Browsing by Author "Trahey, Gregg E"

Cardiovascular disease is the leading cause of death for developed countries, including the United States. In order to diagnose and detect certain cardiac diseases, it is necessary to assess myocardial performance and function. One mechanical property that has been shown to reflect myocardial performance is myocardial stiffness. Acoustic radiation force impulse (ARFI) imaging has been demonstrated to be capable of visualizing variations in local stiffness within soft tissue.

In this thesis, the initial investigations into the visualization of myocardial performance with ARFI imaging are presented. In vivo ARFI images were acquired with a linear array placed on exposed canine hearts. When co-registered with the electrocardiogram (ECG), ARFI images of the heart reflected the expected changes in myocardial stiffness through the cardiac cycle. With the implementation of a quadratic motion filter, motion artifacts within the ARFI images were reduced to below 1.5 &mu m at all points of the cardiac cycle. The inclusion of pre-excitation displacement estimates in the quadratic motion filter further reduced physiological motion artifacts at all points of the cardiac cycle to below 0.5 &mu m.

In order for cardiac ARFI imaging to more quantitatively assess myocardial performance, novel ARFI imaging sequences and methods were developed to address challenges specifically related to cardiac imaging. These improvements provided finer sampling and improved spatial and temporal resolution within the ARFI images. In vivo epicardial ARFI images of an ovine heart were formed using these sequences, and the quality and utility of the resultant ARFI-induced displacement curves were examined.

In vivo cardiac ARFI images were formed of canine left ventricular free walls while the hearts were externally paced by one of two electrodes positioned epicardially on either side of the imaging plane. Directions and speeds of myocardial stiffness propagation were measured within the ARFI imaging field of view. In all images, the myocardial stiffness waves were seen to be traveling away from the stimulating electrode. The stiffness propagation velocities were also shown to be consistent with propagation velocities measured from elastography and tissue velocity imaging as well as the local epicardial ECG.

ARFI-induced displacement curves of an ovine heart were formed and temporally registered with left ventricular pressure and volume measurements. From these plots, the synchronization of myocardial stiffening and relaxation with the four phases (isovolumic contraction, ejection, isovolumic relaxation, and filling) of the cardiac cycle was determined. These ARFI imaging sequences were also used to correlate changes in left ventricular performance with changes in myocardial stiffness. These preliminary results indicated that changes in the ARFI imaging-derived stiffnesses were consistent with those predicted by current, clinically accepted theories of myocardial performance and function.

These results demonstrate the ability of ARFI imaging to visualize changes in myocardial stiffness through the cardiac cycle and its feasibility to provide clinically useful insight into myocardial performance.

Acoustic radiation force impulses (ARFI) have been used to generated transverse-traveling mechanical waves in various biological tissues. The velocity of these waves is related to a medium's stiffness and thus can offer useful diagnostic information. Consequently, shear wave velocimetry has the potential to investigate cardiac disease states that manifest themselves as changes in tissue stiffness (e.g., ischemia).

The work contained herein focuses on employing ARFI-based shear wave velocimetry techniques, similar to those previously utilized on other organs (e.g., breast, liver), for the investigation of cardiac tissue. To this end, ARFI excitations were used to generate slow-moving (under 3 m/s) mechanical waves in exposed myocardium (with access granted through a thoracotomy); these waves were then tracked with ultrasonic methods. Imaging techniques to increase frame-rate, decrease transducer/tissue heating, and reduce the effects of physiological motion were developed. These techniques, along with two shear wave velocimetry methods (i.e., the Lateral Time-to-Peak and Radon sum transformation algorithms), were utilized to successfully track shear wave propagation through the mid-myocardial layer in vitro and in vivo. In vitro experiments focused on the investigation of a shear wave anisotropy through the myocardium. This experimentation suggests a moderate shear wave velocity anisotropy through regions of the mid-myocardial layer. In vivo experiments focused on shear wave anisotropy (which tend to corroborate the aforementioned in vitro results), temporal/spatial stability of shear wave velocity estimates, and estimation of wave velocity through the cardiac cycle. Shear wave velocity was found to cyclically vary through the cardiac cycle, with the largest estimates occurring during systole and the smallest occurring during diastole. This result suggests a cyclic stiffness variation of the myocardium through the cardiac cycle. A novel, on-axis technique, the displacement ratio rate (DRR) method, was developed and compared to conventional shear wave velocitmetry and ARFI imaging results; all three techniques suggest a similar cyclic stiffness variation.

Shear wave velocimetry shows promise in future investigations of myocardial elasticity. The DRR method may offer a means for transthoracic characterization of myocardial stiffness. Additionally, the future use of transesophageal and catheter-based transducers presents a way of generating and tracking shear waves in a clinical setting (i.e., when epicardial imaging is not feasible). Lastly, it is hoped that continued investigations into the physical basis of these ARFI-generated mechanical waves may further clarify the relationship between their velocity in myocardium and material stiffness.

