Robust Eigen-Filter Design for Ultrasound Flow Imaging Using a Multivariate Clustering

Blood flow visualization is a challenging task in the presence of tissue motion. Unsuppressed tissue clutter produces flashing artefacts in ultrasound flow imaging which hampers blood flow detection by dominating part of the blood flow signal in certain challenging clinical imaging applications, ranging from cardiac imaging (maximal tissue vibrations) to microvascular flow imaging (very low blood flow speeds). Conventional clutter filtering techniques perform poorly since blood and tissue clutter echoes share similar spectral characteristics. Eigen-based filtering was recently introduced and has shown good clutter rejection performance; however, flow detection performance in eigen filtering suffers if tissue and flow signal subspaces overlap after eigen components are projected to a single signal feature space for clutter rank selection. To address this issue, a novel multivariate clustering based singular value decomposition (SVD) filter design is developed. The proposed multivariate clustering based filter robustly detects and removes non-blood eigen components by leveraging on three key spatiotemporal statistics: singular value magnitude, spatial correlation and the mean Doppler frequency of singular vectors. A better clutter suppression framework is necessary for high-frame-rate (HFR) ultrasound imaging since it is more susceptible to tissue motion due to poorer spatial resolution (tissue clutter bleeds into flow pixels easily). Hence, to test the clutter rejection performance of the proposed filter, HFR plane wave data was acquired from an in vitro flow phantom testbed and in vivo from a subject’s common carotid artery and jugular vein region induced with extrinsic tissue motion (voluntary probe motion). The proposed method was able to adaptively detect and preserve blood eigen components and enabled fully automatic identification of eigen components corresponding to tissue clutter, blood and noise that removes dependency on the operator for optimal rank selection. The flow detection efficacy of the proposed multivariate clustering based SVD filter was statistically evaluated and compared with current clutter rank estimation methods using the receiver operating characteristic (ROC) analysis. Results for both in vitro and in vivo experiments showed that the multivariate clustering based SVD filter yielded the highest area under the ROC curve at both peak systole (0.98 for in vitro; 0.95 for in vivo) and end diastole (0.96 for in vitro; 0.93 for in vivo) in comparison with other clutter rank estimation methods, signifying its improved flow detection capability. The impact of this work is on the automated as well as adaptive (in contrast to a fixed cut-off) selection of eigen components which can potentially allow to overcome the flow detection challenges associated with fast tissue motion in cardiovascular imaging and slow flow in microvascular imaging which is critical for cancer diagnoses

Ultrasound Imaging Innovations for Visualization and Quantification of Vascular Biomarkers

The existence of plaque in the carotid arteries, which provide circulation to the brain, is a known risk for stroke and dementia. Alas, this risk factor is present in 25% of the adult population. Proper assessment of carotid plaque may play a significant role in preventing and managing stroke and dementia. However, current plaque assessment routines have known limitations in assessing individual risk for future cardiovascular events. There is a practical need to derive new vascular biomarkers that are indicative of cardiovascular risk based on hemodynamic information. Nonetheless, the derivation of these biomarkers is not a trivial technical task because none of the existing clinical imaging modalities have adequate time resolution to track the spatiotemporal dynamics of arterial blood flow that is pulsatile in nature. The goal of this dissertation is to devise a new ultrasound imaging framework to measure vascular biomarkers related to turbulent flow, intra-plaque microvasculature, and blood flow rate. Central to the proposed framework is the use of high frame rate ultrasound (HiFRUS) imaging principles to track hemodynamic events at fine temporal resolution (through using frame rates of greater than 1000 frames per second). The existence of turbulent flow and intra-plaque microvessels, as well as anomalous blood flow rate, are all closely related to the formation and progression of carotid plaque. Therefore, quantifying these biomarkers can improve the identification of individuals with carotid plaque who are at risk for future cardiovascular events. To facilitate the testing and the implementation of the proposed imaging algorithms, this dissertation has included the development of new experimental models (in the form of flow phantoms) and a new HiFRUS imaging platform with live scanning and on-demand playback functionalities. Pilot studies were also carried out on rats and human volunteers. Results generally demonstrated the real-time performance and the practical efficacy of the proposed algorithms. The proposed ultrasound imaging framework is expected to improve carotid plaque risk classification and, in turn, facilitate timely identification of at-risk individuals. It may also be used to derive new insights on carotid plaque formation and progression to aid disease management and the development of personalized treatment strategies

