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abstract: This research investigated using impedance as a minimally invasive oral cancer-screening tool by modeling healthy and diseased tissue. This research developed an ultra-structurally based tissue model for oral mucosa that is versatile enough to be easily modified to mimic the passive electrical impedance responses of multiple benign and cancerous tissue types. This new model provides answers to biologically meaningful questions related to the impedance response of healthy and diseased tissues. This model breaks away from the old empirical top down "black box" Thèvinin equivalent model. The new tissue model developed here was created from a bottom up perspective resulting in a model that is analogous to having a "Transparent Box" where each network element relating to a specific structural component is known. This new model was developed starting with sub cellular ultra-structural components such as membranes, proteins and electrolytes. These components formed the basic network elements and topology of the organelles. The organelle networks combine to form the cell networks. The cell networks combine to make networks of cell layers and the cell layers were combined into tissue networks. This produced the complete "Transparent Box" model for normal tissue. This normal tissue model was modified for disease based on the ultra-structural pathology of each disease. The diseased tissues evaluated include cancers type one through type three; necrotic-inflammation, hyperkeratosis and the compound condition of hyperkeratosis over cancer type two. The impedance responses for each of the disease were compared side by side with the response of normal healthy tissue. Comparative evidence from the models showed the structural changes in cancer produce a unique identifiable impedance "finger print." The evaluation of the "Transparent Box" model for normal tissues and diseased tissues show clear support for using comparative impedance measurements as a clinical tool for oral cancer screening. / Dissertation/Thesis / normal oral mucosal tissue model / cancer type 1 oral mucosal tissue model / cancer type 2 oral mucosal tissue model / cancer type 3 oral mucosal tissue model / hyperkeratosis oral mucosal tissue model / hyperkeratosis over cancer type 2 oral mucosal tissue / necrotic inflammation oral tissue model / Ph.D. Electrical Engineering 2012

Cardiovascular disease is the leading cause of non-communicable disease in the whole world killing 17 million people in 2012. Among the many vascular diseases is vascular calcification (VC) which is the mineral build up in the walls of blood vessels. Medial calcification is the plaque buildup in the medial layer of the blood vesicle that is characterized by arterial stiffness and high blood pressure. Current calcification research involves two dimensional (2D) lab methods such as flat petri dishes to investigate the mechanism that causes and inhibits vascular calcification. Research has shown that the use of three-dimensional (3D) models can be beneficial in mimicking the in vitro environment of the human body for lab practices. We aim to create a 3D benchtop model for vascular calcification research from decellularized carotid porcine arteries to understand the more accurate mechanisms that lead to the plaque buildup in the medial layer of the artery wall.

Diffusion magnetic resonance imaging (dMRI) has made a significant breakthrough in neurological disorders and brain research thanks to its exquisite sensitivity to tissue cytoarchitecture. However, as the water diffusion process in neuronal tissues is a complex biophysical phenomena at molecular scale, it is difficult to infer tissue microscopic characteristics on a voxel scale from dMRI data. The major methodological contribution of this thesis is the development of an integrated and generic Monte Carlo simulation framework, 'Diffusion Microscopist Simulator' (DMS), which has the capacity to create 3D biological tissue models of various shapes and properties, as well as to synthesize dMRI data for a large variety of MRI methods, pulse sequence design and parameters. DMS aims at bridging the gap between the elementary diffusion processes occurring at a micrometric scale and the resulting diffusion signal measured at millimetric scale, providing better insights into the features observed in dMRI, as well as offering ground-truth information for optimization and validation of dMRI acquisition protocols for different applications.We have verified the performance and validity of DMS through various benchmark experiments, and applied to address particular research topics in dMRI. Based on DMS, there are two major application contributions in this thesis. First, we use DMS to investigate the impact of finite diffusion gradient pulse duration (delta) on fibre orientation estimation in dMRI. We propose that current practice of using long delta, which is enforced by the hardware limitation of clinical MRI scanners, is actually beneficial for mapping fibre orientations, even though it violates the underlying assumption made in q-space theory. Second, we employ DMS to investigate the feasibility of estimating axon radius using a clinical MRI system. The results suggest that the algorithm for mapping the direct microstructures is applicable to dMRI data acquired from standard MRI scanners.