This dissertation investigates the clinical utility of adaptive ultrasonic frequency selection using principles of spatial coherence. Presently, the status quo for the selection of settings on an ultrasound scanner leaves much room for improvement. Time constraints and the prevalence of injury to sonographers limit the degree to which scanner settings may optimized for a given patient or acoustic window. One such setting is the frequency, which balances the levels of acoustic noise and resolution. Manufacturers usually include a low- and high-frequency option, but these settings are coarse relative to the overall transducer bandwidth, and leave little room for personalized scanning of each patient. The goal of adaptive frequency selection is to maximize image quality by selecting an optimal frequency at a per-image basis. Automating the process of selecting scanner settings requires a user-independent image quality metric, and conventional metrics, such as contrast and contrast-to-noise ratio (CNR), often require user input to draw a multiple regions-of-interest (ROIs) on the image. This is time consuming as well as prone to further user bias. However, spatial coherence-based metrics, a category of image quality metrics developed by our group and others for use in medical ultrasound, avoids these issues while remaining sensitive to acoustic noise.

This work is presented in four chapters. Chapter 1 provides a review of spatial coherence in medical ultrasound, including image quality characterization techniques, beamforming methods, and a discussion of potential future areas of exploration.

Chapter 2 details a simulation study in which spatial coherence is used to predict the loss in imaging contrast as well as separate the effects of different acoustic noise sources. Results showed agreement between theory and simulations for a multitude of image quality metrics when considering two types of noise: incoherent noise and partially coherent noise. Minimal error was seen between coherence-predicted contrast loss and measured contrast loss. This presented framework shows promise to improve the evaluation of noise reduction strategies.

Chapter 3 details the development of an efficient method to collect frequency-dependent spatial coherence information by leveraging a type of coded transmission known as a chirp. Chirp-collected measurements of coherence were compared to those acquired by individually transmitted conventional pulses over a range of frequencies. Results from ex vivo and in vivo acquisitions showed that chirps replicated the mean coherence in a region-of-interest. This work indicates that the use of chirps is a viable strategy to expedite the collection of frequency-dependent spatial coherence, presenting an avenue for real-time adaptive frequency selection.

Lastly, Chapter 4 details the clinical validation of adaptive frequency selection through a reader study. Image quality improvements shown with coherence-based metrics were corroborated by reader outcomes scores for overall quality, border detection, and target conspicuity. Statistical testing revealed a significant difference between the rated image quality of adaptive images and transducer default images. These results suggest that an optimal frequency can be automatically selected for target detection.

Several groups have proposed classifying atherosclerotic disease by using acoustic radiation

force (ARF) elasticity methods to estimate the mechanical and material

properties of plaque. However, recent evidence suggests that cardiovascular disease

(CVD), in addition to involving pathological changes in arterial tissue, is also a

hemodynamic remodeling problem. As a result, integrating techniques that can

estimate localized hemodynamics relevant to CVD remodeling with existing ARF based

elastography methods may provide a more complete assessment of CVD.

This thesis describes novel imaging approaches for combining clinically-accepted,

ultrasound-based flow velocity estimation techniques (color-flow Doppler and spectral-

Doppler imaging) with ARF-based elasticity characterization of vascular tissue. Techniques

for integrating B-mode, color-flow Doppler, and ARFI imaging were developed

(BACD imaging), validated in tissue-mimicking phantoms, and demonstrated for in

vivo imaging. The resulting system allows for the real-time acquisition (< 20 Hz) of

spatially registered B-mode, flow-velocity, and ARFI displacement images of arterial

tissue throughout the cardiac cycle. ARFI and color-flow Doppler imaging quality,

transducer surface heating, and tissue heating were quantified for different frame-rate

and scan-duration configurations. The results suggest that BACD images can be acquired

at high frame rates with minimal loss of imaging quality for approximately

five seconds, while staying beneath suggested limits for tissue and transducer surface

heating.

Because plaque-burden is potentially a 3D problem, techniques were developed

to allow for the 3D acquisition of color-flow Doppler and ARFI displacement data

using a stage-controlled, freehand scanning approach. The results suggest that a

40mm x 20mm x 25mm BACD volume can be acquired in approximately three seconds.

Jitter, SNR, lesion CNR, soft-plaque detectability, and flow-area assessment were

quantified in tissue mimicking phantoms with a range of elastic moduli relevant

to ARFI imaging applications. Results suggest that both jitter and SNR degrade

with increased sweep velocity, and that degradation is worse when imaging stiffer

materials. The results also suggest that a transition between shearing-dominated

jitter and motion-dominated jitter occurs sooner with faster sweep speeds and in

stiffer materials. These artifacts can be reduced with simple, linear filters. Results

from plaque mimicking phantoms suggest that the estimation of soft-plaque area

and flow area, both important tasks for CVD imaging, are only minimally affected

at faster sweep velocities.

