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

Image segmentation and reconstruction of 3D surfaces from carotid ultrasound images

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200

Classification approach for diagnosis of arteriosclerosis using B-mode ultrasound carotid images

Tese de mestrado. Engenharia Biomédica. Faculdade de Engenharia. Universidade do Porto. 201

In Vivo MRI-Based Three-Dimensional Fluid-Structure Interaction Models and Mechanical Image Analysis for Human Carotid Atherosclerotic Plaques

Introduction. Atherosclerotic plaque rupture may occur without warning leading to severe clinical events such as heart attack and stroke. The mechanisms causing plaque rupture are not well understood. It is hypothesized that mechanical forces may play an important role in the plaque rupture process and that image-based computational mechanical analysis may provide useful information for more accurate plaque vulnerability assessment. The objectives of this dissertation are: a) develop in vivo magnetic resonance imaging (MRI)-based 3D computational models with fluid-structure Interactions (FSI) for human atherosclerotic carotid plaques; b) perform mechanical analysis using 3D FSI models to identify critical stress/strain conditions which may be used for possible plaque rupture predictions. Data, Model, and Methods. Histological, ex vivo/ in vivo MRI data of human carotid plaques were provided by the University of Washington Medical School and Washington University Medical School. Blood flow was assumed to be laminar, Newtonian, viscous and incompressible. The Navier-Stokes equations with arbitrary Lagrangian-Eulerian (ALE) formulation were used as the governing equations for the flow model. The vessel and plaque components were assumed to be hyperelastic, isotropic, nearly-incompressible and homogeneous. The nonlinear Mooney-Rivlin model was used to describe the nonlinear properties of the materials with parameter values chosen to match available experimental data. The fully-coupled FSI models were solved by a commercial finite element software ADINA to obtain full 3D flow and stress/strain distributions for analysis. Validation of the computational models and Adina software were provided by comparing computational solutions with analytic solutions and experimental data. Several novel methods were introduced to address some fundamental issues for construction of in vivo MRI-based 3D FSI models: a) an automated MRI segmentation technique using a Bayes theorem with normal probability distribution was implemented to obtain plaque geometry with enclosed components; b) a pre-shrink process was introduced to shrink the in vivo MRI geometry to obtain the no-load shape of the plaque; c) a Volume Component-Fitting Method was introduced to generate a 3D computational mesh for the plaque model with deformable complex geometry, FSI and inclusions; d) a method using MRI data obtained under in vitro pressurized conditions was introduced to determine vessel material properties. Results. The effects of material properties on flow and wall stress/strain behaviors were evaluated. The results indicate that a 100% stiffness increase may decrease maximal values of maximum principal stress (Stress-P1) and maximum principal strain (Strain-P1) by about 20% and 40%, respectively; flow Maximum-Shear-Stress (FMSS) and flow velocity did not show noticeable changes. By comparing ex vivo and in vivo data of 10 plaque samples, the average axial (25%) and inner circumferential (7.9%) shrinkages of the plaques between loaded and unloaded state were obtained. Effects of the shrink-stretch process on plaque stress/strain distributions were demonstrated based on six adjusted 3D FSI models with different shrinkages. Stress-P1 and Strain-P1 increased 349.8% and 249% respectively with 33% axial stretch. The effects of a lipid-rich necrotic core and fibrous cap thickness on structure/flow behaviors were investigated. The mean values of wall Stress-P1 and Strain-P1 from lipid nodes from a ruptured plaque were significantly higher than those from a non-ruptured plaque (112.3 kPa, 0.235 & 80.1 kPa, 0.185), which was 40.2% and 26.8% higher, respectively (p\u3c0.001). High stress/strain concentrations were found at the thin fibrous cap regions. These results indicate that high stress concentrations and thin fibrous cap thickness might be critical indicators for plaque vulnerability. Conclusion. In vivo image-based 3D FSI models and mechanical image analysis may have the potential to provide quantitative risk indicators for plaque vulnerability assessment

Carotid Artery Wall Imaging: Perspective and Guidelines from the ASNR Vessel Wall Imaging Study Group and Expert Consensus Recommendations of the American Society of Neuroradiology

SUMMARY: Identification of carotid artery atherosclerosis is conventionally based on measurements of luminal stenosis and surface irregularities using in vivo imaging techniques including sonography, CT and MR angiography, and digital subtraction angiography. However, histopathologic studies demonstrate considerable differences between plaques with identical degrees of stenosis and indicate that certain plaque features are associated with increased risk for ischemic events. The ability to look beyond the lumen using highly developed vessel wall imaging methods to identify plaque vulnerable to disruption has prompted an active debate as to whether a paradigm shift is needed to move away from relying on measurements of luminal stenosis for gauging the risk of ischemic injury. Further evaluation in randomized clinical trials will help to better define the exact role of plaque imaging in clinical decision-making. However, current carotid vessel wall imaging techniques can be informative. The goal of this article is to present the perspective of the ASNR Vessel Wall Imaging Study Group as it relates to the current status of arterial wall imaging in carotid artery disease

Segmentation of biomedical images using active contour model with robust image feature and shape prior

In this article, a new level set model is proposed for the segmentation of biomedical images. The image energy of the proposed model is derived from a robust image gradient feature which gives the active contour a global representation of the geometric configuration, making it more robust in dealing with image noise, weak edges, and initial configurations. Statistical shape information is incorporated using nonparametric shape density distribution, which allows the shape model to handle relatively large shape variations. The segmentation of various shapes from both synthetic and real images depict the robustness and efficiency of the proposed method