Clutter Suppression in Ultrasound: Performance Evaluation of Low-Rank and Sparse Matrix Decomposition Methods

Vessel diseases are often accompanied by abnormalities related to vascular shape and size. Therefore, a clear visualization of vasculature is of high clinical significance. Ultrasound Color Flow Imaging (CFI) is one of the prominent techniques for flow visualization. However, clutter signals originating from slow-moving tissue is one of the main obstacles to obtain a clear view of the vascular network. Enhancement of the vasculature by suppressing the clutters is an essential step for many applications of ultrasound CFI. In this thesis, we focus on a state-of-art algorithm framework called Decomposition into Low-rank and Sparse Matrices (DLSM) framework for ultrasound clutter suppression. Currently, ultrasound clutter suppression is often performed by Singular Value Decomposition (SVD) of the data matrix, which is a branch of eigen-based filtering. This approach exhibits two well-known limitations. First, the performance of SVD is sensitive to the proper manual selection of the ranks corresponding to clutter and blood subspaces. Second, SVD is prone to failure in the presence of large random noise in the data set. A potential solution to these issues is the use of DLSM framework. SVD, as a means for singular values, is also one of the widely used algorithms for solving the minimization problem under the DLSM framework. Many other algorithms under DLSM avoid full SVD and use approximated SVD or SVD-free ideas which may have better performance with higher robustness and lower computing time due to the expensive computational cost of full SVD. In practice, these models separate blood from clutter based on the assumption that steady clutter represents a low-rank structure and the moving blood component is sparse. In this thesis, we exploit the feasibility of exploiting low-rank and sparse decomposition schemes, originally developed in the field of computer vision, in ultrasound clutter suppression. Since ultrasound images have different texture and statistical properties compared to images in computer vision, it is of high importance to evaluate how these methods translate to ultrasound CFI. We conduct this evaluation study by adapting 106 DLSM algorithms and validating them against simulation, phantom and in vivo rat data sets. The advantage of simulation and phantom experiments is that the ground truth vessel map is known, and the advantage of the in vivo data set is that it enables us to test algorithms in a realistic setting. Two conventional quality metrics, Signal-to-Noise Ratio (SNR) and Contrast-to-Noise Ratio (CNR), are used for performance evaluation. In addition, computation times required by different algorithms for generating the clutter suppressed images are reported. Our extensive analysis shows that the DLSM framework can be successfully applied to ultrasound clutter suppression

Automated Analysis of 3D Stress Echocardiography

__Abstract__ The human circulatory system consists of the heart, blood, arteries, veins and capillaries. The heart is the muscular organ which pumps the blood through the human body (Fig. 1.1,1.2). Deoxygenated blood flows through the right atrium into the right ventricle, which pumps the blood into the pulmonary arteries. The blood is carried to the lungs, where it passes through a capillary network that enables the release of carbon dioxide and the uptake of oxygen. Oxygenated blood then returns to the heart via the pulmonary veins and flows from the left atrium into the left ventricle. The left ventricle then pumps the blood through the aorta, the major artery which supplies blood to the rest of the body [Drake et a!., 2005; Guyton and Halt 1996]. Therefore, it is vital that the cardiovascular system remains healthy. Disease of the cardiovascular system, if untreated, ultimately leads to the failure of other organs and death

Intravascular Detection of Microvessel Infiltration in Atherosclerotic Plaques: An Intraluminal Extension of Acoustic Angiography

