University of Twente Student Theses
A Protocol Towards Simulating an Image-Based, Motion-Capture-Driven Personalized Musculoskeletal Knee Model
Bahig, Omar (2024) A Protocol Towards Simulating an Image-Based, Motion-Capture-Driven Personalized Musculoskeletal Knee Model.
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| Abstract: | There remains no established treatment for knee osteoarthritis (KOA), underscoring the necessity for personalized musculoskeletal models to tailor treatment approaches. In this assignment, a workflow that would enable simulating an image-based, motion-capture-driven personalized musculoskeletal (MS) knee model to estimate knee joint contact forces is presented, as the initial stage of the TopTreat project, which then can be integrated with a Finite Element model for personalized cartilage testing and treatment. Initially, a healthy female underwent a partial MRI scanning exclusively at her right knee (proximal tibia, distal femur, and patella) and subsequently three gait activities (walking, stepping off, and squatting) were assessed in a motion capture lab. Bones, cartilages, and menisci were segmented from the MRI images, and the generic bone and muscle architecture of the MS model were morphed into the segmented bones. Coordinate systems of the femur, tibia and patella were defined and personalized to the subject’s bone geometries to estimate knee rotational and translational kinematics after driving the model with the recorded gait lab motion. Finally, three contact models were defined using either bones as offsets or the segmented cartilages to estimate the contact forces at the medial and lateral tibiofemoral and patellofemoral compartments. It was found that the maximum morphing error between the source morphed vertex and the target vertex were reported at 6.404mm, 5.130mm, and 4.248mm for the tibia, patella and femur, respectively. Additionally, an error of about 3° was found between the tibia coordinate systems created with the ankle center of the generic model and with the malleoli markers’ location. The optimization process appeared sensitive to the marker’s location on the model especially at the foot segment as it was optimized to 15cm while the subject’s foot length was measured at 22cm. The realization of this workflow revealed the difficulty and complexity of using partial bones to personalize musculoskeletal models mainly due to the absence of the proximal femur and distal tibia scans. Ideally, information at these two regions are required for more reliable personalization. Eventually, it is crucial to validate the model produced by this workflow before utilizing it for the TopTreat project. |
| Item Type: | Essay (Master) |
| Faculty: | TNW: Science and Technology |
| Programme: | Biomedical Engineering MSc (66226) |
| Link to this item: | https://purl.utwente.nl/essays/102199 |
| Export this item as: | BibTeX EndNote HTML Citation Reference Manager |
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