Current clinical assessment of CVD is guided by spectral Doppler velocity methods.

As a result, novel imaging approaches (SAD-SWEI, SAD-GATED) were developed

for combining spectral Doppler methods with existing ARF-based imaging

techniques to allow for the combined assessment of cross-luminal velocity profiles,

wall-shear rate (WSR), ARFI displacement and ARF-induced wave velocities. These

techniques were validated in controlled phantom experiments, and show good agreement

between previously described ARF-techniques and theory. Initial in vivo feasibility

was then evaluated in five human volunteers. Results show that a cyclic

variability in both ARFI displacement and ARF-generated wave velocity occurs during

the cardiac cycle. Estimates of WSR and peak velocity show good agreement

with previous ultrasonic-based assessments of these metrics. In vivo ARFI and Bmode/

WSR images of the carotid vasculature were successfully formed using ECG gating

techniques.

This thesis demonstrates the potential of these methods for the combined assessment

of vascular hemodynamics and elasticity. However, continued investigation

into optimizing sequences to reduce transducer surface heating, removing the angle

dependency of the SAD-SWEI/SAD-GATED methods, and decreasing processing

time will help improve the clinical viability of the proposed imaging techniques.

Adaptive ultrasound systems, designed to automatically and dynamically tune imaging parameters based on image quality feedback, represent a promising solution for reducing the user-dependence of ultrasound. The efficacy of such systems, however, depends on the ability to accurately and reliably measure in vivo image quality with minimal user interaction -- a task for which existing image quality metrics are ill-suited. This dissertation explores the application of backscatter spatial coherence as an alternative image quality metric for adaptive imaging. Adaptive ultrasound methods applying spatial coherence feedback are evaluated in the context of three different applications: 1) the automated selection of acoustic output, 2) model-based clutter suppression in B-mode imaging, and 3) adaptive wall filtering in color flow imaging.

A novel image quality metric, known as the lag-one coherence (LOC), was introduced along with the theory that relates LOC to channel noise and the conventional image quality metrics of contrast and contrast-to-noise ratio (CNR). Simulation studies were performed to validate this theory and compare the variability of LOC to that of conventional metrics. In addition, matched measurements of LOC, contrast, CNR, and temporal correlation were obtained from harmonic phantom and liver images formed with varying mechanical index (MI) to assess the feasibility of adaptive acoustic output selection using LOC feedback. Measurements of LOC in simulation and phantom demonstrated lower variability in LOC relative to contrast and CNR over a wide range of clinically-relevant noise levels. This improved stability was supported by in vivo measurements of LOC that showed increased monotonicity with changes in MI compared to matched measurements of contrast and CNR (88.6% and 85.7% of acquisitions, respectively). The sensitivity of LOC to temporally-stable acoustic noise was evidenced by positive correlations between LOC and contrast (r=0.74) and LOC and CNR (r=0.66) at high acoustic output levels in the absence of thermal noise. Together, these properties translated to repeatable characterization of patient-specific trends in image quality that were able to demonstrate feasibility for the automated selection of acoustic output using LOC and its application for in vivo image quality feedback.

In a second study, a novel model-based adaptive imaging method called Lag-one Spatial Coherence Adaptive Normalization, or LoSCAN, was explored as a means to locally estimate and compensate for the contribution of spatially incoherent clutter from conventional delay-and-sum (DAS) images using measurements of LOC. Suppression of incoherent clutter by LoSCAN resulted in improved image quality without introducing many of the artifacts common to other coherence-based beamforming methods. In simulations with known targets and added channel noise, LoSCAN was shown to restore native contrast and increase DAS dynamic range by as much as 10-15 dB. These improvements were accompanied by DAS-like speckle texture along with reduced focal dependence and artifact compared to other coherence-based methods. Under in vivo liver and fetal imaging conditions, LoSCAN resulted in increased generalized contrast-to-noise ratio (gCNR) in nearly all matched image pairs (N = 366) with average increases of 0.01, 0.03, and 0.05 in good, fair, and poor quality DAS images, respectively, and overall changes in gCNR from -0.01 to 0.20, contrast-to-noise ratio (CNR) from -0.05 to 0.34, contrast from -9.5 to -0.1 dB, and texture mu/sigma from -0.37 to -0.001 relative to DAS.

The application of spatial coherence image quality feedback was further investigated in the context of color flow imaging to perform adaptive wall filter selection. The relationship between velocity estimation accuracy and spatial coherence was demonstrated in simulations with varying flow and clutter conditions. This relationship was leveraged to implement a novel method for coherence-based adaptive wall filtering, which selects a unique wall filter at each imaging location based on local clutter and flow properties captured by measurements of LOC and short-lag spatial coherence (SLSC). In simulations and phantom studies with known flow velocities and clutter, coherence-adaptive wall filtering was shown to reduce velocity estimation bias by suppressing low frequency energy from clutter and minimizing the attenuation of flow signal, while maintaining comparable velocity estimation variance relative to conventional wall filtering. These properties translated to in vivo color flow images of liver and fetal vessels that were able to provide direct visualization of low and high velocity flow under various cluttered imaging conditions without the manual tuning of wall filter cutoffs and/or priority thresholds.

