS Based on an extensive literature research and general functional considerations, a total number of 19 morphological parameters of the tibia have been identified. These were clustered into groups such as tibial slopes, curvatures, dimensions, etc. The full automatic computational framework was developed in MATLAB (The MathWorks, Inc., Natick, Massachusetts, United States) and intended to process 3D surface data (STL-format) of the proximal tibia acquired from CT respectively MRI. In order to test and evaluate the framework overall 436 bone surfaces (gender: 168 men/252 women /16 unknown, side: 209 right/227 left knee, mean age: 62.5 years) of the proximal tibia were available. The tibial surface data were segmented semi-automatically to obtain osteophyte free data, covered the region of approximately 10 cm below the joint gap and originated from patients undergoing TKA. First, the program takes the initial reference coordinate Subsequently, the coordinate (Cobb et al. 2008) and the
3D surface model from a specific input folder and calculates an system located in the barycentre of the model (Figure 1). system is adjusted based on references described by Cobb et al. ankle joint centre. This is used to cut the condyles in the sagittal
and frontal plane. Afterwards the program identifies automatically, according to a predefined process scheme, several reference points in the cutting contours for the parameterization such as turning points and points of inflection. Thereby, a distinction is made between concavity and convexity. In the area between the reference points geometrical primitives (ellipses) are fitted to approximate the bony contour. At this stage, the user/program could manipulate the parameters of the ellipses in order to systematically change the approximated shape. Then, a 3D surface is generated by using a spline-interpolation method. The program was tested and optimized based on 20 datasets and finally applied to the whole data pool. 19 morphological parameters were analysed for every proximal tibia surface and mean plus standard deviation were calculated. RESULTS The developed framework was able to process 408 of 436 datasets (93.6 %) full automatically without any need for interaction, algorithmic or parametric adjustment. The results of selected analyzed parameters are presented in Table 1. It is noticeable that the mean MSTS and LSTS are the same. Looking at the curvature, the mean MSCE is positive whereas the LSCE is negative, which means that the medial plateau is rather concave and the lateral plateau convex in shape. Comparing MSH/LSH and MFW/LFW it can be seen that the medial plateau is in average bigger than the lateral. Table 1: S elected morphological parameters of 408 proximal tibiae
Groups
Parameter
Tibial Slopes
Coronal Tibial Slope (CTS) [°]
4.6
2.2
Medial Sagittal Tibial Slope (MSTS) [°]
6.7
3.3
Lateral Sagittal Tibial Slope (LSTS) [°]
6.7
4.3
Medial Frontal Tibial Slope (MFTS) [°]
15.8
4.3
Lateral Frontal Tibial Slope (LFTS) [°]
5.5
3.9
Medial Sagittal Curvature Extent (MSCE) [mm]
1.2
0.7
Lateral Sagittal Curvature Extent (LSCE) [mm]
-2.6
2.4
Medial Frontal Curvature Extent (MFCE) [mm]
2.7
1.0
Lateral Sagittal Curvature Extent (LSCE) [mm]
1.6
0.9
Mediolateral Width (ML) [mm]
73.5
5.9
Anteroposterior height (AP) [mm]
47.3
4.1
Medial Sagittal Height (MSH) [mm]
46.4
3.9
Lateral Sagittal Height (LSH) [mm]
41.7
5.2
Medial Frontal Width (MFW) [mm]
26.1
4.1
Lateral Frontal Width (LFW) [mm]
24.0
3.4
Curvature
Dimensions
Mean SD
DISCUSSION We presented a framework to automatically parameterize the proximal tibial morphology based on 3D surface data. The framework was robust towards pathological input data and inter-individual differences and only failed when input data strongly varied from the assumed
initial alignment. Compared to literature, the morphological parameters are in good agreement, e.g. Hashemi et al. reported similar values of MSTS and LSTS for 55 healthy patients (-3 to 10 °) (Hashemi et al. 2008). However, they showed recognisable differences between medial and lateral tibial slopes. In our study, the slopes were equal in average which might be based on the used patient data from mostly osteoarthritic knees. Regarding the dimensions, e.g. ML, such as other parameters, was similar to the results of Mahfouz et al. (73.5±5.9 vs. 79.3±3.8) (Mahfouz et al. 2012). Only MSH and LSH differed, which might be due to slightly different definitions. In conclusion, this framework might offer the opportunity to study the effect of proximal tibial morphology on knee biomechanics in combination with validated biomechanical simulation models or experimental setups. New insights could directly be used for patientspecific implant optimization. REFERENCES
Asseln, M.; Hänisch, C.; Al Hares, G.; Eschweiler, J.; Radermacher, K. (Hg.) (2015): Automatic Parameterization Of The Distal Femur Based On 3d Surface Data: A Novel Approach For Systematic Morphological Analysis And Optimization. 15th Annual Meeting of the International Society for Computer Assisted Orthopaedic Surgery. Vancouver, Canada.
Cobb, J. P.; Dixon, H.; Dandachli, W.; Iranpour, F. (2008): The anatomical tibial axis: reliable rotational orientation in knee replacement. In: The Journal of bone and joint surgery. British volume 90 (8), S. 1032–1038. DOI: 10.1302/0301-620X.90B8.19905.
Giffin, J. Robert; Vogrin, Tracy M.; Zantop, Thore; Woo, Savio L. Y.; Harner, Christopher D. (2004): Effects of increasing tibial slope on the biomechanics of the knee. In: The American journal of sports medicine 32 (2), S. 376–382.
Hashemi, Javad; Chandrashekar, Naveen; Gill, Brian; Beynnon, Bruce D.; Slauterbeck, James R.; Schutt, Robert C. et al. (2008): The geometry of the tibial plateau and its influence on the biomechanics of the tibiofemoral joint. In: The Journal of bone and joint surgery. American volume 90 (12), S. 2724–2734. DOI: 10.2106/JBJS.G.01358.
Mahfouz, Mohamed; Abdel Fatah, Emam Elhak; Bowers, Lyndsay Smith; Scuderi, Giles (2012): Three-dimensional morphology of the knee reveals ethnic differences. In: Clinical orthopaedics and related research 470 (1), S. 172–185. DOI: 10.1007/s11999-011-2089-2.
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DISCLOSURE This work has been supported in parts by Conformis, Inc., Bedford, USA.
Figure 1: Framework for parameterization of the proximal tibia
