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http://hdl.handle.net/11375/25960
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DC Field | Value | Language |
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dc.contributor.advisor | Maly, Monica R. | - |
dc.contributor.advisor | Keir, Peter J. | - |
dc.contributor.author | Brenneman Wilson, Elora C. | - |
dc.date.accessioned | 2020-10-21T19:22:25Z | - |
dc.date.available | 2020-10-21T19:22:25Z | - |
dc.date.issued | 2020 | - |
dc.identifier.uri | http://hdl.handle.net/11375/25960 | - |
dc.description.abstract | Understanding articular cartilage mechanics is imperative to gain insight into tissue tolerances and loads, and how these may change with disease. This thesis used a multi-disciplinary approach to quantify cartilage mechanics using magnetic resonance imaging (MRI), a non-invasive tool for the evaluation of soft tissues. Study 1 (Chapter 2) outlined an in vivo study that aimed to identify the effect of 1) running; 2) biological sex; and 3) their interaction on tibial and femoral cartilage thickness changes following running using Statistical Parametric Mapping (SPM). Running caused deformation in all knee compartments, and especially in the lateral tibia (p < 0.0001). Females had thinner cartilage than males (p < 0.009). Finally, clusters indicating an interaction of Running and Sex were identifi ed on the posterior lateral tibia, suggesting females experienced greater cartilage deformation than males (p < 0.012). Studies 2 and 3 (Chapters 3 and 4) integrated MRI and biomechanical datasets to explore the loading environment at the knee. Study 2 investigated the effect of daily cumulative knee load (measured using musculoskeletal modeling and accelerometry) on cartilage response (change in morphology, composition) following running in women. This cumulative loading metric was related to tibial volume change (F(4, 14) = 4:68, p = 0.013, R2 = 0.50), suggesting a potential cartilage conditioning effect. Study 3 outlined the use of an ex vivo porcine stifle model to build a 3D voxelwise statistical map exploring the relationship between cartilage mechanical properties (measured via automated indentation mapping) and cartilage outcomes from MRI (cartilage thickness, T2 change) following static loading. No signi ficant relationships were identi fied; however, this novel integration of indentation mapping and MRI cartilage outcomes shows utility moving forward. Overall, this thesis characterizes cartilage response to biomechanical load using novel tools derived by integrating engineering, biomechanics, and imaging. Together this work provides an exciting opportunity to explore and quantify spatial relationships between mechanical properties and cartilage MRI outcomes. | en_US |
dc.language.iso | en | en_US |
dc.title | Use of magnetic resonance imaging to assess tibiofemoral cartilage behaviour following loading: a multi-disciplinary evaluation of cartilage mechanics | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Kinesiology | en_US |
dc.description.degreetype | Thesis | en_US |
dc.description.degree | Doctor of Philosophy (PhD) | en_US |
dc.description.layabstract | This thesis used a multi-disciplinary approach to characterize articular cartilage mechanics in the knee joint. First, the deformation of knee cartilage following running was quantifi ed using magnetic resonance imaging (MRI), and the effects of 1) running; 2) biological sex; and 3) their interaction were explored using a statistical mapping procedure. Second, daily cumulative knee load (or total load incurred by the knee joint over a day) was related to knee cartilage deformation (via MRI) following running in women. Third, a pig knee model was used to develop a 3D statistical model predicting cartilage mechanical properties from MRI-derived cartilage metrics. This thesis integrated elements of engineering, biomechanics, and medical imaging to derive novel evaluation techniques for the cartilage of the knee joint. This work builds a critical foundation for future development of statistical models that can assist with the assessment of cartilage health and disease management. | en_US |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Description | Size | Format | |
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brenneman_elora_c_202010_phd.pdf | 9.26 MB | Adobe PDF | View/Open |
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