Together, these studies present several promising applications of spatial coherence that are fundamentally unique from existing methods in ultrasound. Results in this work support the broad application of spatial coherence feedback to perform patient, window, and target-specific adjustment of imaging parameters to improve the usability and efficacy of diagnostic ultrasound.

Beamforming of ultrasound signals in the presence of clutter, or partial aperture blockage by an acoustic obstacle can lead to reduced visibility of the structures of interest and diminished diagnostic value of the resulting image. We propose new beamforming methods to recover the quality of ultrasound images under such challenging conditions. Of special interest are the signals from large apertures, which are more susceptible to partial blockage, and from commercial matrix arrays that suffer from low sensitivity due to inherent design/hardware limitations. A coherence-based beamforming method designed for suppressing the in vivo clutter, namely Short-lag Spatial Coherence (SLSC) Imaging, is first implemented on a 1-D array to enhance visualization of liver vasculature in 17 human subjects. The SLSC images show statistically significant improvements in vessel contrast and contrast-to-noise ratio over the matched B-mode images. The concept of SLSC imaging is then extended to matrix arrays, and the first in vivo demonstration of volumetric SLSC imaging on a clinical ultrasound system is presented. The effective suppression of clutter via volumetric SLSC imaging indicates it could potentially compensate for the low sensitivity associated with most commercial matrix arrays. The rest of the dissertation assesses image degradation due to elements blocked by ribs in a transthoracic scan. A method to detect the blocked elements is demonstrated using simulated, ex vivo, and in vivo data from the fully-sampled 2-D apertures. The results show that turning off the blocked elements both reduces the near-field clutter and improves visibility of anechoic/hypoechoic targets. Most importantly, the ex vivo data from large synthetic apertures indicates that the adaptive weighing of the non-blocked elements can recover the loss of focus quality due to periodic rib structure, allowing large apertures to realize their full resolution potential in transthoracic ultrasound.

This dissertation aims to demonstrate the feasibility of direct infarct visualization using 3D medical ultrasound. The dissertation proceeds by providing the first ever demonstration of fully-sampled 3D ultrasonic speckle tracking using raw B-Mode data of the heart. The initial demonstration uses a Cramer-Rao lower bound limited displacement estimator. The dissertation then proceeds to develop an implementable method for biased time-delay estimation. Biased time-delay estimation is shown to surpass the traditional limits described by the Cramer-Rao lower bound in a mean square error sense. Additional characterization of this new class of estimator is performed to demonstrate that with easily obtainable levels of prior information it is possible to estimate displacements that do surpass the Cramer-Rao lower bound. Finally, using 2D and 3D realizations of biased displacement estimation (Bayesian speckle tracking) the passive strain induced in the ventricle walls during atrial systole is shown to be sufficient to distinguish healthy and chronically infarcted myocardium.

Ultrasonic flow detection is a widely used technique to detect vessel, measure blood flow velocities, and monitor perfusion. Conventional techniques include color Doppler imaging and power Doppler (PD) imaging. These methods depend on either the measurement of phase change or the detection of the power of backscattered echoes from blood. Both techniques are susceptible to noise. Common noise sources include thermal noise and clutter. The noise significantly deteriorates the performance of color Doppler imaging, because color Doppler imaging estimates the axial blood velocity from temporal changes in the echo phase, and phase change measurement is sensitive to noise. Power Doppler imaging measures the power of the temporal differences in backscattered echoes, and can provide higher sensitivity with small vessel and slow flow detection than color Doppler imaging at the expense of direction and velocity information. However, it requires a large ensemble length, limiting the frame rate to a few frames per second. The limitations of color Doppler imaging and power Doppler imaging are more severe in deep body vessel imaging due to depth dependent attenuation of the ultrasound waves. Therefore, for deep body vessel imaging, including liver vessel imaging and placental spiral artery imaging, better vessel detection techniques are desirable.

Coherent flow power Doppler (CFPD) imaging was proposed as a sensitive flow detection and imaging technique for slow flow and small vessels. In this work, we present the study on CFPD from principles to clinical evaluation.

The CFPD imaging technique detects blood flow from the spatial coherence of the blood signal. The short-lag spatial coherence (SLSC) beamformer is used for the measurement of spatial coherence. Because blood signals and common noise sources, including thermal noise reverberation clutter, have different spatial coherence properties, CFPD can suppress the noise.

