Accurately quantifying in-vivo knee kinematics is essential for understanding the biomechanical factors contributing to the development and progression of osteoarthritis. While optical motion capture systems are widely used for this purpose, their accuracy is limited by soft tissue artefacts, particularly in overweight individuals, a demographic at increased risk for knee osteoarthritis. Fluoroscopic imaging offers a more direct measurement of bone motion, but requires reliable 2D–3D registration techniques to reconstruct the 6 degree of freedom joint kinematics. Most existing 2D–3D registration workflows rely on CT-based bone models and intensity-based similarity measures. These are impractical for clinical studies due to radiation exposure concerns. MRI-based bone models are considered an alternative but the suitability of intensity-based methods for MRI-based models is limited. Because of this, alternate MRI-based registration methods need to be considered. This thesis explores the feasability of a registration workflow that registers MRI-based models to segmented fluoroscopic images of the knee. Three feature-based similarity measures, the edge matching Score (EMS), shape matching (SM) and normalized edge distance (NED), were implemented. Ten fluoroscopic images of the same healthy participant were registered with the three developed registration pipelines. Validation was performed by comparing the six degree-of-freedom pose to the pose obtained from a manually registered reference standard. The results show that the accuracy of the three similarity measures is comparable, but the shape matching offered superior computational efficiency. Registration was about twice as fast as the other similarity measures with an average registration time of 104.6 seconds per frame. The mean absolute error (MAE) was less than 0.45 mm for translation parallel to the fluoroscope image plane. The translation perpendicular to the image plane remained a significant source of error, with a MAE of less than 4.56 mm. Similarly, the MAE for rotation about the axis perpendicular to the image plane was less than 0.52◦. Rotation about the other two axes was less accurate with a MAE of less than 1.84◦. This study demonstrates the feasibility of using MRI-based bone models and segmented fluoroscopic images for 2D–3D registration of the knee in-vivo. While limitations remain. Particularly in achieving sub millimeter accuracy for the translation perpendicular to the image plane, and sub-degree accuracy for rotation about the axes that are parallel to the image plane, and generalizing to lower-quality images. Despite this, the results are broadly in line with existing literature. Future work should focus on performing more rigorous validation against a more accurate silver- or even gold standard, testing the registration pipeline on fluoroscopic images that are a better representation of clinical use and developing an automated segmentation algorithm to replace the manual segmentation. Additionally, integrating calibrated optical motion capture data to provide an initial pose estimate during optimization could further improve accuracy and reduce the need for human intervention