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Personalisation of musculoskeletal models using Magnetic Resonance Imaging

Montefiori, Erica (2019) Personalisation of musculoskeletal models using Magnetic Resonance Imaging. PhD thesis, University of Sheffield.

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Musculoskeletal (MSK) disorders affecting locomotion represent one of the leading causes for disability in the developed countries, impacting on the patients’ lifestyle and social inclusion, as well as the national healthcare resources. Due to the different aetiologies and progression of such diseases, and to the individual needs of patients, personalised assessment is currently promoted as the gold standard for the diagnosis and treatment of MSK disorders. The introduction of MSK models has recently integrated more traditional measurements of gait-related parameters, enabling the simulation of clinical scenarios and rehabilitation plans within a computational environment, therefore limiting the invasiveness of the experiments. However, the lack of standardised and validated procedures currently limits the adoption of these techniques in the clinical practice and restricts their shareability across the research community. The aim of this PhD thesis was to develop an innovative, robust, and repeatable procedure for the definition of MRI-based subject-specific MSKMs of the lower limb. A fully documented procedure (and associated methodologies) for producing such models was proposed. The final scope of this project is to promote the adoption of personalised modelling in the clinical assessment of lower-limb MSK disorders. The versatility of the proposed modelling approach was successfully tested by applying it in cohorts featured by different age (juvenile and elderly), genders and health conditions (juvenile idiopathic arthritis and osteopenia). In particular the model was tested for its ability to: discriminate joint kinematics and joint loadings that are typical of different populations; identify informative biomechanical parameters to characterise disease and disease progression in juvenile idiopathic arthritis; quantify the effect of different physiological muscle features, such as volumes and geometry, on the estimate of joint loading. As a result of the work carried out as part of the above studies, a significant advance in the standardisation and automation of the procedures needed for building fully personalised MRI-based models of the MSK system has been achieved. The model outputs were proved to have good repeatability and reproducibility and to be informative in all above applications. The proposed approach also showed a clear potential toward complementing traditional clinical gait analysis approaches by providing information on the muscle and joint internal forces, otherwise not easily accessible in-vivo. Future work will aim at reducing the cost, operator time, and errors associated to MRI-based MSK modelling by further improving and automating the image processing techniques and even replacing the MRI with affordable and portable technologies, such as ultrasound-based systems.

Item Type: Thesis (PhD)
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Mechanical Engineering (Sheffield)
Identification Number/EthosID: uk.bl.ethos.786594
Depositing User: Miss Erica Montefiori
Date Deposited: 30 Sep 2019 14:21
Last Modified: 01 Nov 2019 10:20
URI: http://etheses.whiterose.ac.uk/id/eprint/24926

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