The performance of CFPD in flow detection was evaluated with simulations and flow phantom experiments under various imaging conditions, and compared with the performance of PD. It is found that CFPD provides an improvement of Doppler signal-to-noise ratio (SNR) of 7.5-12.5 dB over PD in slow flow and small vessel imaging. The improvement in SNR translates to higher Doppler image contrast, faster frame rate, or lower limit-of-detection (LOD). In similar imaging conditions of slow flow, CFPD may detect up to 50% slower flow than PD.

The CFPD imaging technique was also implemented with novel pulse sequences, including plane-wave synthetic transmit aperture imaging, and diverging-wave synthetic transmit aperture imaging. For plane-wave synthetic transmit aperture imaging, the angular coherence theory was proposed to describe the coherence of backscattered waves corresponding to plane wave transmits at different steering angles. In addition, we also propose the coherent Kasai and Loupas estimators, which utilizes the coherence information of flow signals to provide velocity estimates with reduced uncertainty.

To demonstrate the clinical relevance of CFPD, we built a real-time CFPD imaging system and conducted a pilot clinical study with it. In the system, the CFPD technique was implemented on a Verasonics Vantage 256 research scanner. The software beamformer and CFPD processing were implemented on the graphics processing unit (GPU). The Doppler frame rate of the system is 10 frames per second for a field-of-view (FOV) of 10 cm axially and 4 cm laterally.

In the pilot clinical study, the liver vasculatures of 15 healthy human volunteers were imaged by a trained sonographer using the real-time CFPD system. The raw data corresponding to a 132 Doppler videos were captured and processed offline. The SNR of the vessels in the CFPD and PD images were measured and analyzed. In all of the 132 data sets, CFPD provides higher SNR than PD. The average improvement in SNR is 8.6 dB. From the visual analysis of the images, it can be seen that the improvement in SNR leads to more sensitive detection of small vessels in deeper parts of the liver.

Radiofrequency ablation (RFA) is commonly used to treat cardiac arrhythmias, by generating a series of discrete RFA lesions in the myocardium to isolate arrhythmogenic conduction pathways. The size of each lesion is controlled by the temperature of the tissue at the surface or the duration of RF power delivery, but feedback on the extent and transmurality of the generated lesion are unavailable with current technology. Intracardiac Echocardiography (ICE) may provide a solution through Acoustic Radiation Force Impulse (ARFI) imaging or Shear Wave Imaging (SWI), which each generate images of local mechanical compliance from very small ultrasonically-induced waves. This work compares ARFI and SWI in an ex-vivo experiment for lesion boundary assessment and lesion gap resolution.

Medical ultrasound imaging is portable, real-time, and inexpensive, with countless applications across a range of pathologies and imaging targets. Despite these advantages, many patients suffer from suboptimal image quality, hampered by acoustic clutter which can reduce contrast and obscure targets of interest. Obesity, in particular, has been linked to increased rates of inadequate visualization and reduced diagnostic efficacy of ultrasound imaging. Rising obesity rates support the need for improved image quality in challenging imaging environments.

Advanced beamforming methods may offer an opportunity to mitigate sources of acoustic clutter and improve image quality. Many methods have been proposed in the literature, which have been shown to improve aspects of image quality over conventional delay-and-sum beamforming. However, these methods often exchange enhanced contrast for coarse speckle texture, distort the native echogenicty of the imaging target, and/or employ ad hoc approaches to image formation that lack a sound basis in physical principles.

This dissertation presents a new paradigm for image formation: an estimation-based approach to image the statistical properties of tissue. The foundation for this approach is the fundamental observation that targets in medical ultrasound consist of inherently unresolvable, diffuse scatterers. Backscattered echoes from diffuse targets can be characterized by their statistical properties, which are classically described by the van Cittert-Zernike (VCZ) theorem under a statistically stationary, spatially incoherent scattering model.

This work applies the VCZ theorem to a piecewise-stationary scattering model. This application yields a key insight: the spatial covariance of the received echo data is the linear superposition of covariances from distinct spatial regions in the imaging target. This relationship is derived from first principles and validated through simulation studies demonstrating superposition and scaling.

Under the framework of spatial covariance decomposition, a novel method to image the statistical properties of stochastic targets is derived. Multi-covariate Imaging of Sub-resolution Targets, or MIST, employs an estimation-based method to image the on-axis contributions to the echo data covariance matrix. MIST covariance models are defined based on a spatial decomposition of the theoretical transmit intensity distribution into contributions received on- and off-axis. The mathematical foundations of the MIST estimator are analytically derived, and imaging performance is evaluated in simulation, phantom, and in vivo studies, which demonstrate consistent improvements in contrast-to-noise ratio (CNR) and speckle signal-to-noise ratio (SNR) across imaging targets, while preserving target echogenicity and lateral resolution.

