Multi-scale imaging and modelling of bone

The multi-level organization of bone facilitates the exploitation of in-vivo micro-scale information which is currently lacking for clinical applications. The three sub-projects presented in this thesis investigate the human skeletal system at multiple scales using magnetic resonance imaging (MRI) with the aim of providing new techniques for extracting finer scale information in-vivo. At the whole organ level, human knee joint kinematics was studied using a combined MRI strategy. This new strategy enables the in-vivo investigation of tibiofemoral locomotion under body weight-bearing conditions by modelling the knee flexion angle as a function of the femur and tibia cartilage surfaces in contact. The resultant "contact" trajectory may potentially be used to understand the mechanical cause of cartilage degeneration and as a biomarker to detect abnormalities in the lower limb. At the molecular level, in-vivo MR diffusion tensor imaging (DTI) has been performed for the first time in the human tibia epiphysis. By tracking the water molecules inside the red marrow, the organization of trabecular bone network may be understood as the streamlines formed by anisotropic diffusion trajectories. This sub-project aims to understand the organization of trabecular bone networks non-invasively, which is usually performed ex-vivo through biopsies. The feasibility and reproducibility of DTI is studied. Finally, a new MR imaging protocol named multi-directional sub-pixel enhancement (mSPENT) is proposed and developed to quantify the trabecular bone structural arrangement at the meso-scale. By modulating a dephasing gradient to manipulate the underlying spin system inside each voxel, the resulting mSPENT image contrast varies with gradient at different directions based on the magnetization at the corresponding voxel. A tensor-based method is further developed to model this contrast change, leading to a localized quantification of tissue structural orientation beyond the conventional MR imaging resolution

Automated segmentation and characterisation of white matter hyperintensities

Neuroimaging has enabled the observation of damage to the white matter that occurs frequently in elderly population and is depicted as hyperintensities in specific magnetic resonance images. Since the pathophysiology underlying the existence of these signal abnormalities and the association with clinical risk factors and outcome is still investigated, a robust and accurate quantification and characterisation of these observations is necessary. In this thesis, I developed a data-driven split and merge model selection framework that results in the joint modelling of normal appearing and outlier observations in a hierarchical Gaussian mixture model. The resulting model can then be used to segment white matter hyperintensities (WMH) in a post-processing step. The validity of the method in terms of robustness to data quality, acquisition protocol and preprocessing and its comparison to the state of the art is evaluated in both simulated and clinical settings. To further characterise the lesions, a subject-specific coordinate frame that divides the WM region according to the relative distance between the ventricular surface and the cortical sheet and to the lobar location is introduced. This coordinate frame is used for the comparison of lesion distributions in a population of twin pairs and for the prediction and standardisation of visual rating scales. Lastly the cross-sectional method is extended into a longitudinal framework, in which a Gaussian Mixture model built on an average image is used to constrain the representation of the individual time points. The method is validated through a purpose-build longitudinal lesion simulator and applied to the investigation of the relationship between APOE genetic status and lesion load progression

Computer modeling and signal analysis of cardiovascular physiology

This dissertation aims to study cardiovascular physiology from the cellular level to the whole heart level to the body level using numerical approaches. A mathematical model was developed to describe electromechanical interaction in the heart. The model integrates cardio-electrophysiology and cardiac mechanics through excitation-induced contraction and deformation-induced currents. A finite element based parallel simulation scheme was developed to investigate coupled electrical and mechanical functions. The developed model and numerical scheme were utilized to study cardiovascular dynamics at cellular, tissue and organ levels. The influence of ion channel blockade on cardiac alternans was investigated. It was found that the channel blocker may significantly change the critical pacing period corresponding to the onset of alternans as well as the alternans’ amplitude. The influence of electro-mechanical coupling on cardiac alternans was also investigated. The study supported the earlier assumptions that discordant alternans is induced by the interaction of conduction velocity and action potential duration restitution at high pacing rates. However, mechanical contraction may influence the spatial pattern and onset of discordant alternans. Computer algorithms were developed for analysis of human physiology. The 12-lead electrocardiography (ECG) is the gold standard for diagnosis of various cardiac abnormalities. However, disturbances and mistakes may modify physiological waves in ECG and lead to wrong diagnoses. This dissertation developed advanced signal analysis techniques and computer software to detect and suppress artifacts and errors in ECG. These algorithms can help to improve the quality of health care when integrated into medical devices or services. Moreover, computer algorithms were developed to predict patient mortality in intensive care units using various physiological measures. Models and analysis techniques developed here may help to improve the quality of health care