The knee joint is one of the most complex and weight-bearing joints in the body, making it highly susceptible to injury from various activities. Knee surgery often becomes necessary when conservative treatments fail to alleviate pain and other related disorders. In 2020, research indicated that there were nearly 60,000 total knee replacements (TKRs) for women and approximately 50,000 TKRs for men across England, Wales, Northern Ireland, and the Isle of Man. Projections suggest that by 2060, the demand for hip and knee replacements in the UK will rise by 40%. Robotic knee surgery, a minimally invasive and computer-assisted orthopaedic surgery (CAOS), allows for precise surgical movements, leading to quicker recovery and reduced postoperative pain. However, according to NHS Patient Reported Outcome Measures (PROMs), around 4% of patients in England remain dissatisfied with their knee replacement outcomes, primarily due to implant malalignment. Virtual 3D knee models, generated from CT and MRI scans, play a critical role in improving implant alignment before surgery. These models enable preoperative planning by allowing surgeons to virtually model the patient's knee in 3D, optimizing implant selection and simulating postoperative range of motion. However, the mechanical functionality of the knee joint remains poorly understood, and researchers are actively exploring improvements through finite element analysis (FEA). FEA is a valuable tool for simulating the mechanical behaviour of the knee under various conditions, helping surgeons and biomedical engineers analyse stress distribution, implant stability, and soft tissue interactions. Although existing finite element (FE) knee models provide highly detailed meshes of anatomical structures like bones, cartilage, ligaments, and tendons, these models are complex, time-consuming to create, and prone to human error, making them unsuitable for analysing large image datasets. This brings us to our primary research question: What is the impact of using simplified soft tissue models on finite element simulations of subject-specific knee joints? Can we create an FE model that incorporates elastic, homogeneous soft tissue around knee bones instead of modelling individual ligaments and cartilage? This approach is inspired by a study by Arjmand, which replaced soft tissue in the proximal tibia with an incompressible cylindrical medium. However, that model did not adequately represent the joint's volume or surface topology. In our study, we propose a simplified FE model where all soft tissues and bony structures are contiguous, maximizing anatomical accuracy. One of the critical steps in creating subject-specific 3D models for FEA is segmentation, which, as our systematic review revealed, suffers from significant variability. Variability in the segmentation process introduces uncertainty into the quantitative data, affecting the reliability of the resulting models. To assess this variability, we conducted inter- and intra-observer variability tests, which are commonly performed in various fields but are notably lacking in the literature for knee joint surgeries. Our secondary aims included determining the intra- and inter-examiner variability in semi-automatic segmentation performed by one operator and 15 operators, respectively. Additionally, we sought to determine the optimal threshold values for knee joint tissues during segmentation, using thresholding techniques. We segmented the tibia at various thresholds and compared the results to a reference tibia segmented at 205 HU. The effect of thresholding proved significant, impacting the final model by causing under- or over-segmentation. The optimal threshold values were identified as 205 HU for the tibia, 160 HU for the femur, 200 HU for the patella, and 232 HU for the fibula. In a pilot study, intra-observer variability was assessed by having one participant segment the knee five times, with the results compared using the Cloud-to-Cloud (C2C) method. The highest similarity (93.39%) was observed between the fourth and fifth segmentations, indicating that operator experience influences the segmentation process. Following ethical approval, 15 volunteers were trained to segment the femur, tibia, and patella five times using ITK-Snap software. Graphical assess intra-observer variability in MATLAB. Inter-observer variability for DSC was calculated using the intraclass correlation coefficient (ICC) in IBM SPSS. The ICC for DSC was 0.975 for the femur, 0.981 for the tibia, and 0.959 for the patella, indicating excellent reliability in the segmentation process. The femur and patella exhibited high DSC and Jaccard Index values, while the tibia had the highest Hausdorff Distance. After confirming the segmentation process’s reliability, we segmented the knee twice more, including the soft tissues, making the model subject-specific. These models were imported into Ansys for FEA, where the soft tissue was modelled as isotropic, homogeneous, and hyperplastic with a neo-Hookean material model (shear modulus: 1 MPa, Poisson's ratio: 0.45). The von Mises strain in the soft tissue following an applied force on the tibia was 1.42 µm for the first knee and 2.43 µm for the second, reflecting a 71% difference. The von Mises stress was 637 Pa and 728 Pa, respectively, showing a 14.2% difference. The articular cartilage experienced the highest stress and strain. Our study successfully simplified the modelling of soft tissue in knee FE models while achieving convergence. The results demonstrated that simulation outcomes are highly sensitive to even minor variations in segmentation. Despite the tibias lower similarity (higher Hausdorff distances), the overall agreement between operators remained consistent. Our findings show good to excellent reliability for segmenting the tibia, patella, and femur in 4D CT images of the knee joint across multiple observers. comparisons were performed using CloudCompare, and quantitative metrics, including Hausdorff Distance, Dice Similarity Coefficient (DSC), and Jaccard Index, were computed to assess intra-observer variability in MATLAB. Inter-observer variability for DSC was calculated using the intraclass correlation coefficient (ICC) in IBM SPSS. The ICC for DSC was 0.975 for the femur, 0.981 for the tibia, and 0.959 for the patella, indicating excellent reliability in the segmentation process. The femur and patella exhibited high DSC and Jaccard Index values, while the tibia had the highest Hausdorff Distance. After confirming the segmentation process’s reliability, we segmented the knee twice more, including the soft tissues, making the model subject-specific. These models were imported into Ansys for FEA, where the soft tissue was modelled as isotropic, homogeneous, and hyperplastic with a neo-Hookean material model (shear modulus: 1 MPa, Poisson's ratio: 0.45). The von Mises strain in the soft tissue following an applied force on the tibia was 1.42 µm for the first knee and 2.43 µm for the second, reflecting a 71% difference. The von Mises stress was 637 Pa and 728 Pa, respectively, showing a 14.2% difference. The articular cartilage experienced the highest stress and strain. Our study successfully simplified the modelling of soft tissue in knee FE models while achieving convergence. The results demonstrated that simulation outcomes are highly sensitive to even minor variations in segmentation. Despite the tibias lower similarity (higher Hausdorff distances), the overall agreement between operators remained consistent. Our findings show good to excellent reliability for segmenting the tibia, patella, and femur in 4D CT images of the knee joint across multiple observers.
| Date of Award | 5 Feb 2025 |
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| Original language | English |
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| Awarding Institution | - University Of Strathclyde
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| Sponsors | University of Strathclyde |
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| Supervisor | Phil Riches (Supervisor) & Craig Childs (Supervisor) |
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