In a pilot clinical study, MIST image quality was evaluated in fifteen patients at the Duke Fetal Diagnostic Center, using data collected with the Verasonics Vantage 256 research scanner from a variety of fetal structures in first- and second-trimester pregnancies. Patient body habitus varied from underweight to obese (body mass indices of 17.5--58.3). Across 152 images from all patients, MIST demonstrated improved contrast (93.2% of images), CNR (99.1%) and speckle SNR (99.5%) over matched B-Mode images. Image quality improvements were consistent across patient body habitus and between fundamental and harmonic imaging modes, showing promising indications for MIST in fetal applications.

To characterize the intrinsic tradeoffs associated with MIST, the effects of varying two key parameters on image quality were explored: (1) the spatial cutoff delineating the on- from off-axis covariance models and (2) the degree of spatial averaging of the measured echo data covariance matrix. The results demonstrated a fundamental tradeoff between resolution and speckle texture. This fundamental tradeoff was compared to similar tradeoffs in spatial and frequency compounding. MIST was shown to provide greater improvements in speckle texture at a comparable resolution to each method. Across these tunable parameters, MIST also demonstrated stable performance in noise and fidelity to native contrast. These results present a framework for parameter selection in MIST to maximize speckle SNR without an appreciable loss in resolution.

Like many coherence-based imaging methods, MIST suffers from reduced image quality outside the depth of field for focused ultrasound transmissions. To extend the depth of field, synthetic aperture focusing was applied to MIST under focused, plane wave and diverging wave transmit geometries. Synthetic aperture MIST demonstrated consistent improvements in image quality over conventional dynamic receive MIST, with approximately equivalent results between transmit geometries. In an in vivo liver example, synthetic aperture MIST images demonstrated 16.8 dB and 16.6% improvements in contrast and CNR, respectively, over dynamic receive MIST images, as well as 17.4 dB and 32.3% improvements over synthetic aperture B-Mode. Simulation and experimental results indicate wide applicability of MIST to synthetic aperture focusing methods.

Lastly, MIST imaging performance in multi-dimensional arrays was evaluated through a preliminary simulation study. MIST images were formed using 1-D, 1.75-D, and 2-D transducer geometries on a number of targets with a range of native contrast values. MIST image quality was demonstrated to be stable in the presence of noise across array geometries. Preliminary results showed substantial improvements in contrast, speckle SNR, and lesion detectability metrics with only a modest increase in system complexity.

In summary, Multi-covariate Imaging of Sub-resolution Targets is a novel approach to image the statistical properties of diffuse scattering targets, based on a spatial decomposition of aperture domain covariance into on- and off-axis contributions. Simulated and experimental results indicate significant improvements of image quality over conventional methods, promising preliminary clinical data, and feasibility under modern focusing schemes and advanced hardware. This work suggests MIST may greatly benefit image quality patients in patients for whom conventional methods fail.

Coronary heart disease contributed to approximately one in four deaths in the United States in 2014, and is caused by a restriction of blood flow to myocardial tissue. Stress echocardiography is a clinical technique used to assess myocardial ischemia by observing changes (or lack thereof) in ventricular wall motion in response to cardiac stress. The American Society of Echocardiography (ASE) recommends that left ventricle functionality be quantified using a 16 or 17 segment model of the left ventricle (LV). To properly assess the function of the ventricle, clear endocardial border delineation is necessary.

However, an increasing prevalence of obesity has been linked to a rise in the number of unreadable ultrasound scans. Image degradation is attributed to tissue inhomogeneities and subcutaneous fat layers, giving rise to phase aberration errors and acoustical clutter from near-field reverberation. In the event that two or more segments are inadequately visualized, the ASE recommends the use of contrast agents. Though contrast agents are effective, they are invasive and increase the procedure time and costs.

Recent work has shown that clutter can be suppressed using a novel image reconstruction technique based on the second order statistics of ultrasound echoes called short-lag spatial coherence (SLSC). Unlike conventional B-mode imaging, which forms images of the echo magnitude, SLSC forms images of the spatial coherence of the echo. By suppressing clutter, a sufficient improvement in the visualization of the endocardial border could minimize the need for contrast agents and potentially reduce the level of expertise necessary to interpret images. Though promising in preliminary studies, SLSC has a high computational demand that limited previous studies to offline image reconstruction. The goal of this research was to implement spatial coherence imaging in real-time, and to assess its performance in echocardiography.

First, the existing spatial coherence estimation methodology was investigated, and three computationally efficient modifications were proposed: a reduced kernel, a downsampled receive aperture, and the use of an ensemble correlation coefficient. The proposed methods were implemented in simulation and in vivo studies. Reducing the kernel to a single sample improved computational throughput and improved axial resolution. Downsampling the receive aperture was found to have negligible effect on estimator variance, and improved computational throughput by an order of magnitude for a downsample factor of 4. The ensemble correlation estimator was found to have lower variance than the currently used average correlation estimator. Combining the three methods, the throughput was improved 105-fold in simulation with a downsample factor of 4 and 20-fold in vivo with a downsample factor of 2.

