Advanced signal processing methods for plane-wave color Doppler ultrasound imaging

Conventional medical ultrasound imaging uses focused beams to scan the imaging scene line-by-line, but recently however, plane-wave imaging, in which plane-waves are used to illuminate the entire imaging scene, has been gaining popularity due its ability to achieve high frame rates, thus allowing the capture of fast dynamic events and producing continuous Doppler data. In most implementations, multiple low-resolution images from different plane wave tilt angles are coherently averaged (compounded) to form a single high-resolution image, albeit with the undesirable side effect of reducing the frame rate, and attenuating signals with high Doppler shifts. This thesis introduces a spread-spectrum color Doppler imaging method that produces high-resolution images without the use of frame compounding, thereby eliminating the tradeoff between beam quality, frame rate and the unaliased Doppler frequency limit. The method uses a Doppler ensemble formed of a long random sequence of transmit tilt angles that randomize the phase of out-of-cell (clutter) echoes, thereby spreading the clutter power in the Doppler spectrum without compounding, while keeping the spectrum of in-cell echoes intact. The spread-spectrum method adequately suppresses out-of-cell blood echoes to achieve high spatial resolution, but spread-spectrum suppression is not adequate for wall clutter which may be 60 dB above blood echoes. We thus implemented a clutter filter that re-arranges the ensemble samples such that they follow a linear tilt angle order, thereby compacting the clutter spectrum and spreading that of the blood Doppler signal, and allowing clutter suppression with frequency domain filters. We later improved this filter with a redesign of the random sweep plan such that each tilt angle is repeated multiple times, allowing, after ensemble re-arrangement, the use of comb filters for improved clutter suppression. Experiments performed using a carotid artery phantom with constant flow demonstrate that the spread-spectrum method more accurately measures the parabolic flow profile of the vessel and outperforms conventional plane-wave Doppler in both contrast resolution and estimation of high flow velocities. To improve velocity estimation in pulsatile flow, we developed a method that uses the chirped Fourier transform to reduce stationarity broadening during the high acceleration phase of pulsatile flow waveforms. Experimental results showed lower standard deviations compared to conventional intensity-weighted-moving-average methods. The methods in this thesis are expected to be valuable for Doppler applications that require measurement of high velocities at high frame rates, with high spatial resolution

An investigation of real time ultrasound Doppler techniques for tissue motion and deformation analysis

Cardiovascular disease accounts for more than 50% of all deaths in the Western world. Atherosclerosis is responsible for the vast majority of these diseases. There are a range of risk factors for atherosclerosis that affect the endothelial lining vessel wall cells to cause endothelial dysfunction, which then predisposes to a localized build-up of 'plaque' tissue that narrows the lumen of the arteries. Plaque rupture promotes localized vasospasm, thrombosis and embolism causing downstream tissue death, resulting in severe disability or death from, for instance, heart attack (in the coronary circulation) or stroke (in the cerebral circulation). Narrowing of the lumen and plaque rupture are associated with high tissue stresses and tissue under perfusion, which will alter local arterial and myocardial wall dynamics and elastic properties. Hence visualization of tissue dynamic and deformation property changes is crucial to detect atherosclerosis in the earliest stages to prevent acute events.The objective of this dissertation research is to develop new techniques based on Doppler ultrasound to investigate and visualize changes in tissue dynamic and deformation properties due to atherosclerosis in cardiac and vascular applications. A new technique, to correct for the Doppler angle dependence for tissue motion analysis has been developed. It is based on multiple ultrasound beams, and has been validated in vitro to study tissue dynamic properties. It can measure tissue velocity magnitude with low bias (5%) and standard deviation (10%), and tissue velocity orientation with a bias less than 5 degrees and a standard deviation below 5 degrees. A new Doppler based method, called strain rate, has also been developed and validated in vitro for the quantification of regional vessel or myocardial wall deformation. Strain rate is derived from the velocity information and can assess tissue deformation with an accuracy of 5% and a standard deviation less than 10%. Some examples of cardiac strain rate imaging have been gathered and are described in this thesis. Strain rate, as all Doppler based techniques, suffers from angle dependence limitation. A method to estimate one-component strain rate in any direction in the two-dimensional image not necessarily along the ultrasound beam has been developed. The method allows correcting for the strain rate bias along any user-defined direction. It is also shown that the full strain rate tensor can theoretically be extracted from the velocity vector field acquired by multiple beam tissue vector velocity technique. In vitro experiments have shown that qualitative two-component strain rate tensor can be derived. Two-component vector velocity from the moving tissue was acquired and two two-component strain rate images were derived. The images showed agreement with the expected deformation pattern

An Experimental Study of Different Signal Processing Methods on Ultrasonic Velocity Profiles in a Single Phase Flow

Ultrasonic velocity profile (UVP) measurement methods have been continuously developed in the field of engineering. A UVP can visualize a fluid flow along a benchmark line. This provides a significant advantage over other conventional methods such as differential pressure, turbine, and vortex. This paper presents an experimental study of using different signal processing methods including autocorrelation (AC), fast Fourier transform (FFT), maximum likelihood estimation (MLE), multiple signal classification (MUSIC), and Estimation of signal parameter via rotational invariance technique (ESPRIT) under diverse situations as the number of pulse repetitions (Nprf), frequency of repetitions (fprf), velocity profiles, computation – time requirements and flowrates. Experimental results express that there is an optimal number and frequency of pulse repetitions for each signal processing method that depended on fprf, Nprf, and flowrate. Moreover, computation-time and statistical tests were verified from experimental results. From the comparisons, MLE was experimentally the best algorithm even though the trade-off of moderate computation-time requirements was realized. However, considering the optimization of both accuracy and computation-time consumption, MLE was determined as the preferred signal processing method based on UVP for estimating flowrate in existing water reactors. &nbsp