Cardiovascular disease is the leading cause of death worldwide, surpassing both stroke and cancer related mortality with 17.5 million deaths in 2014 alone. Atherosclerosis is the build-up of fatty deposits within arteries and is responsible for the majority of cardiovascular related deaths. Over the past decade, research in atherosclerosis has identified that a key limitation in the appropriate management of the disease is detecting and identifying dangerous fatty plaque build-ups before they dislodge and cause major cardiovascular events, such as embolisms, stroke, or myocardial infarctions. It has been noted that plaques vulnerable to rupture have several key features that may be used to distinguish them from asymptomatic plaques. One key identifier of a dangerous plaque is the presence of blood flow within the plaque itself since this is an indicator of growth and instability of the plaque. Recently, a superharmonic imaging method known as “acoustic angiography” has been shown to resolve microvasculature with unprecedented quality and could be a possible method of detecting blood vessel infiltration within these plaques. This dissertation describes the material and methods used to move the application of “acoustic angiography” to a reduced form factor typical of intravascular catheters and to demonstrate its ability to detect microvasculature. The implementation of this approach is described in terms of the contrast agents used to generate superharmonic signals, the dual-frequency transducers to image them, and the hardware needed to operate them in order to establish how these design choices can impact the quality of the images produced. Furthermore, this dissertation demonstrates how image processing methods such as adaptive windowing or automated sound speed correction can further enhance image quality of vascular targets. The results of these chapters show how acoustic angiography may be optimized using engineering considerations both in signal acquisition and post processing. Overall, these studies demonstrate that acoustic angiography can be performed using a catheter-deployable dual-frequency transducer to detect microvasculature through superharmonic imaging methods.Doctor of Philosoph

Preclinical MRI of the Kidney

This Open Access volume provides readers with an open access protocol collection and wide-ranging recommendations for preclinical renal MRI used in translational research. The chapters in this book are interdisciplinary in nature and bridge the gaps between physics, physiology, and medicine. They are designed to enhance training in renal MRI sciences and improve the reproducibility of renal imaging research. Chapters provide guidance for exploring, using and developing small animal renal MRI in your laboratory as a unique tool for advanced in vivo phenotyping, diagnostic imaging, and research into potential new therapies. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and thorough, Preclinical MRI of the Kidney: Methods and Protocols is a valuable resource and will be of importance to anyone interested in the preclinical aspect of renal and cardiorenal diseases in the fields of physiology, nephrology, radiology, and cardiology. This publication is based upon work from COST Action PARENCHIMA, supported by European Cooperation in Science and Technology (COST). COST (www.cost.eu) is a funding agency for research and innovation networks. COST Actions help connect research initiatives across Europe and enable scientists to grow their ideas by sharing them with their peers. This boosts their research, career and innovation. PARENCHIMA (renalmri.org) is a community-driven Action in the COST program of the European Union, which unites more than 200 experts in renal MRI from 30 countries with the aim to improve the reproducibility and standardization of renal MRI biomarkers

Preclinical MRI of the kidney : methods and protocols

This Open Access volume provides readers with an open access protocol collection and wide-ranging recommendations for preclinical renal MRI used in translational research. The chapters in this book are interdisciplinary in nature and bridge the gaps between physics, physiology, and medicine. They are designed to enhance training in renal MRI sciences and improve the reproducibility of renal imaging research. Chapters provide guidance for exploring, using and developing small animal renal MRI in your laboratory as a unique tool for advanced in vivo phenotyping, diagnostic imaging, and research into potential new therapies. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and thorough, Preclinical MRI of the Kidney: Methods and Protocols is a valuable resource and will be of importance to anyone interested in the preclinical aspect of renal and cardiorenal diseases in the fields of physiology, nephrology, radiology, and cardiology. This publication is based upon work from COST Action PARENCHIMA, supported by European Cooperation in Science and Technology (COST). COST (www.cost.eu) is a funding agency for research and innovation networks. COST Actions help connect research initiatives across Europe and enable scientists to grow their ideas by sharing them with their peers. This boosts their research, career and innovation. PARENCHIMA (renalmri.org) is a community-driven Action in the COST program of the European Union, which unites more than 200 experts in renal MRI from 30 countries with the aim to improve the reproducibility and standardization of renal MRI biomarkers

Neuroimaging - Clinical Applications

Modern neuroimaging tools allow unprecedented opportunities for understanding brain neuroanatomy and function in health and disease. Each available technique carries with it a particular balance of strengths and limitations, such that converging evidence based on multiple methods provides the most powerful approach for advancing our knowledge in the fields of clinical and cognitive neuroscience. The scope of this book is not to provide a comprehensive overview of methods and their clinical applications but to provide a "snapshot" of current approaches using well established and newly emerging techniques