Thesis

A novel method for knee ligament tension determination using dynamic CT imaging and finite element analysis

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Awarding institution
  • University of Strathclyde
Date of award
  • 2026
Thesis identifier
  • T17607
Person Identifier (Local)
  • 201757803
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Abstract
  • Background: The knee joint is a critical component of the human skeletal system, essential for facilitating movement and supporting load-bearing activities. Each ligament contributes uniquely to restricting the joint’s range of motion across multiple degrees of freedom, ensuring biomechanical stability. However, ligament tensions in vivo are not well known. By estimating ligament tensions during knee flexion, we will not only understand the biomechanics of the native knee better but will inform the soft tissue balancing during knee arthroplasty. This study therefore proposes a novel methodology to quantify ligament tensions in the knee using FEA, informed by fourdimensional computed tomography (4dCT). Methods: High-resolution 4dCT scans from a healthy participant captured the dynamics of the knee joint across a full range of motion. An FEA model of the knee was constructed, and a novel iterative scheme was used to estimate the tension in the patella tendon and four main ligaments of the knee. Point clouds were used to track the knee during movement, self-validating the model, and providing insight into the accuracy of the model in replicating the knee's kinematic behaviour. Results: Integration of 4dCT imaging with iterative finite element analysis (FEA) generated a robust model of knee biomechanics, accurately estimating ligament tensions during flexion. Cloud Compare analysis of patellar point clouds showed 75–86% of nodes within ±2.8–4 mm for early flexion (F02–F06), improving to 93–98% within ±1–3.6 mm in later frames, indicating enhanced alignment accuracy. For the tibia, spatial errors were more variable. During the initial flexion phase, 85–88.3% of nodes were within ±5 mm of the reference. Accuracy decreased in the midrange of motion, with only 63.9–74.8% of nodes within this threshold, before recovering in the final phase of flexion to 91–92.1% within ±5 mm. Combining FEA with 4dCT imaging refined in vivo ligament tension estimates, correlating with observed kinematic motion across flexion angles, validating the model’s efficacy in capturing knee biomechanics. Conclusions: This study successfully developed and validated an innovative methodology for assessing ligament tension and knee kinematics, combining 4dCT imaging with iterative FEA. The approach provided a more accurate quantification of native ligament tensions compared to traditional methods, addressing a critical gap in soft tissue balancing for TKR. The high accuracy of the FEA model, as evidenced by its alignment with 4dCT data, underscores its potential as a reliable tool for biomechanical analysis and clinical applications.
Advisor / supervisor
  • Riches, Philip
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DOI

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