Spatial coherence estimation techniques were also expanded to 2D matrix array transducers. SLSC images generated with a 2D array yielded superior contrast-to-noise ratio (CNR) and texture signal-to-noise ratio (SNR) measurements over SLSC images made on a corresponding 1D array and over B-mode imaging. SLSC images generated with square subapertures were found to be superior to SLSC images generated with subapertures of equal surface area that spanned the whole array in one dimension. Subaperture beamforming was found to have little effect on SLSC imaging performance for subapertures up to 8x8 elements in size on a 64x64 element transducer. Additionally, the use of 8x8, 4x4, and 2x2 element subapertures provided an 8, 4, and 2 times improvement in channel SNR along with a 2640-, 328-, and 25-fold reduction in computation time, respectively.

The improved spatial coherence estimation methodology was implemented using a GPU-based software beamformer to develop a real-time SLSC imaging system suitable for echocardiography. The system went through several iterations, with the final form consisting of a stand-alone CUDA C++ library for GPU-based beamforming, and a second CUDA C++ library to interface a research ultrasound scanner with the first. The resulting system was capable of live spatial coherence imaging at more than 30 frames per second, a rate sufficient for echocardiography.

The system was then used in a clinical study to image 15 stress echocardiography patients with poor image quality. A fundamental and harmonic imaging study was conducted. The latter study, which had greater clinical significance, was an assessment of the visibility of 17 LV segments using conventional tissue harmonic imaging (THI) and harmonic spatial coherence imaging (HSCI). A cardiologist rated the visibility of each of 17 LV segments as 0=invisible, 1=poorly visualized, or 2=well visualized, where scores of 0 and 1 indicated a need for contrast agent. There was a clear superiority of HSCI over THI in a comparison of overall segment scores (p < 0.0001 by symmetry test unadjusted for clustering). When comparing the number of segments with clinically acceptable image quality per patient, HSCI again showed superiority over THI (p < 0.0001 by McNemar test adjusted for clustering). In one patient, HSCI improved visualization sufficiently to eliminate the need for contrast agents altogther. These results indicate that spatial coherence imaging may provide sufficient improvements in LV wall visualization in certain patients to proceed without contrast agents.

The research in spatial coherence estimation techniques also proved fruitful in other areas of ultrasound imaging, such as ultrasound molecular imaging (USMI). USMI is accomplished by detecting microbubble (MB) contrast agents that have bound to specific biomarkers, and can be used for the early detection of cancer. However, USMI in humans is challenging because of the signal degradation caused by the presence of heterogenous subcutaneous tissue. In a phantom and in vivo study, USMI performance was assessed using conventional contrast-enhanced ultrasound (CEUS) imaging and SLSC-CEUS. In a USMI-mimicking phantom, SLSC-CEUS was found to be more robust than DAS to additive thermal noise, with a 9 dB and 15 dB SNR improvement without and with -6 dB thermal noise, respectively. USMI performance was also measured in vivo using VEGFR2-targeted MBs in mice with subcutaneous human hepatocellular carcinoma tumors. SLSC-CEUS improved the SNR in each of 10 tumors by an average of 65%, corresponding to 4.3 dB SNR. These results indicate that the SLSC beamformer is well-suited for USMI applications because of its high sensitivity and robust properties.

These studies are a demonstration of the feasibility of real-time spatial coherence imaging using current technology, and an exposition of its utility in medical ultrasound imaging.

The rupture of arterial plaques is the most common cause of ischemic complications including stroke, the fourth leading cause of death and number one cause of long term disability in the United States. Unfortunately, because conventional diagnostic tools fail to identify plaques that confer the highest risk, often a disabling stroke and/or sudden death is the first sign of disease. A diagnostic method capable of characterizing plaque vulnerability would likely enhance the predictive ability and ultimately the treatment of stroke before the onset of clinical events.

This dissertation evaluates the hypothesis that Acoustic Radiation Force Impulse (ARFI) imaging can noninvasively identify lipid regions, that have been shown to increase a plaque's propensity to rupture, within carotid artery plaques in vivo. The work detailed herein describes development efforts and results from simulations and experiments that were performed to evaluate this hypothesis.

To first demonstrate feasibility and evaluate potential safety concerns, finite-element method simulations are used to model the response of carotid artery plaques to an acoustic radiation force excitation. Lipid pool visualization is shown to vary as a function of lipid pool geometry and stiffness. A comparison of the resulting Von Mises stresses indicates that stresses induced by an ARFI excitation are three orders of magnitude lower than those induced by blood pressure. This thesis also presents the development of a novel pulse inversion harmonic tracking method to reduce clutter-imposed errors in ultrasound-based tissue displacement estimates. This method is validated in phantoms and was found to reduce bias and jitter displacement errors for a marked improvement in image quality in vivo. Lastly, this dissertation presents results from a preliminary in vivo study that compares ARFI imaging derived plaque stiffness with spatially registered composition determined by a Magnetic Resonance Imaging (MRI) gold standard in human carotid artery plaques. It is shown in this capstone experiment that lipid filled regions in MRI correspond to areas of increased displacement in ARFI imaging while calcium and loose matrix components in MRI correspond to uniformly low displacements in ARFI imaging.