L'imagerie par résonance magnétique de diffusion (IRMd) a fait une percée significative dans les troubles neurologiques et les recherches sur le cerveau grâce à son extraordinaire sensibilité à la cytoarchitecture des tissus. Cependant, comme le processus de diffusion de l'eau dans les tissus neuronaux est un phénomène biophysique complexe à l'échelle moléculaire, il est difficile d'en déduire les caractéristiques microscopiques des tissus à l'échelle du voxel, à partir des données d'IRMd. La contribution méthodologique majeure de cette thèse est le développement d'un cadre de simulation de type Monte Carlo intégré et générique, appelé `Diffusion Microscopist Simulator' (DMS), qui permet d'élaborer des modèles de tissus biologiques tridimensionnels aux géométries et propriétés variées et qui permet de synthétiser des données d'IRMd correspondantes pour une grande variété d'IRM, de séquences d'impulsions et de paramètres. L'outil DMS vise à combler le fossé entre les processus de diffusion élémentaires, qui se produisent à une échelle micrométrique, et le signal de diffusion résultant, mesuré à l'échelle millimétrique, qui offre un meilleur aperçu des caractéristiques observées dans l'IRMd, tout en offrant une information vérité terrain pour l'optimisation et la validation des protocoles d'acquisition de l'IRMd pour différentes applications.Nous avons vérifié les performances et la validité du simulateur à travers différents tests, et appliqué cet outil pour aborder des thèmes de recherche particuliers à l'IRMd. Il y a deux contributions majeures dans cette thèse. Tout d'abord, nous avons utilisé l'outil DMS pour étudier l'impact de la durée d'impulsions de gradient de diffusion finies (delta) sur l'estimation de l'orientation des fibres par l'IRMd. Nous avons démontré que la pratique actuelle qui utilise un delta long, imposée par la limitation physique des scanners d'IRM cliniques, est en fait bénéfique pour la cartographie des orientations des fibres, même si elle viole l'hypothèse sous-jacente faite dans la théorie de l'espace q. Deuxièmement, nous avons employé le simulateur pour étudier la possibilité d'estimer le rayon des axones en utilisant un système d'IRM clinique. Les résultats suggèrent que la technique d'inférence de la taille des axones reposant sur un modèle analytique de la réponse IRM d'un axone au processus de diffusion est applicable aux données d'IRMd acquises avec des scanners IRM standards. / Diffusion magnetic resonance imaging (dMRI) has made a significant breakthrough in neurological disorders and brain research thanks to its exquisite sensitivity to tissue cytoarchitecture. However, as the water diffusion process in neuronal tissues is a complex biophysical phenomena at molecular scale, it is difficult to infer tissue microscopic characteristics on a voxel scale from dMRI data. The major methodological contribution of this thesis is the development of an integrated and generic Monte Carlo simulation framework, ‘Diffusion Microscopist Simulator' (DMS), which has the capacity to create 3D biological tissue models of various shapes and properties, as well as to synthesize dMRI data for a large variety of MRI methods, pulse sequence design and parameters. DMS aims at bridging the gap between the elementary diffusion processes occurring at a micrometric scale and the resulting diffusion signal measured at millimetric scale, providing better insights into the features observed in dMRI, as well as offering ground-truth information for optimization and validation of dMRI acquisition protocols for different applications.We have verified the performance and validity of DMS through various benchmark experiments, and applied to address particular research topics in dMRI. Based on DMS, there are two major application contributions in this thesis. First, we use DMS to investigate the impact of finite diffusion gradient pulse duration (delta) on fibre orientation estimation in dMRI. We propose that current practice of using long delta, which is enforced by the hardware limitation of clinical MRI scanners, is actually beneficial for mapping fibre orientations, even though it violates the underlying assumption made in q-space theory. Second, we employ DMS to investigate the feasibility of estimating axon radius using a clinical MRI system. The results suggest that the algorithm for mapping the direct microstructures is applicable to dMRI data acquired from standard MRI scanners.

Extensions to k-Wave toolbox used for ultrasound modelling are described. Aim of extensions is to reduce time and space complexity by presenting alternative representations of tissues and transducers in simulation. This project clarifies basic principles and features of k-Wave, describes design of new representations and finally describes implementation of the suggested extensions.