Detection of Spatial and Temporal Interactions in Renal Autoregulation Dynamics

Renal autoregulation stabilizes renal blood flow to protect the glomerular capillaries and maintain glomerular filtration rates through two mechanisms: tubuloglomerular feedback (TGF) and the myogenic response (MR). It is considered that the feedback mechanisms operate independently in each nephron (the functional unit of the kidney) within a kidney, but renal autoregulation dynamics can be coupled between vascular connected nephrons. It has also been shown that the mechanisms are time-varying and interact with each other. Understanding of the significance of such complex behavior has been limited by absence of techniques capable of monitoring renal flow signals among more than 2 or 3 nephrons simultaneously. The purpose of this thesis was to develop approaches to allow the identification and characterization of spatial and temporal properties of renal autoregulation dynamics. We present evidence that laser speckle perfusion imaging (LSPI) effectively captures renal autoregulation dynamics in perfusion signals across the renal cortex of anaesthetized rats and that spatial heterogeneity of the dynamics is present and can be investigated using LSPI. Next, we present a novel approach to segment LSPI of the renal surface into phase synchronized clusters representing areas with coupled renal autoregulation dynamics. Results are shown for the MR and demonstrate that when a signal is present phase synchronized regions can be identified. We then describe an approach to identify quadratic phase coupling between the TGF and MR mechanisms in time and space. Using this approach we can identify locations across the renal surface where both mechanisms are operating cooperatively. Finally, we show how synchronization between nephrons can be investigated in relation to renal autoregulation effectiveness by comparing phase synchronization estimates from LSPI with renal autoregulation system properties estimated from renal blood flow and blood pressure measurements. Overall, we have developed approaches to 1) capture renal autoregulation dynamics across the renal surface, 2) identify regions with phase synchronized renal autoregulation dynamics, 3) quantify the presence of the TGF-MR interaction across the renal surface, and 4) determine how the above vary over time. The described tools allow for investigations of the significance and mechanisms behind the complex spatial interactions and time-varying properties of renal autoregulation dynamics

Ultrasound Determination of Absolute Backscatter from Arterial Wall Structures

This thesis presents an ultrasound technique for measuring the absolute integrated backscatter (IBS) of arterial wall structures through an intervening inhomogeneous soft tissue layer. The aberrating effect of this tissue layer is minimized by normalizing the measured IBS from the wall region of interest with the IBS from an adjacent range cell in blood. The technique may become a tool to differentiate between stable and vulnerable plaques in the carotid artery

Quantifying atherosclerosis in vasculature using ultrasound imaging

Cerebrovascular disease accounts for approximately 30% of the global burden associated with cardiovascular diseases [1]. According to the World Stroke Organisation, there are approximately 13.7 million new stroke cases annually, and just under six million people will die from stroke each year [2]. The underlying cause of this disease is atherosclerosis – a vascular pathology which is characterised by thickening and hardening of blood vessel walls. When fatty substances such as cholesterol accumulate on the inner linings of an artery, they cause a progressive narrowing of the lumen referred to as a stenosis. Localisation and grading of the severity of a stenosis, is important for practitioners to assess the risk of rupture which leads to stroke. Ultrasound imaging is popular for this purpose. It is low cost, non-invasive, and permits a quick assessment of vessel geometry and stenosis by measuring the intima media thickness. Research is showing that 3D monitoring of plaque progression may provide a better indication of sites which are at risk of rupture. Various metrics have been proposed. From these, the quantification of plaques by measuring vessel wall volume (VWV) using the segmented media-adventitia boundaries (MAB) and lumen-intima boundaries (LIB) has been shown to be sensitive to temporal changes in carotid plaque burden. Thus, methods to segment these boundaries are required to help generate VWV measurements with high accuracy, less user interaction and increased robustness to variability in di↵erent user acquisition protocols.ii This work proposes three novel methods to address these requirements, to ultimately produce a highly accurate, fully automated segmentation algorithm which works on intensity-invariant data. The first method proposed was that of generating a novel, intensity-invariant representation of ultrasound data by creating phase-congruency maps from raw unprocessed radio-frequency ultrasound information. Experiments carried out showed that this representation retained the necessary anatomical structural information to facilitate segmentation, while concurrently being invariant to changes in amplitude from the user. The second method proposed was the novel application of Deep Convolutional Networks (DCN) to carotid ultrasound images to achieve fully automatic delineation of the MAB boundaries, in addition to the use of a novel fusion of amplitude and phase congruency data as an image source. Experiments carried out showed that the DCN produces highly accurate and automated results, and that the fusion of amplitude and phase yield superior results to either one alone. The third method proposed was a new geometrically constrained objective function for the network's Stochastic Gradient Descent optimisation, thus tuning it to the segmentation problem at hand, while also developing the network further to concurrently delineate both the MAB and LIB to produce vessel wall contours. Experiments carried out here also show that the novel geometric constraints improve the segmentation results on both MAB and LIB contours. In conclusion, the presented work provides significant novel contributions to field of Carotid Ultrasound segmentation, and with future work, this could lead to implementations which facilitate plaque progression analysis for the end�user