Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.786594
Title: Personalisation of musculoskeletal models using Magnetic Resonance Imaging
Author: Montefiori, Erica
ISNI:       0000 0004 7972 0405
Awarding Body: University of Sheffield
Current Institution: University of Sheffield
Date of Award: 2019
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Abstract:
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.
Supervisor: Mazza', Claudia ; Marzo, Alberto Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.786594  DOI: Not available
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