Basic Science to Clinical Research: Segmentation of Ultrasound and Modelling in Clinical Informatics

The world of basic science is a world of minutia; it boils down to improving even a fraction of a percent over the baseline standard. It is a domain of peer reviewed fractions of seconds and the world of squeezing every last ounce of efficiency from a processor, a storage medium, or an algorithm. The field of health data is based on extracting knowledge from segments of data that may improve some clinical process or practice guideline to improve the time and quality of care. Clinical informatics and knowledge translation provide this information in order to reveal insights to the world of improving patient treatments, regimens, and overall outcomes. In my world of minutia, or basic science, the movement of blood served an integral role. The novel detection of sound reverberations map out the landscape for my research. I have applied my algorithms to the various anatomical structures of the heart and artery system. This serves as a basis for segmentation, active contouring, and shape priors. The algorithms presented, leverage novel applications in segmentation by using anatomical features of the heart for shape priors and the integration of optical flow models to improve tracking. The presented techniques show improvements over traditional methods in the estimation of left ventricular size and function, along with plaque estimation in the carotid artery. In my clinical world of data understanding, I have endeavoured to decipher trends in Alzheimer’s disease, Sepsis of hospital patients, and the burden of Melanoma using mathematical modelling methods. The use of decision trees, Markov models, and various clustering techniques provide insights into data sets that are otherwise hidden. Finally, I demonstrate how efficient data capture from providers can achieve rapid results and actionable information on patient medical records. This culminated in generating studies on the burden of illness and their associated costs. A selection of published works from my research in the world of basic sciences to clinical informatics has been included in this thesis to detail my transition. This is my journey from one contented realm to a turbulent one

Anisotropic Diffusion Filter with Memory based on Speckle Statistics for Ultrasound Images

Ultrasound imaging exhibits considerable difficulties for medical visual inspection and for the development of automatic analysis methods due to speckle, which negatively affects the perception of tissue boundaries and the performance of automatic segmentation methods. With the aim of alleviating the effect of speckle, many filtering techniques are usually considered as a preprocessing step prior to automatic analysis methods or visual inspection. Most of the state-of-the-art filters try to reduce the speckle effect without considering its relevance for the characterization of tissue nature. However, the speckle phenomenon is the inherent response of echo signals in tissues and can provide important features for clinical purposes. This loss of information is even magnified due to the iterative process of some speckle filters, e.g., diffusion filters, which tend to produce over-filtering because of the progressive loss of relevant information for diagnostic purposes during the diffusion process. In this work, we propose an anisotropic diffusion filter with a probabilistic-driven memory mechanism to overcome the over-filtering problem by following a tissue selective philosophy. Specifically, we formulate the memory mechanism as a delay differential equation for the diffusion tensor whose behavior depends on the statistics of the tissues, by accelerating the diffusion process in meaningless regions and including the memory effect in regions where relevant details should be preserved. Results both in synthetic and real US images support the inclusion of the probabilistic memory mechanism for maintaining clinical relevant structures, which are removed by the state-of-the-art filters

Microbubbles in vascular imaging

Ultrasound is integral in diagnostic imaging of vascular disease. It is a common first line imaging modality in the detection of deep vein thrombosis (DVT) and carotid atherosclerosis. The therapeutic use of ultrasound in vascular disease is also clinically established through ultrasound thrombolysis for acute DVT. Contrast agents are widely used in other imaging modalities, however, contrast enhanced ultrasound (CEUS) using microbubbles remains a largely specialist clinical investigation with truly established roles in hepatic imaging only. Aim The aim of this thesis was to investigate diagnostic and therapeutic roles of CEUS in vascular disease. Diagnostically, carotid plaque characteristics were evaluated for stroke risk stratification in patients with carotid atherosclerosis. Therapeutically, microbubble augmented ultrasound thrombolysis was investigated in-vitro as a novel technique for acute thrombus removal in the prevention of post thrombotic syndrome. Methods A validated in-vitro flow model of DVT was adapted and developed for a formal feasibility study of microbubble augmented ultrasound thrombolysis. Two cross sectional studies of patients with 50-99% carotid stenosis were performed assessing firstly, plaque ulceration and secondly plaque perfusion using CEUS. Results Using commercially available microbubbles and ultrasound platform, significantly improved thrombus dissolution was demonstrated using CEUS over ultrasound alone in the in-vitro flow model of acute DVT. In particular, increased destruction of the thrombus fibrin mesh network was observed. CEUS demonstrated greater sensitivity than carotid duplex in the detection of carotid plaque ulceration with a trend toward symptomatic carotid plaques. A reduced plaque perfusion detected by both semi-qualitative and quantitative analysis was associated with a symptomatic status in patients with a 50-99% stenosis. Conclusion CEUS is a viable adjunct to vascular imaging with ultrasound. Microbubble augmented ultrasound thrombolysis is a feasible, non-invasive, non-irradiating intervention which warrants further investigation in-vivo. Carotid plaque CEUS may contribute to future scoring systems in stroke risk stratification but requires prospective validation.Open Acces