This dissertation provides evidence to support that ARFI imaging may provide important prognostic and diagnostic information regarding stroke risk via measurements of plaque stiffness. More generally, the results have important implications for all acoustic radiation force based imaging methods used clinically.

Clutter is a problematic noise artifact in a variety of ultrasound applications. Clinical tasks complicated by the presence of clutter include detecting cancerous lesions in abdominal organs (e.g. livers, bladders) and visualizing endocardial borders to assess cardiovascular health. In this dissertation, an analytical expression for contrast loss due to clutter is derived, clutter is quantified in abdominal images, and sources of abdominal clutter are identified. Novel clutter reduction methods are also presented and tested in abdominal and cardiac images.

One of the novel clutter reduction methods is Short-Lag Spatial Coherence (SLSC) imaging. Instead of applying a conventional delay-and-sum beamformer to measure the amplitude of received echoes and form B-mode images, the spatial coherence of received echoes are measured to form SLSC images. The world's first SLSC images of simulated, phantom, and in vivo data are presented herein. They demonstrate reduced clutter and improved contrast, contrast-to-noise, and signal-to-noise ratios compared to conventional B-mode images. In addition, the resolution characteristics of SLSC images are quantified and compared to resolution in B-mode images.

A clinical study with 14 volunteers was conducted to demonstrate that SLSC imaging offers 19-33% improvement in the visualization of endocardial borders when the quality of B-mode images formed from the same echo data was poor. There were no statistically significant improvements in endocardial border visualization with SLSC imaging when the quality of matched B-mode images was medium to good.

Implemented on an intracardiac echocardiography transducer, acoustic radiation force methods may provide a useful means of characterizing the heart's elastic properties. Elasticity imaging may be of benefit for diagnosis and characterization of infarction and heart failure, as well as for guidance of ablation therapy for the treatment of arrhythmias. This thesis tests the hypothesis that with appropriately designed imaging sequences, intracardiac acoustic radiation force impulse (ARFI) imaging and shear wave elasticity imaging (SWEI) are viable tools for quantification of myocardial elasticity, both temporally and spatially. Multiple track location SWEI (MTL-SWEI) is used to show that, in healthy in vivo porcine ventricles, shear wave speeds follow the elasticity changes with contraction and relaxation of the myocardium, varying between 0.9 and 2.2 m/s in diastole and 2.6 and 5.1 m/s in systole. Infarcted tissue is less contractile following infarction, though not unilaterally stiffer. Single-track-location SWEI (STL-SWEI) is proven to provide suppression of speckle noise and enable improved resolution of structures smaller than 2 mm in diameter compared to ARFI and MTL-SWEI. Contrast to noise ratio and lateral edge resolution are shown to vary with selection of time step for ARFI and arrival time regression filter size for STL-SWEI and MTL-SWEI.

In 1.5 mm targets, STL-SWEI achieves alternately the tightest resolution (0.3 mm at CNR = 3.5 for a 0.17 mm filter) and highest CNR (8.5 with edge width = 0.7 mm for a 0.66 mm filter) of the modalities, followed by ARFI and then MTL-SWEI.

In larger, 6 mm targets, the CNR-resolution tradeoff curves for ARFI and STL-SWEI overlap for ARFI time steps up to 0.5 ms and kernels $\leq$1 mm for STL-SWEI. STL-SWEI can operate either with a 25 dB improvement over MTL-SWEI in CNR at the same resolution, or with edge widths 5$\times$ as narrow at equivalent CNR values, depending on the selection of regression filter size. Ex vivo ablations are used to demonstrate that ARFI, STL-SWEI and MTL-SWEI each resolve ablation lesions between 0.5 and 1 cm in diameter and gaps between lesions smaller than 5 mm in 3-D scans. Differences in contrast, noise, and resolution between the modalities are discussed. All three modalities are also shown to resolve ``x''-shaped ablations up to 22 mm in depth with good visual fidelity and correspondence to surface photographs, with STL-SWEI providing the highest quality images. Series of each type of image, registered using 3-D data from an electroanatomical mapping system, are used to build volumes that show ablations in in vivo canine atria. In vivo images are shown to be subject to increased noise due to tissue and transducer motion, and the challenges facing the proposed system are discussed. Ultimately, intracardiac acoustic radiation force methods are demonstrated to be promising tools for characterizing dynamic myocardial elasticity and imaging radiofrequency ablation lesions.