Musculoskeletal models have represented for decades one of the most important research tools to understand how the human body works, and to have a better comprehension of the dysfunctions of the locomotor system. In the last years, progress in musculoskeletal mechanics, medical imaging and computational power have tremendously increased the detail and complexity of musculoskeletal models. Today, the use of musculoskeletal simulations holds significant promise for applications in the biomedical industry, in order to improve the diagnosis and treatment for patients, and in the near future model predictions are expected to gain a central role in personalized healthcare. Work presented in this thesis aimed to develop accurate and efficient methods to create subject-specific musculoskeletal models of the lower extremity. A new comprehensive and complete dataset, called Twente Lower Extremity Model 2.0, based on CT and MRI scans and dissection measurements, was purposely built to be easily combined with innovative image-based scaling techniques. TLEM 2.0 represented the starting point of a streamlined modeling workflow to create subject-specific models, using medical images and functional measurements, in a semi-automatic way with limited manual intervention. This modeling workflow was applied to ten healthy subjects in order to build a unique set of ten subject-specific musculoskeletal models, then effects of personalization and improvements in model predictions were assessed using the novel FDG-PET technique. This thesis shows that it is possible today to build a subject-specific model of the lower extremity in an efficient way, and the improvements in the model predictions make the personalization process worth the effort. Future research on developing more accurate validation techniques are needed to gain the reliability necessary for critical scenarios, such as surgical planning and clinical decision-making process, in order to reach the ambitious goal of application of subject-specific musculoskeletal models to clinical practice on a large scale.
|Award date||26 May 2016|
|Place of Publication||Enschede|
|Publication status||Published - 26 May 2016|